Problem Of Urban Traffic Congestion Research Articles

Traffic flow prediction based on nonlinear weight decreasing PSO-SVR univariate time series prediction algorithm

The aim of this paper is to investigate the application of car flow prediction in the field of transport in order to solve the problem of urban traffic congestion. For this purpose, we adopt the nonlinear weight decreasing PSO-SVR univariate time series prediction algorithm to predict the car flow, and divide the data set into training set and test set according to the ratio of 7:3. By analysing the scatterplot and line graph between the predicted and actual values of the training and test sets, we find that the prediction effect is better, but there is a certain deviation. Specifically, the scatter plot shows a Y=X distribution, but the actual values have a wide range of variation relative to the predicted values. Meanwhile, the line graph shows that the actual traffic flow and the predicted traffic flow have the same trend, but the actual traffic flow value changes more, while the predicted traffic flow value changes less. This may be due to the long time span resulting in too many cycles, and shortening the time span can reduce the number of cycles and thus improve the prediction accuracy. Further analysis of the MAE values for the training and test sets shows that the MAE values for both the training and test sets are relatively small, 1.8437 and 2.6408, respectively, where the MAE for the test set is on the large side, but the overall prediction results of the model are better. Therefore, the nonlinear weight decreasing PSO-SVR univariate time-series prediction algorithm used in this paper can provide powerful decision support for traffic management departments and help them to better formulate traffic planning and management strategies.

A Review of GIS Technology Applications in Transportation Planning and Management

With the rapid development of the economy, the scale of urban construction is expanding, the corresponding transportation infrastructure is also improving, and the traffic congestion problem is becoming more and more serious. The application of GIS technology to transportation management can effectively alleviate the problem of urban traffic congestion at this stage, thus improving the operation of road traffic. This paper describes the advantages of GIS technology in transportation planning and management from the aspects of accuracy, measurement convenience, visualization, scientific, dynamic, data management, and introduces its application initiatives in road design, traffic prediction, vehicle guidance, traffic system design, traffic analysis. The application of GIS technology in transportation planning is becoming more and more extensive and profound, the technology can provide ideas and technical support for modern transportation planning, but also can combine the construction of road transportation system with information technology, effectively improve the rationality and scientificity of the transportation planning program, and play a positive and significant role in reducing traffic congestion and improving the efficiency of traffic operation.

Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

Performance analysis of machine learning methods for short-term traffic prediction

With the rapid development of urbanization and the rapid increase of the number of motor vehicles, the problem of urban traffic congestion has become increasingly prominent. The accurate prediction of short-term traffic flow is considered as a promising solution, which can provide a key decision-making basis for route planning and traffic flow scheduling, so that can greatly alleviate or even prevent congestion. Researchers have used many machine learning methods to predict traffic flow, but few people pay attention to the boundaries of different machine algorithms. In this paper, we use AdaBoost, Random Forest, SVM and BP neural network to predict short-term traffic flow in California, which aims to compare the differences in prediction performance of different algorithms and analyze their potential reasons. The results show that, the integration methods such as AdaBoost and Random Forest are quite appropriate to solve the short-term traffic flow, which can obtain an accuracy more than 95%, while prediction made by SVM is less precise than the two aforementioned methods with a 79% accuracy. And BP neural network may be inappropriate if the parameters remain default. The different results are due to the periodicity of the database. Integration methods can recognize the periodicity while the SVM and BP neural network fail to do it. When employing the SVM and BP neural network, the datasets need to be divided within a period to avoid being disturbed by cyclically. Besides, the precise of BP neural network can be improved when adjusting the parameters to the optimal.

Analysis of Traffic Characteristics in Beijing --Based on the Traffic Performance Index

This study aims to deal with the increasingly serious urban traffic congestion problem, taking Beijing as a case study. With the increase in population, traffic congestion has become a serious problem affecting peoples life quality and social and economic development. This study uses the time series analysis and uses the ARIMA (1,0,0) (0,1,1)12 model to forecast the traffic performance index (TPI) data of the past ten years, and deeply analyzes the traffic congestion situation of the next twelve months. The results show that this model can capture the seasonal trend of traffic congestion well, and provide strong decision support for traffic management departments. Through the interpretation and analysis of the forecast, this study gives a series of traffic management suggestions for different months to achieve the alleviation and optimization of urban traffic congestion. In conclusion, this study provides a useful reference for urban traffic management and planning and contributes to the construction of a more efficient, convenient, and sustainable urban transportation system.

