Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
Big data traffic management in vehicular ad-hoc network IJECEIAES
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...cscpconf
The intensive development of traffic engineering and technologies that are integrated into vehicles, roads and their surroundings, bring opportunities of real time transport mobility modeling. Based on such model it is then possible to establish a predictive layer that is capable of predicting short and long term traffic flow behavior. It is possible to create the real time model of traffic mobility based on generated data. However, data may have different geographical, temporal or other constraints, or failures. It is therefore appropriate to develop tools that artificially create missing data, which can then be assimilated with real data. This paper presents a mechanism describing strategies of generating artificial data using microsimulations. It describes traffic microsimulation based on our solution of multiagent framework over which a system for generating traffic data is built. The system generates data of a structure corresponding to the data acquired in the real world.
Evolutionary reinforcement learning multi-agents system for intelligent traf...IJECEIAES
Due to the rapid growth of urban vehicles, traffic congestion has become more serious. The signalized intersections are used all over the world and still established in the new construction. This paper proposes a self-adapted approach, called evolutionary reinforcement learning multi-agents system (ERL-MA), which combines computational intelligence and machine learning. The concept of this work is to build an intelligent agent capable of developing senior skills to manage the traffic light control system at any type of junction, using two powerful tools: learning from the confronted experience and the assumption using the randomization concept. The ERL-MA is an independent multi-agents system composed of two layers: the modeling and the decision layers. The modeling layer uses the intersection modeling using generalized fuzzy graph technique. The decision layer uses two methods: the novel greedy genetic algorithm (NGGA), and the Q-learning. In the Q-learning method, a multi Q-tables strategy and a new reward formula are proposed. The experiments used in this work relied on a real case of study with a simulation of one-hour scenario at Pasubio area in Italy. The obtained results show that the ERL-MA system succeeds to achieve competitive results comparing to urban traffic optimization by integrated automation (UTOPIA) system using different metrics.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
The intensive development of traffic engineering and technologies that are integrated into
vehicles, roads and their surroundings, bring opportunities of real time transport mobility
modeling. Based on such model it is then possible to establish a predictive layer that is capable
of predicting short and long term traffic flow behavior. It is possible to create the real time
model of traffic mobility based on generated data. However, data may have different
geographical, temporal or other constraints, or failures. It is therefore appropriate to develop
tools that artificially create missing data, which can then be assimilated with real data. This
paper presents a mechanism describing strategies of generating artificial data using
microsimulations. It describes traffic microsimulation based on our solution of multiagent
framework over which a system for generating traffic data is built. The system generates data of
a structure corresponding to the data acquired in the real world.
Intelligent Traffic Management System using Shortest Pathijtsrd
Due to current significant increases in population and consequently in traffic congestion in most metropolitan cities in the world, designing of an intelligent traffic management system ITMS in order to detect the path with the shortest travel time is critical for emergency, health, and courier services. The aim of this research study was to develop a theoretical traffic detection system and capable of estimating the travel time associated with each street segment based on the traffic data updated every 20 seconds, which successively finds the path with the shortest travel time in the network by using a dynamic programming technique. Furthermore, in this study we model the travel time associated with each street segment based on the historical and real time data considering that the traffic speed on each road segment is piecewise constant. It would be useful to implement such algorithms in GIS systems such as Google map in such a way that the service delivery drivers can avoid congested routes by receiving real time traffic information. Bharti Kumari | Vinod Mahor "Intelligent Traffic Management System using Shortest Path" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50598.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/50598/intelligent-traffic-management-system-using-shortest-path/bharti-kumari
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
Big data traffic management in vehicular ad-hoc network IJECEIAES
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...cscpconf
The intensive development of traffic engineering and technologies that are integrated into vehicles, roads and their surroundings, bring opportunities of real time transport mobility modeling. Based on such model it is then possible to establish a predictive layer that is capable of predicting short and long term traffic flow behavior. It is possible to create the real time model of traffic mobility based on generated data. However, data may have different geographical, temporal or other constraints, or failures. It is therefore appropriate to develop tools that artificially create missing data, which can then be assimilated with real data. This paper presents a mechanism describing strategies of generating artificial data using microsimulations. It describes traffic microsimulation based on our solution of multiagent framework over which a system for generating traffic data is built. The system generates data of a structure corresponding to the data acquired in the real world.
