We present a new Following Car Algorithm in Microscopic Urban Traffic Models which integrates some real-life factors that need to be considered, such as the effect of random distributions in the car speed,acceleration, entry of lane… Our architecture is based on Multi-Agent Randomized Systems (MARS) developed in earlier publications
This document provides a review of fuzzy microscopic traffic flow models. It discusses how fuzzy logic can be used to model traffic flow and driver behavior by introducing uncertainty into variables like speed and headway. It describes fuzzy cellular automata models that represent traffic as vehicles characterized by fuzzy numbers for position and velocity. It also covers fuzzy logic car-following models that use linguistic terms and rules to model car-following behavior, and fuzzy route choice models that calculate possibility indexes to determine the most likely route. The goal of these fuzzy models is to more realistically simulate traffic flow and account for the imprecise nature of traffic data.
Vehicle Headway Distribution Models on Two-Lane Two-Way Undivided RoadsAM Publications
The time headway between vehicles is an important flow characteristic that affects the safety, level of service, driver behavior, and capacity of a transportation system. The present study attempted to identify suitable probability distribution models for vehicle headways on 2-lane 2-way undivided (2/2 UD) road sections. Data was collected from three locations in the city of Semarang: Abdulrahman Saleh St. (Loc. 1), Taman Siswa St. (Loc. 2) and Lampersari St. (Loc.3). The vehicle headways were grouped into one-second interval. Three mathematical distributions were proposed: random (negative-exponential), normal, and composite, with vehicle headway as variable. The Kolmogorov-Smirnov test was used for testing the goodness of fit. Traffic flows at the selected locations were considered low, with traffic volume ranged between 400 to 670 vehicles per hour per lane. The traffic volume on Loc.1 was 484 vehicles per hour, that on Loc. 2 was 405 vehicles per hour, and that on Loc. 3 was 666 vehicles per hour. Random distribution showed good fit at all locations under study with 95% confidence level. Normal distribution showed good fit at Loc. 1 and Loc. 2, whereas composite distribution fit only at Loc. 1. It was suggested that random distribution is to be used as an input in generating traffic in traffic analysis at highway sections where traffic volume are under 500 vehicles per hour.
Modeling business management systems transportationSherin El-Rashied
Introduction
How IT &Business Process Fit Together
What is modeling?
What is Simulation?
Modeling & Simulation in Business Process Management
The Seven-Step Model-Building Process
Transportation
An overview on transportation modeling
Transport model scope & structure
Car Traffic Jam Problem
Aim of Transportation Model
Types of Traffic Models
Microscopic Traffic model & Simulation
Cellular Automaton model
Conclusion
Solving Transportation Problem by Software Application
Class Example
This document describes a study that used an ARIMA time series model to estimate traffic arrival patterns at three signalized intersections on Route 18 in New Jersey. Simulation data from Paramics was used to collect vehicle counts and headways under different demand levels. The ARIMA model was found to predict arrival patterns more accurately than the conventional Poisson model, particularly for over dispersed and under dispersed traffic scenarios. Specifically, the ARIMA model had smaller deviations from the simulation data for metrics like headway distribution, vehicle counts per cycle, and variance-to-mean ratio. This indicates that the ARIMA time series model provides a better approach for estimating real-world traffic arrival patterns compared to traditional distributions like Poisson.
This document discusses developing a traffic simulation model to characterize heterogeneous or mixed traffic conditions in India. It reviews literature on quantifying the mix of different vehicle types and studying the impact of slow moving vehicles. The objective is to model traffic in Agartala, Delhi, Guwahati, and Kolkata on single lane urban roads. Field data will be collected using video cameras and analyzed using simulation software. The expected outcome is a simulation model that provides a better understanding of heterogeneous traffic flow to improve transportation infrastructure utilization and regulation.
Application of a Markov chain traffic model to the Greater Philadelphia RegionJoseph Reiter
A macroscopic traffic model based on the Markov chain process is developed for urban traffic networks. The method utilizes existing census data rather than measurements of traffic to create parameters for the model. Four versions of the model are applied to the Philadelphia regional highway network and evaluated based on their ability to predict segments of highway that possess heavy traffic.
Solving real world delivery problem using improved max-min ant system with lo...ijaia
This paper presents a solution to real-world delive
ry problems (RWDPs) for home delivery services wher
e
a large number of roads exist in cities and the tra
ffic on the roads rapidly changes with time. The
methodology for finding the shortest-travel-time to
ur includes a hybrid meta-heuristic that combines a
nt
colony optimization (ACO) with Dijkstra’s algorithm
, a search technique that uses both real-time traff
ic
and predicted traffic, and a way to use a real-worl
d road map and measured traffic in Japan. We
previously proposed a hybrid ACO for RWDPs that use
d a MAX-MIN Ant System (MMAS) and proposed a
method to improve the search rate of MMAS. Since tr
affic on roads changes with time, the search rate i
s
important in RWDPs. In the current work, we combine
the hybrid ACO method with the improved MMAS.
Experimental results using a map of central Tokyo a
nd historical traffic data indicate that the propos
ed
method can find a better solution than conventional
methods.
This document presents a study on developing an artificial intelligence system to manage real-time traffic. The study developed a traffic simulator using Python to model vehicle and traffic light behavior at an intersection. A linear regression model was then used to control the traffic lights dynamically based on current traffic conditions, collected from sensors. Testing showed the AI-based dynamic system improved traffic flow compared to a static traffic light system, allowing more vehicles to pass through the intersection in a given time period. The authors conclude the linear regression model provides better real-time traffic management than existing approaches and suggest further improving it with deep learning techniques.
This document provides a review of fuzzy microscopic traffic flow models. It discusses how fuzzy logic can be used to model traffic flow and driver behavior by introducing uncertainty into variables like speed and headway. It describes fuzzy cellular automata models that represent traffic as vehicles characterized by fuzzy numbers for position and velocity. It also covers fuzzy logic car-following models that use linguistic terms and rules to model car-following behavior, and fuzzy route choice models that calculate possibility indexes to determine the most likely route. The goal of these fuzzy models is to more realistically simulate traffic flow and account for the imprecise nature of traffic data.
Vehicle Headway Distribution Models on Two-Lane Two-Way Undivided RoadsAM Publications
The time headway between vehicles is an important flow characteristic that affects the safety, level of service, driver behavior, and capacity of a transportation system. The present study attempted to identify suitable probability distribution models for vehicle headways on 2-lane 2-way undivided (2/2 UD) road sections. Data was collected from three locations in the city of Semarang: Abdulrahman Saleh St. (Loc. 1), Taman Siswa St. (Loc. 2) and Lampersari St. (Loc.3). The vehicle headways were grouped into one-second interval. Three mathematical distributions were proposed: random (negative-exponential), normal, and composite, with vehicle headway as variable. The Kolmogorov-Smirnov test was used for testing the goodness of fit. Traffic flows at the selected locations were considered low, with traffic volume ranged between 400 to 670 vehicles per hour per lane. The traffic volume on Loc.1 was 484 vehicles per hour, that on Loc. 2 was 405 vehicles per hour, and that on Loc. 3 was 666 vehicles per hour. Random distribution showed good fit at all locations under study with 95% confidence level. Normal distribution showed good fit at Loc. 1 and Loc. 2, whereas composite distribution fit only at Loc. 1. It was suggested that random distribution is to be used as an input in generating traffic in traffic analysis at highway sections where traffic volume are under 500 vehicles per hour.
