A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

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.

Research on traffic congestion mechanism and countermeasures based on dynamic traffic assignment

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.

CHRT:Clustering-Based Hybrid Re-Routing System for Traffic Congestion Avoidance

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.

Road traffic congestion measurement considering impacts on travelers

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.

Traffic Congestion Mechanism in Two Ramp Systems

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.

Exploring critical attributes during air traffic congestion with a fuzzy DEMATEL–ANP technique: a case study in Ninoy Aquino International Airport

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.

Evolution of Road Traffic Congestion Control:A Survey from Perspective of Sensing,Communication,and Computation

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.

Analysis on the Feasibility of Collecting Traffic Congestion Charges in Beijing

On the basis of analyzing connotation of congestion pricing,through the qualitative analysis of the main content and implementation effect of the congestion policy in cities such as Singapore,London and Stockholm,the basic conditions for urban congestion charges were summarized.Compared with Beijing’s urban development,traffic development status,urban transport infrastructure and social factors,the current policy of traffic congestion charging is still not available from the perspective of the current status of car ownership in Beijing,residents’ travel composition,and population density distribution.

TCEVis:Visual analytics of traffic congestion influencing factors based on explainable machine learning

Traffic congestion is becoming increasingly severe as a result of urbanization,which not only impedes people’s ability to travel but also hinders the economic development of cities.Modeling the correlation between congestion and its influencing factors using machine learning methods makes it possible to quickly identify congested road segments.Due to the intrinsic black-box character of machine learning models,it is difficult for experts to trust the decision results of road congestion prediction models and understand the significance of congestion-causing factors.In this paper,we present a model interpretability method to investigate the potential causes of traffic congestion and quantify the importance of various influencing factors using the SHAP method.Due to the multidimensionality of these factors,it can be challenging to visually represent the impact of all factors.In response,we propose TCEVis,an interactive visual analytics system that enables multi-level exploration of road conditions.Through three case studies utilizing actual data,we demonstrate that the TCEVis system offers advantages for assisting traffic managers in analyzing the causes of traffic congestion and elucidating the significance of various influencing factors.

Bi-objective optimization models for mitigating traffic congestion in urban road networks

Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world.In this context,this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation.Four congestion mitigation strategies are identified based on deviation and relative deviation of link volume from the corresponding capacity.Consequently,four biobjective mathematical programming optimal flow distribution(OFD)models are proposed.The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volumeto-capacity links compared to UE and SO models.Among the models,the system optimality with minimal sum and maximum absolute relative-deviation models(SO-SAR and SO-MAR)showed superior results for different performance measures.The SO-SAR model yielded 50%and 30%fewer links at higher link utilization factors than UE and SO models,respectively.Also,it showed more than 25%improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of1.04 compared to the other OFD and UE models.Conversely,the SO-MAR model yielded the least total distance and total system travel time,resulting in lower fuel consumption and emissions,thus contributing to sustainability.The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.

Processes and events in the center: a taxi trajectory-based approach to detecting traffic congestion and analyzing its causes

A novel approach is introduced for the detection of the location and direction of traffic congestion using GPS data from taxis.This approach uses a dynamic model that conceptualizes events,processes,and states.The states are the locations of the taxis,the processes are the motion of taxis,and the events are congestion.The model is implemented as a graph database,which represents the relationships between states,events,processes,and things(such as points of interest and road grid).Algorithms for constructing and updating the relationships and taxi behaviors dynamic retrieval method in Neo4j are presented and are used to demonstrate the capabilities in dynamic expression and reasoning.An implementation of Shanghai in 2015finally demonstrated the ability of congestion direction detection and the cause searching of traffic congestion.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

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.

Study on the Reduced Traffic Congestion Method Based on Dynamic Guidance Information

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.

A Decision Framework for Automatic Guided Vehicle Routing Problem with Traffic Congestions

Automatic guided vehicles are widely used in various types of warehouses including the automated container terminals.This paper provides a decision framework for port managers to design and schedule automatic guided vehicle routing plans under time-varying traffic conditions.A large number of computational experiments on a grid graph are conducted to validate the efficiency of the proposed decision framework.We also proposed one efficient queueing rule in automatic guided vehicle routing scheduling.Although the complexity of the problem is high,computational results show that our proposed decision framework can provide high quality solutions within a relatively short computation time.

