Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of ...Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.展开更多
Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial...Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.展开更多
Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal c...Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.展开更多
Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban t...Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban traffic travel pressure and reduce the frequency of accidents. Due to the randomness and fast changing speed of urban dynamic traffic data flow, most of the existing prediction methods lack the ability to model the dynamic temporal and spatial correlation of traffic data, so they cannot produce satisfactory prediction results. A spatio-temporal convolution network (ST-CNN) is proposed to solve the traffic flow prediction problem. The model consists of two parts: 1) a convolution block used to extract spatial features;2) a block of time used to characterize time. Data has been fully mined through two modules to output the prediction results of spatio-temporal characteristics, and at the same time, skip connection (direct connection) has been made between the two modules to avoid the problem of gradient explosion. The experimental results on two data sets show that ST-CNN is better than the baseline model.展开更多
Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traff...Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic management.It has been a challenging problem due to three aspects:1)The diversity of traffic patterns due to heterogeneous layouts of urban intersections;2)The nature of complex spatiotemporal correlations;3)The requirement of dynamically adjusting the parameters of traffic models in a real-time system.To cater to these challenges,this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning(AUTOSIM).In particular,simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry attributes.Through a meta-learning technique,AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal correlations.Besides,AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a meta-learner.Through computational experiments,we demonstrate the effectiveness of the meta-learningbased framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.展开更多
An urban traffic ecosystem is a spatial structure composed of air,population,vehicles,roads,green spaces,and regions.Traffic ecological resilience is a critical issue in high-quality urban development.From the perspec...An urban traffic ecosystem is a spatial structure composed of air,population,vehicles,roads,green spaces,and regions.Traffic ecological resilience is a critical issue in high-quality urban development.From the perspective of system optimization,it is important to study the level of urban traffic ecological resilience and analyze its influencing factors.In this study,we evaluated traffic ecological resilience,characterized its spatio-temporal differentiation,and explored its influencing factors by constructing a system of urban traffic ecological resilience and by analyzing the environmental protection and urban construction data in 31 Chinese cities during 2011-2018.By conducting Kernel density analysis,standard deviation ellipse,comprehensive weight determination,panel data regression analysis,andχ2test,we found that traffic ecological resilience was low on the whole and exhibited the temporal trend of“decreasing first and then increasing”and the spatial characteristic of“high in the east,second in the middle,and low in the west”.The cities with high traffic ecological resilience density values were located in Southeast China and tended to move from northwest to southeast.Governance capability,market activity,technological innovation capability,opening degree,and financial resources had significant effects on urban traffic ecological resilience.Finally,we gave some suggestions for improving the urban traffic ecological resilience in Chinese cities as well as other developing countries in the world.展开更多
The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea a...The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea arises from previous results in the literature, where ubiquitous computing achieves significant and promising results in diverse scenarios. Some works in the state-of- the-art clearly state that traditional traffic light signals are not capable of offering higher service levels when they should control the vehicular mobility because of different constraints. In this sense, the paper proposes an adaptation of the main ideas of ubiquitous computing as a metaphor to facilitate the interaction between users and traffic infrastructures in order to improve the users’ experience on the road.展开更多
The biggest environmental problem caused by the construction of tunnels adjacent to high-rise buildings is the settlement of buildings.The paper analyzes the influence of tunnel excavation on the deformation of the su...The biggest environmental problem caused by the construction of tunnels adjacent to high-rise buildings is the settlement of buildings.The paper analyzes the influence of tunnel excavation on the deformation of the superstructure and the deformation mode of the superstructure.It introduces the indicators and standards for the construction control of tunnel adjacent to the building at home and abroad.Combined with the Yuzhong tunnel project under construction in Chongqing,the main monitoring indicators and control standards of the Yuzhong Tunnel passing through the main buildings are given after comprehensive analysis and considerations,which provide a reference for the deformation control indicators of similar urban traffic tunnels adjacent to high-rise buildings.展开更多
