Urban Road Traffic Congestion Research Articles

Research on the urban green transportation development level evaluation based on the triangular pythagorean fuzzy multiple attribute decision making

With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity.

Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road

How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance.

The Real-Time Dynamic Prediction of Optimal Taxi Cruising Area Based on Deep Learning

A real-time, effective, and dynamic taxi cruising recommendation strategy is essential to solving the problem of taxi cruising passenger difficulty and urban road traffic congestion. This study focuses on two aspects of the real-time accessible range and pick-up ratio (PR) and proposes a real-time dynamic identification method for taxi optimal cruise-seeking area. Firstly, based on the cumulative opportunity method, a univariate temporal convolutional network (UTCN) accessible range dynamic prediction model is proposed to predict the real-time accessible range of taxis. Secondly, based on the gradient boosting decision tree (GBDT) model, the influencing factors with a high correlation with the PR are selected from the four dimensions of traffic characteristics, environmental meteorology, and time and space variables. Then, a multivariate univariate temporal convolutional network (MTCN) global grid PR prediction model is constructed, and the optimal taxi cruising area is identified based on the maximum PR. The results show that the taxi accessible range and PR of the same grid in different periods change with time, and based on the model comparison, the accessible range and PR prediction results of UTCN and MTCN algorithms in different periods are the best to identify the optimal cruising area of taxis in different periods. The main contribution of this study is that the proposed optimal cruising area prediction model has timeliness, accessibility, and dynamics. It can not only improve the probability of taxis receiving passengers and avoid taxis cruising aimlessly, but also solve the shortage of taxis in hotspots, thus shortening the waiting time of passengers. This provides a scientific basis for improving taxi cruising efficiency and the government’s formulation of taxi operation management policies, which can effectively promote the sustainable development of urban traffic.

Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model

Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models.

A Systematic Review on Urban Road Traffic Congestion

A Systematic Review on Urban Road Traffic Congestion

A Blockchain-Based Shared Bus Service Scheduling and Management System

With the continuous development of urbanization, it has become an important issue to effectively alleviate urban road traffic congestion and improve traffic efficiency. By combining blockchain technology and shared buses, this paper builds an intelligent traffic-service scheduling management system based on blockchain. The system effectively solves the core problems of shared buses, improves data security and privacy protection, realizes intelligent scheduling and route planning, and simplifies cross-organization cooperation and settlement processes. The research shows that the system can reduce the distance and number of buses, and improve the service quality and operation efficiency while ensuring the waiting time of passengers. The results of this paper verify the feasibility and advantages of the system, bring innovation and improvement to the field of traffic management, and promote the sustainable development of urban intelligent traffic management system. Future research could further explore the application of blockchain technology in traffic management to achieve more intelligent and sustainable urban traffic management.

An improved discrete particle swarm optimization approach for a multi-objective optimization model of an urban logistics distribution network considering traffic congestion

To optimize urban logistics networks, this paper proposes a multi-objective optimization model for urban logistics distribution networks (ULDN). The model optimizes vehicle usage costs, transportation costs, penalty costs for failing to meet time windows, and carbon emission costs, while also considering the impact of urban road traffic congestion on total costs. To solve the model, a DPSO (Discrete Particle Swarm Optimization) algorithm based on the basic principle of PSO (Particle Swarm Optimization) is proposed. The DPSO introduces multiple populations to handle multiple targets and uses a variable neighbourhood search strategy to improve the search ability of particles, which helps to improve the local search ability of the algorithm. Simulation results demonstrate the effectiveness of the proposed model in avoiding traffic congestion, reducing carbon emissions costs, and time penalty costs. The optimization comparison results between DPSO and PSO also verify the superiority of the DPSO algorithm. The proposed model can be applied to real-world urban logistics networks to improve their efficiency, reduce costs, and minimize environmental impact.