Research on spatiotemporal characteristics of urban crowd gathering based on Baidu heatmap

With the rapid development of urban transportation and the increase in per capita car ownership, the problem of urban traffic congestion is becoming increasingly prominent. Due to the uneven distribution of crowd in different regions of the city, it is difficult to determine and solve the traffic dynamics congestion. In order to solve the problem that it is difficult to determine the dynamics of traffic congestion areas caused by uneven distribution of vitality in different regions of mountainous cities, a crowded mega mountainous city is selected as research object and it proposes a model to calculate the change characteristics of regional crowd gathering. Baidu Heatmap is used as it could distinguish crowd gathering in certain urban core area. The heat map pictures in dozens of consecutive days is extracted and researchers conducted pixel statistical classification on thermal map images. Based on the pixel data of different levels of the pictures, the calculation model is established and an algorithm based on particle swarm optimization is proposed. The calibration of the relative active population equivalent density is conducted, and the distribution characteristics of crowd gathering in time and space are analyzed. The results show that there are obvious spatiotemporal characteristics for this selected city. In time, holidays have an important impact on crowd gathering. The peak time of crowd gathering on weekdays is different from that on rest days. The research in this paper has a direct practical value for the identification of traffic congestion areas and the corresponding governance measures. The dynamic identification of population gathering areas in mountainous mega cities, demand prediction for various transportation regions, and future population OD(Origin—Destination) planning are of great significance.

Research on Optimization Algorithm for Urban Traffic Flow Based on Computer Simulation

With the acceleration of urbanization, the problem of urban traffic congestion is becoming increasingly prominent. In order to address this issue, this paper proposes an optimization algorithm for urban traffic flow based on computer simulation. The algorithm simulates urban traffic flow, analyzes traffic bottlenecks and congestion points, and proposes corresponding optimization strategies. Through experiments and data analysis, the effectiveness and feasibility of the algorithm are verified. The achievements of this research have important implications for urban traffic management and optimization.

The Performance Evaluation Model of Bus APP

With the high development of economy and the continuous growth of the population of major cities in the world, the problem of traffic jams has become a serious problem that local governments must solve. In addition, facing the impact of global warming, how to save energy and reduce carbon emission is another critical problem that local governments need to face. An effective smart bus APP can enhance people’s satisfaction with the use of the APP while it can increase their willingness to take the bus, thereby reducing the number of people driving their own cars. It is of some help to alleviate the problem of urban traffic congestion. At the same time, it also reduces the total amount of carbon emitted from cars so as to achieve the effects of energy saving and carbon reduction. Therefore, this paper, through questionnaires, gathered opinions from passengers who used the bus APP and established a performance evaluation model of the bus APP. This model can assist the designer or management engineer of the bus APP to find out the service items of the bus APP that need to be improved, in order to make the bus APP more intelligent and efficient to use.

Review on short-term traffic flow prediction methods under big data

The goal of traffic forecast is to predict the related traffic situation in the future according to the historical concept. The predicted angle can be divided into short-term prediction and long-term prediction. This method can be used to solve the increasingly serious urban traffic congestion problem, and researchers have proposed a deep learning model to help decision makers in the field of traffic control. It has made great contributions to improving future road capacity and optimizing intelligent transportation services. In this paper, short-term traffic forecast related documents under big data are helpful after sorting out, and the traffic flow data characteristics of intelligent transportation system and related correction methods are analysed. Secondly, it then classifies the applications involved by big data algorithm calculation according to various related principles, and summarizes the prediction accuracy, computational complexity, applicable interval and computation time of each type of algorithm in an overview. According to the relevant data, the combination forecasting model is effectively diversified, and combined with the related combination forecasting, I hope to improve the forecasting accuracy of the future development prospect.

Goal-oriented Urban Integrated Transportation Planning under the Concept of Green Mobility

With the development of urban motorization, urban traffic congestion and traffic pollution problems are becoming more and more serious, and the new era of "double carbon" strategic goals have also made important requirements for the transportation sector. The traditional integrated transportation planning has the limitation of being established only to meet the transportation demand, and it also takes less account of the importance of sustainable development. Therefore, this paper reflects on the traditional comprehensive transportation planning from the perspective of the "double carbon" strategic goal, and identifies the problems, combines the current characteristics that there are many green travel modes in the city but not in an orderly way, builds a green travel system in the comprehensive urban transportation planning, integrates the green travel concept into the urban development, and creates a green travel transportation planning system in the urban area. The research results are in line with the requirements of the "double carbon" strategy and the sustainable development of resources, environment and transportation. The research results of this study are in line with the realistic needs and have certain theoretical reference value and practical guidance for urban transportation managers in terms of theory, methodology and practical application.

