Bayero University Kano, Post graduate Program, Civil Engineering Department, Masters in Highway Engineering, Advance traffic Engineering Assignment Presentation on a review of Microscopic model using artificial intelligence.
Application of artificial intelligence in microscopic model
Abstract—This paper provides a brief overview of the Intelligent Traffic Management System based on Artificial
Neural Networks (ANN). It is being utilized to enhance the present traffic management system and human resource
reliance. The most basic problem with the current traffic lights is their dependency on humans for their working.
The technologies used in the making of this automated traffic lights are Internet of Things, Machine Learning and
Artificial Intelligence. The basic steps used in Internet of Things are reported along with different ANN trainings.
This ANN model can be used for the minimization of traffic on roads and less waiting time at traffic lights. As a
result, we can make traffic lights more automated which in turn eventually deceases our dependency on human
resources
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
Public transport service is one of the most preferred
modes of transportation in today’s smart cities. People prefer
public transport mainly for the cost benefit reasons. The
problems faced by the people while using the public transport
can be overcome by the technology such as Internet of Things
(IOT). In this paper, we present how this technology can be
applied to eliminate the problems faced by the passengers of the
public bus transport service. The Internet of Things technology is
used to provide the passengers waiting at the bus stop with real
time information of the arriving buses. Information such as
arrival time, crowd density and traffic information of the
arriving buses are predetermined and provided to the passengers
waiting at the bus stop. The display boards fitted at the bus stops
provide the real time bus navigation information to the waiting
passengers. This Smart Bus Navigation system enables the
passengers to make smart decisions regarding their bus journey.
This system reduces the anxiety and the waiting time of the
passenger’s at the bus stop. The smart bus navigation system
creates a positive impact and increases the number of people who
prefer to use the public mode of transportation.
Deployment of Intelligent Transport Systems Based on User Mobility to be Endo...ijbuiiir1
The emerging increase in vehicles and very high traffic, demands the need for improved Intelligent Transport Systems (ITS). The available ITSs do not meet all the requirements of the present day situation in providing safetravels and avoidance of congestionin spite of its limitations on road. Intelligent Transport Systemsrequiremore research and implementation of better solutions on the traffic network with increased mobility and more rapid acquisition of data by sense network technology. In this paper a review is made on the present ITS where research is required so that improvement in the course of implementing reality mining can enhance the behavior of ITS. This will breed a forward leap in the improvement of safety and convenience of personal and commercial travel and in turn guarantee an ultimate drop in fatality in the society
Fuzzy Logic Model for Traffic CongestionIOSR Journals
Abstract: Traffic congestion has become a serious problem in the urban districts. This is mainly due to the
rapid increase in the number and the use of vehicles. Travel time, travel safety, environmental quality, and life
quality are all adversely affected by traffic congestion. Many traffic control systems have been developed and
installed to alleviate the problem with limited success. Traffic demands are still high and increasing. The main
focus of this report is to introduce a versatile fuzzy logic traffic flow model capable of making optimal traffic
predictions. This model can be used to evaluate various traffic-light timing plans. More importantly, it provides
a framework for implementing adaptive traffic signal controllers based on fuzzy logic technology. When
implemented it solved the problem of waiting time, travel cost, accident, traffic congestion.
Key words: Traffic Congestion, fuzzy logic, Traffic Density, fuzzy controller, conventional controller.
Abstract—This paper provides a brief overview of the Intelligent Traffic Management System based on Artificial
Neural Networks (ANN). It is being utilized to enhance the present traffic management system and human resource
reliance. The most basic problem with the current traffic lights is their dependency on humans for their working.
The technologies used in the making of this automated traffic lights are Internet of Things, Machine Learning and
Artificial Intelligence. The basic steps used in Internet of Things are reported along with different ANN trainings.