Advanced information and communication technologies 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,...Advanced information and communication technologies 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
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
Traffic network is an importance asp ect of researching controllable parameters of an urban spatial morpholo-gy.Based on GIS,traffic network str ucture complexity can be understood by using fractal geometry in which t...Traffic network is an importance asp ect of researching controllable parameters of an urban spatial morpholo-gy.Based on GIS,traffic network str ucture complexity can be understood by using fractal geometry in which th e length-radius dimension describes change of network density,and ramification-radius dimension describes complexity and accessibility of urban network.It i s propitious to analyze urban traffic network and to understand dynamic c hange process of traffic network using expanding f ractal-dimension quantification.Meanwhile the length-radius dimension and ramifica-tion-radius dimension could be rega rd as reference factor of quantitative describing urban traffic network.展开更多
This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collec...This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.展开更多
With the great prosperity of national economy, there has been a dramatic rise of vehicles on city road, which makes increasing pressure of road transportation. Currently, many countries are confronting the severe situ...With the great prosperity of national economy, there has been a dramatic rise of vehicles on city road, which makes increasing pressure of road transportation. Currently, many countries are confronting the severe situation of traffic jam in different degrees. Nevertheless, there are many triggers contributing to this congestion, one of which is the blocking of residential quarters towards vital traffic line. Therefore, it is extremely necessary to study whether the opening of residential quarters can improve the road capacity of the entire city and remit the traffic pressure. Our paper is based on graph theory, density theory and random utility theory. First of all, we demonstrate a mathematical model of road traffic. Secondly, we explore the influence of residential quarters opening on urban traffic, taking three factors into account listed as road traffic capacity, road network density and network average running time. On the basis of above contents, the impact analysis of vehicle traffic caused by pedestrians is added afterwards. Finally, our paper takes three different types of residential areas into account as an example to empirically analyze the tangible impact of the opening, and finally come to the benefit of the traffic system after the opening.展开更多
In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The mod...In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.展开更多
Urban environment pattern depends heavily upon urban traffic pattern, the balance between traffic (implicit production) and environment leads to the urban sustainable development. An integrated urban traffic environme...Urban environment pattern depends heavily upon urban traffic pattern, the balance between traffic (implicit production) and environment leads to the urban sustainable development. An integrated urban traffic environment model consists of three components of urban production variables (population density, GDP, salary, etc. in blocks), urban traffic variables and urban environmental variables; and two links between urban traffic planning variables and urban environment variables, and between spatial interaction model (SIM) and traffic planning variables as well. The model is quite useful in urban environment impact assessment; urban traffic management; urban sustainable development planning; and urban development decision\|making.展开更多
This paper investigates urban traffic data by analysing the long-range correlation with detrended fluctuation analysis.Through a large number of real data collected by the travel time detection system in Beijing,the v...This paper investigates urban traffic data by analysing the long-range correlation with detrended fluctuation analysis.Through a large number of real data collected by the travel time detection system in Beijing,the variation of flow in different time periods and intersections is studied.According to the long-range correlation in different time scales, it mainly discusses the effect of intersection location in road net,people activity customs and special traffic controls on urban traffic flow.As demonstrated by the obtained results,the urban traffic flow represents three-phase characters similar to highway traffic.Moreover,compared by the two groups of data obtained before and after the special traffic restrictions(vehicles with special numbered plates only run in a special workday) enforcement,it indicates that the rules not only reduce the flow but also avoid irregular fluctuation.展开更多