A Hybrid Method of Traffic Congestion Prediction and Control

With the increasing complexity of urban transportation system, serious traffic congestion brings inconvenience to travel. It is also very difficult to predict and control traffic congestion. Therefore, this paper takes urban traffic condition and traffic congestion as the research object, and conducts in-depth research on traffic condition prediction model and traffic congestion control method. Firstly, a traffic state prediction method based on improved particle swarm optimization (IPSO) optimized radial basis function (RBF) and long and short term memory network (LSTM)/ support vector machine (SVM) feature fusion model was proposed for urban traffic state prediction. Experiments were carried out based on the regional traffic data of Shenyang Station, and compared with other algorithms, which verified the superiority of the feature fusion model based on IPSO-RBF and LSTM/SVM in this paper. Secondly, aiming at the problem of urban traffic congestion, a congestion section control method based on traffic allocation is proposed. Through VISSIM simulation and comparison with other traffic control schemes, the superiority of the congestion section optimization method proposed in this paper is verified.

Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems

In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is mostly divided by urban administrative divisions, which is difficult to reflect the difference of congestion degree within administrative divisions or traffic congestion zoning. In this paper, we introduce the Self-Organizing Feature Mapping (SOFM) model, construct the urban traffic congestion zoning index system based on the resilience and vulnerability of urban traffic systems, and establish the urban traffic congestion zoning model, which is divided into four, five, six, and seven according to the different structures of competition layer topology. The four vulnerability damage capacity indicators of traffic volume, severe congestion mileage, delay time and average operating speed, and two resilience supply capacity indicators of traffic systems, namely, road condition and number of lanes, are used as model input vectors; the data of Guiyang city from January to June 2021 are used as data sets to input four SOFM models for training and testing and the best SOFM model with six competitive topologies is constructed. Finally, the Support Vector Machine (SVM) is used to identify the optimal partition boundary line for traffic congestion. The results show that the four models predict the urban traffic congestion zoning level correctly over 95% on the test set, each traffic congestion zoning evaluation index in the urban area shows different obvious spatial clustering characteristics, the urban traffic congestion area is divided into six categories, and the city is divided into 16 zoning areas considering the urban traffic congestion control types (prevention zone, control zone, closure control zone). The spatial boundary is clear and credible, which helps to improve the spatial accuracy when predicting urban traffic congestion zoning and provides a new methodological approach for urban traffic congestion zoning and zoning boundary delineation.

Urban Customized Bus Design for Private Car Commuters

With the deepening of the urbanization process, the ownership of urban private cars continues to increase, resulting in severe urban traffic congestion and environmental problems. The customized bus, as an emerging public transportation mode, is considered an effective means to alleviate the above problems. This article employs electronic registration identification (ERI) data of vehicles to design customized buses for private cars, consisting of two components: 1) discovering private car commuters and 2) designing customized bus schemes. First, based on the spatial–temporal similarity and high-frequency characteristics of commuting trips, we mined the urban private car commuters and their corresponding commuting trips as the demand for customized buses. Then, we constructed the customized bus model, which targets the number of served passengers with the constraints, such as the trip time window, bus capacity, passenger load rate, etc. In the model, intermediate stops are not set to ensure bus punctuality and passenger experience, and buses of various capacities are employed to ensure effectiveness and efficiency. The differential evolution algorithm was utilized to find the optimal solution for the model. In the experiments, we carried out relevant verification based on Chongqing’s one-week ERI data. The experimental results showed customized bus schemes for various cases and verified the superior performance of our algorithm by comparing it with general optimization algorithms. Besides, through numerical calculation and traffic simulation, the excellent potential for customized buses in reducing urban transportation energy consumption and urban road congestion is illustrated.

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.

Congestion-Aware Rideshare Dispatch for Shared Autonomous Electric Vehicle Fleets

The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively.

Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model

Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model’s parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance.

A Deep Learning–Based Approach for Moving Vehicle Counting and Short-Term Traffic Prediction From Video Images