This ANN model can be used for the minimization of traffic on roads and less waiting time at traffic lights. As a
result, we can make traffic lights more automated which in turn eventually deceases our dependency on human
resources
Techniques for Smart Traffic Control: An In-depth ReviewEditor IJCATR
Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted
scholars to investigate other solutions to traffic congestion. One area gaining interest is the use of smart traffic control systems (STCS)
to make traffic routing decisions. These systems use real time data and try to mimic human reasoning thus prove promising in vehicle
traffic control and management. This paper is a review on the motivations behind the emergence of STCS and the different types of
these systems in use today for road traffic management. They include – fuzzy expert systems (FES), artificial neural networks (ANN)
and wireless sensor networks (WSN). We give an in depth study on the design, benefits and limitations of each technique. The paper
cites and analyses a number of successfully tested and implemented STCS. From these reviews we are able to derive comparisons of
the STCS discussed in this paper. For instance, for a learning or adaptive system, ANN is the best approach; for a system that just
routes traffic based on real time data and does not need to derive any data patterns afterwards, then FES is the best approach; for a
cheaper alternative to the FES, then WSN is the least costly approach. All prove effective in traffic control and management with
respect to the context in which each of them is used.
Public transport service is one of the most preferred
modes of transportation in today’s smart cities. People prefer
public transport mainly for the cost benefit reasons. The
problems faced by the people while using the public transport
can be overcome by the technology such as Internet of Things
(IOT). In this paper, we present how this technology can be
applied to eliminate the problems faced by the passengers of the
public bus transport service. The Internet of Things technology is
used to provide the passengers waiting at the bus stop with real
time information of the arriving buses. Information such as
arrival time, crowd density and traffic information of the
arriving buses are predetermined and provided to the passengers
waiting at the bus stop. The display boards fitted at the bus stops
provide the real time bus navigation information to the waiting
passengers. This Smart Bus Navigation system enables the
passengers to make smart decisions regarding their bus journey.
This system reduces the anxiety and the waiting time of the
passenger’s at the bus stop. The smart bus navigation system
creates a positive impact and increases the number of people who
prefer to use the public mode of transportation.
Deployment of Intelligent Transport Systems Based on User Mobility to be Endo...ijbuiiir1
The emerging increase in vehicles and very high traffic, demands the need for improved Intelligent Transport Systems (ITS). The available ITSs do not meet all the requirements of the present day situation in providing safetravels and avoidance of congestionin spite of its limitations on road. Intelligent Transport Systemsrequiremore research and implementation of better solutions on the traffic network with increased mobility and more rapid acquisition of data by sense network technology. In this paper a review is made on the present ITS where research is required so that improvement in the course of implementing reality mining can enhance the behavior of ITS. This will breed a forward leap in the improvement of safety and convenience of personal and commercial travel and in turn guarantee an ultimate drop in fatality in the society
Fuzzy Logic Model for Traffic CongestionIOSR Journals
Abstract: Traffic congestion has become a serious problem in the urban districts. This is mainly due to the
rapid increase in the number and the use of vehicles. Travel time, travel safety, environmental quality, and life
quality are all adversely affected by traffic congestion. Many traffic control systems have been developed and
installed to alleviate the problem with limited success. Traffic demands are still high and increasing. The main
focus of this report is to introduce a versatile fuzzy logic traffic flow model capable of making optimal traffic
predictions. This model can be used to evaluate various traffic-light timing plans. More importantly, it provides
a framework for implementing adaptive traffic signal controllers based on fuzzy logic technology. When
implemented it solved the problem of waiting time, travel cost, accident, traffic congestion.
Key words: Traffic Congestion, fuzzy logic, Traffic Density, fuzzy controller, conventional controller.
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.
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%.
Deployment of Intelligent Transport Systems Based on User Mobility to be Endo...ijcnes
The emerging increase in vehicles and very high traffic, demands the need for improved Intelligent Transport Systems (ITS). The available ITSs do not meet all the requirements of the present day situation in providing safetravels and avoidance of congestionin spite of its limitations on road. Intelligent Transport Systemsrequiremore research and implementation of better solutions on the traffic network with increased mobility and more rapid acquisition of data by sense network technology. In this paper a review is made on the present ITS where research is required so that improvement in the course of implementing reality mining can enhance the behavior of ITS. This will breed a forward leap in the improvement of safety and convenience of personal and commercial travel and in turn guarantee an ultimate drop in fatality in the society.
