The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A...The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.展开更多
In this study,a strategy is proposed to use the congestion index as a new input feature.This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter(PM_(2.5))conce...In this study,a strategy is proposed to use the congestion index as a new input feature.This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter(PM_(2.5))concentrations.To assess the effectiveness of this strategy,we conducted an ablation experiment on the congestion index and implemented a multi-scale input model.Compared with conventional models,the strategy reduces the root mean square error(RMSE)of all benchmark models by>6.07%on average,and the bestperforming model reduces it by 12.06%,demonstrating excellent performance improvement.In addition,evenwith high traffic emissions,the RMSE during peak hours is still below 9.83μg/m^(3),which proves the effectiveness of the strategy by effectively addressing pollution hotspots.This study provides new ideas for improving urban environmental quality and public health and anticipates inspiring further research in this domain.展开更多
Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road ...Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road occupancy and vehicle density.Therefore,the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment.Conventional prediction systems face difficulties in identifying highly congested areas,which leads to reduced prediction accuracy.The problem is addressed by integrating Graph Neural Networks(GNN)with the Lion Swarm Optimization(LSO)framework to tackle the congestion prediction problem.Initially,the traffic information is collected and processed through a normalization process to scale the data and mitigate issues of overfitting and high dimensionality.Then,the traffic flow and temporal characteristic features are extracted to identify the connectivity of the road segment.From the connectivity and node relationship graph,modeling improves the overall prediction accuracy.During the analysis,the lion swarm optimization process utilizes the concepts of exploration and exploitation to understand the complex traffic dependencies,which helps predict high congestion on roads with minimal deviation errors.There are three core optimization phases:roaming,hunting,and migration,which enable the framework to make dynamic adjustments to enhance the predictions.The framework’s efficacy is evaluated using benchmark datasets,where the proposed work achieves 99.2%accuracy and minimizes the prediction deviation value by up to 2.5%compared to other methods.With the new framework,there was a more accurate prediction of realtime congestion,lower computational cost,and improved regulation of traffic flow.This system is easily implemented in intelligent transportation systems,smart cities,and self-driving cars,providing a robust and scalable solution for future traffic management.展开更多
Advanced information and communication technolo-gies can be used to facilitate traffic incident management.If an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a...Advanced information and communication technolo-gies can be used to facilitate traffic incident management.If an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work.Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled.Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is delivered.The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.展开更多
In order to alleviate urban traffic congestion and provide fast vehicle paths,a hidden Markov model(HMM)based on multi-feature data of urban regional roads is constructed to solve the problems of low recognition rate ...In order to alleviate urban traffic congestion and provide fast vehicle paths,a hidden Markov model(HMM)based on multi-feature data of urban regional roads is constructed to solve the problems of low recognition rate and poor instability of traditional model algorithms.At first,the HHM is obtained by training.Then according to dynamic planning principle,the traffic states of intersections are obtained by the Viterbi algorithm.Finally,the optimal path is selected based on the obtained traffic states of intersections.The experiment results show that the proposed method is superior to other algorithms in road unobstruction rate and recognition rate under complex road conditions.展开更多
Re-routing system has become an important technology to improve traffic efficiency.The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic,making the planned routes unable to co...Re-routing system has become an important technology to improve traffic efficiency.The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic,making the planned routes unable to cope with the changing traf-fic conditions.Based on real-time traffic information,it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion.Moreover,how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system.To deal with these challenges,this paper proposes CHRT,a clustering-based hybrid re-routing system for traffic congestion avoidance.CHRT develops a multi-layer hybrid architecture.The central server accesses the global view of traffic,and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead.Then,a clustering-based priority mechanism is proposed,which sets priorities for clusters based on realtime traffic information to avoid secondary congestion.Furthermore,to plan the optimal routes for vehicles while alleviating global traffic congestion,this paper presents a multi-metric re-routing algorithm.Through extensive simulations based on the SUMO traffic simulator,CHRT reduces vehicle traveling time,fuel consumption,and CO2 emissions compared to other systems.In addition,CHRT globally alleviates traffic congestion and improves traffic efficiency.展开更多
Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic proc...Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic process. A Dynamic Traffic Assignment modeling fundamental combined with an urban congestion analysis method is studied in this paper. Three methods are based on congestion analysis, and the stochastic user optimal DTA models are especially considered. Correspondingly, a dynamic system optimal model is suggested for responding congestion countermeasures and an ideal user optimal model for predicted congestion countermeasure respectively.展开更多
The aim of this paper is to study traffic properties in an on/off-ramp system with a bus stop close to the on/off ramp. The location of the bus stop in the on/off-ramp （thereafter downstream or upstream case） is dis...The aim of this paper is to study traffic properties in an on/off-ramp system with a bus stop close to the on/off ramp. The location of the bus stop in the on/off-ramp （thereafter downstream or upstream case） is discussed. The simulation results show that in the two ramp systems, the reasons for traffic congestions are different. In the on-ramp system, buses and cars coming from on-ramp interweave each other, while in the off-ramp system, buses interweave with cars exiting to off-ramp. Thus, in the on-ramp （off-ramp） system, the upstream （downstream） bus stop is helpful to reduce the interweaving situation. Moreover, the negative effect will disappear when the distance between the bus stop and the on/off-ramp is more than 20 cells （i.e. 150 m）. These qualitative findings may provide some suggestions on traffic management and optimization.展开更多
The article intends to find a method to quantify traffic congestion＇s impacts on travelers to help transportation planners and policy decision makers well understand congestion situations. Three new congestion indica...The article intends to find a method to quantify traffic congestion＇s impacts on travelers to help transportation planners and policy decision makers well understand congestion situations. Three new congestion indicators, including transportation environment satisfaction （TES）, travel time satisfaction （TTS）, and traffic congestion frequency and feeling （TCFF）, are defined to estimate urban traffic congestion based on travelers＇ feelings. Data of travelers＇ attitude about congestion and trip information were collected from a survey in Shanghai, China. Based on the survey data, we estimated the value of the three indi- cators. Then, the principal components analysis was used to derive a small number of linear combinations of a set of variables to estimate the whole congestion status. A linear regression model was used to find out the significant variables which impact respondents＇ feelings. Two ordered logit models were used to select significant variables of TES and TTS. Attitudinal factor variables were also used in these models. The results show that attitudinal factor variables and cluster category variables are as important as sociodemographic variables in the models. Using the three congestion indicators, the government can collect travelers＇ feeling about traffic congestion and estimate the transportation policy that might be applied to cope with traffic congestion.展开更多
One-way traffic organization is a direct, efficient and economic method to solve traffic congestion and expand traffie capacity. With its evolution, advantages and disadvantages introduced its setting conditions demon...One-way traffic organization is a direct, efficient and economic method to solve traffic congestion and expand traffie capacity. With its evolution, advantages and disadvantages introduced its setting conditions demonstrated. The general method and processes are summarized in planning for urban one-way streets project, viz. investigation, drawing out and evaluation of project, selecting of project and beneficial analysis. Fuzzy synthetical evaluation other fields is employed to evaluate the project. Its evaluation system and method is introduced and Delphi method is adopted to obtain evaluation index. Finally, taking Harbin city as an example, the application process of above-mentioned method is illuminated. Accordingly, it is proved that the method is exercisable.展开更多
The constant growth of air travel in the Philippines has brought about significant consequences to air traffic congestion. Given limited resources, major airports seek to address this issue while considering various a...The constant growth of air travel in the Philippines has brought about significant consequences to air traffic congestion. Given limited resources, major airports seek to address this issue while considering various attributes generally affecting air transportation. This paper adopts fuzzy decision-making trial and evaluation laboratory-analytic network process （DEMATEL-ANP） to identify the most critical attributes in the commercial aviation industry. A case study participated by key experts of Ninoy Aquino International Airport was conducted to illustrate the proposed approach. The fuzzy DEMATELANP model performed satisfactorily as it was able to extract the global priority vectors of attributes under a fuzzy environment. The results showed that aviation safety is most prioritized, as can also be seen from the significant influence it brings on other attributes. Following next to safety in terms of priority axe attributes that address the general air transportation system such as economic value, environmental value, social value, equitable treatment of competing airline, customer goodwill, and utilization of runway and terminal. Then, attributes relating to passenger cost, fuel cost, extra crew cost, landing/take-off fee, and cost of using flight routes axe of last priority. Given the order of priorities and criticality of each attribute, shortterm and long-term policies can be framed accordingly to propose air traffic flow management actions that can best address the issue on congestion.展开更多