Evolutionary reinforcement learning multi-agents system for intelligent traf...IJECEIAES
Due to the rapid growth of urban vehicles, traffic congestion has become more serious. The signalized intersections are used all over the world and still established in the new construction. This paper proposes a self-adapted approach, called evolutionary reinforcement learning multi-agents system (ERL-MA), which combines computational intelligence and machine learning. The concept of this work is to build an intelligent agent capable of developing senior skills to manage the traffic light control system at any type of junction, using two powerful tools: learning from the confronted experience and the assumption using the randomization concept. The ERL-MA is an independent multi-agents system composed of two layers: the modeling and the decision layers. The modeling layer uses the intersection modeling using generalized fuzzy graph technique. The decision layer uses two methods: the novel greedy genetic algorithm (NGGA), and the Q-learning. In the Q-learning method, a multi Q-tables strategy and a new reward formula are proposed. The experiments used in this work relied on a real case of study with a simulation of one-hour scenario at Pasubio area in Italy. The obtained results show that the ERL-MA system succeeds to achieve competitive results comparing to urban traffic optimization by integrated automation (UTOPIA) system using different metrics.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
The intensive development of traffic engineering and technologies that are integrated into
vehicles, roads and their surroundings, bring opportunities of real time transport mobility
modeling. Based on such model it is then possible to establish a predictive layer that is capable
of predicting short and long term traffic flow behavior. It is possible to create the real time
model of traffic mobility based on generated data. However, data may have different
geographical, temporal or other constraints, or failures. It is therefore appropriate to develop
tools that artificially create missing data, which can then be assimilated with real data. This
paper presents a mechanism describing strategies of generating artificial data using
microsimulations. It describes traffic microsimulation based on our solution of multiagent
framework over which a system for generating traffic data is built. The system generates data of
a structure corresponding to the data acquired in the real world.
Intelligent Traffic Management System using Shortest Pathijtsrd
Due to current significant increases in population and consequently in traffic congestion in most metropolitan cities in the world, designing of an intelligent traffic management system ITMS in order to detect the path with the shortest travel time is critical for emergency, health, and courier services. The aim of this research study was to develop a theoretical traffic detection system and capable of estimating the travel time associated with each street segment based on the traffic data updated every 20 seconds, which successively finds the path with the shortest travel time in the network by using a dynamic programming technique. Furthermore, in this study we model the travel time associated with each street segment based on the historical and real time data considering that the traffic speed on each road segment is piecewise constant. It would be useful to implement such algorithms in GIS systems such as Google map in such a way that the service delivery drivers can avoid congested routes by receiving real time traffic information. Bharti Kumari | Vinod Mahor "Intelligent Traffic Management System using Shortest Path" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50598.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/50598/intelligent-traffic-management-system-using-shortest-path/bharti-kumari
A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Inter...ijtsrd
In this paper, a two stage fuzzy logic system has been proposed to control an isolated intersection adaptively. The aim of this work is to minimize the average waiting time for a different traffic flow rates in real time means. In the first stage, the system consists of two modules named next phase selection module and the green phase extension module. In the second stage the system consists of the decision named module. The study was performed using SUMO traffic simulator. A comparison is made between a fuzzy logic controller and a conventional fixed time controller. As a result, fuzzy logic controller has shown better performance. Taha Mahmood | Muzamil Eltejani Mohammed Ali | Akif Durdu ""A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data using SUMO Simulator"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23873.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23873/a-two-stage-fuzzy-logic-adaptive-traffic-signal-control-for-an-isolated-intersection-based-on-real-data-using-sumo-simulator/taha-mahmood
On the development of methodology for planning and cost modeling of a wide ar...IJCNCJournal
The most important stages in designing a
computer
network
in a
wider geographical area include:
definition of requirements, topological description
,
identification and calculation of relevant parameters
(
i
.
e
.
traffic matrix
)
, determining the shortest path between nodes, quantification of the effect of various
levels
of technical and technological development of urban areas involved, the cost of technology
,
and the
cost of services. The
se
parameters differ for WAN networks in different regions
–
their calculation depends
directly
on
the data “
i
n the field
”
: number of inhabitants, distance between populated areas,
network
traffic
density
,
as well as
available
bandwidth
. The
main
reason for identification and evaluation of these
parameters
is
to develop a model that could
meet the
constraints
im
posed by poten
tial beneficiaries.
In this
paper
,
we develop a methodology for planning and cost
-
modeling of a wide area network
and
validate it
in
a case study,
under the
supposition
that
behavioral interactions of individuals and groups play a significant
role and have
to be taken into consideration
by employing either simple or composite indicators of
socioeconomic status
.
Integrated tripartite modules for intelligent traffic light systemIJECEIAES
The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.
A Framework for Traffic Planning and Forecasting using Micro-Simulation Calib...ITIIIndustries
This paper presents the application of microsimulation for traffic planning and forecasting, and proposes a new framework to model complex traffic conditions by calibrating and adjusting traffic parameters of a microsimulation model. By using an open source micro-simulator package, TRANSIMS, in this study, animated and numerical results were produced and analysed. The framework of traffic model calibration was evaluated for its usefulness and practicality. Finally, we discuss future applications such as providing end users with real time traffic information through Intelligent Transport System (ITS) integration.