Simulation Based Analysis of Bee Swarm Inspired Hybrid Routing Protocol Param...Editor IJCATR
Vehicular Ad-hoc Networks (VANET's) are basically emanated from Mobile Ad hoc networks (MANET's) in which
vehicles act as the mobile nodes, the nodes are vehicles on the road and mobility of these vehicles are very high. The main objective of
VANET is to enhance the safety and amenity of road users. It provides intelligent transportation services in vehicles with the
automobile equipment to communicate and co-ordinates with other vehicles in the same network that informs the driver’s about the
road status, unseen obstacles, internet access and other necessary travel service information’s. The evaluation of vehicular ad hoc
networks applications in based on the simulations. A Realistic Mobility model is a basic component for VANET simulation that
ensures that conclusion drawn from simulation experiments will carry through to real deployments. This paper attempts to evaluate the
performance of a Bee swarm inspired Hybrid routing protocol for vehicular ad hoc network, that protocol should be tested under a
realistic condition including, representative data traffic models, and the realistic movement of the mobile nodes which are the vehicles.
In VANET the simulation of Realistic mobility model has been generated using SUMO and MOVE software and network simulation
has been performed using NS2 simulator, we conducted performance evaluation based on certain metric parameters such as packet
delivery ratio, end-to-end delay and normalized overhead ratio.
Review Paper on Intelligent Traffic Control system using Computer Vision for ...JANAK TRIVEDI
In today scenario city will try to modify in the form of smart city with better facilities in terms of education, social-economic life,
better transportation availability, noise free – Eco-friendly environment availability, and ICT- Information and communication technology
enabler for development in the city. In this paper, we are reviewing different work already done or draft by some research in the field of traffic
control system – for better monitoring, tracking and managing using a computer vision system. Nowadays, most of the city installed with
C.C.T.V. – camera for monitoring the traffic related activity.
The realistic mobility evaluation of vehicular ad hoc network for indian auto...ijasuc
In recent years, continuous progress in wireless communication has opened a new research field in
computer networks. Now a day’s wireless ad-hoc networking is an emerging research technology that
needs attention of the industry people and the academicians. A vehicular ad-hoc network uses vehicles as
mobile nodes to create mobility in a network.
It’s a challenge to generate realistic mobility for Indian networks as no TIGER or Shapefile map is
available for Indian Automotive Networks.
This paper simulates the realistic mobility of the Vehicular Ad-hoc Networks (VANETs). The key feature of
this work is the realistic mobility generation for the Indian Automotive Intelligent Transport System (ITS)
and also to analyze the throughput, packet delivery fraction (PDF) and packet loss for realistic scenario.
The experimental analysis helps in providing effective communication for safety to the driver and
passengers.
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.
Improvement of Traffic Monitoring System by Density and Flow Control For Indi...IJSRD
The growth and scale of vehicles today makes management of traffic a constant problem. The existing traffic control system works based on a timing mechanism, meaning an equal time slot is provided for each junction. This is inefficient for non-uniform flow of vehicles. Hence there is a need for a system which is adaptive in nature. Routes should have an option of being granted more time slots depending on the requirements for the given route. This paper proposes a traffic congestion control system which would be adaptive in nature and provide time slot to each route based on traffic density.