The Impact of Road Functions on Road Congestions Based on POI Clustering: An Empirical Analysis in Xi’an, China

In new-tier cities with rapid urbanization, the reorganization of urban spatial functions and the development of road networks have brought novel challenges to traffic congestion control. Urban land use patterns have a significant correlation with urban traffic congestion. However, whether and how land use patterns of cities close to the roads affect road congestion is less to be discussed. This article investigated the relationship between land use patterns close to the urban trunk road network and traffic congestion in new tier cities Xi’an, China. We adopted the DBSCAN algorithm to cluster POIs and use the mixed POI clusters to label the socio-economic functions of roads. We found the spatial heterogeneity of POIs on the trunk road network and identified the impacts of the scales and types of POI on road congestion based on the empirical analysis. Compared to the POIs as origin and destination of the trips, the POIs as stopover points of the trips cause significantly more road congestion. The POIs with bidirectional flows at entrances/exits are more likely to cause road congestion than the POIs with unidirectional flows. Moreover, the POIs with flexible traffic flows increase road congestion, while the POIs with predicted traffic flows have no statistically significant correlation with road congestion. The results help urban planners to plan the scale, type, and location of POIs close to roads and to optimize the socio-economic functions of roads and alleviate road congestion.

Analysis of Intelligent Transportation System Application Based on Internet of Things and Big Data Technology under the Background of Information Society

With the growth of national economic strength, urban modernization has become an indispensable part. Among them, urban road traffic congestion has become the main problem affecting work and traffic. Traffic pressure promotes the massive use of Internet of Things and other technologies in the transportation system. The integration of computer, Internet of Things and transportation system forms a new intelligent transportation concept. With the advent of intelligent information society, the application of intelligent transportation is imminent. In view of the shortcomings of urban intelligent transportation, this paper uses Internet of Things technology and big data technology to study it. Firstly, it analyzes the function and value of IoT intelligent transportation, analyzes the system components from the construction principle, and makes full use of intelligent technology to improve the transportation system. Secondly, the optimal path algorithm is proposed in the IoT urban intelligent transportation system model. The advantages and disadvantages of genetic algorithm and heuristic algorithm are analyzed, and ant colony algorithm is used for further optimization. In order to verify the feasibility of the intelligent transportation system, technologies such as big data edge computing are also used to comprehensively evaluate and study the use of the system. Finally, we analyze the results of the practical application of the intelligent transportation system under the Internet of Things technology. The research results show that the urban intelligent transportation system, supported by the Internet of Things technology, can realize the dynamic planning of the optimal route, help the people to improve their driving experience in the high safety and intellectualization, and greatly avoid a series of problems caused by congestion.

Evaluating the Congestion Relief Impacts of Public Transport in Monetary Terms

Traffic congestion is a major urban transport problem. Efficient public transport (PT) can be one of the potential solutions to the problem of urban road traffic congestion. Public transport systems can carry a significant amount of trips during congested hours, improving overall transportation capacity, and can release the burden of excess demand on congested road networks. This paper presents a comparative assessment of international research valuing the congestion relief impacts of PT. It explores previous research valuing congestion relief impacts and examines secondary evidence demonstrating changes in mode split associated with changes in public transport. The research establishes a framework for estimating the monetary value of the congestion reduction impacts of public transport. Congestion relief impacts are valued at between 4.4 and 151.4 cents (Aus$, 2008) per marginal vehicle km of travel, with an average of 45.0 cents. Valuations are higher for circumstances with greater degrees of traffic congestion and also where both travel time and vehicle operating cost savings are considered. A simplified congestion relief valuation model is presented to estimate the congestion relief benefits of PT based on readily-available transport data. Using the average congestion valuation and mode shift evidence, the model has been applied to a number of cities to estimate the monetary value of the congestion relief impact of public transport. Overall, the analysis presents a simplified method to investigate the impact of public transport on traffic congestion. Further research is warranted to develop a comprehensive approach for establishing a measure of the congestion relief impact of public transport.

Research on Decision Evaluation Model of HOV Lane Setting

In order to achieve the purpose of improving the travel efficiency of commuters in the periphery of the city, expanding the beneficiary groups of urban rail transit, and alleviating urban road traffic congestion, when planning and setting up HOV in the periphery of the city, it is necessary to analyze the feasibility of HOV lane setting from both the demand conditions and the setting conditions. This paper combines machine learning to construct a decision-making evaluation model for HOV lane setting and studies the optimal layout model and algorithm of HOV lanes in service rail transit commuter chain. The setting, planning, and layout of HOV lanes are a two-way interactive process of traveler's path selection and designer's road planning. Finally, after the model is constructed, the performance of the system model is verified. The results show that the system studied in this paper can be used for traffic data and lane planning analysis. Therefore, in the process of urban operation, the HOV model constructed in this paper is mainly used to alleviate urban traffic and improve urban operation efficiency.