Modeling business management systems transportationSherin El-Rashied
Introduction
How IT &Business Process Fit Together
What is modeling?
What is Simulation?
Modeling & Simulation in Business Process Management
The Seven-Step Model-Building Process
Transportation
An overview on transportation modeling
Transport model scope & structure
Car Traffic Jam Problem
Aim of Transportation Model
Types of Traffic Models
Microscopic Traffic model & Simulation
Cellular Automaton model
Conclusion
Solving Transportation Problem by Software Application
Class Example
This document describes a study that used an ARIMA time series model to estimate traffic arrival patterns at three signalized intersections on Route 18 in New Jersey. Simulation data from Paramics was used to collect vehicle counts and headways under different demand levels. The ARIMA model was found to predict arrival patterns more accurately than the conventional Poisson model, particularly for over dispersed and under dispersed traffic scenarios. Specifically, the ARIMA model had smaller deviations from the simulation data for metrics like headway distribution, vehicle counts per cycle, and variance-to-mean ratio. This indicates that the ARIMA time series model provides a better approach for estimating real-world traffic arrival patterns compared to traditional distributions like Poisson.
This document discusses developing a traffic simulation model to characterize heterogeneous or mixed traffic conditions in India. It reviews literature on quantifying the mix of different vehicle types and studying the impact of slow moving vehicles. The objective is to model traffic in Agartala, Delhi, Guwahati, and Kolkata on single lane urban roads. Field data will be collected using video cameras and analyzed using simulation software. The expected outcome is a simulation model that provides a better understanding of heterogeneous traffic flow to improve transportation infrastructure utilization and regulation.
Application of a Markov chain traffic model to the Greater Philadelphia RegionJoseph Reiter
A macroscopic traffic model based on the Markov chain process is developed for urban traffic networks. The method utilizes existing census data rather than measurements of traffic to create parameters for the model. Four versions of the model are applied to the Philadelphia regional highway network and evaluated based on their ability to predict segments of highway that possess heavy traffic.
Solving real world delivery problem using improved max-min ant system with lo...ijaia
This paper presents a solution to real-world delive
ry problems (RWDPs) for home delivery services wher
e
a large number of roads exist in cities and the tra
ffic on the roads rapidly changes with time. The
methodology for finding the shortest-travel-time to
ur includes a hybrid meta-heuristic that combines a
nt
colony optimization (ACO) with Dijkstra’s algorithm
, a search technique that uses both real-time traff
ic
and predicted traffic, and a way to use a real-worl
d road map and measured traffic in Japan. We
previously proposed a hybrid ACO for RWDPs that use
d a MAX-MIN Ant System (MMAS) and proposed a
method to improve the search rate of MMAS. Since tr
affic on roads changes with time, the search rate i
s
important in RWDPs. In the current work, we combine
the hybrid ACO method with the improved MMAS.
Experimental results using a map of central Tokyo a
nd historical traffic data indicate that the propos
ed
method can find a better solution than conventional
methods.
This document presents a study on developing an artificial intelligence system to manage real-time traffic. The study developed a traffic simulator using Python to model vehicle and traffic light behavior at an intersection. A linear regression model was then used to control the traffic lights dynamically based on current traffic conditions, collected from sensors. Testing showed the AI-based dynamic system improved traffic flow compared to a static traffic light system, allowing more vehicles to pass through the intersection in a given time period. The authors conclude the linear regression model provides better real-time traffic management than existing approaches and suggest further improving it with deep learning techniques.
A macroscopic traffic model based on the Markov chain process is developed for urban traffic networks. The method utilizes existing census data rather than measurements of traffic to create parameters for the model. Four versions of the model are applied to the Philadelphia regional highway network and evaluated based on their ability to predict segments of highway that possess heavy traffic.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
INTEGRATION OF GIS AND OPTIMIZATION ROUTINES FOR THE VEHICLE ROUTING PROBLEMijccmsjournal
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in transportation field due to the spatial nature of such problems. In this context, we couple a geographical information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
Integration Of Gis And Optimization Routines For The Vehicle Routing Problemijccmsjournal
This document discusses integrating geographic information systems (GIS) and optimization routines to efficiently solve vehicle routing problems (VRP). Specifically, it proposes coupling a GIS with a particle swarm optimization metaheuristic. This allows generating a geographic solution by mapping optimized vehicle routes rather than just a numeric solution. The approach is demonstrated on a real-world VRP case study for a region in Tunisia. Customer locations, roads, and potential routes are modeled in GIS. Particle swarm optimization is then used to determine the minimum cost vehicle routes while respecting vehicle capacities. This integrated GIS-optimization approach allows visualizing optimized routing solutions on maps for practical transportation planning.
Presentation by Professor Toshio YOSHII of Ehime University of Japan, delivered as a guest seminar during a visit to the Institute for Transport Studies, July 2014.
It is well known that traffic accident tends to occur more in congested flow state than in flee flow state. The developing simulation can estimate the traffic accident risk considering these traffic states. The traffic accident risk shows the likelihood of the occurrence of accidents. 3 traffic states are considered in the analysis, which are free flow, congested flow and mixed flow. The simulation can estimate traffic states at each link and using these states the risk estimation model can estimate traffic accident risks. The risk estimation model has been developed by Poisson regression analysis. The results of the Poisson regression analysis is presented.
This document summarizes a research paper that proposes using a genetic algorithm to solve the travelling salesman problem (TSP). It begins by defining the TSP and explaining that it is NP-hard. The document then reviews various existing approaches that have used genetic algorithms and other metaheuristics to solve TSP. It proposes a genetic algorithm with tournament selection, two-point crossover, and interchange mutation operators. The algorithm is tested on sample problems with 15 cities and is shown to find optimal or near-optimal solutions. In conclusion, the document argues that genetic algorithms can efficiently find good solutions to TSP, especially when combined with knowledge from heuristic methods.
This document summarizes recent research on trajectory planning algorithms for autonomous vehicles. It discusses graph search algorithms like A* that plan optimal paths but have limitations in dynamic environments. Improvements like D* and Focused D* allow recomputing only changed portions of the path. Kinematic A* adds vehicle constraints to generate smoother, safer paths. Overall, the document analyzes how these algorithms aim to enable reliable trajectory planning in unknown, changing environments.
Autonomous smart traffic control is proposed to relieve traffic congestion for bus scheduling, to intelligently accomplish tasks such as on-demand dynamic passenger pickup or drop-off.
Differential game theory for Traffic Flow ModellingSerge Hoogendoorn
Lecture given at the INdAM symposium in Rome, 2017. The lecture shows how you can use differential games to model traffic flows, focussing on pedestrian simulation.