The analysis of huge data is a complex task that cannot be executed without a proper system. Geographic information systems (GISs) have been used by many transportation agencies and police departments to analyze and m...The analysis of huge data is a complex task that cannot be executed without a proper system. Geographic information systems (GISs) have been used by many transportation agencies and police departments to analyze and manage urban traffic accident (UTA) data and for decision making aimed at decreasing accident rates and increasing safety. The exact location of accidents and environmental characteristics must be analyzed as UTAs occur in specific locations with specific characteristics. ArcGIS software is the best choice for obtaining meaningful information and analysis results from UTAs in an observational time span. GIS technology is a fundamental element for investigating and evaluating the complex spatial relationship among different components and urban traffic accident is one of them. Micro or macro analysis of UTAs through the spatial prospective within the geographical environment and urban structure can make a deep micro understanding of UTAs patterns in addition to assisting in decision making. UTAs can be considered complex events that occur in two aspects which are spatial and temporal or space and time in other word. A GIS can integrate more than two different and unrelated databases. The evaluation among different spatial objects in a geographical environment and associated factors in urban structure which are included but not limited to land use category, road transportation network qualification, population density, etc., is one of the GIS specification. Traffic safety organizations and UTA researchers use GISs as a key technology to support their research and operational needs. In particular, GIS-T is an often-used GIS application used for planning and decision-making in transportation.展开更多
Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this syste...Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings or tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, a novel method employing a back propagation (BP) neural network is developed to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, the speed of its related road sections can be used to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the proposed method of traffic speed estimation is very effective.展开更多
The basic principles of GPS (Global Positioning System) and DGPS (Differential GPS) are described. The principle and structure of vehicle navigation systems, and its application to the urban traffic flow guidance are ...The basic principles of GPS (Global Positioning System) and DGPS (Differential GPS) are described. The principle and structure of vehicle navigation systems, and its application to the urban traffic flow guidance are analyzed. Then, an area coordinated adaptive control system based on DGPS and a traffic flow guidance information system based on DGPS are put forward, and their working principles and functions are researched. This is to provides a new way for the development of urban road traffic control systems.展开更多
基金supported by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation“Research and System Development of Highway Asset Digitalization Technology inUse Based onHigh-PrecisionMap”(Project Number:202203)in part by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation:Research and Demonstration Application of Key Technologies for Precise Sensing of Expressway Thrown Objects(No.202204).
文摘Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.
基金the National Natural Science Foundation of China under Grant No.62272087Science and Technology Planning Project of Sichuan Province under Grant No.2023YFG0161.
文摘Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.
基金supported by National Key R&D Program of China(Grant No.2018YFE0204302)National Natural Science Foundation of China(Grant No.52062015,No.61703160)+1 种基金the Talent Research Start-up Fund of Nanjing University of Aeronautics and Astronautics(YAH22019)Jiangsu High Level'Shuang-Chuang'Project.
文摘Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.
文摘Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban traffic travel pressure and reduce the frequency of accidents. Due to the randomness and fast changing speed of urban dynamic traffic data flow, most of the existing prediction methods lack the ability to model the dynamic temporal and spatial correlation of traffic data, so they cannot produce satisfactory prediction results. A spatio-temporal convolution network (ST-CNN) is proposed to solve the traffic flow prediction problem. The model consists of two parts: 1) a convolution block used to extract spatial features;2) a block of time used to characterize time. Data has been fully mined through two modules to output the prediction results of spatio-temporal characteristics, and at the same time, skip connection (direct connection) has been made between the two modules to avoid the problem of gradient explosion. The experimental results on two data sets show that ST-CNN is better than the baseline model.
基金supported by the National Natural Science Foundation of China(62173329)。
文摘Online traffic simulation that feeds from online information to simulate vehicle movement in real-time has recently seen substantial advancement in the development of intelligent transportation systems and urban traffic management.It has been a challenging problem due to three aspects:1)The diversity of traffic patterns due to heterogeneous layouts of urban intersections;2)The nature of complex spatiotemporal correlations;3)The requirement of dynamically adjusting the parameters of traffic models in a real-time system.To cater to these challenges,this paper proposes an online traffic simulation framework called automated urban traffic operation simulation via meta-learning(AUTOSIM).In particular,simulation models with various intersection layouts are automatically generated using an open-source simulation tool based on static traffic geometry attributes.Through a meta-learning technique,AUTOSIM enables an automated learning process for dynamic model settings of traffic scenarios featured with different spatiotemporal correlations.Besides,AUTOSIM is capable of adapting traffic model parameters according to dynamic traffic information in real-time by using a meta-learner.Through computational experiments,we demonstrate the effectiveness of the meta-learningbased framework that is capable of providing reliable supports to real-time traffic simulation and dynamic traffic operations.