Traffic Control System by Incorporating Message Forwarding ApproachCSCJournals
During the last few years, continuous progresses in wireless communications have opened new research fields in computer networking, aimed at extending data networks connectivity to environments where wired solutions are impracticable. Among these, vehicular traffic is attracting a growing attention from both academia and industry, due to the amount and importance of related distributive applications to mobile entertainment. VANETs are self-organized networks built up from moving vehicles, and are part of the broader class of MANETs. Because of these peculiar characteristics, VANETs require new networking techniques, whose feasibility and performance are usually tested by means of simulation. In order to meet performance goals, it is widely agreed that VANETs must rely heavily on node-to-node communication. In VANET, each vehicle acts as a node and communicates with other vehicles within the range or communicates with base stations. The main idea is to deploy a wireless communication network that has a capability of sending and receiving messages between transmitter and mobile devices in the particular network. Results can be shown using an effective VEINS Simulator. This Simulator can produce detailed vehicular movement traces and can simulate different traffic conditions through fully customizable scenarios. The Framework is expected to be employed using such simulator that makes use of traffic modulator, network simulator and coupling module that integrates the traffic and network.
DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
SIMULATION & VANET: TOWARDS A NEW RELIABLE AND OPTIMAL DATA DISSEMINATION MODELpijans
Ad hoc networks was developed in the 2000s, they was highly used in dynamic environment, particularly
for inter- vehicular communication (VANETs : Vehicular Ad hoc Networks).
Since that time, many researches and developments process was dedicated to VANET networks. These were
motivated by the current vehicular industry trend that is leading to a new transport system generation
based on the use of new communication technologies in order to provide many services to passengers, the
fact that improves the driving and travel’s experience.
These systems require traffic information sharing and dissemination, such as the alert message emitting,
that be exchanged for drivers protection on the road. Sharing such information between vehicles helps to
anticipate potentially dangerous situations, as well as planning better routes during congestion situations.
The current paper attempts to model and simulate VANET Networks, aiming to analyze and evaluate
security information dissemination approaches and mechanisms used in this type of networks in several
exchanges conditions. The second objective is to identify their limitations and suggest a new improved
approach. This study was conducted as part of our research project entitled “Simulation & VANETs”,
where we justify and validate our approach using modeling and simulation techniques and tools used in
this domain.
Optimization of smart traffic lights to prevent traffic congestion using fuzz...TELKOMNIKA JOURNAL
not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
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A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Inter...ijtsrd
In this paper, a two stage fuzzy logic system has been proposed to control an isolated intersection adaptively. The aim of this work is to minimize the average waiting time for a different traffic flow rates in real time means. In the first stage, the system consists of two modules named next phase selection module and the green phase extension module. In the second stage the system consists of the decision named module. The study was performed using SUMO traffic simulator. A comparison is made between a fuzzy logic controller and a conventional fixed time controller. As a result, fuzzy logic controller has shown better performance. Taha Mahmood | Muzamil Eltejani Mohammed Ali | Akif Durdu ""A two Stage Fuzzy Logic Adaptive Traffic Signal Control for an Isolated Intersection Based on Real Data using SUMO Simulator"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23873.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23873/a-two-stage-fuzzy-logic-adaptive-traffic-signal-control-for-an-isolated-intersection-based-on-real-data-using-sumo-simulator/taha-mahmood
On the development of methodology for planning and cost modeling of a wide ar...IJCNCJournal
The most important stages in designing a
computer
network
in a
wider geographical area include:
definition of requirements, topological description
,
identification and calculation of relevant parameters
(
i
.
e
.
traffic matrix
)
, determining the shortest path between nodes, quantification of the effect of various
levels
of technical and technological development of urban areas involved, the cost of technology
,
and the
cost of services. The
se
parameters differ for WAN networks in different regions
–
their calculation depends
directly
on
the data “
i
n the field
”
: number of inhabitants, distance between populated areas,
network
traffic
density
,
as well as
available
bandwidth
. The
main
reason for identification and evaluation of these
parameters
is
to develop a model that could
meet the
constraints
im
posed by poten
tial beneficiaries.
In this
paper
,
we develop a methodology for planning and cost
-
modeling of a wide area network
and
validate it
in
a case study,
under the
supposition
that
behavioral interactions of individuals and groups play a significant
role and have
to be taken into consideration
by employing either simple or composite indicators of
socioeconomic status
.
Integrated tripartite modules for intelligent traffic light systemIJECEIAES
The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.
A Framework for Traffic Planning and Forecasting using Micro-Simulation Calib...ITIIIndustries
This paper presents the application of microsimulation for traffic planning and forecasting, and proposes a new framework to model complex traffic conditions by calibrating and adjusting traffic parameters of a microsimulation model. By using an open source micro-simulator package, TRANSIMS, in this study, animated and numerical results were produced and analysed. The framework of traffic model calibration was evaluated for its usefulness and practicality. Finally, we discuss future applications such as providing end users with real time traffic information through Intelligent Transport System (ITS) integration.