Back-Bone Assisted HOP Greedy Routing for VANETijsrd.com
Using advanced wireless local area network technologies, vehicular ad hoc networks (VANETs) have become viable and valuable for their wide variety of novel applications, such as road safety, multimedia content sharing, commerce on wheels, etc., currently, geographic routing protocols are widely adopted for VANETs as they do not require route construction and route maintenance phases. Again, with connectivity awareness, they perform well in terms of reliable delivery. Further, in the case of sparse and void regions, frequent use of the recovery strategy elevates hop count. Some geographic routing protocols adopt the minimum weighted algorithm based on distance or connectivity to select intermediate intersections. However, the shortest path or the path with higher connectivity may include numerous intermediate intersections. As a result, these protocols yield routing paths with higher hop count. In this paper, we propose a hop greedy routing scheme that yields a routing path with the minimum number of intermediate intersection nodes while taking connectivity into consideration. Moreover, we introduce back-bone nodes that play a key role in providing connectivity status around an intersection. Apart from this, by tracking the movement of source as well as destination, the back-bone nodes enable a packet to be forwarded in the changed direction. Simulation results signify the benefits of the proposed routing strategy in terms of high packet delivery ratio and shorter end-to-end delay.
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.
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%.
Deployment of Intelligent Transport Systems Based on User Mobility to be Endo...ijcnes
The emerging increase in vehicles and very high traffic, demands the need for improved Intelligent Transport Systems (ITS). The available ITSs do not meet all the requirements of the present day situation in providing safetravels and avoidance of congestionin spite of its limitations on road. Intelligent Transport Systemsrequiremore research and implementation of better solutions on the traffic network with increased mobility and more rapid acquisition of data by sense network technology. In this paper a review is made on the present ITS where research is required so that improvement in the course of implementing reality mining can enhance the behavior of ITS. This will breed a forward leap in the improvement of safety and convenience of personal and commercial travel and in turn guarantee an ultimate drop in fatality in the society.
Simulation Based Analysis of Bee Swarm Inspired Hybrid Routing Protocol Param...Editor IJCATR
Vehicular Ad-hoc Networks (VANET's) are basically emanated from Mobile Ad hoc networks (MANET's) in which
vehicles act as the mobile nodes, the nodes are vehicles on the road and mobility of these vehicles are very high. The main objective of
VANET is to enhance the safety and amenity of road users. It provides intelligent transportation services in vehicles with the
automobile equipment to communicate and co-ordinates with other vehicles in the same network that informs the driver’s about the
road status, unseen obstacles, internet access and other necessary travel service information’s. The evaluation of vehicular ad hoc
networks applications in based on the simulations. A Realistic Mobility model is a basic component for VANET simulation that
ensures that conclusion drawn from simulation experiments will carry through to real deployments. This paper attempts to evaluate the
performance of a Bee swarm inspired Hybrid routing protocol for vehicular ad hoc network, that protocol should be tested under a
realistic condition including, representative data traffic models, and the realistic movement of the mobile nodes which are the vehicles.
In VANET the simulation of Realistic mobility model has been generated using SUMO and MOVE software and network simulation
has been performed using NS2 simulator, we conducted performance evaluation based on certain metric parameters such as packet
delivery ratio, end-to-end delay and normalized overhead ratio.
Review Paper on Intelligent Traffic Control system using Computer Vision for ...JANAK TRIVEDI
In today scenario city will try to modify in the form of smart city with better facilities in terms of education, social-economic life,
better transportation availability, noise free – Eco-friendly environment availability, and ICT- Information and communication technology
enabler for development in the city. In this paper, we are reviewing different work already done or draft by some research in the field of traffic
control system – for better monitoring, tracking and managing using a computer vision system. Nowadays, most of the city installed with
C.C.T.V. – camera for monitoring the traffic related activity.
The realistic mobility evaluation of vehicular ad hoc network for indian auto...ijasuc
In recent years, continuous progress in wireless communication has opened a new research field in
computer networks. Now a day’s wireless ad-hoc networking is an emerging research technology that
needs attention of the industry people and the academicians. A vehicular ad-hoc network uses vehicles as
mobile nodes to create mobility in a network.
It’s a challenge to generate realistic mobility for Indian networks as no TIGER or Shapefile map is
available for Indian Automotive Networks.
This paper simulates the realistic mobility of the Vehicular Ad-hoc Networks (VANETs). The key feature of
this work is the realistic mobility generation for the Indian Automotive Intelligent Transport System (ITS)
and also to analyze the throughput, packet delivery fraction (PDF) and packet loss for realistic scenario.