Urban road congestion patterns under the COVID-19 pandemic: A case study in Shanghai

The novel coronavirus (COVID-19) pandemic has had a significant impact on human mobility around the world. Many cities issued “stay-at-home” orders during the outbreak of COVID-19, and many commuters have also changed their travel modes in the post pandemic period; e.g., transit/bus passengers have switched to driving or car-sharing. Urban road traffic congestion patterns are significantly different than they were pre-pandemic, and understanding such changes can be an opportunity to improve future emergency traffic management and control. Previous studies on this topic have focused on natural disasters or major accidents/incidents. However, very few studies have analyzed the empirical traffic congestion patterns that have occurred during a pandemic. This study takes Shanghai as an example, and conducts a retrospective analysis of empirical spatio-temporal road traffic congestion during the COVID-19 pandemic. The three-month road traffic speed data in the 446 Traffic Analysis Zones (TAZs) collected from Baidu Maps was used in this study. The algorithm of Singular Value Decomposition (SVD) was employed to investigate the inherent composition of the spatio-temporal variation simultaneously influenced by several factors. Three principal components were identified from the spatio-temporal variation, including the stable, main part of variation; the part of the variation that is affected by commuting; and the part of the variation that is affected by migrant populations and the pandemic. The results may suggest ways to improve the emergency management and control of urban roadways in other metropolitan areas worldwide during and after the COVID-19 pandemic period.

The Prediction of Urban Road Traffic Congestion by Using a Deep Stacked Long Short-Term Memory Network

Traffic congestion is an overwhelming problem faced by road travelers all over the world. A time-efficient and accurate prediction of upcoming traffic congestion can reduce this problem through enabling the proactive planning of routes. Recent research suggests that prediction accuracy requires the extraction of hidden features of the road network from the historical traffic data. In general, this data is either limited (with a longer sampling time) or not provided by providers. In urban areas, traffic lights, weather conditions, city events, accidents, and people’s habits significantly influence the traffic flow according to the structure of road network. Therefore, a mechanism is required to extract traffic data by scraping images from the route planners’ websites to predict traffic congestion. In this article, we devise such a method and introduce a fuzzy logic and stochastic estimation algorithm to detect congestion levels at the intersections of the road network. We then build a deep stacked long short-term memory network, in combination with online training, for the multipoint future prediction of congestion. We name the proposed model a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy logic and deep learning-based traffic congestion predictor</i> (FDLTCP) and compare the proposed predictor with the gated recurrent unit and stacked auto-encoders. Experimental evaluations demonstrate the effectiveness of FDLTCP, in terms of mean square error and other critical performance metrics, to perform future predictions.

Multicause Automatic Real-Time Identification of Urban Road Traffic Congestion Based on Bayesian Network

Urban road traffic congestion is caused by various factors, such as dynamic and real-time, complexity, and changeability, and existing methods are subjective, have low accuracy and poor real-time, and unable to automatically identify. This study proposes a multicause automatic real-time identification of urban road traffic congestion on the basis of the Bayesian network. First, the method performs systematic mechanism analysis and simulation verification on the relationship between the dynamic observable variables of urban road traffic and the multiple congestion causes to construct the Bayesian network structure. Second, the obtained measured historical data are used for parameter learning and training to obtain the complete Bayesian network model. Finally, multiple causes of traffic congestion can be automatically and simultaneously identified in real time when inputting the observable variables of traffic under road working conditions into the Bayesian network model. This method has high flexibility that can help express the correlation of nodes better and has strong interpretability, which can fully use expert experience and knowledge and achieve automatization in real time. The results in the research on Quanxiu Street, Quanzhou City show that the construction of the multicause automatic real-time identification of urban road traffic congestion based on the Bayesian network is reasonable. Five congestion causes have higher recognition accuracy rates than contrast methods, which include pedestrian influence, peak traffic, parking occupied road, unreasonable signal timing, and impact of traffic crossing the road.