Traffic Control System by Incorporating Message Forwarding ApproachCSCJournals
During the last few years, continuous progresses in wireless communications have opened new research fields in computer networking, aimed at extending data networks connectivity to environments where wired solutions are impracticable. Among these, vehicular traffic is attracting a growing attention from both academia and industry, due to the amount and importance of related distributive applications to mobile entertainment. VANETs are self-organized networks built up from moving vehicles, and are part of the broader class of MANETs. Because of these peculiar characteristics, VANETs require new networking techniques, whose feasibility and performance are usually tested by means of simulation. In order to meet performance goals, it is widely agreed that VANETs must rely heavily on node-to-node communication. In VANET, each vehicle acts as a node and communicates with other vehicles within the range or communicates with base stations. The main idea is to deploy a wireless communication network that has a capability of sending and receiving messages between transmitter and mobile devices in the particular network. Results can be shown using an effective VEINS Simulator. This Simulator can produce detailed vehicular movement traces and can simulate different traffic conditions through fully customizable scenarios. The Framework is expected to be employed using such simulator that makes use of traffic modulator, network simulator and coupling module that integrates the traffic and network.
DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
SIMULATION & VANET: TOWARDS A NEW RELIABLE AND OPTIMAL DATA DISSEMINATION MODELpijans
Ad hoc networks was developed in the 2000s, they was highly used in dynamic environment, particularly
for inter- vehicular communication (VANETs : Vehicular Ad hoc Networks).
Since that time, many researches and developments process was dedicated to VANET networks. These were
motivated by the current vehicular industry trend that is leading to a new transport system generation
based on the use of new communication technologies in order to provide many services to passengers, the
fact that improves the driving and travel’s experience.
These systems require traffic information sharing and dissemination, such as the alert message emitting,
that be exchanged for drivers protection on the road. Sharing such information between vehicles helps to
anticipate potentially dangerous situations, as well as planning better routes during congestion situations.
The current paper attempts to model and simulate VANET Networks, aiming to analyze and evaluate
security information dissemination approaches and mechanisms used in this type of networks in several
exchanges conditions. The second objective is to identify their limitations and suggest a new improved
approach. This study was conducted as part of our research project entitled “Simulation & VANETs”,
where we justify and validate our approach using modeling and simulation techniques and tools used in
this domain.
Optimization of smart traffic lights to prevent traffic congestion using fuzz...TELKOMNIKA JOURNAL
not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Towards a new intelligent traffic system based on deep learning and data integration
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 4649~4660
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4649-4660 4649
Journal homepage: http://ijece.iaescore.com
Towards a new intelligent traffic system based on deep learning
and data integration
Nadia Slimani1
, Siham Yousfi2
, Mustapha Amghar1
, Nawal Sbiti1
1
Computer Systems and Productivity Team, Computer Science Department in Mohammadia School of Engineering,
Mohammed V University, Rabat, Morocco
2
Meridian Team, LYRICA Laboratory, School of Information Sciences, Mohammed V University, Rabat, Morocco
Article Info ABSTRACT
Article history:
Received Sep 27, 2022
Revised Dec 15, 2022
Accepted Dec 17, 2022
Time series forecasting is an important technique to study the behavior of
temporal data in order to forecast the future values, which is widely applied
in intelligent traffic systems (ITS). In this paper, several deep learning
models were designed to deal with the multivariate time series forecasting
problem for the purpose of long-term predicting traffic volume. Simulation
results showed that the best forecasts are obtained with the use of two hidden
long short-term memory (LSTM) layers: the first with 64 neurons and the
second with 32 neurons. Over 93% of the forecasts were made with less than
±2.0% error. The analysis of variances is mainly due to peaks in some
extreme conditions. For this purpose, the data was then merged between two
different sources: electromagnetic loops and cameras. Data fusion is based
on a calibration of the reliability of the sources according to the visibility
conditions and time of the day. The integration results were then compared
with the real data to prove the improvement of the prediction results in peak
periods after the data fusion step.
Keywords:
Data integration
Deep learning
Image recognition
Intelligent traffic systems
Long short-term memory
Multivariate time series
Traffic forecasting This is an open access article under the CC BY-SA license.
Corresponding Author:
Nadia Slimani
Computer Systems and Productivity Team, EMI, Computer Science Department in Mohammadia School of
Engineering, Mohammed V University
Rabat, Morocco
Email: slimani.nadia@gmail.com
1. INTRODUCTION
Accurate and timely traffic flow information is currently critical to individual travelers, industries
and government departments. It has the potential to help road users to make better travel decisions for
avoiding accident risks, reducing traffic congestion and carbon emissions, and improving the efficiency of
traffic operations. This is an essential step in traffic management to address the dilemma of rising
transportation demand and limited land availability [1]. According to the study conducted by
Duchenaux et al. [2], increasing road capacity does not systematically lead to optimal congestion reduction,
and may even causes serious traffic conditions. To achieve better traffic flow prediction performance, many
prediction methods have been proposed covering a wide spectrum, such as parametric methods,
non-parametric methods and hybrid methods [3]–[8].
Like other industries and fields, transportation is impacted by the emergence of vehicular computing
hardware, vehicular and infrastructure sensors and the abundance of information sources. Thus, those
advanced technologies have entered the transportation topic in era of intelligent transport system (ITS) and
big data. Traffic flow forecasting relies strongly on historical and real-time traffic data of time series
collected from various sources. The recent years have seen a significant amount of transportation data
collected from multiple sources including electromagnetic loops, radar, cameras, mobile devices. Although
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4649-4660
4650
there are several techniques and models for predicting traffic flow, most of them are still somewhat
unsatisfactory especially for traffic forecasting during peak periods. This prompts us to rethink the traffic
flow prediction problem based on deep architecture models coupled with time series analysis and the
integration of those systems.