The experimental analysis helps in providing effective communication for safety to the driver and
passengers.
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.
Improvement of Traffic Monitoring System by Density and Flow Control For Indi...IJSRD
The growth and scale of vehicles today makes management of traffic a constant problem. The existing traffic control system works based on a timing mechanism, meaning an equal time slot is provided for each junction. This is inefficient for non-uniform flow of vehicles. Hence there is a need for a system which is adaptive in nature. Routes should have an option of being granted more time slots depending on the requirements for the given route. This paper proposes a traffic congestion control system which would be adaptive in nature and provide time slot to each route based on traffic density.
Back-Bone Assisted HOP Greedy Routing for VANETijsrd.com
Using advanced wireless local area network technologies, vehicular ad hoc networks (VANETs) have become viable and valuable for their wide variety of novel applications, such as road safety, multimedia content sharing, commerce on wheels, etc., currently, geographic routing protocols are widely adopted for VANETs as they do not require route construction and route maintenance phases. Again, with connectivity awareness, they perform well in terms of reliable delivery. Further, in the case of sparse and void regions, frequent use of the recovery strategy elevates hop count. Some geographic routing protocols adopt the minimum weighted algorithm based on distance or connectivity to select intermediate intersections. However, the shortest path or the path with higher connectivity may include numerous intermediate intersections. As a result, these protocols yield routing paths with higher hop count. In this paper, we propose a hop greedy routing scheme that yields a routing path with the minimum number of intermediate intersection nodes while taking connectivity into consideration. Moreover, we introduce back-bone nodes that play a key role in providing connectivity status around an intersection. Apart from this, by tracking the movement of source as well as destination, the back-bone nodes enable a packet to be forwarded in the changed direction. Simulation results signify the benefits of the proposed routing strategy in terms of high packet delivery ratio and shorter end-to-end delay.
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1. BAYERO UNIVERSITY KANO
FACULTY OF ENGINEERING
DEPARTMENT OF CIVIL ENGINEERING
CIV 8331: ANDVANCE TRAFFIC ENGINEERING
ASSIGNMENT
ON
Review the microscopic travel model
using artificial intelligence.
BY
KABIR ABUBAKAR
SPS/20/MCE/00023
SUPERVISED BY:
Prof. H.M. ALHASSAN
2. OUTLINES
Introduction
What Artificial intelligence is and main areas of its
utilization in transportation
Main reasons for successful growing of AI at present
Main areas of AI using in transportation
Artificial neural nets using in transportation
Some neural nets application description in road
transport
Driver behaviour modelling
Advantages and disadvantage of using neural nets in
transportation
Future research work
Conclusion
3. 1.0 INTRODUCTION
• Intelligent technologies which are penetrating to different parts of human life
don't ignore transportation. As an example we can take intelligent transport
systems and automated transport systems which use information, transportation
and communication technologies implemented to vehicles or to infrastructure.
These systems aim to increase people or goods mobility along with increasing road
safety and transportation comfort, reduction of transport collisions and impacts on
environment.
• Information technologies usage became inherent component of “human
“development. Ability to effectively process and use information and knowledge
became one of the most important parts of economic growth and prosperity. In
transportation, the still changing environment of many participants, special
attention should be paid to artificial intelligence – progressive information
technology.
4. 2. What Artificial intelligence is and main areas
of its utilization in transportation
• There are a lot of definitions of Artificial intelligence (AI), for better
imagination what AI is I choose following two from Marvin Minsky and
John L. Gordon:
• Marvin Minsky: Artificial intelligence is the science of making machines or
systems do things that would require intelligence if done by men:
• John L. Gordon: The aim of Artificial Intelligence is to create intelligent
machines and through this, to understand the principles of intelligence. At
the moment, we can settle for creating less stupid machines.
According to these definitions we can say that AI systems are
characterized by:
• They think like people
• They act like people
• They think reasonably (rational)
For their implementation is the necessary to get information and
knowledge and using information and knowledge to achieve the goal or
solution.