文摘An urban traffic ecosystem is a spatial structure composed of air,population,vehicles,roads,green spaces,and regions.Traffic ecological resilience is a critical issue in high-quality urban development.From the perspective of system optimization,it is important to study the level of urban traffic ecological resilience and analyze its influencing factors.In this study,we evaluated traffic ecological resilience,characterized its spatio-temporal differentiation,and explored its influencing factors by constructing a system of urban traffic ecological resilience and by analyzing the environmental protection and urban construction data in 31 Chinese cities during 2011-2018.By conducting Kernel density analysis,standard deviation ellipse,comprehensive weight determination,panel data regression analysis,andχ2test,we found that traffic ecological resilience was low on the whole and exhibited the temporal trend of“decreasing first and then increasing”and the spatial characteristic of“high in the east,second in the middle,and low in the west”.The cities with high traffic ecological resilience density values were located in Southeast China and tended to move from northwest to southeast.Governance capability,market activity,technological innovation capability,opening degree,and financial resources had significant effects on urban traffic ecological resilience.Finally,we gave some suggestions for improving the urban traffic ecological resilience in Chinese cities as well as other developing countries in the world.
文摘The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea arises from previous results in the literature, where ubiquitous computing achieves significant and promising results in diverse scenarios. Some works in the state-of- the-art clearly state that traditional traffic light signals are not capable of offering higher service levels when they should control the vehicular mobility because of different constraints. In this sense, the paper proposes an adaptation of the main ideas of ubiquitous computing as a metaphor to facilitate the interaction between users and traffic infrastructures in order to improve the users’ experience on the road.
基金National Key R&D Program of China Special Funding(2017YFC0805305)National Natural Science Foundation of China(41601574)Chinese Academy of Engineering Institute-Local Cooperation Project(2019-CQ-ZD-4)。
文摘The biggest environmental problem caused by the construction of tunnels adjacent to high-rise buildings is the settlement of buildings.The paper analyzes the influence of tunnel excavation on the deformation of the superstructure and the deformation mode of the superstructure.It introduces the indicators and standards for the construction control of tunnel adjacent to the building at home and abroad.Combined with the Yuzhong tunnel project under construction in Chongqing,the main monitoring indicators and control standards of the Yuzhong Tunnel passing through the main buildings are given after comprehensive analysis and considerations,which provide a reference for the deformation control indicators of similar urban traffic tunnels adjacent to high-rise buildings.
基金supported by the National Natural Science Foundation of China(61374148)
文摘Advanced information and communication technologies 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Traffic network is an importance asp ect of researching controllable parameters of an urban spatial morpholo-gy.Based on GIS,traffic network str ucture complexity can be understood by using fractal geometry in which th e length-radius dimension describes change of network density,and ramification-radius dimension describes complexity and accessibility of urban network.It i s propitious to analyze urban traffic network and to understand dynamic c hange process of traffic network using expanding f ractal-dimension quantification.Meanwhile the length-radius dimension and ramifica-tion-radius dimension could be rega rd as reference factor of quantitative describing urban traffic network.
文摘This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.