The intelligent transportation system (ITS) is one of the effective solutions to the problem of urban traffic congestion, and it is also one of the important topics of smart city construction. One particular application is the traffic monitoring and flow prediction. However, there are still challenges regarding both aspects. On the one hand, the current traffic monitoring relies heavily on the single object detection method that cannot achieve accurate statistics of moving target counting and, meanwhile, has limited speed advantage; on the other hand, the existing traffic flow prediction models rarely consider different weather conditions. Therefore, the present article attempts to propose a packaged solution, which combines a new target tracking and moving vehicle counting method and an improved long short-term memory (LSTM) network for traffic flow forecast with weather conditions. More specifically, the DCN V2 convolution kernel and MultiNetV3 framework are used to replace YOLOv4’s conventional convolution kernel and backbone network to realize multi-target tracking and counting, respectively. Subsequently, combined with the temporal characteristics of historical traffic flow, this article introduces weather conditions into the LSTM network and realizes the short-term prediction of traffic flow at the road junction level. This study carries out a series of experiments using the real traffic video data with a 2-month time span at a popular road junction in the downtown of Shenzhen, China. The results suggest that the proposed algorithms outperform the previous methods in terms of the 10% higher accuracy of target detection tracking and about a half reduction of traffic prediction error, when considering weather conditions.

Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model

An effective way to solve the problem of urban traffic congestion is to predict the road traffic status accurately and take effective traffic control measures in time. Considering the impact of visibility on traffic, the pavement status and time characteristics were finely divided, and a regression decision tree was used to establish the traffic flow velocity prediction model with pavement status, time characteristics, and working day characteristics as characteristic parameters. Furthermore, based on the perspective of avoiding using velocity as a single parameter to classify the road traffic status levels, the Kmeans clustering algorithm was used to obtain the classification label results. Moreover, the traffic flow velocity and pavement status were used as characteristic parameters of the classification decision tree to establish the multi-parameter road traffic status prediction model. The experimental result showed that the prediction accuracy of the proposed road traffic status prediction model was 81.31%, and this method has good applicability and certain application value for road traffic status prediction.

Comprehensive service quality evaluation of public transit based on extension cloud model

Prioritizing the development of public transit and enhancing its attractiveness is an important way to solve the problem of urban traffic congestion and achieve sustainable development. To improve the service quality and overall opera-tional efficiency of urban public transit, an evaluation index system related to the comprehensive experience of pas-sengers, service supply quality of public transit enterprises, and supervision of management departments was intro-duced from both the demand and the supply of public transit travel services. Based on the data distribution characteris-tics of the boxplot in statistics, the evaluation level and corresponding value range of each index were determined, and the comprehensive weight of the index was determined using the linear weighting method combining the analytic hierarchy process and the entropy weight method, so as to reduce the influence of single weighting method on the evaluation results of comprehensive service quality of public transit. An evaluation method of public transit compre-hensive service quality based on the extension cloud model was established. The evaluation results of the model were obtained by calculating the cloud affiliation and comprehensive certainty, and a reliability factor was used to test the evaluation results, which solved the problem of randomness and fuzziness in the process of comprehensive service quality evaluation of public transportation and made the evaluation results closer to the reality. Finally, the estab-lished comprehensive evaluation model was applied to a city for example analysis, and the corresponding evaluation level was obtained as good. The value of the reliability factor in the model was less than 0.01, indicating that the model has good applicability and a certain application value for the comprehensive service quality evaluation of public transit. The evaluation method fully considered a variety of evaluation indicators, specified the evaluation level of comprehensive service quality of public transit, and the evaluation results provide a theoretical basis for public transport enterprise to make targeted improvement measures.

Network Design Algorithm Implementation for Resilient Transportation System under Continuous Risk Perturbation with Big Data Analysis

With the rapid economic development and urbanization process accelerating, motor vehicle ownership in large cities is increasing year by year; urban traffic congestion, parking difficulties, and other problems are becoming increasingly serious; in ordinary daily life, continuous risk of disturbance, having a flexible transportation system network is more able to alleviate daily congestion in the city, and the main thing about flexible transportation network is its algorithm. It is worth noting that congestion in many cities is generally reflected in the main roads, while many secondary roads and branch roads are underutilized, and the limited road resources in cities are not fully utilized. As an economic and effective road traffic management measure, one-way traffic can balance the spatial and temporal distribution of traffic pressure within the road network, make full use of the existing urban road network capacity, and solve the traffic congestion problem. Therefore, it is of great theoretical and practical significance to develop a reasonable and scientific one-way traffic scheme according to the characteristics of traffic operation in different regions. Based on the fixed demand model, the influence of traffic demand changes is further considered, the lower-level model is designed as an elastic demand traffic distribution model, the excess demand method is used to transform the elastic demand problem into an equivalent fixed demand problem based on the extended network, and the artificial bee colony algorithm based on risk perturbation is designed to solve the two-level planning model. The case study gives a one-way traffic organization optimization scheme that integrates three factors, namely, the average load degree overload limit of arterial roads, the detour coefficient, and the number of on-street parking spaces on feeder roads, and performs sensitivity analysis on the demand scaling factor.