5. 2.1. Main reasons for successful growing of AI
at present
However AI theory is developing some decade years already its using had to
wait for progress in IT technologies area. Including AI in transport machines or
systems requires:
• The huge development of IT technologies.
• The development of computer components – mainly speedy processors, high
capacity memory devices etc.
• Digitalization of sound and image – for inputs.
Computer networks creation and growth as wireless nets, logistic systems,
Internet are:
• Satellite and mobile nets.
• Progress in transport devices area.
Thanks to this current technical progress Artificial Intelligence contains ways
and means to be used in transportation such as neural nets, AI planning,
evolution algorithms, knowing and expert systems, fuzzy logic, multi-agent
systems, vector regression, data mining, optimizing techniques, etc.
6. 2.2. Main areas of AI using in transportation
AI at present provides instruments and allows solving problems in each kind of transport and
their interaction (air, road, railway and water transport) and is used in areas such as:
• Real time transport managing
• Design, operation, time schedule and administration of logistical systems and freight
transport
• Transport policy, planning, projecting and managing
• Questions related to environment, toll – roads, reliability and safety
• Transport systems operation
• Usage and administration of new technologies
• Travel demands analysis, predictions and transport organization
• Travel information systems and services
• Transport and surroundings intelligence technologies
• Pedestrian and herd behavior analysis and simulations
• City planning of sustainable mobility
• Service oriented architecture of vehicles and vehicles in communication infrastructure.
• Transport technology review and evaluation
• Artificial transport systems and simulations
AI techniques allow using applications for entire transport system managing – vehicle, driver,
infrastructure and the way in which these components dynamically offer transport services.
All purpose AI instruments and their power are suitable for complicated and diversified
transport systems.
7. 3. Artificial neural nets using in transportation
According to diversity of AI and to its growing usage I am only able to
describe in this article neural networks use in some areas of road
transport.
Nowadays IT era force us to handle more and more information in very
short time. That is why it is inevitable to construct and use such technical
devices which are able to sort out important information from quantity
and according to its design suitable solution for current situation, perhaps
even predict following situation. These complicated problems are partially
solved by neural networks utilizing knowledge about data organizing and
administration in human brain.
8. 3.1. Artificial neural nets definition
Artificial neural nets can be defined as massive parallel computing system
open to saving and following execution of information while simulating
human brain in collecting data during learning process and saving of these
data using inter-neural connections.
Artificial neural nets are one of the options in situations where there are
no strict rules according to which it is possible to simulate result of the
situation or where these rules are too complex or incomplete. Statistical
methods, multi-agent systems or adaptive computing systems are further
alternatives. It is suitable to use standard AI methods when rules are
known.
Artificial neural networks (ANN) are another attempt to model associative
reasoning and pattern matching typical of human brain. At present, these
networks only model the process that connects input data with output
data by exploiting computer ability to perform an iterative series of fast
numerical computations (Hajek and Hurdal , 1993).
9. Artificial neural nets CONT
Neural networks represent a valuable methodological tool in the
field of transportation research, particularly in the areas of traffic
pattern recognition, classification and prediction, congestion and
incident detection, driver route choice modeling (Dougherty, Kirby
and Boyle, 1994). For instance, as far as traffic distribution is
concerned, conventional systems are based on time interval
dependent actions. In other words, owing to the difficulty of
dynamic traffic assignment modeling, it is generally assumed that
network traffic state (i.e the traffic pattern) is static in a given time
interval. The adaptive resonance theory (ART) is an ANN model able
to provide more reasonable traffic pattern recognition results than
the most common methods, by allowing parallel processing and
tolerance adjustability (Faghri and Hua, 1992).
Moreover, ANN have been used in signal timing control, through a
network based pattern classification and evaluation procedure of
monitoring and control strategies.
10. 4. Some neural nets application description in
road transport
The following section describes some important and interesting
applications of neural networks in a road transport and explains NN
using in these solutions.