The present work will focus on the prediction problem, through the analysis of multivariate time
series (MTS). Unlike univariate time series that summarize only one variable at a time, MTS arises when
multiple interconnected streams of data are recorded over time, those are typically produced by devices
with multiple (heterogeneous) sensors and are characterized by interactions in time but also between its
different dimensions. are among the recent models of MTS [9]. The first one is based on the Fourier
transform. The second model is based on random forests allowing the transformation of an MTS into a
symbolic representation in the form of a string of characters. This method can suffer from scalability
(complexity) problems especially for long time sequences. As a result, the classification of MTS is still an
open problem.
Our project’s main goal is to forecast the number of vehicles passing in highway through each toll
plaza per hour and per payment mode (electronic toll and manual toll) by designing and implementing of a
machine learning solution for data integration that allows, from the fusion between two data sources
available, to have a more accurate and precise prediction in peak periods. The goal is to enable managers,
through those predictions, to manage and control traffic through these projections in order to decrease
congestion and thus promote road safety, as well as to anticipate the size of the infrastructures and the
resources to be given (reduce or increase the number of lanes and toll collectors).
This article presents a new approach to solve the problem of traffic flow prediction mentioned
above, it is articulated as: as an introduction, the importance of an accurate traffic forecasting for financial
and security purposes are highlighted. The second part will be devoted to the theoretical concepts of the state
of the art of multivariate time series forecasting and the existing machine learning models applied in traffic
prediction. Then, several useful add-ons and models will be reviewed in the third part according to their
contribution in the process of building up a traffic prediction model. The predictive performance will be
compared based on different metrics. The fourth part will review an existing open challenge in the traffic
prediction task, which is the need to enhance predictions in peak periods by data integration from multiple
sources.
2. THE PROPOSED METHOD
2.1. Dataset overview
Our study is based on real databases provided by an infrastructure manager of highways. The dataset
contains traffic flow data coming from two sources:
− Traffic sensors in each toll lane offering the electromagnetic loops data: The dataset contains traffic
stream data recorded inside every hour during 4 successive years: 2016, 2017, 2018 and 2019. Through
the different sensors installed, the vehicles are classified into three classes based on their length and axles.
The following physical and quantifiable factors are used to distinguish classes: i) the length of the vehicle,
ii) the number of axles, and iii) the overall height of the vehicle. The meta-data of the files includes the
date (day and hour), station name and code. Also, a decomposition of the traffic by the class of the
vehicle (lightweight vehicle: class 1, truck: class 2, vehicle with at least three axles: class 3, examples of
recorded traffic are presented in Table 1.
Table 1. Dataset example
Station 222 234 237
Day 14/12/2019 03/11/2019 31/12/2019
Hour 3 17 21
ELE_CL1 1 3 18
SUB_CL1 0 1 6
CAD_CL1 0 1 1
CSH_CL1 11 47 49
ELE_CL2 2 8 0
SUB_CL2 4 6 2
CAD_CL2 1 1 4
CSH_CL2 12 25 57
ELE_CL3 3 1 7
SUB_CL3 5 10 2
CAD_CL3 1 7 5
CSH_CL3 2 9 15
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− Closed-circuit television (CCTV): Video recordings obtained from two types of cameras. One placed at
20 Km from the toll plaza recording the vehicles passing through this area and the other near each plaza
recording the incoming and outgoing vehicles (of which a sample is illustrated in Figure 1).
Figure 1. Overview of a video recording made at the Fez West highway station-Morocco
2.2. Descriptive analysis of the dataset
The boxplot, also called Tukey box presented in Figure 2, is a graphical representation that
illustrates the shape and spread of the dataset distribution: the median, quartiles and extreme values. The
dataset contains 26,257 observations of traffic flow information about a specific axle in Morocco’s highway
recorded in four years and segmented by vehicle’s classification and payment method. We observe that the
total traffic in the studied station varies between 0 and 1,733 vehicles per hour, with a value of median equal
to 674 that shares the central box. The Figure 2 represents the boxplot of the sub-series “Cash”, “Electronic
toll”, and “Subscription card” for class 2 vehicles.
Figure 2. Dataset statistics recorded between 2016 and 2018 for class 2 vehicles
2.3. Background and methodology
2.3.1. Univariate and multivariate time series
This study deals with a time series problem, which are sets of observations that are distinguished by
the important role played by the order in which they were collected. The object is the study of variables over
time. There are two types of time series [10]:
− Univariate time series: a time series made up entirely of single observations (also known as scalars) that
are progressively recorded across equal time intervals. Time is an implicit variable in the time series,
despite the fact that a univariate time series data set is typically presented as a single column. The time
variable, or index, need not be specified explicitly if the data are equally spaced apart. The series can
occasionally be directly plotted using the time variable. However, the time series model itself does not
make use of it.