This document reviews a fuzzy logic-based microscopic traffic simulation model. It discusses how fuzzy logic can be applied to problems in traffic engineering that involve uncertainty, such as incident detection and congestion modeling. The review examines literature on using fuzzy set theory for incident detection algorithms. It also discusses problems with current research in the area and potential future directions, such as incorporating fuzzy logic into lane changing rules in microscopic models. The conclusion is that fuzzy logic approaches to traffic signal control can better handle high congestion and uneven traffic flows compared to traditional controls.
A Review on Road Traffic Models for Intelligent Transportation System (ITS)IJSRD
Traffic flow models seek to describe the interaction of vehicles with their drivers and the infrastructure. Almost all the models directly or indirectly characterize the relationship among the traffic variables: the position, the speed, the flow, and the density of vehicles. These relationships can be based on either the behavior of individual vehicles in a traffic network in relation to the dynamics of other vehicles, the overall characteristics of the flow of vehicles in a traffic network, or a combination of the behavior of individual vehicles in a traffic network and the overall traffic flow characteristics. This paper describes the different models for automatic Traffic control system.
The railway capacity optimization problem deals with the maximization of the number of trains running on
a given network per unit time. In this study, we frame this problem as a typical asymmetrical Travelling
Salesman Problem (ATSP), with the ATSP nodes representing the train arrival /departure events and the
ATSP total cost representing the total time-interval of the schedule. The application problem is then
optimized using the standard Ant Colony Optimization (ACO) algorithm. The simulation experiments
validate the formulation of the railway capacity problem as an ATSP and the ACO algorithm produces
optimal solutions superior to those produced by the domain experts.
This document discusses traffic simulation and modelling. It covers different types of traffic models including microscopic, mesoscopic, and macroscopic models. Microscopic models track individual vehicles, macroscopic models aggregate traffic flow data, and mesoscopic models have aspects of both. Simulation models are presented as an alternative to analytical models which require extensive field data collection. The advantages of simulation include being cheaper than field studies and allowing testing of alternative strategies. Current traffic simulation software can model traffic flow at different scales.
A comparison between M/M/1 and M/D/1 queuing models to vehicular traffic atKa...IOSR Journals
This document compares the M/M/1 and M/D/1 queuing models in analyzing vehicular traffic in Kanyakumari district, India. Data on arrival and service rates was collected from various locations and times. Both models were used to calculate metrics like average number of customers in the system, average queue length, average waiting time, and average time spent in the queue. The results showed that the M/M/1 model produced slightly higher values for these metrics compared to the M/D/1 model. Overall, the study found queuing theory can help minimize traffic congestion by analyzing traffic patterns and intensities.
Neural-Geo-Temporal approach to travel demand modellingAndre Dantas
This document describes a neural-geo-temporal modelling (NGTM) approach for analyzing the evolution of urban travel demand. The NGTM uses neural networks and geographic information systems to model spatial and temporal interactions between land use patterns and transportation systems over time. It aims to overcome limitations of traditional travel demand models. The document outlines the theoretical conception of the NGTM and presents a case study applying it to analyze travel demand changes in Nagoya, Japan between 1971-1991. Key features of the integrated NN-GIS database for the case study area are also described.
The document discusses various path planning techniques for mobile robots to navigate between a starting point and destination while avoiding collisions. It describes methods like visibility graphs, roadmaps, cell decomposition, and potential fields. It also covers implementing techniques like breadth-first search on visibility graphs and optimizing robot trajectories using factors like travel time, distance and sensor information.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Building A Web Observatory Extension: Schema.orggloriakt
The document discusses a presentation about extending the Schema.org vocabulary to facilitate web science collaboration through semantic markup. The presentation covers building a Web Observatory, the Schema.org vocabulary, and recommendations for extending the Schema.org vocabulary for a Web Observatory project. It also mentions that a team from Rensselaer Polytechnic Institute won first place in a developer challenge by using semantic technologies and software they developed.
An employing a multistage fuzzy architecture for usability of open source sof...ijcsit
The Demand for Open Source Software (OSS) is increasing day by day. However, its end users still face
challenges using such software. Therefore, this study is conducted to propose a fuzzy usability model to be
an approach for evaluating the usability of the open source software. In order to propose such model, six common usability characteristics have been considered, namely: Learnability, understandability, attractiveness, operability, efficiency, and memorability. Some of these characteristics are related to the open source software's features (Learnability, usability, attractiveness, operability); and the rest are related to the end users. Finally, the Matalab Simulink software (Fuzzy Logic Toolbox) has been employed to simulate and to validate the proposed approach model.
A macroscopic traffic model based on the Markov chain process is developed for urban traffic networks. The method utilizes existing census data rather than measurements of traffic to create parameters for the model. Four versions of the model are applied to the Philadelphia regional highway network and evaluated based on their ability to predict segments of highway that possess heavy traffic.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
INTEGRATION OF GIS AND OPTIMIZATION ROUTINES FOR THE VEHICLE ROUTING PROBLEMijccmsjournal
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in transportation field due to the spatial nature of such problems. In this context, we couple a geographical information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
Integration Of Gis And Optimization Routines For The Vehicle Routing Problemijccmsjournal
This document discusses integrating geographic information systems (GIS) and optimization routines to efficiently solve vehicle routing problems (VRP). Specifically, it proposes coupling a GIS with a particle swarm optimization metaheuristic. This allows generating a geographic solution by mapping optimized vehicle routes rather than just a numeric solution. The approach is demonstrated on a real-world VRP case study for a region in Tunisia. Customer locations, roads, and potential routes are modeled in GIS. Particle swarm optimization is then used to determine the minimum cost vehicle routes while respecting vehicle capacities. This integrated GIS-optimization approach allows visualizing optimized routing solutions on maps for practical transportation planning.
Presentation by Professor Toshio YOSHII of Ehime University of Japan, delivered as a guest seminar during a visit to the Institute for Transport Studies, July 2014.
It is well known that traffic accident tends to occur more in congested flow state than in flee flow state. The developing simulation can estimate the traffic accident risk considering these traffic states. The traffic accident risk shows the likelihood of the occurrence of accidents. 3 traffic states are considered in the analysis, which are free flow, congested flow and mixed flow. The simulation can estimate traffic states at each link and using these states the risk estimation model can estimate traffic accident risks. The risk estimation model has been developed by Poisson regression analysis. The results of the Poisson regression analysis is presented.
This document summarizes a research paper that proposes using a genetic algorithm to solve the travelling salesman problem (TSP). It begins by defining the TSP and explaining that it is NP-hard. The document then reviews various existing approaches that have used genetic algorithms and other metaheuristics to solve TSP. It proposes a genetic algorithm with tournament selection, two-point crossover, and interchange mutation operators. The algorithm is tested on sample problems with 15 cities and is shown to find optimal or near-optimal solutions. In conclusion, the document argues that genetic algorithms can efficiently find good solutions to TSP, especially when combined with knowledge from heuristic methods.