4.1. Driving of unmanned vehicles or computer controlled cars
One of the most valid successes at present is using artificial neural
nets in the road transport. When appeared vehicles on the road
without steering wheels of a man for the first time they aroused a
wave of interest. Simply because it is amazing to see how the car
passes several thousand kilometres without a driver and no crash.
Currently is their development in full drift and they have already
driven more than a half million kilometres. In one US state there is a
law allowing unmanned vehicles on the roads. Unmanned vehicles
controlled by computer covered many different transport situations
without an incident.
11. Unmanned vehicles development is related to development of Automatic
Transport Systems ATS. These systems use only electric vehicles and different
transport organization as usual. Main feature is using only unmanned vehicles
and eliminating other drivers from limited ATS area what will bring more
discipline and less accidents in the transport. Only electric vehicles are
planned in this project to clear away exhaust fumes and to enrich
environment. Such projects are currently realized in some countries, in others
is its development financed.
4.2. Driver behaviour modelling
Sometimes using GPS or other navigation doesn’t have to be the best solution.
Driver has to decide about by-pass road or using highroad etc. There are
models allowing designing such transport systems which consider safety and
effectiveness of human mobility. Feed forward neural nets are used in
analysing desirable road from interactive simulators.
Such system was created as following:
Neural net was designed with volunteers providing trial journeys. During the
journey they were deciding about the most effective and the most suitable
way from many different criterions. According to the actions of volunteers
neural net training set was created. This neural net learned same decisions as
volunteering drivers and was able to choose journey from unknown data.
12. 4.3. Creation of models which can forecast
following of traffic signs by driver
There are algorithms created to solve this problem. Current models use fuzzy
logic and neural nets combination to overcome limitations of existing
algorithms. Using neural nets to solve such problems is preferable due to their
ability to solve nonlinear relations and because they are immune against
mistakes obtained from imperfect inputs. NN are suitable for reactive behavior
modelling which is often described as rules connecting perceived situation
with attributable measures.
Models which can forecast following of traffic signs by driver can be used as a
part in Intelligent transport system (ITS) or ATS.
4.4. Systems for advising maintenance and repair of paths and roads can
foresee problems on the roads caused by weather or abrasion
ITS and ATS need to have such parts which offer overall view of roads and
paths state for either road participants or transport companies which are in
charge of road conditions.
Systems for advising maintenance and repair of paths and roads can be
divided to two subsystems – diagnostic and prognostic.
Diagnostic subsystem can be classified as pattern detecting problem. Neural
nets are used here to process road surface snapshots and assigning them to
different defect categories.
13. Diagnostic subsystem also automatically detects by-pass roads or
damaged roads.
Prognostic subsystem is complex according to its conformity – road
repair actions are not only dependant on actual road condition but
also on traffic intensity and on financial needs required for road
repair. Data collection for all potential situations is extremely
difficult. Suitable solution might be connecting more neural nets to
one system.
4.5. Systems for classification and registering of passing vehicles
NN are in this case used to process input data from signalers built
beside the roads (video cam with high performing snapshot
processing, sensors, etc.) Their main contribution is noticeable
during bad external conditions. These systems were successfully run
in licensed trademark reading.
4.6. Traffic net analysis and Journey planning and optimizing
These systems use neural nets to diagnose traffic jams and analyse
season changes in the traffic and can plan the most effective route
what can shorten journeys, lower accidents and finally save
environment. The most difficult part is setting parameters for the
problem which is nonlinear.
14. 4.7. Traffic streaming forecast
Very important parts of ATS or ITS are systems to recognize and predict
congestions to inform all road users about actual situation.
The benefit of a neural network to solve this problem is that it absorbs
patterns in data and so can learn to generalize. The main features of a
neural network approach are trials of its application to a congestion
recognition problem to short term and long term forecasting of flows
Models to recognize and predict congestions include:
• Short term forecast – forecasting few minutes, can be part of transport
managing system.
• Long term forecast – forecasting months or years, important in planning
and building roads.