Road traffic congestion can inevitably de-grade road infrastructure and decrease travel efficiency in urban traffic networks,which can be relieved by employing appropriate congestion control.Accord-ing to different de...Road traffic congestion can inevitably de-grade road infrastructure and decrease travel efficiency in urban traffic networks,which can be relieved by employing appropriate congestion control.Accord-ing to different developmental driving forces,in this paper,the evolution of road traffic congestion control is divided into two stages.The ever-growing num-ber of advanced sensing techniques can be seen as the key driving force of the first stage,called the sens-ing stage,in which congestion control strategies ex-perienced rapid growth owing to the accessibility of traffic data.At the second stage,i.e.,the communica-tion stage,communication and computation capabil-ity can be regarded as the identifying symbols for this stage,where the ability of collecting finer-grained in-sight into transportation and mobility reality improves dramatically with advances in vehicular networks,Big Data,and artificial intelligence.Specifically,as the pre-requisite for congestion control,in this paper,ex-isting congestion detection techniques are first elab-orated and classified.Then,a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control,vehi-cle route guidance,and their combined techniques is provided.In this regard,the evolution of these strate-gies with continuous development of sensing,com-munication,and computation capability are also intro-duced.Finally,the paper concludes with several re-search challenges and trends to fully promote the in-tegration of advanced techniques for traffic congestion mitigation in transportation systems.展开更多
An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level progr...An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover,practical processing approaches were suggested, which may improve the operability of the model-based solutions.展开更多
Maintenance and rehabilitation projects of interstate facilities typically mandate lane closures. Lane closures require merging maneuvers that often result in reduced speeds and traffic bottlenecks. Work zone impacts ...Maintenance and rehabilitation projects of interstate facilities typically mandate lane closures. Lane closures require merging maneuvers that often result in reduced speeds and traffic bottlenecks. Work zone impacts on traffic operations are magnified when project durations are extended. Conventionally, work zone traffic control plans are developed to address work zone impacts. This study evaluated various merge control strategies at interstate work zones peak and off-peak traffic conditions and summarized related impacts. A comprehensive microscopic simulation model was developed in full consideration of driver/vehicle behavior at work zones. The analysis of simulation results revealed that merge control strategies, when implemented during peak and off-peak conditions, can preserve the level of service and provide favorable mobility, safety, and environmental impacts. In addition, results indicate that transportation agencies’ practice of scheduling work zone activities during the off-peak may not be the most optimum approach. Overall, the findings of this study highlight the need for evaluation of work zone scheduling practices in full consideration of agency, user, and project costs.展开更多
Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although the government has implemented ...Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents,it still exists and is getting worse.This paper proposes an intelligent,adaptive,practical,and feasible deep learning method for intelligent traffic control.It uses an Internet of Things(IoT)sensor,a camera,and a Convolutional Neural Network(CNN)tool to control traffic in real time.An image segmentation algorithm analyzes inputs from the cameras installed in designated areas.This study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed,high-congestion situations.The presented algorithm calculates traffic density and cars’speeds to determine which lane gets high priority first.A real case study has been conducted on MATLAB to verify and validate the results of this approach.This algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red lights.An assessment between some literature works and the presented algorithm is also provided.In contrast to traditional traffic management methods,this intelligent and adaptive algorithm reduces traffic congestion,automobile waiting times,and accidents.展开更多
This paper studies how to generate the reasonable information of travelers' decision in real network. This problem is very complex because the travelers' decision is constrained by different human behavior. Th...This paper studies how to generate the reasonable information of travelers' decision in real network. This problem is very complex because the travelers' decision is constrained by different human behavior. The network conditions can be predicted by using the advanced dynamic OD(Origin-Destination, OD) estimation techniques. Based on the improved mesoscopic traffic model, the predictable dynamic traffic guidance information can be obtained accurately.A consistency algorithm is designed to investigate the travelers' decision by simulating the dynamic response to guidance information. The simulation results show that the proposed method can provide the best guidance information. Further,a case study is conducted to verify the theoretical results and to draw managerial insights into the potential of dynamic guidance strategy in improving traffic performance.展开更多