This document summarizes recent research on trajectory planning algorithms for autonomous vehicles. It discusses graph search algorithms like A* that plan optimal paths but have limitations in dynamic environments. Improvements like D* and Focused D* allow recomputing only changed portions of the path. Kinematic A* adds vehicle constraints to generate smoother, safer paths. Overall, the document analyzes how these algorithms aim to enable reliable trajectory planning in unknown, changing environments.
Autonomous smart traffic control is proposed to relieve traffic congestion for bus scheduling, to intelligently accomplish tasks such as on-demand dynamic passenger pickup or drop-off.
Differential game theory for Traffic Flow ModellingSerge Hoogendoorn
Lecture given at the INdAM symposium in Rome, 2017. The lecture shows how you can use differential games to model traffic flows, focussing on pedestrian simulation.
This document reviews a fuzzy logic-based microscopic traffic simulation model. It discusses how fuzzy logic can be applied to problems in traffic engineering that involve uncertainty, such as incident detection and congestion modeling. The review examines literature on using fuzzy set theory for incident detection algorithms. It also discusses problems with current research in the area and potential future directions, such as incorporating fuzzy logic into lane changing rules in microscopic models. The conclusion is that fuzzy logic approaches to traffic signal control can better handle high congestion and uneven traffic flows compared to traditional controls.
A Review on Road Traffic Models for Intelligent Transportation System (ITS)IJSRD
Traffic flow models seek to describe the interaction of vehicles with their drivers and the infrastructure. Almost all the models directly or indirectly characterize the relationship among the traffic variables: the position, the speed, the flow, and the density of vehicles. These relationships can be based on either the behavior of individual vehicles in a traffic network in relation to the dynamics of other vehicles, the overall characteristics of the flow of vehicles in a traffic network, or a combination of the behavior of individual vehicles in a traffic network and the overall traffic flow characteristics. This paper describes the different models for automatic Traffic control system.
The railway capacity optimization problem deals with the maximization of the number of trains running on
a given network per unit time. In this study, we frame this problem as a typical asymmetrical Travelling
Salesman Problem (ATSP), with the ATSP nodes representing the train arrival /departure events and the
ATSP total cost representing the total time-interval of the schedule. The application problem is then
optimized using the standard Ant Colony Optimization (ACO) algorithm. The simulation experiments
validate the formulation of the railway capacity problem as an ATSP and the ACO algorithm produces
optimal solutions superior to those produced by the domain experts.
This document discusses traffic simulation and modelling. It covers different types of traffic models including microscopic, mesoscopic, and macroscopic models. Microscopic models track individual vehicles, macroscopic models aggregate traffic flow data, and mesoscopic models have aspects of both. Simulation models are presented as an alternative to analytical models which require extensive field data collection. The advantages of simulation include being cheaper than field studies and allowing testing of alternative strategies. Current traffic simulation software can model traffic flow at different scales.
A comparison between M/M/1 and M/D/1 queuing models to vehicular traffic atKa...IOSR Journals
This document compares the M/M/1 and M/D/1 queuing models in analyzing vehicular traffic in Kanyakumari district, India. Data on arrival and service rates was collected from various locations and times. Both models were used to calculate metrics like average number of customers in the system, average queue length, average waiting time, and average time spent in the queue. The results showed that the M/M/1 model produced slightly higher values for these metrics compared to the M/D/1 model. Overall, the study found queuing theory can help minimize traffic congestion by analyzing traffic patterns and intensities.
Neural-Geo-Temporal approach to travel demand modellingAndre Dantas
This document describes a neural-geo-temporal modelling (NGTM) approach for analyzing the evolution of urban travel demand. The NGTM uses neural networks and geographic information systems to model spatial and temporal interactions between land use patterns and transportation systems over time. It aims to overcome limitations of traditional travel demand models. The document outlines the theoretical conception of the NGTM and presents a case study applying it to analyze travel demand changes in Nagoya, Japan between 1971-1991. Key features of the integrated NN-GIS database for the case study area are also described.
The document discusses various path planning techniques for mobile robots to navigate between a starting point and destination while avoiding collisions. It describes methods like visibility graphs, roadmaps, cell decomposition, and potential fields. It also covers implementing techniques like breadth-first search on visibility graphs and optimizing robot trajectories using factors like travel time, distance and sensor information.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Building A Web Observatory Extension: Schema.orggloriakt
The document discusses a presentation about extending the Schema.org vocabulary to facilitate web science collaboration through semantic markup. The presentation covers building a Web Observatory, the Schema.org vocabulary, and recommendations for extending the Schema.org vocabulary for a Web Observatory project. It also mentions that a team from Rensselaer Polytechnic Institute won first place in a developer challenge by using semantic technologies and software they developed.
An employing a multistage fuzzy architecture for usability of open source sof...ijcsit
The Demand for Open Source Software (OSS) is increasing day by day. However, its end users still face
challenges using such software. Therefore, this study is conducted to propose a fuzzy usability model to be
an approach for evaluating the usability of the open source software. In order to propose such model, six common usability characteristics have been considered, namely: Learnability, understandability, attractiveness, operability, efficiency, and memorability. Some of these characteristics are related to the open source software's features (Learnability, usability, attractiveness, operability); and the rest are related to the end users. Finally, the Matalab Simulink software (Fuzzy Logic Toolbox) has been employed to simulate and to validate the proposed approach model.
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web:www.canhosaigon365.com
SENSITIVITY ANALYSIS OF INFORMATION RETRIEVAL METRICS ijcsit
Average Precision, Recall and Precision are the main metrics of Information Retrieval (IR) systems performance. Using Mathematical and empirical analysis, in this paper, we show the properties of those metrics. Mathematically, it is demonstrated that all those parameters are very sensitive to relevance judgment which is not usually very reliable. We show that position shifting downwards of the relevant document within the ranked list is followed by Average Precision decreasing. The variation of Average Precision parameter value is highly present in the positions 1 to 10, while from the 10th position on, this variation is negligible. In addition, we try to estimate the regularity of the Average Precision value changes, when we assume that we are switching the arbitrary number of relevance judgments within the existing ranked list, from non-relevant to relevant. Empirically, it is shown hat 6 relevant documents at the end of the 20 document list, have approximately same Average Precision value as a single relevant document at the beginning of this list, while Recall and Precision values increase linearly, regardless of the document position in the list. Also, we show that in the case of Serbian-to-English human translation query followed by English-to-Serbian machine translation, relevance judgment is significantly changed and therefore, all the parameters for measuring the IR system performance are also subject to change.