An Adjoint StateMethod for Numerical Approximation of Continuous Traffic Congestion Equilibria

The equilibriummetric forminimizing a continuous congested trafficmodel is the solution of a variational problem involving geodesic distances.The continuous equilibrium metric and its associated variational problem are closely related to the classical discrete Wardrop’s equilibrium.We propose an adjoint state method to numerically approximate continuous traffic congestion equilibria through the continuous formulation.The method formally derives an adjoint state equation to compute the gradient descent direction so as to minimize a nonlinear functional involving the equilibrium metric and the resulting geodesic distances.The geodesic distance needed for the state equation is computed by solving a factored eikonal equation,and the adjoint state equation is solved by a fast sweeping method.Numerical examples demonstrate that the proposed adjoint state method produces desired equilibrium metrics and outperforms the subgradient marching method for computing such equilibrium metrics.

Traffic Attractors and Congestion in the Urban Context, the Case of the City of Rome

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.

Urban traffic congestion in China:causes and countermeasures

Based on the analysis of the current situation and causes of urban traffic congestion in China,this paper points out that the rapidly rising gap between demand and supply in transportation,inappropriate urban space arrangement,and inefficient traffic management are the underlying causes of traffic congestion.Drawing on the advanced experience of foreign countries,this paper also offers suggestions for countermeasures against urban traffic congestion in China.

An Intelligent Adaptive Dynamic Algorithm for a Smart Traffic System

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.

Characterizing Peak-Time Traffic Jam Incidents in Kampala Using Exploratory Data Analysis

Traffic congestion is associated with increased environmental pollutions, as well as reduced socio-economic productivity due to significant delays in travel times. The consequences are worse in least developed countries where motorized road transport networks are often inefficiently managed in addition to being largely underdeveloped. Recent research on traffic congestion has mostly focused on infrastructural aspects of road networks, with little or no emphasis at all on motorists’ on-the-road behavior (MB). The current study thus aimed to bridge this knowledge gap by characterizing traffic jam incidents (TJI) observed over a period of 80 days in Uganda’s Capital City, Kampala. MB as well as road network infrastructural factors such as road blockage (RB), were captured for each of the observed TJI. A total of 483 peak-time TJI were recorded, and exploratory data analysis (EDA) subsequently performed on the TJI dataset. EDA involved Hierarchical clustering analysis (HCA) and K-means clustering of the TJI dataset, as well as a detailed descriptive statistical analysis of both the entire dataset and the emerging TJI clusters. A highlight finding of this study is that 48.2% of the observed TJIs were as a result of on-the-road motorist behavior. Furthermore, the intervention of traffic police officers in a bid to regulate traffic flow was equally responsible for 25.9% of the TJIs observed in this study. Overall, these results indicate that whereas road infrastructural improvement is warranted in order to improve traffic flow, introducing interventions to address inappropriate on-the-road motorists’ behavior could alone improve traffic flow in Kampala, by over 48%. Additionally, in-order to effectively regulate traffic flow in Kampala and other least developed cities with similar traffic congestion management practices, motorists’ on-the-road behavior ought to be factored into any data-driven mechanisms deployed to regulate traffic flow and thus potentially significantly curbing traffic congestion.

Numerical Simulation of Diffusion Type Traffic Flow Model Using Second-Order Lax-Wendroff Scheme Based on Exponential Velocity Density Function

In order to control traffic congestion, many mathematical models have been used for several decades. In this paper, we study diffusion-type traffic flow model based on exponential velocity density relation, which provides a non-linear second-order parabolic partial differential equation. The analytical solution of the diffusion-type traffic flow model is very complicated to approximate the initial density of the Cauchy problem as a function of x from given data and it may cause a huge error. For the complexity of the analytical solution, the numerical solution is performed by implementing an explicit upwind, explicitly centered, and second-order Lax-Wendroff scheme for the numerical solution. From the comparison of relative error among these three schemes, it is observed that Lax-Wendroff scheme gives less error than the explicit upwind and explicit centered difference scheme. The numerical, analytical analysis and comparative result discussion bring out the fact that the Lax-Wendroff scheme with exponential velocity-density relation of diffusion type traffic flow model is suitable for the congested area and shows a better fit in traffic-congested regions.