The inflow caused by tourists in peak seasons exerts an uncontrollable pressure on the existing infrastructure. The Ayubia National Park in Pakistan faces traffic delays and capacity restraints on the connecting roads...The inflow caused by tourists in peak seasons exerts an uncontrollable pressure on the existing infrastructure. The Ayubia National Park in Pakistan faces traffic delays and capacity restraints on the connecting roads in peak season. The study focuses on the formulation of critical strategies by deploying amendments in the transport network. The methodology contains three parts</span><span style="font-family:Verdana;">:</span><span style="font-family:Verdana;"> 1) a questionnaire was designed to inquire about several variables from the visitors</span><span style="font-family:Verdana;">;</span><span style="font-family:Verdana;">2) the</span><span style="font-family:""> </span><span style="font-family:Verdana;">second part was traffic count data collection and analysis. Based on the response collected, the impact of multiple strategies on the network was analyzed using TransCad</span><span style="font-family:Verdana;">;3) </span><span style="font-family:Verdana;">in the third part</span><span style="font-family:Verdana;">, </span><span style="font-family:Verdana;">the results obtained were shared with experts to gain their valuable opinions. It was observed that the time of the day based access restriction to heavy vehicles could lead to dropping the Volume to capacity ratio from 1.7 to 1.2. However, the experts were also of the view that network changes can enhance and improve the visitors’ experience.展开更多
Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility features.They play a crucial role in modern reconnaissance,inspection,intel...Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility features.They play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance missions.Coverage path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large region.Therefore,energy consumption is one of the most challenging issues that need to be optimized.In this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP problem.The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region.To do so,the flight path is optimized and the number of turns is reduced to minimize the energy consumption.The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint.Then,the coverage path planning problem is formulated,where the exact solution is determined using the CPLEX solver.For small-scale problems,the CPLEX shows a better solution in a reasonable time.However,the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems.Thus,to solve the model for large-scale problems,simulated annealing forCPP is developed.The results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX solver.Finally,we compare the simulated annealing against the greedy algorithm.The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.展开更多
Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Th...Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Thus,improving the performance of the network and making it attractive to social media-based systems.Security issues are one of the most challenges encountered in FC.In this paper,we propose an anomalybased Intrusion Detection and Prevention System(IDPS)against Man-in-theMiddle(MITM)attack in the fog layer.The system uses special nodes known as Intrusion Detection System(IDS)nodes to detect intrusion in the network.They periodically monitor the behavior of the fog nodes in the network.Any deviation from normal network activity is categorized as malicious,and the suspected node is isolated.ExponentiallyWeighted Moving Average(EWMA)is added to the system to smooth out the noise that is typically found in social media communications.Our results(with 95%confidence)show that the accuracy of the proposed system increases from 80%to 95%after EWMA is added.Also,with EWMA,the proposed system can detect the intrusion from 0.25–0.5 s seconds faster than that without EWMA.However,it affects the latency of services provided by the fog nodes by at least 0.75–1.3 s.Finally,EWMA has not increased the energy overhead of the system,due to its lightweight.展开更多
Recently,Internet of Drones(IoD)has garnered significant attention due to its widespread applications.However,deploying IoD for area coverage poses numerous limitations and challenges.These include interference betwee...Recently,Internet of Drones(IoD)has garnered significant attention due to its widespread applications.However,deploying IoD for area coverage poses numerous limitations and challenges.These include interference between neighboring drones,the need for directional antennas,and altitude restrictions for drones.These challenges necessitate the development of efficient solutions.This research paper presents a cooperative decision-making approach for an efficient IoDdeployment to address these challenges effectively.The primary objective of this study is to achieve an efficient IoDdeployment strategy thatmaximizes the coverage regionwhile minimizing interference between neighboring drones.In deployment problem,the interference increases as the number of deployed drones increases,resulting in bad quality of communication.On the other hand,deploying a few drones cannot satisfy the coverage demand.To accomplish this,an enhanced version of a concise population-based meta-heuristic algorithm,namely Improved Particle SwarmOptimization(IPSO),is applied.The objective function of IPSO is defined based on the coverage probability,which is primarily influenced by the characteristics of the antennas and drone altitude.A radio frequency(RF)model