A S URVIVABILITY M ODEL FOR S AUDI ICT S TART - UPSijcsit
nnovation and entrepreneurship are critical elements in the transition to the knowledge-based economy
and future competition. Unfortunately, innovation tends to
b
e absent in Arab states for many reasons. To
promote innovation in Saudi Arabia, for instance, it is necessary to support inventors’ ideas to turn
inventions into start-up companies, which are companies in their early stage. At the same time, it seems
that there is a need for more academic research to study the success factors of Saudi information and
communication technology (ICT) start-up companies. ICT start-ups are important to the economy because
they are needed for the progress of all industries. Therefore, this study will identify the factors that lead to
successful ICT start-up projects. Then, it will develop a model for the best practices in the interplay among
the defined factors that will increase the opportunity to initiate successful start-ups. This research involves
a factor analysis study based on a quantitative method to measure the interdependences among the success
factors for ICT start-ups. The identified factors are verified using a sample of Saudi
start-up companies.
The study will contribute to enhancing the technological content to diversify the Saudi economy in order to
prepare for the post-oil era
P ERFORMANCE M EASUREMENTS OF F EATURE T RACKING AND H ISTOGRAM BASED T ...ijcsit
In this paper, feature tracking based and histogram
based traffic congestion detection systems are
developed. Developed all system are designed to run
as real time application. In this work, ORB (Orien
ted
FAST and Rotated BRIEF) feature extraction method h
ave been used to develop feature tracking based
traffic congestion solution. ORB is a rotation inva
riant, fast and resistant to noise method and conta
ins the
power of FAST and BRIEF feature extraction methods.
Also, two different approaches, which are standard
deviation and weighed average, have been applied to
find out the congestion information by using
histogram of the image to develop histogram based t
raffic congestion solution. Both systems have been
tested on different weather conditions such as clou
dy, sunny and rainy to provide various illumination
at
both daytime and night. For all developed systems p
erformance results are examined to show the
advantages and drawbacks of these systems.
P REFIX - BASED L ABELING A NNOTATION FOR E FFECTIVE XML F RAGMENTATIONijcsit
XML is
gradually
emplo
yed as
a standard of data exchange
in
web
environment
since its inception
in the
90s
until
present
.
It
serves
as a data exchange between system
s
and other application
s
.
Meanwhile t
he data
volume has grown substantially
in the web and
thus effective methods
of
storing and retrieving
these
data
is
essential
.
One recommended way is
p
hysically or virtually
fragments
the large chunk of data
and
distributes
the fragments
into different nodes.
F
ragmentation design
of XML document
contains of two
parts: fragmentat
ion operation and fragmentation method. The
three
fragmentation o
peration
s are
Horizontal, Vertical
and Hybrid. It
determines how the XML should be fragmented.
This
paper
aims
to give
an overview on the fragmentation design consideration
and
subsequently,
propose a
fragmentation
technique
using
number addressing
.
This document provides tips and sample answers for common interview questions for a maintenance technician position. It discusses how to answer questions about yourself, your strengths, career goals, reasons for leaving previous jobs, weaknesses, knowledge of the organization, and ways you have improved your skills. For each question, it offers steps and examples to effectively convey your qualifications and experience in a positive light.
Ijcsit12REQUIREMENTS ENGINEERING OF A WEB PORTAL USING ORGANIZATIONAL SEMIOTI...ijcsit
The requirements of software are key elements that contribute to the quality and users satisfaction of the
final system. In this work, Requirements Engineering (RE) of web sites is presented using an organizational
semiotics perspective. They are shown as being part of an organization, with particular practices, rules
and views considering stakeholders several differences and opinions. The main contribution of this paper is
to relate an experience, from elicitation to validation, showing how organizational semiotics artifacts were
exploited in a collaborative and participatory way to RE of a web portal. A case study is described in order
to demonstrate the feasibility of using such artifacts to RE when we think about the system as being part of
a social organization.
This document provides tips and sample answers for common interview questions for a legal assistant position. It discusses how to answer questions about yourself, your strengths, career goals, reasons for leaving previous jobs, weaknesses, knowledge of the organization, and ways you've improved your skills. For each question, it offers steps and strategies for crafting effective responses that highlight your relevant qualifications and experience. Sample answers are provided for questions about your background, work history, goals, and steps taken to further your knowledge.
T OWARDS A S YSTEM D YNAMICS M ODELING M E- THOD B ASED ON DEMATELijcsit
This document proposes a new method for constructing system dynamics models that combines the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique with system dynamics modeling. DEMATEL is first used to systematically define and quantify causal relationships between variables in a system. The results from DEMATEL, including impact relation maps and a total influence matrix, are then used to derive the causal loop diagram and define variable weights in the stock-flow chart equations of the system dynamics model. This combined method aims to overcome limitations and subjectivity in traditional system dynamics modeling that relies solely on decision makers' mental models.
This document provides tips and sample answers for common marketing coordinator interview questions. It discusses how to answer questions about yourself, your strengths, career goals, reasons for leaving previous jobs, weaknesses, knowledge of the organization, and ways you've improved your marketing skills. For each question, it offers steps and guidelines for effective responses, including focusing answers on the job requirements, providing evidence of strengths, and avoiding negative statements. Sample answers are provided for questions about the applicant's background, experience, goals, and knowledge of the company.
Comparing of switching frequency on vector controlled asynchronous motorijscai
Nowadays, asynchronous motors have wide range use in many industrial applications. Field oriented
control (FOC) and direct torque control (DTC) are commonly used methods in high performance vector
control for asynchronous motors. Therefore, it is very important to identify clearly advantages and
disadvantages of both systems in the selection of appropriate control methods for many industrial
applications. This paper aims to present a new and different perspective regarding the comparison of the
switching behaviours on the FOC and the DTC drivers. For this purpose, the experimental studies have
been carried out to compare the inverter switching frequencies and torque responses of the asynchronous
motor in the FOC and the DTC systems under different working conditions. The dSPACE 1103 controller
board was programmed with Matlab/Simulink software. As expected, the experimental studies showed that
the FOC controlled motors has a lessened torque ripple. On the other hand, the FOC controlled motor
switching frequency has about 65-75% more than the DTC controlled under both loaded and unloaded
working conditions
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
interface for communication between agents.
class for communication management.
Agent Factory: class for agent creation.
Agent Directory: class for agent registration.
Agent Behavior: abstract class for agent behavior definition.
Concrete Agent: concrete agent implementation.
The core of the architecture is based on three main classes:
- Manager - represents the highest level of hierarchy, manages lower level agents.
- Agent - represents basic autonomous entity, encapsulates behavior and communication.
- Structure - represents geographical area, contains reference to lower level agents.
Agents are organized hierarchically according to geographical areas they represent. Manager is
the root of hierarchy, structures represent areas and agents are located
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.
This document provides an overview of a student's assignment reviewing fuzzy microscopic traffic flow models. It discusses how fuzzy logic can be used to introduce uncertainty into traffic simulation models to better reflect real-world conditions. It reviews different types of fuzzy microscopic models, including fuzzy cellular models that use fuzzy numbers to represent vehicle parameters and transitions between time steps, and fuzzy logic car-following models that use fuzzy reasoning and linguistic terms to describe driver behavior. The goal is to understand how these fuzzy microscopic models work.