文摘With the great prosperity of national economy, there has been a dramatic rise of vehicles on city road, which makes increasing pressure of road transportation. Currently, many countries are confronting the severe situation of traffic jam in different degrees. Nevertheless, there are many triggers contributing to this congestion, one of which is the blocking of residential quarters towards vital traffic line. Therefore, it is extremely necessary to study whether the opening of residential quarters can improve the road capacity of the entire city and remit the traffic pressure. Our paper is based on graph theory, density theory and random utility theory. First of all, we demonstrate a mathematical model of road traffic. Secondly, we explore the influence of residential quarters opening on urban traffic, taking three factors into account listed as road traffic capacity, road network density and network average running time. On the basis of above contents, the impact analysis of vehicle traffic caused by pedestrians is added afterwards. Finally, our paper takes three different types of residential areas into account as an example to empirically analyze the tangible impact of the opening, and finally come to the benefit of the traffic system after the opening.
文摘In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.
文摘Urban environment pattern depends heavily upon urban traffic pattern, the balance between traffic (implicit production) and environment leads to the urban sustainable development. An integrated urban traffic environment model consists of three components of urban production variables (population density, GDP, salary, etc. in blocks), urban traffic variables and urban environmental variables; and two links between urban traffic planning variables and urban environment variables, and between spatial interaction model (SIM) and traffic planning variables as well. The model is quite useful in urban environment impact assessment; urban traffic management; urban sustainable development planning; and urban development decision\|making.
基金Project supported by the National High Technology Research and Development Program of China(Grant Nos.2008AA01Z208 and 2009AA01Z405)the Applied Basic Research Program of Sichuan Province of China(Grant No.2010JY0013)the Youth Foundation of Sichuan Province of China(Grant No.2009-28-419)
文摘This paper investigates urban traffic data by analysing the long-range correlation with detrended fluctuation analysis.Through a large number of real data collected by the travel time detection system in Beijing,the variation of flow in different time periods and intersections is studied.According to the long-range correlation in different time scales, it mainly discusses the effect of intersection location in road net,people activity customs and special traffic controls on urban traffic flow.As demonstrated by the obtained results,the urban traffic flow represents three-phase characters similar to highway traffic.Moreover,compared by the two groups of data obtained before and after the special traffic restrictions(vehicles with special numbered plates only run in a special workday) enforcement,it indicates that the rules not only reduce the flow but also avoid irregular fluctuation.
文摘The analysis of huge data is a complex task that cannot be executed without a proper system. Geographic information systems (GISs) have been used by many transportation agencies and police departments to analyze and manage urban traffic accident (UTA) data and for decision making aimed at decreasing accident rates and increasing safety. The exact location of accidents and environmental characteristics must be analyzed as UTAs occur in specific locations with specific characteristics. ArcGIS software is the best choice for obtaining meaningful information and analysis results from UTAs in an observational time span. GIS technology is a fundamental element for investigating and evaluating the complex spatial relationship among different components and urban traffic accident is one of them. Micro or macro analysis of UTAs through the spatial prospective within the geographical environment and urban structure can make a deep micro understanding of UTAs patterns in addition to assisting in decision making. UTAs can be considered complex events that occur in two aspects which are spatial and temporal or space and time in other word. A GIS can integrate more than two different and unrelated databases. The evaluation among different spatial objects in a geographical environment and associated factors in urban structure which are included but not limited to land use category, road transportation network qualification, population density, etc., is one of the GIS specification. Traffic safety organizations and UTA researchers use GISs as a key technology to support their research and operational needs. In particular, GIS-T is an often-used GIS application used for planning and decision-making in transportation.
基金funded by National Key Technology R&D Program of China (No.2006BAG01A03)
文摘Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings or tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, a novel method employing a back propagation (BP) neural network is developed to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, the speed of its related road sections can be used to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the proposed method of traffic speed estimation is very effective.
文摘The basic principles of GPS (Global Positioning System) and DGPS (Differential GPS) are described. The principle and structure of vehicle navigation systems, and its application to the urban traffic flow guidance are analyzed. Then, an area coordinated adaptive control system based on DGPS and a traffic flow guidance information system based on DGPS are put forward, and their working principles and functions are researched. This is to provides a new way for the development of urban road traffic control systems.