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− Multivariate time series: is used to describe a time series with multiple time dependent variables. Each
variable has some relationship with other variables in addition to being dependent on its prior values. It is
possible to anticipate future values using this reliance. This sort of time series can be split into two
categories: those that predict one value from several factors and those that predict two or more values
from multiple variables.
2.3.2. Recurrent neural networks
A Recurrent neural networks (RNN) is a class of artificial neural networks (ANN) whose
associations between nodes can form a cycle, allowing the output from some nodes to influence the input to
other nodes in the same network [11]. The idea of RNN was designed by Jordan in 1980s [12] to exhibit
temporal dynamic behavior [13]. The result of traffic prediction tasks depends on both the current situation as
well as the previous state. To solve this problem, RNN was developed to represent traffic characteristics
without previous information and to explain the impacts from successive traffic records [14]. As a result, it is
better able to capture the temporal correlations between the traffic data [15]. Figure 3 illustrates how an RNN
is divided into several repetition modules based on time. There is typically only one gate in an RNN repeat
module.
Figure 3. The functioning of RNN
If the input time sequence is presented 𝑋 = [𝑥1, 𝑥2, … , 𝑥𝑇], where 𝑥𝑖 is the data observed at time 𝑖,
and the output vector is 𝐻 = [ℎ1, ℎ2, … , ℎ𝑇], and ℎ𝑖 is the output based on the 𝑥𝑖𝑡ℎ input and output in the
prior timestamp, which is represented as:
ℎ𝑖 = 𝐹(𝑊. [ℎ𝑖−1, 𝑥𝑖] + 𝑏)
where 𝐹 is the activate function, 𝑊 is the weight and 𝑏 is the bias.
We may infer that the outputs of the previous repeat modules as well as the current timestamp input
data make up the input set of the current repeat module. Based on the output given by the gate structure
contained inside the module, the module evaluates how much of the information in the previous timestamps
could have an influence on the current prediction result and how much information should be transited to the
following timestamp. The training of RNNs also makes use of back-propagation techniques. There might be
a gradient explosion or gradient disappearance issue during the optimization stage if the input series is long
enough. To address this problem, the gate control was developed. Using a gating mechanism to govern how
information is distributed is the idea behind gate control.
Long-short term memory (LSTM) module was presented in 1997 by Hochreiter and Schmidhuber as
a solution to the long-term dependency issue that existed in fundamental RNN [16]. In the recurrent hidden
layer of the LSTM are unique components referred as memory blocks. The memory blocks include unique
multiplicative units called gates to regulate the information flow as well as memory cells with self-
connections that store the network’s temporal state. In the original architecture, each memory block had an
input gate and an output gate. The flow of input activations into the memory cell is controlled by the input
gate. According to Figure 4, the output gate regulates how cell activations exit into the remainder of the
network. The memory block afterwards received the forget gate [17]. Before adding it as input to the cell
through the self-recurrent connection, the forget gate scales the internal state of the cell [18], [19]. To
simulate the internal calculating process of the LSTM, Zhou et al. described four phases in [20]. With, 𝑋𝑡 is
the input value of the LSTM cell at time 𝑡, 𝐶𝑡 is the state value memory cell at time 𝑡, ℎ𝑡 is the output value
at time 𝑡, σ denotes the 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 activation function and 𝑡𝑎𝑛ℎ means the 𝑡𝑎𝑛ℎ activation function.
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Figure 4. Structure of LSTM
3. RESEARCH MEHOD FOR PREDICTION MODEL
3.1. Feature extraction
The feature extraction step is essential for the realization of deep learning models. For this purpose,
we present in Figure 5 a visualization of the major factors that can impact the highway traffic. By analyzing
the transactional data offered by electromagnetic loops, we first notice that we are facing a time series
problem that changes according to the following logic: i) a decrease in traffic during Ramadan and the day of
“Eid al Adha” (Muslim celebration of sacrifice); ii) an increase in traffic during Sundays, school vacations-
especially the first and last days-, summer vacations, “Eid al mawlid Nabawi” (Muslim celebration of the
birth of the Prophet), “Eid al Fitr” (Muslim celebration of the breaking of the fast) and a period of two days
after “Eid al Adha”.
Figure 5. Visualization of some features impacting the behavior of studied time series
3.2. Tools and work environment
For the current study, Python is used through its different libraries for data extraction, creation of
regression algorithms and data visualization. This language is very popular and covers a range of usage areas:
web development, artificial intelligence (AI), machine learning, operating systems, mobile application
development, video games and many others. Besides, the prediction model deployment study is based on
TensorFlow which is a computational framework for creating machine learning models. It offers access to
community resources that enable developers to quickly create and deploy machine learning-based
applications.
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Number
of
vehicles
Time
Ramadan School holiday Electronic toll Cash
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3.3. Data processing
The data processing refers to the whole process of collecting, transforming and classifying data. In
order to exploit the python libraries, the data is collected, then normalized between 0 and 1 by using the
MinMaxScaler class of the Scikit-learn library [21]. Then, the data are sorted in ascending order of day and
time and completed by the above-mentioned features as shown in the Table 2.