Information Spread in the Context of Evacuation OptimizationDr. Mirko Kämpf
The document describes a simulation of evacuation from a building using an agent-based model. Agents represent individuals, groups, or people with communication devices. The simulation analyzes how information spreads during evacuation and compares results between open and restricted geometries. Statistical analysis methods are applied to detect phases or transitions in the system. The impacts of different communication technologies and evacuation strategies are also studied. The goal is to define requirements for communication networks and sensors to optimize the evacuation process based on the simulation results.
This document provides an overview of traffic flow modeling and simulation methods for intelligent transportation systems. It discusses both macroscopic and microscopic modeling approaches. Macroscopic models view traffic as a continuous flow and use partial differential equations involving density, speed, and flow rate over time and space. Microscopic models treat each vehicle individually using ordinary differential equations to model driver behavior and car-following dynamics. The document also reviews several traffic simulation software tools and concludes that modeling and simulation can help design and evaluate new transportation control strategies before implementation.
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost
pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of
optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in
transportation field due to the spatial nature of such problems. In this context, we couple a geographical
information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical
solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
This document discusses using machine learning algorithms to predict traffic flow and reduce congestion at intersections. It compares linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor models on a UK road traffic dataset. All models performed well according to evaluation metrics, indicating they are suitable for an adaptive traffic light system. The system was implemented using a random forest regressor model and simulations showed it reduced traffic congestion by 30.8%, justifying its effectiveness.
A Presentation of fuzzy input. To model and analyze traffic flow, different approaches have been proposed, such as mathematical, statistical, simulation, and artificial intelligence methods. One of the artificial intelligence methods that has been applied to traffic flow is fuzzy logic, which is a form of multi-valued logic that deals with uncertainty, vagueness, and imprecision.
1
Intermodal Autonomous Mobility-on-Demand
Mauro Salazar1,2, Nicolas Lanzetti1,2, Federico Rossi2, Maximilian Schiffer2,3, and Marco Pavone2
Abstract—In this paper we study models and coordination poli-
cies for intermodal Autonomous Mobility-on-Demand (AMoD),
wherein a fleet of self-driving vehicles provides on-demand
mobility jointly with public transit. Specifically, we first present
a network flow model for intermodal AMoD, where we capture
the coupling between AMoD and public transit and the goal is
to maximize social welfare. Second, leveraging such a model,
we design a pricing and tolling scheme that allows the system
to recover a social optimum under the assumption of a perfect
market with selfish agents. Third, we present real-world case
studies for the transportation networks of New York City and
Berlin, which allow us to quantify the general benefits of
intermodal AMoD, as well as the societal impact of different
vehicles. In particular, we show that vehicle size and powertrain
type heavily affect intermodal routing decisions and, thus, system
efficiency. Our studies reveal that the cooperation between AMoD
fleets and public transit can yield significant benefits compared
to an AMoD system operating in isolation, whilst our proposed
tolling policies appear to be in line with recent discussions for
the case of New York City.
I. INTRODUCTION
TRAFFIC congestion is soaring all around the world. Besidesmere discomfort for passengers, congestion causes severe
economic and environmental harm, e.g., due to the loss of
working hours and pollutant emissions such as CO2, partic-
ulate matter, and NOx [1]. In 2013, traffic congestion cost
U.S. citizens 124 Billion USD [2]. Notably, transportation
remains one of a few sectors in which emissions are still
increasing [3]. Governments and municipalities are struggling
to find sustainable ways of transportation that can match
mobility needs and reduce environmental harm as well as
congestion.
To achieve sustainable modes of transportation, new mobil-
ity concepts and technology changes are necessary. However,
the potential to realize such concepts in urban environments is
limited, since upgrades to available infrastructures (e.g., roads
and subway lines) and their capacity are often extremely costly
and require decades-long planning timelines. Thus, mobility
concepts that use existing infrastructure in a more efficient way
are especially attractive. In this course, mobility-on-demand
services appear to be particularly promising. Herein, two main
concepts exist. On the one hand, free floating car sharing
systems strive to reduce the total number of private vehicles
in city centers. However, these systems offer limited flexibility
and are generally characterized by low adoption rates that
result from low vehicle availabilities due to the difficulty of
1Institute for Dynamic Systems and Control ETH Zürich, Zurich (ZH),
Switzerland {samauro,lnicolas}@ethz.ch
2Department of Aeronautics and Astro.
Integrating Fuzzy Mde- AT Framework For Urban Traffic SimulationWaqas Tariq
This document summarizes a research paper that proposes integrating fuzzy modeling concepts with Model Driven Engineering (MDE) and Activity Theory (AT) to develop a framework for simulating urban traffic systems. The framework uses AT concepts like activity, subject, object, tools etc. to model the traffic system. It then applies fuzzy set theory to quantify uncertainty in the modeling. MDE is used to successively refine models from analysis to design. The framework was applied to develop a platform independent model of an urban traffic control system using UML. Fuzzy relationships were defined between model elements to represent uncertainty in message passing between system entities. The framework allows modeling both behavioral and structural aspects of the traffic system using fuzzy concepts integrated with MDE and
This document provides an introduction, literature review, and discussion of determining the range of thresholds for fuzzy input in traffic flow modeling. It discusses how fuzzy logic can be used to represent traffic parameters like density, speed, and volume linguistically rather than with precise values. The document explores applications of fuzzy logic in traffic management, advantages and disadvantages, and recommends a multidimensional analysis using data, simulations, and machine learning to establish effective threshold ranges that capture traffic dynamics.
Study of statistical models for route prediction algorithms in vanetAlexander Decker
This document summarizes and compares three statistical models for predicting vehicle routes in Vehicular Ad-Hoc Networks (VANETs): Markov models, Hidden Markov models (HMM), and Variable Order Markov models (VMM). It describes how each model works, including Markov models predicting the next road segment based on the current one, HMM using both transitions and observations to predict states, and VMM capturing longer dependencies while avoiding size increases of higher-order Markov models. The document also provides pseudocode for route prediction algorithms using each statistical model.
Application of Fuzzy Logic in Transport Planningijsc
Fuzzy logic is shown to be a very promising mathematical approach for modelling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation planning problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. This paper presents an analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes.
APPLICATION OF FUZZY LOGIC IN TRANSPORT PLANNINGijsc
Fuzzy logic is shown to be a very promising mathematical approach for modelling traffic and
transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic
premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed
to solve various traffic and transportation planning problems. Emphasis is put on the importance of fuzzy
logic systems as universal approximators in solving traffic and transportation problems. This paper
presents an analysis of the results achieved using fuzzy logic to model complex traffic and transportation
processes.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
Linear Regression Model Fitting and Implication to Self Similar Behavior Traf...IOSRjournaljce
We present a simple linear regression model fit in the direction of self-similarity behavior of internet user’s arrival data pattern. It has been reported that Internet traffic exhibits self-similarity. Motivated by this fact, real time internet users arrival patterns considered as traffic and the results carried out and proven that it has the self-similar nature by various Hurst index methods. The present study provides a mathematical model equation in terms linear regression as a tool to predict the arrival pattern of Internet users data at web centers. Numerical results, analysis discussed and presented here plays a significant role in improvement of the services and forecasting analysis of arrival protocols at web centers in the view of quality of service (QOS).