Traffic flows form a complex system, especially in the context of urban sprawl. Direct estimation of traffic flows requires significant efforts and knowing in advance where to focus the study and where to place tools ...Traffic flows form a complex system, especially in the context of urban sprawl. Direct estimation of traffic flows requires significant efforts and knowing in advance where to focus the study and where to place tools to directly measure traffic flows, and consequently traffic congestion could lead to significant savings in time and money. In the case of Rome municipality, we have monitored a situation in which a very high rate of urban fragmentation has occurred in the last 30 years, making the direct estimation of traffic difficult. The work described here can help to solve the problem of estimating traffic flows and in particular the congestion phenomenon through a very cheap approach by using remote sensing and geographical information system technology. This method is based on the identification of attractor points that draw traffic flows such as malls, schools, offices, shops, etc. that is to say, points in a territory that attract a certain number of people with vehicles (estimated with a scale) in specific periods of the day. The identification of those points and the calculation of the urban density through the satellite image processing have allowed the creation of a congestion map for the study area. Then the road network and the buildings have been classified according to the congestion values. The results highlight the most critical and congested areas that affect the traffic flows and impact the quality of life.展开更多
At present the entire world is under the risk of severe environmental problems, due to the expansion of industries, urban population and commercial activities, the city like Delhi （India） faces transportation, envir...At present the entire world is under the risk of severe environmental problems, due to the expansion of industries, urban population and commercial activities, the city like Delhi （India） faces transportation, environmental and economic challenges. Such type of situations demand the addition of knowledge based layer to help the operators to be familiar with exact traffic problem and give the best choice of strategic control actions to the city. In current situation there is a necessity to build systematic, knowledge based tool to analyze and manage the recent or potential air quality issues and traffic noise issues. The paper comprises the creation of knowledge from the information which is extracted from the various data by using knowledge based modules （spreadsheets, database, software, etc.） and some management, optimization models. Such type of knowledge based management tool may act as a Decision Support System （DSS） which will be very supportive in traffic control system. The technology of knowledge-based systems may facilitate in designing and executing suitable knowledge structures to formulate conceptual models for traffic analysis and management and to use such approach for on-line strategic traffic management operations.展开更多
Current traffic signals in Jordan suffer from severe congestion due to many factors,such as the considerable increase in the number of vehicles and the use of fixed timers,which still control existing traffic signals....Current traffic signals in Jordan suffer from severe congestion due to many factors,such as the considerable increase in the number of vehicles and the use of fixed timers,which still control existing traffic signals.This condition affects travel demand on the streets of Jordan.This study aims to improve an intelligent road traffic management system(IRTMS)derived from the human community-based genetic algorithm(HCBGA)to mitigate traffic signal congestion in Amman,Jordan’s capital city.The parameters considered for IRTMS are total time and waiting time,and fixed timers are still used for control.By contrast,the enhanced system,called enhanced-IRTMS(E-IRTMS),considers additional important parameters,namely,the speed performance index(SPI),speed reduction index(SRI),road congestion index(R i),and congestion period,to enhance IRTMS decision.A significant reduction in congestion period was measured using E-IRTMS,improving by 13% compared with that measured using IRTMS.Meanwhile,the IRTMS result surpasses that of the current traffic signal system by approximately 83%.This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI,SRI,and R_(i) compared with IRTMS.展开更多
Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Ne...Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.展开更多
文摘The aim of this study is to determine the level to which the public is aware about ITS(intelligent transportation systems)technologies and how they perceive the potential advantages and inhibitors of ITS in Michigan.A survey was performed with 200 participants living in Michigan,in urban,suburban and rural areas.Questions covered in the survey included how often and how bad traffic congestion occurred,how familiar travelers were with ITS technologies(adaptive traffic signals,real time monitoring of the traffic)and how much support travelers would provide for ITS initiatives.Results reveal that there is a high degree of traffic congestion awareness,there is low public awareness of ITS technologies.While respondents who were aware of ITS solutions had positive views about deploying them,especially in urban areas,they were less supportive of ITS solutions than they were among those who did not know much about these.Factors including area of residence,commute time and age were perceived to influence ITS along with more positive attitudes to ITS amongst urban dwellers and younger respondents.Analysis of key barriers to ITS implementation reflected high initial costs,challenges with technical integration and users’concerns surrounding privacy.