is derived to evaluate the coverage quality,considering both Line of Sight(LOS)and Non-Line of Sight(NLOS)down-link coverage probabilities for ground communication.It is assumed that each drone is equipped with a directional antenna to optimize coverage in a given region.Extensive simulations are conducted to assess the effectiveness of the proposed approach.Results demonstrate that the proposed method achieves maximum coverage with minimum transmission power.Furthermore,a comparison is made against Collaborative Visual Area Coverage Approach(CVACA),and a game-based approach in terms of coverage quality and convergence speed.The simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage and convergence speed.Comparisons validate the superiority of our approach over existing methods.To assess the robustness of the proposed RFmodel,we have considered two distinct ranges of noise:range1 spanning from−120 to−90 dBm,and range2 spanning from−90 to−70 dBmfor different numbers of UAVs.In summary,this research presents a cooperative decision-making approach for efficient IoD deployment to address the challenges associatedwith area coverage and achieves an optimal coveragewithminimal interference.展开更多
The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our lives.However,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and th...The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our lives.However,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and threats.Many interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this problem.Given the resource limitations of fog computing environments,a lightweight IDS is essential.This paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based IDS.We test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm rate.We compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate metrics.Our findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource use.The practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog node.The proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge devices.We prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting approaches.Extensive experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.展开更多
Enforcement of trafc rules and regulations involves a wide range of complex tasks,many of which demand the use of modern technologies.variable speed limits(VSL)control is to change the current speed limit according to...Enforcement of trafc rules and regulations involves a wide range of complex tasks,many of which demand the use of modern technologies.variable speed limits(VSL)control is to change the current speed limit according to the current trafc situation based on the observed trafc conditions.The aim of this study is to provide a simulation-based methodological framework to evaluate(VSL)as an effective Intelligent Transportation System(ITS)enforcement system.The focus of the study is on measuring the effectiveness of the dynamic trafc control strategy on trafc performance and safety considering various performance indicators such as total travel time,average delay,and average number of stops.United Arab Emirates(UAE)was selected as a case study to evaluate the effectiveness of this strategy.A micro simulation software package VISSIM with add-on module VisVAP is used to evaluate the impacts of VSL.It has been observed that VSL control strategy reduced the average delay time per vehicle to around 7%,travel time by 3.2%,and number of stops by 48.5%.Dynamic trafc control strategies also alleviated congestion by increasing the capacity of the bottleneck section and improving safety.Results of this study would act as a guidance for engineers and decision makers to new trafc control system implementation.展开更多
Although the pick-up/drop-off(PUDO)strategy in carpooling offers the convenience of short-distance walking for passengers during boarding and disembarking,there is a noticeable hesitancy among commuters to adopt this ...Although the pick-up/drop-off(PUDO)strategy in carpooling offers the convenience of short-distance walking for passengers during boarding and disembarking,there is a noticeable hesitancy among commuters to adopt this travel method,despite its numerous benefits.Here,this paper establishes a tripartite evolutionary game theory(EGT)model to verify the evolutionary stability of choosing the PUDO strategy of drivers and passengers and offering subsidies strategy of carpooling platforms in carpooling system.The model presented in this paper serves as a valuable tool for assessing the dissemination and implementation of PUDO strategy and offering subsidies strategy in carpooling applications.Subsequently,an empirical analysis is conducted to examine and compare the sensitivity of the parameters across various scenarios.The findings suggest that:firstly,providing subsidies to passengers and drivers,along with deductions for drivers through carpooling platforms,is an effective way to promote wider adoption of the PUDO strategy.Then,the decision-making process is divided into three stages:initial stage,middle stage,and mature stage.PUDO strategy progresses from initial rejection to widespread acceptance among drivers in the middle stage and,in the mature stage,both passengers and drivers tend to adopt it under carpooling platform subsidies;the factors influencing the costs of waiting and walking times,as well as the subsidies granted to passengers,are essential determinants that require careful consideration by passengers,drivers,and carpooling platforms when choosing the PUDO strategy.Our work provides valuable insight into the PUDO strategy’s applicability and the declared results provide implications for traffic managers and carpooling platforms to offer a suitable incentive.展开更多