Neural Network Based Parking via Google Map GuidanceIJERA Editor
This document describes an intelligent parking guidance system that uses neural networks and algorithms to predict travel times between locations and allocate parking spaces. It consists of an Intelligent Trip Modeling System (ITMS) that uses a Speed Prediction Neural Network System (SPNNS) and Dynamic Traversing Speed Profile (DTSP) algorithm to accurately predict traffic speed and travel times. The system also includes an intelligent parking guidance component that provides information on nearby parking availability and allows users to reserve spaces based on their predicted time of arrival. The overall goal is to help drivers efficiently find parking by predicting travel times and allocating spaces in advance.
This document presents an approach for generating valuable traffic density data to simulate route planning for patrol cars. It involves extracting location data from GPS and tracking devices of patrol cars over time. This data is used to calculate route frequencies, which are then encoded with color to represent density on a map. The route density data is then correlated with crime hotspot information to propose a new route planning simulation for law enforcement. This aims to more efficiently dispatch patrol cars by considering both traffic patterns and crime trends.
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FOLLOWING CAR ALGORITHM WITH MULTI AGENT RANDOMIZED SYSTEM
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 4, August 2013
DOI : 10.5121/ijcsit.2013.5411 143
FOLLOWING CAR ALGORITHM WITH MULTI AGENT
RANDOMIZED SYSTEM
Mounir Gouiouez, Noureddine Rais, Mostafa Azzouzi Idrissi1
1
Laboratoire d’Informatique et Modélisation LIM, FSDM
University Sidi Mohammed Ben Abdellah, Fez, Morocco
raissn@gmail.com
ABSTRACT
We present a new Following Car Algorithm in Microscopic Urban Traffic Models which integrates some
real-life factors that need to be considered, such as the effect of random distributions in the car speed,
acceleration, entry of lane… Our architecture is based on Multi-Agent Randomized Systems (MARS)
developed in earlier publications.
1 INTRODUCTION
Traffic simulation or the simulation of transportation systems are computer models where the
movements of individual vehicles travelling around road networks are determined by using
mathematical models to evaluate and analysis the various traffic conditions. In the literature,
simulation is a process based on building a computer model that suitably represents a real or
proposed system which enables to improve their performance.
In simulating mobile systems, it is important to use mobility models that reflect as close as
possible the reality of their behaviour in real-world including a traffic generation model, and take
into account driver behaviour or specific characteristics of the urban environment.
Hence, Microscopic Traffic Simulation has proven to be one of the most useful tools for analysis
of various traffic systems .In the microscopic simulation, every individual vehicle is modelled as
a single simulation object. They are the only modelling tools available with the capability to
examine certain complex traffic problems [9] [10].
In the mathematical analysis, Traffic flow has been considered as a stochastic process. Adams in
1936 [22] proposed the idea of vehicles arrival at the entry of lane as a random process and
verified its relevance to theory and observations. Afterwards, Greenberg in 1966 [23] has found a
connection between the microscopic traffic flow theory and the lognormal follower headway
distribution. In 1993, Heidemann [24] developed a new approach in which he applied the theory
of stochastic processes to analytically derive the headway distribution as a function of traffic
density. His approach may provide a link between the macroscopic traffic flow theory and the
headway distributions.
Multi-Agent System (MAS) has brought a new vision to study the microscopic phenomena and
complex situations with emphasizes the interactions of components of the systems. In literature,
the MAS is one of the newest area of research in the artificial intelligence (AI), it has started in
the early 90s with Minsky [25], Ferber [26], as an attempt to enrich the limits of classical AI [11].
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 4, August 2013
144
The foundations of the MAS are interested in modelling human behaviours in the real world with
mental notions such as knowledge, beliefs, intentions, desires, choices, commitments [27].
There are various definitions of the concept agent [12, 13, 14] in the contemporary literature;
however, the adopted definition which covers the characteristics of agents developed in the new
model, is that proposed by Jennings, Sycara and Wooldridge: an agent is a computer system,
located in an environment, which is autonomous and flexible to meet the objectives for which it
was designed. As far as MAS is concerned, according to Ferber [26], a MAS is a system
composed of the following: Environment, a set of objects in space; a set of agents who are active
entities of the system; a set of relationships that binds objects together; a set of operations
allowing agents to perceive, destroy, create, transform, and manipulate objects.
Therefore, the main objective of the research is to develop a new microscopic approach in the
MAS combining the notion of theoretical mathematic model, especially the statistic model, and
the main characteristics of the Multi-agent System. Such a combination would pave the way for a
real description of the phenomena.
The application of the statistic model on the traffic problems had been used in the last decades.
Certain applications, such as Poisson Law distribution, were discussed by Kinzer [15] in 1933,
Adams [16] in 1936, and Green shield [17] in 1947.
In order to improve the accuracy of simulators, therefore the accuracy of the results obtained, we
proposed a new hybrid approach to improve the effectiveness of the simulation. In previous
papers [28, 29, 30], we developed a new architecture of MAS to handle Urban Road Traffic, and
Multi Agent Randomized System (MARS) allowing agent’s stochastic behaviour.
In this paper, we develop a new Following Car Algorithm to describe influences and impacts
between agents in Urban Road Traffic. The first section presents system architecture; the second
explains MARS; the third develops the proposed following car algorithm. Finally, Section four
concludes the paper.
2 MAS FOR VEHICLE ROUTING
In phenomena of road traffic, vehicles continually enter and leave the system and are dispersed
over the spatially distributed road infrastructure. Therefore our architecture schematically
depicted in Fig. 1 consists of a number of autonomous entities, called Execution Agents (EAs) –
vehicles - that are situated or embedded in an environment designed by Zone Agent (ZA). The
Main Agent (MA), which is the main element of this architecture, serves to distribute the (EAs).
ZA is the agent responsible of building network from a database that contains all the elements
necessary to build a network (roads, crossroads ...). Besides, during the simulation, it provides all
information about the positions of EAs to the Control Agent (CA).
CA is designed to build a re-active and persistent architecture. This agent records the evolution of
architecture caused by changes of existing resources in the interaction database which discern the
change in the behaviours of the EAs.
In the proposed approach, CA collect all information of the execution agents. Thus, the
accumulated information could be shared between agents, increasing overall efficiency of the
system. During registration, the MA aims to retrieve EAs’ characteristics in the interaction
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 4, August 2013
145
database. Hence, EAs update their knowledge about the other agents. This process of update is
decided according to the collected information by the CA.
Generatesthenetwork
Network
database
Interaction database
Main Agent
Control Agent
Zone Agent
Figure 1. Architecture of MAS2RT
3 TRAFFIC GENERATION
3.1 Distribution model
Modelling the entry of vehicles in the lane presents a crucial step in urban traffic flow simulation.