基金supported by the Enterprises Research Project(Nos.W2021JSKF0922 and W2023JSKF0116)the Key industrialization Projects of Intelligent Manufacturing Institute,Hefei University of Technology(No.IMICZ2019001).
文摘In this study,a strategy is proposed to use the congestion index as a new input feature.This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter(PM_(2.5))concentrations.To assess the effectiveness of this strategy,we conducted an ablation experiment on the congestion index and implemented a multi-scale input model.Compared with conventional models,the strategy reduces the root mean square error(RMSE)of all benchmark models by>6.07%on average,and the bestperforming model reduces it by 12.06%,demonstrating excellent performance improvement.In addition,evenwith high traffic emissions,the RMSE during peak hours is still below 9.83μg/m^(3),which proves the effectiveness of the strategy by effectively addressing pollution hotspots.This study provides new ideas for improving urban environmental quality and public health and anticipates inspiring further research in this domain.
基金Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01641)。
文摘Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road occupancy and vehicle density.Therefore,the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment.Conventional prediction systems face difficulties in identifying highly congested areas,which leads to reduced prediction accuracy.The problem is addressed by integrating Graph Neural Networks(GNN)with the Lion Swarm Optimization(LSO)framework to tackle the congestion prediction problem.Initially,the traffic information is collected and processed through a normalization process to scale the data and mitigate issues of overfitting and high dimensionality.Then,the traffic flow and temporal characteristic features are extracted to identify the connectivity of the road segment.From the connectivity and node relationship graph,modeling improves the overall prediction accuracy.During the analysis,the lion swarm optimization process utilizes the concepts of exploration and exploitation to understand the complex traffic dependencies,which helps predict high congestion on roads with minimal deviation errors.There are three core optimization phases:roaming,hunting,and migration,which enable the framework to make dynamic adjustments to enhance the predictions.The framework’s efficacy is evaluated using benchmark datasets,where the proposed work achieves 99.2%accuracy and minimizes the prediction deviation value by up to 2.5%compared to other methods.With the new framework,there was a more accurate prediction of realtime congestion,lower computational cost,and improved regulation of traffic flow.This system is easily implemented in intelligent transportation systems,smart cities,and self-driving cars,providing a robust and scalable solution for future traffic management.
基金supported by the National Natural Science Foundation of China(61374148)
文摘Advanced information and communication technolo-gies can be used to facilitate traffic incident management.If an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work.Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled.Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is delivered.The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.
基金Natural Science Foundation of Gansu Provincial Science&Technology Department(No.1504GKCA018)。
文摘In order to alleviate urban traffic congestion and provide fast vehicle paths,a hidden Markov model(HMM)based on multi-feature data of urban regional roads is constructed to solve the problems of low recognition rate and poor instability of traditional model algorithms.At first,the HHM is obtained by training.Then according to dynamic planning principle,the traffic states of intersections are obtained by the Viterbi algorithm.Finally,the optimal path is selected based on the obtained traffic states of intersections.The experiment results show that the proposed method is superior to other algorithms in road unobstruction rate and recognition rate under complex road conditions.
基金This work was partially supported by the National Key R&D Program of China under Grant 2019YFB1803301the Key Research and Development Program of Shanxi under Grant 201903D121117+1 种基金Beijing Nova Program of Science and Technology under Grant Z191100001119028the National Natural Science Foundation of China under Grant 62001320.