Nowadays,resilience has become an indispensable term in several aspects and areas of research and life.Reaching consensus on what actually constitutes"resilience,""community,"and"community res...Nowadays,resilience has become an indispensable term in several aspects and areas of research and life.Reaching consensus on what actually constitutes"resilience,""community,"and"community resilience"is still a task that guarantees a vivid exchange of opinions,sometimes escalating into debates,both in the scientific community and among practitioners.Figuring out how to practically apply resilience principles goes even a step further.This study attempts to circumvent the need for a universal agreement on the definition of"community resilience,"which may still be immature,if not impossible,at this time.We accomplish this by proposing a practical methodological approach with concrete methods on how to agree and implement commonly accepted community resilience principles in the context of technology development and pilot testing for disaster management.The proposed approach was developed,tested,and validated in the context of the Horizon 2020 EU-funded project Search and Rescue.Major aspects of the approach,along with considerations for further improvement and adaptation in different contexts,are addressed in the article.展开更多
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give approp...Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article's Creative Commons licence,unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use,you will need to obtain permission directly from the copyright holder.展开更多
The traffic in developing countries presents its own specificity,notably due to the heterogeneous traffic and a weak-lane discipline.This leads to differences in driver behavior between these countries and developed c...The traffic in developing countries presents its own specificity,notably due to the heterogeneous traffic and a weak-lane discipline.This leads to differences in driver behavior between these countries and developed countries.Knowing that the analysis of the drivers from developed countries leads the design of the majority of driver models,it is not surprising that the simulations performed using these models do not match thefield data of the developing countries.This article presents a systematic review of the literature on modeling driving behaviors in the context of developing countries.The study focuses on the microsimulation approaches,and specifically on the multiagent paradigm,that are considered suitable for reproducing driving behaviors with accuracy.The major contributions from the recent literature are analyzed.Three major scientific challenges and related minor research directions are described.展开更多
文摘The inflow caused by tourists in peak seasons exerts an uncontrollable pressure on the existing infrastructure. The Ayubia National Park in Pakistan faces traffic delays and capacity restraints on the connecting roads in peak season. The study focuses on the formulation of critical strategies by deploying amendments in the transport network. The methodology contains three parts</span><span style="font-family:Verdana;">:</span><span style="font-family:Verdana;"> 1) a questionnaire was designed to inquire about several variables from the visitors</span><span style="font-family:Verdana;">;</span><span style="font-family:Verdana;">2) the</span><span style="font-family:""> </span><span style="font-family:Verdana;">second part was traffic count data collection and analysis. Based on the response collected, the impact of multiple strategies on the network was analyzed using TransCad</span><span style="font-family:Verdana;">;3) </span><span style="font-family:Verdana;">in the third part</span><span style="font-family:Verdana;">, </span><span style="font-family:Verdana;">the results obtained were shared with experts to gain their valuable opinions. It was observed that the time of the day based access restriction to heavy vehicles could lead to dropping the Volume to capacity ratio from 1.7 to 1.2. However, the experts were also of the view that network changes can enhance and improve the visitors’ experience.
基金funded by Project Number INML2104 under the Interdisci-Plinary Center of Smart Mobility and Logistics,KFUPM.
文摘Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility features.They play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance missions.Coverage path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large region.Therefore,energy consumption is one of the most challenging issues that need to be optimized.In this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP problem.The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region.To do so,the flight path is optimized and the number of turns is reduced to minimize the energy consumption.The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint.Then,the coverage path planning problem is formulated,where the exact solution is determined using the CPLEX solver.For small-scale problems,the CPLEX shows a better solution in a reasonable time.However,the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems.Thus,to solve the model for large-scale problems,simulated annealing forCPP is developed.The results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX solver.Finally,we compare the simulated annealing against the greedy algorithm.The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.
基金The Authors would like to acknowledge the support of King Fahd University of Petroleum and Minerals for this research.