The distribution model is used to simulate the entry of vehicles in the network and describes how
vehicles arrive at a section. The theoretical study of conditions affecting the traffic of vehicles on
a lane requires the constant use of probability theory. Modelling the vehicle arrivals at the level
entry can be produced in two related methods. The first one focuses on number of vehicles
arrived in a fix interval of time, the second one measures the time interval between the successive
arrivals of vehicles. The vehicle arrival is obviously a random process.Observing the arrivals of
vehicles at the entry of the road, one can notice different patterns; some vehicles arrive at the
same time, others arrive at random instances.
The distribution of vehicles follows arbitrary patterns which makes it impossible to predict the
number of vehicles in each lane. Thus, considering sequence (Tn)n≥0 in which Tn presents the
time of entry of vehicle n in the lanes. This process applies to many situations such as the arrival
of customers at the CTM, the emission of radioactive particles… Generally speaking; this type of
process is relevant to recurrent cases. In the new model, the entry of vehicles is considered as
following:
The sequence (Tn)n≥0 presents the successive time of the entries, is random variables in R+, the set
of positive real numbers, a stochastic process which verifies:
1. T0<T1<T2<…<Tn, the series is strictly increasing, Tn tends to +∞ as n tends to +∞
2. j≠k, Tj-Tj-1 and Tk-Tk-1 are independents.
3. Stationary, that is the number of realizations, in some interval [a, a+t] depends only of the
length t and not of the origin a of that interval, and hence will be denoted N
4. t,P(N − N = 1) = . h + o(h) as h → 0
5. t,P(N − N > 1) = o(h) as h → 0
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 4, August 2013
146
These properties are characteristic of the Poisson process. The coefficient of proportionality
involved in the 4th
property is the average number of vehicles per unit of time. We
note: N ~ P(t).If N ~ P(t), then the waiting time between two successive realizations
T (= ∆t) is a random variable distributed according to the exponential law
Exp() with density e
for t > 0 and average
.
In the following, U refers to independent realizations of the uniform distribution on [0,1]. Then
instants of introducing vehicles can be generated by:
T = T + Δt = T − ln(U) /λ (1)
The speed V of vehicle j at its introduction instant T can be generated using normal distribution
with mean V and standard deviation σ using Box-Muller Algorithm by:
V = V + σ −2ln (U ) cos(2πU ) (2)
4 MICRO-SIMULATION ALGORITHMS
Most micro-simulation algorithms use various driver behaviours to simulate the react of each
vehicle on a network (acceleration, deceleration, and speed), however the MAS can improve
communication between the vehicle and provide a more realistic simulation.
The position and speed of each vehicle on the network is updated once per second based
on CA agent.
Default vehicle and driver characteristics can also be modified to properly analyze and
interpret the simulation.
Once a vehicle is assigned performance and driver characteristics, its movement through
the network is determined by two primary algorithms:
• generating vehicles
• Car following
There are other algorithms which influence vehicle behavior, such as those which govern
queue discharge and traffic signal control, but car following, lane changing, and gap
acceptance are perhaps the most important and are common to all traffic simulation
models. As CORSIM, AIMSUN
4.1 Algorithms generating vehicles
When T reaches the valueT , the jth
vehicle starts with the speed V (see algorithms generating
vehicles)
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 4, August 2013
147
Figure 2 .Distribution Model
T0j is calculated by incrementing T0j-1 with the value ∆tj which is distributed according to the
exponential law Exp(), where is the average number of vehicles per unit of time; while
V0j is distributed according to a normal distribution with average Vem and standard
deviation σv.
4.2 Car following
The vehicles are distributed randomly on the network according to the distribution model defined
for each junction of the network. The vehicles react to the information concerning their next
action. Models differ according to the various answers to the key questions: What is the nature of
the adequate action ? To what stimulus it does react? And how to measure the characteristics of
the other agents? The first and simplest model correspond to the case when the response is
represented by the acceleration or deceleration of the vehicle.
In what follows, we use simplified notations and situations to be enhanced later. We consider
roads with only one lane. Indexes j and k nominate vehicles; j being the last introduced one in the
considered section. Acceleration is supposed a positive parameter “a” and deceleration a negative
parameter “d”.
Every vehicle k moving in the road is modelled as an agent, which has its own entry time T0k,
position XTk at time T>T0k, speed VTk at T> T0k, and acceleration a>0 or deceleration d<0. To
reduce algorithm complexity, we drop the “T” index; xTk is denoted xk meaning the actual
position of vehicle k. Same for Vk,
The algorithm generates the entry time T0k of vehicle k, but k can’t get in the network until road is
free, i.e xk-xk-1 > α. ∆.Vk. Otherwise, k will be delayed and T0k incremented to stay being the
effective entry time of k. Here α is a determined parameter. We take α=2 in our simulations, so
that, when started, vehicle k doesn’t reach and hit him leader. While ∆ is just the time increment
(to simplify ∆= 1).
When in the network, k will react to leading vehicle, if any, designed by k-1: xk = X − X ; is
the distance between k and k-1; Vk =V − V , is the difference between speed of k and that
of k-1. If k and k-1 are not too close (Xk > α.V ) and Vk > 0 (V < V ), vehicle k has to
accelerate. If k and k-1 are too close (Xk < α.V ) and Vk < 0 (V > V ), k has to decelerate.
Model of Barcèlo and al. [19] simplify to:
V = V + 2.5a 1 −
V
V
0.025 +
V
V
(3)
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 4, August 2013
148
V = d + d − d 2(X − S − X ) − V −
V
d
(4)
Otherwise, k will continue with the same speed. Here Sk is the effective length of vehicle k,
maintained constant in our simulations. Vehicle k will continue its progress in the network: Xk
becomes Xk + Vk.
Figure 3 .Proposed Following Car Algorithm
Accelerate (3)
Xk > Vk
Initialisations : T=0 ; j=0, T1=1, V1=Vem, C1=0
T=T+1
k=k+1
Y
N
k>j
Ck=1
N
Xk= Xk +Vk
k=0
Vk< 0
N
N
N
Y
T>=Tj+1
Cj=1
N
j=j+1, cJ=1; Xj=0
Generate Tj+1(1) &
(2)
Y
Xk<XS
Y
Ck =0
Y
Ck-1=0 Y
Tj+1=1+Tj+1
Calculate xk ; Vk
Decelerate (4)
Y Y
Vk< 0
Y
N
N
N
Xa>Vj+1
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 4, August 2013
149
5 CONCLUSION
This work discussed the randomly distributed simulation of the road traffic. It described the main
aspects of the general computer simulation and the specific features of the computer simulation in
the field of road traffic. The first section introduced the topic. The second section provided a
general description of the Microscopic traffic modelling software. The third section proposed
Multi-Agent architecture proceeded with the description of the main issues of the distribution
model and the interaction model as well as the stochastic distribution model. The fourth described
proposed Following Car Algorithm. We are now running simulations using NetLog and
Jade/Java, results will permit to adjust parameters and distributions to make our model closer to
reality.
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