4.8. Transport economics
Neural nets can also be used in solving problems in the area no one would
expect – impact of noise on real estate price close to transport arteries.
Used neural net consisted of instrument which could analyse many
variables – real estate condition, age, largeness and of course noise factor
of vehicles
15. 4.9. Traffic sign recognition
There are devices that can detect, recognise and follow traffic signs
from moving vehicle. Recognition is done by colour segmentation
and neural nets classification. Existing systems can not only
recognise traffic signs but also locate and gather them.
• Locating is realised by approximating location from GPS device and
location of traffic sign acquired from video cam or video file.
• Traffic signs gathering help to build traffic sign database which in
the same time composes training data set.
• Traffic signs are characterized by features from which the most
important for detecting and recognising are colour and shape.
• Detection is done by classical methods based on tresholding and
colour segmentation using different colour models or shape models
(in black and white images) or their combination. 3D modelling is
also often used.
There are more methods which use machine learning algorithms for
classification and detection.
16. 5.1. Advantages of using neural nets in
transportation
Following advantages of using neural nets applies broadly as well as in
road transport:
1. Neural nets allow parallel data processing and by using appropriate
hardware it is possible to allocate calculations on more parallel processors.
This capability of NN is essential for example to construct unmanned
vehicle due to a processing a huge number of inputs from surroundings
during driving the vehicle.
2. Neural net doesn’t need any information about process structure to
which it is implicated, it learns and does not to be reprogrammed.
Instead of it is possible use just suitably chosen training set and
appropriate network architecture. Train with back propagation of errors
set the parameters (weights and thresholds) of network so that we get
acceptable solution. The solution can be finalized by simulations and
experimentation rather than rigorous and formal approach to the
problem.
17. 3. If neural net is used with learning algorithm it can be adapted to changes
in parameters.
4. Neural nets are suitable for identification, classification and sorting of
models – using in recognition of road signs, registration plates, driving
licenses, people faces and others.
5. If neural nets are implemented without learning algorithm they are quite
fast.
Learning algorithm is a huge programme process that can significantly slow
the NN.
6. NN allows abstracting managing rules for different regulators (such as
human being or regulator with long computing time) and replace them.
Very important in unmanned vehicles – human solutions in the cars are
relatively so slow that cause most of road accidents. A decisions of NN
system is disproportionately faster.
7. NN allows data reduction to smaller dimension.
8. NN is universal approximator – they allow approximation of any function
with any accuracy.
18. 5.2. Disadvantages of using neural nets in
transportation
1. Artificial intelligence and NN also need a huge hardware
support.
2. There is no methodology for neural net architecture and
functions for neural description. Implementation is done
by experiments and mistakes what increase time demand
on solution.
3. The architecture of a neural network is different from the
architecture of microprocessors therefore needs to be
emulated.
4. Learning process can take very long time.
5. During the learning process can became the situation
when neurons reach the state of saturation consequently
their outputs lead to extreme values for example sensible
error signals.
19. 6. Future research work
The findings of this research has clarified about ANN being a robust model
because of its ability of covering multiple tasks associated with AI, and the
fact that it does not concentrate or need deep understanding of the
processes. The advantage of it also includes the relationship between the
inputs and outputs for the purpose of recognising the patterns, which may
further help in the management of voluminous data through the well-
adjusted and performance when surrounding by noisy data.
The fast computation tool can help in reducing the time, while improving
the performance. Arguably, there are several researches that have only
focused on one or two of the traffic parameters for the purpose of
developing the model, which means that the future researchers can focus
on enhancing the predictive operations through the use of more than two
features.
In addition, the future researchers can further work on more than one
hidden layer for the model’s structure.
20. 7.0 Conclusion
Artificial Intelligence and Neural nets included in
there have broad utilization in every area of
transport. Their applications can be found in all
systems involving road transport management, such
as
• Automatic transport systems using electric computer
managed vehicles
• Intelligent road systems
• Intelligent highway systems
• Traffic road logistics and many other.
Nowadays all rich developed countries involve in
development of these systems which costs high
financial means.