文摘Re-routing system has become an important technology to improve traffic efficiency.The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic,making the planned routes unable to cope with the changing traf-fic conditions.Based on real-time traffic information,it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion.Moreover,how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system.To deal with these challenges,this paper proposes CHRT,a clustering-based hybrid re-routing system for traffic congestion avoidance.CHRT develops a multi-layer hybrid architecture.The central server accesses the global view of traffic,and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead.Then,a clustering-based priority mechanism is proposed,which sets priorities for clusters based on realtime traffic information to avoid secondary congestion.Furthermore,to plan the optimal routes for vehicles while alleviating global traffic congestion,this paper presents a multi-metric re-routing algorithm.Through extensive simulations based on the SUMO traffic simulator,CHRT reduces vehicle traveling time,fuel consumption,and CO2 emissions compared to other systems.In addition,CHRT globally alleviates traffic congestion and improves traffic efficiency.
文摘Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic process. A Dynamic Traffic Assignment modeling fundamental combined with an urban congestion analysis method is studied in this paper. Three methods are based on congestion analysis, and the stochastic user optimal DTA models are especially considered. Correspondingly, a dynamic system optimal model is suggested for responding congestion countermeasures and an ideal user optimal model for predicted congestion countermeasure respectively.
基金Supported by the National Basic Research Program of China under Grant No.2006CB705500the National Natural Science Foundation of China under Grant Nos.70631001,70701004,and 71071012
文摘The aim of this paper is to study traffic properties in an on/off-ramp system with a bus stop close to the on/off ramp. The location of the bus stop in the on/off-ramp （thereafter downstream or upstream case） is discussed. The simulation results show that in the two ramp systems, the reasons for traffic congestions are different. In the on-ramp system, buses and cars coming from on-ramp interweave each other, while in the off-ramp system, buses interweave with cars exiting to off-ramp. Thus, in the on-ramp （off-ramp） system, the upstream （downstream） bus stop is helpful to reduce the interweaving situation. Moreover, the negative effect will disappear when the distance between the bus stop and the on/off-ramp is more than 20 cells （i.e. 150 m）. These qualitative findings may provide some suggestions on traffic management and optimization.
基金supported by the Key Natural Science Foundation of China:Urban Transportation Planning Theory and Methods under the Information Environment, Grant No. 50738004/E0807
文摘The article intends to find a method to quantify traffic congestion＇s impacts on travelers to help transportation planners and policy decision makers well understand congestion situations. Three new congestion indicators, including transportation environment satisfaction （TES）, travel time satisfaction （TTS）, and traffic congestion frequency and feeling （TCFF）, are defined to estimate urban traffic congestion based on travelers＇ feelings. Data of travelers＇ attitude about congestion and trip information were collected from a survey in Shanghai, China. Based on the survey data, we estimated the value of the three indi- cators. Then, the principal components analysis was used to derive a small number of linear combinations of a set of variables to estimate the whole congestion status. A linear regression model was used to find out the significant variables which impact respondents＇ feelings. Two ordered logit models were used to select significant variables of TES and TTS. Attitudinal factor variables were also used in these models. The results show that attitudinal factor variables and cluster category variables are as important as sociodemographic variables in the models. Using the three congestion indicators, the government can collect travelers＇ feeling about traffic congestion and estimate the transportation policy that might be applied to cope with traffic congestion.
基金Sponsored by the National Natural Science of China(Grant No. 50278026)
文摘One-way traffic organization is a direct, efficient and economic method to solve traffic congestion and expand traffie capacity. With its evolution, advantages and disadvantages introduced its setting conditions demonstrated. The general method and processes are summarized in planning for urban one-way streets project, viz. investigation, drawing out and evaluation of project, selecting of project and beneficial analysis. Fuzzy synthetical evaluation other fields is employed to evaluate the project. Its evaluation system and method is introduced and Delphi method is adopted to obtain evaluation index. Finally, taking Harbin city as an example, the application process of above-mentioned method is illuminated. Accordingly, it is proved that the method is exercisable.