文摘Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Thus,improving the performance of the network and making it attractive to social media-based systems.Security issues are one of the most challenges encountered in FC.In this paper,we propose an anomalybased Intrusion Detection and Prevention System(IDPS)against Man-in-theMiddle(MITM)attack in the fog layer.The system uses special nodes known as Intrusion Detection System(IDS)nodes to detect intrusion in the network.They periodically monitor the behavior of the fog nodes in the network.Any deviation from normal network activity is categorized as malicious,and the suspected node is isolated.ExponentiallyWeighted Moving Average(EWMA)is added to the system to smooth out the noise that is typically found in social media communications.Our results(with 95%confidence)show that the accuracy of the proposed system increases from 80%to 95%after EWMA is added.Also,with EWMA,the proposed system can detect the intrusion from 0.25–0.5 s seconds faster than that without EWMA.However,it affects the latency of services provided by the fog nodes by at least 0.75–1.3 s.Finally,EWMA has not increased the energy overhead of the system,due to its lightweight.
基金funded by Project Number INML2104 under the Interdisciplinary Center of Smart Mobility and Logistics at King Fahd University of Petroleum and Minerals.This study also was supported by the Special Research Fund BOF23KV17.
文摘Recently,Internet of Drones(IoD)has garnered significant attention due to its widespread applications.However,deploying IoD for area coverage poses numerous limitations and challenges.These include interference between neighboring drones,the need for directional antennas,and altitude restrictions for drones.These challenges necessitate the development of efficient solutions.This research paper presents a cooperative decision-making approach for an efficient IoDdeployment to address these challenges effectively.The primary objective of this study is to achieve an efficient IoDdeployment strategy thatmaximizes the coverage regionwhile minimizing interference between neighboring drones.In deployment problem,the interference increases as the number of deployed drones increases,resulting in bad quality of communication.On the other hand,deploying a few drones cannot satisfy the coverage demand.To accomplish this,an enhanced version of a concise population-based meta-heuristic algorithm,namely Improved Particle SwarmOptimization(IPSO),is applied.The objective function of IPSO is defined based on the coverage probability,which is primarily influenced by the characteristics of the antennas and drone altitude.A radio frequency(RF)model is derived to evaluate the coverage quality,considering both Line of Sight(LOS)and Non-Line of Sight(NLOS)down-link coverage probabilities for ground communication.It is assumed that each drone is equipped with a directional antenna to optimize coverage in a given region.Extensive simulations are conducted to assess the effectiveness of the proposed approach.Results demonstrate that the proposed method achieves maximum coverage with minimum transmission power.Furthermore,a comparison is made against Collaborative Visual Area Coverage Approach(CVACA),and a game-based approach in terms of coverage quality and convergence speed.The simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage and convergence speed.Comparisons validate the superiority of our approach over existing methods.To assess the robustness of the proposed RFmodel,we have considered two distinct ranges of noise:range1 spanning from−120 to−90 dBm,and range2 spanning from−90 to−70 dBmfor different numbers of UAVs.In summary,this research presents a cooperative decision-making approach for efficient IoD deployment to address the challenges associatedwith area coverage and achieves an optimal coveragewithminimal interference.
基金supported by the interdisciplinary center of smart mobility and logistics at King Fahd University of Petroleum and Minerals(Grant number INML2400).
文摘The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our lives.However,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and threats.Many interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this problem.Given the resource limitations of fog computing environments,a lightweight IDS is essential.This paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based IDS.We test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm rate.We compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate metrics.Our findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource use.The practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog node.The proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge devices.We prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting approaches.Extensive experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.
文摘Enforcement of trafc rules and regulations involves a wide range of complex tasks,many of which demand the use of modern technologies.variable speed limits(VSL)control is to change the current speed limit according to the current trafc situation based on the observed trafc conditions.The aim of this study is to provide a simulation-based methodological framework to evaluate(VSL)as an effective Intelligent Transportation System(ITS)enforcement system.The focus of the study is on measuring the effectiveness of the dynamic trafc control strategy on trafc performance and safety considering various performance indicators such as total travel time,average delay,and average number of stops.United Arab Emirates(UAE)was selected as a case study to evaluate the effectiveness of this strategy.A micro simulation software package VISSIM with add-on module VisVAP is used to evaluate the impacts of VSL.It has been observed that VSL control strategy reduced the average delay time per vehicle to around 7%,travel time by 3.2%,and number of stops by 48.5%.Dynamic trafc control strategies also alleviated congestion by increasing the capacity of the bottleneck section and improving safety.Results of this study would act as a guidance for engineers and decision makers to new trafc control system implementation.