The implementation of the RNNs models is done according to the process described in Figure 6:
after downloading the dataset, we perform a pre-processing that will prepare our data for the following steps.
We will establish traffic-impacting features based on a visual analysis of the traffic time function, and then
proceed to data normalization which reduces the complexity of the models. The next step defines the inputs
and outputs, then divide the dataset into two parts: 80% of the first observations as training data, and the
remaining 20% as model test data and transform the time series problem into a supervised learning problem
in order to train the created deep learning models.
Table 2. Number of vehicles and some features for the day of April 17th
, 2017
Hours
14:00 15:00 16:00 17:00 18:00
Number of vehicles Electronic 3 4 15 34 28
Cash 468 447 446 545 483
Features Sunday 0 0 0 0 0
Weekday 1 1 1 1 1
School vacation 0 0 0 0 0
Figure 6. Steps for RNN model design
4. RESULTS AND DISCUSSION
After having tested several combinations of architectures applied to the LSTM model, the Table 3
summarizes a comparative of the obtained results. Finding the model that best depicts our data and predicts
how the model will perform in the future is an unavoidable step. The architecture which recorded the best
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performances consists of 14 inputs, two hidden LSTM layers and two outputs. The hidden layers are
composed of 64 and 32 neurons respectively as shown in Figure 7. This finding was established after the use
of three commonly used error measures to evaluate the performance of a regression model:
− Mean square error (MSE): the average of the squared differences between the predicted and expected
target values in a data set. It can be calculated using the mean squared error function in the scikit-learn
library.
− The root mean square error (RMSE): an extension of the MSE. The link between these two quantities is
explained:
𝑀𝑆𝐸 =
1
𝑁
∑ (𝑌𝑖 − 𝑌
̂𝑖
𝑁
𝑖=1 )² and 𝑅𝑀𝑆𝐸 = √
1
𝑁
∑ (𝑌𝑖 − 𝑌
̂𝑖)
2
𝑁
𝑖=1
with 𝑌
̂𝑖 is the predicted value, 𝑌𝑖 is the recorded value, and 𝑁 is number of observations.
Table 3. Comparative of some different architectures applied to the LSTM model with 2 hidden layers
Number of neurons Activation function Dropout layer RMSE Electronic RMSE Manual
128, 64 ReLU 0.1 21.17 22.79
0.2 18.19 20.61
Softmax 0.1 23.23 26.24
0.2 25.28 20.11
64, 32 ReLU 0.1 14.05 18.58
0.2 18.83 20.13
Softmax 0.1 18.87 22.35
0.2 24.18 20.71
Figure 7. Architecture of RNN model
Figures 8 and 9 show a temporal representation of predicted and actual traffic, the predicted values
are plotted in orange, and the actually recorded values are represented by the blue color. We notice that the
two series overlap in the majority of the points, except during the peak traffic periods. Thus, over 93% of the
forecasts were made with less than ±2.0% error. This discrepancy is mainly due to the quality of the model’s
predictions during peak periods, which we propose to improve in the following section by integrating traffic
data from different data sources.
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Figure 8. Plot of predicted and actual values for electronic toll collection, LSTM
Figure 9. Plot of predicted and actual values for manual toll collection, LSTM
4.1. Improving results in peak periods by multiform collection and data fusion in highway context
Data integration aims to provide users with a uniform picture of the data and unified access to data
that needs information from many sources [22]. Ziegler explains the reasons behind the value of integrating
data from different sources [23], which are twofold: i) reliability of information by comparing and merging it
with a collection of current information systems and ii) a deeper comprehension of the issue through the
synthesis of facts from many sources into pertinent and useful knowledge.
A complicated framework with a variety of approaches is frequently needed for effective data
integration from heterogeneous large volumes of data [24]. Such a framework’s construction would be
challenging. Factors like redundancy, reliability and severity [25] have to be considered and validated for
combining data from disparate sources [26] into meaningful and more accurate predictions of traffic flow
[27]. Furthermore, few studies concerning data integration in a real highway context exist prior to this present
work, revealing a gap in the scientific field, especially the management of traffic peaks remains an open
challenge [28]. Figure 10 shows the scheme pursued for data integration.
Two processing engines are necessary for the execution of our framework: the first is the video
handling motor, which examines the CCTV recordings, and the second is the log handling motor, which
checks electromagnetic loop data. Three times per day, the video streams were recorded (morning, afternoon
and night). Utilizing you only look once (YOLOv4) software for real-time object detection system [29], as
shown in Figure 11, we can detect cars with the best speed and accuracy. Utilizing deep SORT [30], the
vehicles can be tracked. A sample of the result from the video processing is shown in Figure 12.
