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.

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.

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.

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.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile AdHocNetworks(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.

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.

Emission Patterns under Alternative Congestion and Motor Vehicle Pollution Mitigation Policies in Shanghai

As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission control of motor vehicle and congestion reduction in such a city to explore how to reconcile mobility enhancement with the environment. Results of a dynamic simulation displayed time paths of emissions from motor vehicles in Shanghai over the period from 2000 to 2020. The simulation results showed that early policies on emission control of motor vehicle could bring about far-reaching effects on emission reduc- tion, and take advantage of available low-polluting technologies and technical innovation over time. Travel demand management would play an important role in curbing congestion and reducing motor vehicle pollution by calming down car ownership rise and deterring inefficient trips as well as reducing fuel waste caused by congestion.

Evaluation of Merge Control Strategies at Interstate Work Zones under Peak and Off-Peak Traffic Conditions

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.

Traffic Regulation at Critical Intersections: A Case Study of Odole Intersection, Akure, Ondo State, Nigeria

Traffic congestion on major roads consequent upon existing bottlenecks at intersections is a major problem in Akure Metropolis. To change this trend, this research was carried out in order to design effective traffic control mechanisms at the “troubled spots” in the metropolis. Odole intersection was identified as one of the critical intersections during a reconnaissance survey and as such, selected for study. Data on geometric features were collected using Oedometer and Google Earth software. Peak and off-peak traffic volume data were collected between 7:30 and 8:30 am and between 12.00 noon and 1.00 pm respectively every other day using Cine camera. Furthermore, discharge headway and delay data were collected using stop watch. The geometric and traffic data collected were analysed using Microsoft Excel. An appraisal of Odole Intersection indicated that the major contributors to traffic are Motorcycles (70.88%) and Passenger Cars (28.72%). Other modes of transportation account for about 0.4% of vehicles traversing the intersection. The critical traffic volume at the intersection was over 4000 veh/hr and the average delay was 22 seconds. An Average delay of 22 seconds at the intersection was an indication that the operating level of service was C (i.e. fairly stable traffic condition with average delays attributable to traffic control by personnel). By juxtaposing the results of geometric and traffic data analyses with the pros and cons of various traffic control mechanisms, traffic control by signalisation was selected and designed to suit Odole intersection. In tandem with the results of this research, appropriate measures have been recommended to ameliorate traffic and commuting problems in the metropolis.

Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm

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.

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

A Comparison of Machine Learning Techniques in the Carpooling Problem

Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiative is one of the most representative efforts to propitiate a responsible use of particular vehicles. Thus, the paper introduces a carpooling model considering the users’ preference to reach an appropriate match among drivers and passengers. In particular, the paper conducts a study of 6 of the most avid classified techniques in machine learning to create a model for the selection of travel companions. The experimental results show the models’ precision and assess the best cases using Friedman’s test. Finally, the conclusions emphasize the relevance of the proposed study and suggest that it is necessary to extend the proposal with more drives and passengers’ data.

Major impact of queue-rule choice on the performance of dynamic networks with limited buffer size

We investigate the similarities and differences among three queue rules,the first-in-first-out(FIFO)rule,last-in-firstout(LIFO)rule and random-in-random-out(RIRO)rule,on dynamical networks with limited buffer size.In our network model,nodes move at each time step.Packets are transmitted by an adaptive routing strategy,combining Euclidean distance and node load by a tunable parameter.Because of this routing strategy,at the initial stage of increasing buffer size,the network density will increase,and the packet loss rate will decrease.Packet loss and traffic congestion occur by these three rules,but nodes keep unblocked and lose no packet in a larger buffer size range on the RIRO rule networks.If packets are lost and traffic congestion occurs,different dynamic characteristics are shown by these three queue rules.Moreover,a phenomenon similar to Braess’paradox is also found by the LIFO rule and the RIRO rule.