An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities

Traffic lights have been used for decades to control and manage traffic flows crossing road intersections to increase traffic efficiency and road safety. However, relying on fixed time cycles may not be ideal in dealing with the increasing congestion level in cities. Therefore, we propose a new Adaptive Traffic Light Control System (ATLCS) to assist traffic management authorities in efficiently dealing with traffic congestion in cities. The main idea of our ATLCS consists in synchronizing a number of traffic lights controlling consecutive junctions by creating a delay between the times at which each of them switches to green in a given direction. Such a delay is dynamically updated based on the number of vehicles waiting at each junction, thereby allowing vehicles leaving the city centre to travel a long distance without stopping (i.e., minimizing the number of occurrences of the ‘stop and go’ phenomenon), which in turn reduces their travel time as well. The performance evaluation of our ATLCS has shown that the average travel time of vehicles traveling in the synchronized direction has been significantly reduced (by up to 39%) compared to non-synchronized fixed time Traffic Light Control Systems. Moreover, the overall achieved improvement across the simulated road network was 17%.

DESIGN OF TRAFFIC SIGNAL CONTROL SYSTEMS BASED ON FUZZY CONTROL

With a gradual increase in urban road traffic volume and traffic congestion degree, how to improve and solve the current traffic pressures has been a problem requiring urgent solution. To improve the traffic congestion status in urban road intersections and heighten the road traffic efficiency, the principle of fuzzy control is employed in this paper. Moreover, the multi-phase signal traffic control of intersections is performed together with vehicle queue lengths in lanes corresponding to the key traffic flow, and a traffic signal fuzzy control system is designed. Finally, this paper compares the simulation results between the fuzzy control system designed herein and the existing fixed traffic signal control methods. The comparative test results have shown that the fuzzy control method can well better actual congestion and traffic efficiency at intersections to a greater degree than the fixed timing method does.

A dynamic road incident information delivery strategy to reduce urban 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.

A Two-level Traffic Light Control Strategy for Preventing Incident-Based Urban Traffic Congestion

This work designs a two-level strategy at signalized intersections for preventing incident-based urban traffic congestion by adopting additional traffic warning lights. The first-level one is a ban signal strategy that is used to stop the traffic flow driving toward some directions, and the second-level one is a warning signal strategy that gives traffic flow a recommendation of not driving to some directions. As a visual and mathematical formalism for modeling discrete-event dynamic systems, timed Petri nets are utilized to describe the cooperation between traffic lights and warning lights, and then verify their correctness. A two-way rectangular grid network is modeled via a cell transmission model. The effectiveness of the proposed two-level strategy is evaluated through simulations in the grid network. The results reveal the influences of some major parameters, such as the route-changing rates of vehicles, operation time interval of the proposed strategy, and traffic density of the traffic network on a congestion dissipation process. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

The Study on In-City Capacity Affected by Pedestrian Crossing

Currently, the urban road traffic congestion is serious and the traffic accident is happening at a high frequency; thus it has not satisfied the travel needs of security and affects the quality of urban trips. In order to effectively relieve the confliction of people and motor vehicle, to make sure of the safety of pedestrians crossing the road, and to improve the capacity of urban roads, this passage focuses on studying the influence of pedestrians crossing the roads on the capacity of urban roads in three pedestrian crossing approaches including freely crossing the street, uncontrolled crossing of the pedestrian crosswalk, and controlled crossing of the pedestrian crosswalk. Firstly, it confirms the general formula of the road capacity when pedestrians are crossing the road based on three preassumptions, combined with the survey data, and then constructs the empirical mathematical model of pedestrian crossing on the capacity impact. Lastly, it takes the step of case calculation and simulation evaluation and calculates errors between them, finding that the error between the model calculation and software simulation is small. The efficiency of the model is validated and improved.

Real-time Road Congestion Detection Based on Image Texture Analysis

Proposing a fast detection algorithm for urban road traffic congestion based on image processing technology. Firstly, to speed up the processing and to freely select the interesting area, the human-computer interaction vehicle area detection was put forward. Then, by using the difference of texture features between congestion image and unobstructed image, proposing vehicle density estimation based on texture analysis. Through image grayscale relegation, gray level co-occurrence matrix calculation and feature extraction, the energy and entropy features that could reflect vehicle density were obtained from vehicle area. After features training, the decision threshold could be obtained and traffic congestion was carried out. Experimental results showed that the accuracy of algorithm was as high as 99%, and the processing speed could satisfy the real-time requirement in engineering.