基金the Engineering Research and Development for Technology (ERDT) of the Philippine Department of Science and Technology (DOST) for the financial support provided through the full graduate scholarship grant of the first author
文摘The constant growth of air travel in the Philippines has brought about significant consequences to air traffic congestion. Given limited resources, major airports seek to address this issue while considering various attributes generally affecting air transportation. This paper adopts fuzzy decision-making trial and evaluation laboratory-analytic network process （DEMATEL-ANP） to identify the most critical attributes in the commercial aviation industry. A case study participated by key experts of Ninoy Aquino International Airport was conducted to illustrate the proposed approach. The fuzzy DEMATELANP model performed satisfactorily as it was able to extract the global priority vectors of attributes under a fuzzy environment. The results showed that aviation safety is most prioritized, as can also be seen from the significant influence it brings on other attributes. Following next to safety in terms of priority axe attributes that address the general air transportation system such as economic value, environmental value, social value, equitable treatment of competing airline, customer goodwill, and utilization of runway and terminal. Then, attributes relating to passenger cost, fuel cost, extra crew cost, landing/take-off fee, and cost of using flight routes axe of last priority. Given the order of priorities and criticality of each attribute, shortterm and long-term policies can be framed accordingly to propose air traffic flow management actions that can best address the issue on congestion.
基金the National Key R&D Program of China(2019YFB1600100)National Nat-ural Science Foundation of China(U1801266)the Youth Innovation Team of Shaanxi Universities.
文摘Road traffic congestion can inevitably de-grade road infrastructure and decrease travel efficiency in urban traffic networks,which can be relieved by employing appropriate congestion control.Accord-ing to different developmental driving forces,in this paper,the evolution of road traffic congestion control is divided into two stages.The ever-growing num-ber of advanced sensing techniques can be seen as the key driving force of the first stage,called the sens-ing stage,in which congestion control strategies ex-perienced rapid growth owing to the accessibility of traffic data.At the second stage,i.e.,the communica-tion stage,communication and computation capabil-ity can be regarded as the identifying symbols for this stage,where the ability of collecting finer-grained in-sight into transportation and mobility reality improves dramatically with advances in vehicular networks,Big Data,and artificial intelligence.Specifically,as the pre-requisite for congestion control,in this paper,ex-isting congestion detection techniques are first elab-orated and classified.Then,a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control,vehi-cle route guidance,and their combined techniques is provided.In this regard,the evolution of these strate-gies with continuous development of sensing,com-munication,and computation capability are also intro-duced.Finally,the paper concludes with several re-search challenges and trends to fully promote the in-tegration of advanced techniques for traffic congestion mitigation in transportation systems.
基金Projects(71171200,51108465,71101155)supported by the National Natural Science Foundation of China
文摘An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover,practical processing approaches were suggested, which may improve the operability of the model-based solutions.
文摘Maintenance and rehabilitation projects of interstate facilities typically mandate lane closures. Lane closures require merging maneuvers that often result in reduced speeds and traffic bottlenecks. Work zone impacts on traffic operations are magnified when project durations are extended. Conventionally, work zone traffic control plans are developed to address work zone impacts. This study evaluated various merge control strategies at interstate work zones peak and off-peak traffic conditions and summarized related impacts. A comprehensive microscopic simulation model was developed in full consideration of driver/vehicle behavior at work zones. The analysis of simulation results revealed that merge control strategies, when implemented during peak and off-peak conditions, can preserve the level of service and provide favorable mobility, safety, and environmental impacts. In addition, results indicate that transportation agencies’ practice of scheduling work zone activities during the off-peak may not be the most optimum approach. Overall, the findings of this study highlight the need for evaluation of work zone scheduling practices in full consideration of agency, user, and project costs.
基金This research work was funded by Institutional Fund Projects under Grant No.(IFPIP:707-829-1443)The authors gratefully acknowledge technical and financial support provided by theMinistry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents,it still exists and is getting worse.This paper proposes an intelligent,adaptive,practical,and feasible deep learning method for intelligent traffic control.It uses an Internet of Things(IoT)sensor,a camera,and a Convolutional Neural Network(CNN)tool to control traffic in real time.An image segmentation algorithm analyzes inputs from the cameras installed in designated areas.This study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed,high-congestion situations.The presented algorithm calculates traffic density and cars’speeds to determine which lane gets high priority first.A real case study has been conducted on MATLAB to verify and validate the results of this approach.This algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red lights.An assessment between some literature works and the presented algorithm is also provided.In contrast to traditional traffic management methods,this intelligent and adaptive algorithm reduces traffic congestion,automobile waiting times,and accidents.