基金the National Natural Science Foundation of China under Grant Nos.72171172 and 62088101the Shanghai Municipal Science and Technology,China Major Project under Grant No.2021SHZDZX0100the Shanghai Municipal Commission of Science and Technology,China Project under Grant No.19511132101.
文摘Although the pick-up/drop-off(PUDO)strategy in carpooling offers the convenience of short-distance walking for passengers during boarding and disembarking,there is a noticeable hesitancy among commuters to adopt this travel method,despite its numerous benefits.Here,this paper establishes a tripartite evolutionary game theory(EGT)model to verify the evolutionary stability of choosing the PUDO strategy of drivers and passengers and offering subsidies strategy of carpooling platforms in carpooling system.The model presented in this paper serves as a valuable tool for assessing the dissemination and implementation of PUDO strategy and offering subsidies strategy in carpooling applications.Subsequently,an empirical analysis is conducted to examine and compare the sensitivity of the parameters across various scenarios.The findings suggest that:firstly,providing subsidies to passengers and drivers,along with deductions for drivers through carpooling platforms,is an effective way to promote wider adoption of the PUDO strategy.Then,the decision-making process is divided into three stages:initial stage,middle stage,and mature stage.PUDO strategy progresses from initial rejection to widespread acceptance among drivers in the middle stage and,in the mature stage,both passengers and drivers tend to adopt it under carpooling platform subsidies;the factors influencing the costs of waiting and walking times,as well as the subsidies granted to passengers,are essential determinants that require careful consideration by passengers,drivers,and carpooling platforms when choosing the PUDO strategy.Our work provides valuable insight into the PUDO strategy’s applicability and the declared results provide implications for traffic managers and carpooling platforms to offer a suitable incentive.
基金funded in part by the Horizon 2020 EU-funded project‘‘Search and Rescue’’under Grant Agreement No.882897。
文摘Nowadays,resilience has become an indispensable term in several aspects and areas of research and life.Reaching consensus on what actually constitutes"resilience,""community,"and"community resilience"is still a task that guarantees a vivid exchange of opinions,sometimes escalating into debates,both in the scientific community and among practitioners.Figuring out how to practically apply resilience principles goes even a step further.This study attempts to circumvent the need for a universal agreement on the definition of"community resilience,"which may still be immature,if not impossible,at this time.We accomplish this by proposing a practical methodological approach with concrete methods on how to agree and implement commonly accepted community resilience principles in the context of technology development and pilot testing for disaster management.The proposed approach was developed,tested,and validated in the context of the Horizon 2020 EU-funded project Search and Rescue.Major aspects of the approach,along with considerations for further improvement and adaptation in different contexts,are addressed in the article.
文摘Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article's Creative Commons licence,unless indicated otherwise in a credit line to the material.If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use,you will need to obtain permission directly from the copyright holder.
基金supported by the ERAMUS+Higher Education Learning under Grant No.1953215 of Hasselt University Belgium.Alexandre Lombard is supported by the National Inter-UT Project SMART-E2AU 2018-2022 of the“Universitéde Technologie de Belfort-Montbéliard”,France.Stéphane GallandThomas Martinet are supported by the EU project H2020 REDREAM,under Grant No.957837.
文摘The traffic in developing countries presents its own specificity,notably due to the heterogeneous traffic and a weak-lane discipline.This leads to differences in driver behavior between these countries and developed countries.Knowing that the analysis of the drivers from developed countries leads the design of the majority of driver models,it is not surprising that the simulations performed using these models do not match thefield data of the developing countries.This article presents a systematic review of the literature on modeling driving behaviors in the context of developing countries.The study focuses on the microsimulation approaches,and specifically on the multiagent paradigm,that are considered suitable for reproducing driving behaviors with accuracy.The major contributions from the recent literature are analyzed.Three major scientific challenges and related minor research directions are described.