According to the previous sections, it was noted that the data collected from electromagnetic loops
can provide imperfect or erroneous predictions during potential peak traffic days (Sundays, vacations…). In
the other side, the performance of video data varies depending on the time of day, visibility and camera
position. In order to improve prediction results in peak periods, we thought of integrating these two
complementary data sources. For this purpose, we create three features: i) visibility: the creation of this
characteristic starts with the collection of the weather data, as showed in Figure 13; ii) time of day (day=1 or
night=0); and iii) probable peak days: Sunday and first and last day of vacations and celebrations.
Depending on visibility and traffic peak conditions, we rate the dependability of each data source.
For instance, the video has a high “Weight Vid” index on sunny days and a lower value on nights when it is
raining. Similar to electromagnetic loops data, electromagnetic loops data has a high “Weight Tran” index
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during off-peak times and a low weight at peak times. The function designed to distribute weights across
those data sources is depicted in Figure 14.
The established model offers generally accurate predictions based on loop data, with the exception
of traffic peaks brought on by particular unique events, such as religious holidays, cultural or sporting events,
and travel restrictions following a health emergency. While the video processing engine offers lower error
rates during peak hours, these are the only times when visibility is reduced due to opacity at night, rain, or
fog. The instances where both sources simultaneously provided inaccurate information are rare, though.
Consequently, there is interest in combining these complementary data sources to enhance traffic flow
predictions as shown in Table 4.
Figure 10. Data integration process
Figure 11. Illustration of YOLO algorithm
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Figure 12. Utilizing deep sort and YOLOv4 to track and detect vehicles
Figure 13. Data sent by OpenWeatherMap API Figure 14. Assigning weights between data sources
Table 4. A sample of feature fusion for the day of August 28th
, 2021 for station 222
Time TELEP_Tran TELEP_vid TELEP_real Visibility Peak Night Weight_Tran Weight_Vid
06:00 402 410 410 1 0 0 0,8 0,2
12:00 325 340 346 0 1 0 0,6 0,4
21:00 841 895 897 1 1 1 0,4 0,6
Finally, we assess the performance forecasts using data from electromagnetic loops and videos. The
peaks shown in loop data and the low visibility in videos are to blame for the differences seen between the
predicted values and the actual values. Integration makes it possible to select a dependable data source, which
enhances the accuracy of predictions as shown in Table 5.
Table 5. RMSE of electromagnetic loops and video data integration forecasts
RMSE Manual toll RMSE Electronic toll
Loops data 34.43 94.26
Video data 93.96 70.25
Integration 27.95 10.27
5. CONCLUSION
This paper presents several long-term prediction models based on recurrent neural networks with a
real traffic data set. The architecture which recorded the best performances consists of 14 inputs, two hidden
LSTM layers with 64 and 32 neurons respectively and two outputs. Thus, over 93% of the forecasts were
made with less than ±2.0% error. This discrepancy is mainly due to the quality of the model’s predictions
during peak periods. In order to improve the prediction results especially under abnormal traffic conditions,
we perform a smart system for data integration from heterogeneous data sources (loops and camera) that aims
to provide the most correct and complete as possible traffic predictions. This paper verifies the robustness of
the prediction model built, in various weather conditions. This work paves the way for several lines of
research such as adaptive model learning and the progressive incorporation of models based on large
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databases which are present in a small proportion of the reviewed works and which require real-time
updating of traffic data, which implies updating of model parameters and better consideration of exogenous
factors.
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BIOGRAPHIES OF AUTHORS
Nadia Slimani Engineer graduated from Mohammedia School of Engineering
(EMI) in Rabat and currently Ph.D. student in Mohammedia School of Engineering. She has a
long experience with a national transport operator in Morocco. She also attended the higher
cycle of management at the ISCAE of Rabat. Her research and publication interests include
machine learning, traffic forecasting and intelligent transportation systems. She can be
contacted at email: slimani.nadia@gmail.com. https://www.researchgate.net/profile/Nadia-
Slimani-2.
Siham Yousfi holds a Ph.D. in computer science from Mohammedia School of
Engineering (EMI) in Rabat and currently Professor in School of Information Science and a
member of Computer Systems and Productivity Team (EMI) and Meridian Team (ESI). In
addition, she is a reviewer for leading computer science journals. She can be contacted at
email: siham.yousfi@gmail.com. https://www.researchgate.net/profile/Siham-Yousfi.
Mustapha Amghar holder of a Ph.D. in industrial computing from Liege
University in Belgium as well as a university habilitation in research from the Mohammedia
School of Engineering in Morocco. He is currently Professor in Electrical Department at
Mohammedia School of Engineering. For more than 30 years, he has been carrying out
consulting missions for public and private organizations. He can be contacted at email:
amghar@emi.ac.ma. https://www.researchgate.net/profile/Mustapha-Amghar.
Nawal Sbiti holder of a Ph.D. in automation and industrial computing from
Mohammedia School of Engineering in Morocco as well as a university habilitation in
research from the same institution. She is currently Professor in Electrical Department at
Mohammedia School of Engineering. For more than 30 years, she has been carrying out
consulting missions for public and private organizations. She can be contacted at email:
sbiti@emi.ac.ma.