基金Supported by National Natural Science Foundation of China under Grant Nos.71471104,71771019,71571109,and 71471167The University Science and Technology Program Funding Projects of Shandong Province under Grant No.J17KA211+1 种基金The Project of Public Security Department of Shandong Province under Grant No.GATHT2015-236The Major Social and Livelihood Special Project of Jinan under Grant No.20150905
文摘This paper studies how to generate the reasonable information of travelers' decision in real network. This problem is very complex because the travelers' decision is constrained by different human behavior. The network conditions can be predicted by using the advanced dynamic OD(Origin-Destination, OD) estimation techniques. Based on the improved mesoscopic traffic model, the predictable dynamic traffic guidance information can be obtained accurately.A consistency algorithm is designed to investigate the travelers' decision by simulating the dynamic response to guidance information. The simulation results show that the proposed method can provide the best guidance information. Further,a case study is conducted to verify the theoretical results and to draw managerial insights into the potential of dynamic guidance strategy in improving traffic performance.
文摘Traffic flows form a complex system, especially in the context of urban sprawl. Direct estimation of traffic flows requires significant efforts and knowing in advance where to focus the study and where to place tools to directly measure traffic flows, and consequently traffic congestion could lead to significant savings in time and money. In the case of Rome municipality, we have monitored a situation in which a very high rate of urban fragmentation has occurred in the last 30 years, making the direct estimation of traffic difficult. The work described here can help to solve the problem of estimating traffic flows and in particular the congestion phenomenon through a very cheap approach by using remote sensing and geographical information system technology. This method is based on the identification of attractor points that draw traffic flows such as malls, schools, offices, shops, etc. that is to say, points in a territory that attract a certain number of people with vehicles (estimated with a scale) in specific periods of the day. The identification of those points and the calculation of the urban density through the satellite image processing have allowed the creation of a congestion map for the study area. Then the road network and the buildings have been classified according to the congestion values. The results highlight the most critical and congested areas that affect the traffic flows and impact the quality of life.
文摘At present the entire world is under the risk of severe environmental problems, due to the expansion of industries, urban population and commercial activities, the city like Delhi （India） faces transportation, environmental and economic challenges. Such type of situations demand the addition of knowledge based layer to help the operators to be familiar with exact traffic problem and give the best choice of strategic control actions to the city. In current situation there is a necessity to build systematic, knowledge based tool to analyze and manage the recent or potential air quality issues and traffic noise issues. The paper comprises the creation of knowledge from the information which is extracted from the various data by using knowledge based modules （spreadsheets, database, software, etc.） and some management, optimization models. Such type of knowledge based management tool may act as a Decision Support System （DSS） which will be very supportive in traffic control system. The technology of knowledge-based systems may facilitate in designing and executing suitable knowledge structures to formulate conceptual models for traffic analysis and management and to use such approach for on-line strategic traffic management operations.
文摘Current traffic signals in Jordan suffer from severe congestion due to many factors,such as the considerable increase in the number of vehicles and the use of fixed timers,which still control existing traffic signals.This condition affects travel demand on the streets of Jordan.This study aims to improve an intelligent road traffic management system(IRTMS)derived from the human community-based genetic algorithm(HCBGA)to mitigate traffic signal congestion in Amman,Jordan’s capital city.The parameters considered for IRTMS are total time and waiting time,and fixed timers are still used for control.By contrast,the enhanced system,called enhanced-IRTMS(E-IRTMS),considers additional important parameters,namely,the speed performance index(SPI),speed reduction index(SRI),road congestion index(R i),and congestion period,to enhance IRTMS decision.A significant reduction in congestion period was measured using E-IRTMS,improving by 13% compared with that measured using IRTMS.Meanwhile,the IRTMS result surpasses that of the current traffic signal system by approximately 83%.This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI,SRI,and R_(i) compared with IRTMS.
基金the Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2024-1008.
文摘Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.
