Characteristics Of Traffic Congestion Research Articles

Revitalizing Urban Mobility:A Comprehensive Analysis of Traffic Flow and Sustainable Solutions at the Un-signalized Taxila Intersection

In recent years, traffic congestion has become one of many cities’ most significant financial and ecological concerns worldwide. It also costs citizens a substantial amount of time and fuel. Many experts believe that identifying traffic congestion characteristics is the first step in reducing traffic congestion. This study aimed to determine the extent of traffic congestion and fuel consumption at Taxila Chowk. The research’s primary methodological tools were a traffic volume survey and peak hour volume study using Aimsun software. According to this study, auto-rickshaws, bicycles, motorcycles, and trucks were the most common modes of transportation. Traffic flow is particularly heavy in the morning, noon, and evening hours. It was revealed that the H.M.C. road is the most congestion-prone route compared to the other four routes. Also, the distinctive design of rickshaws and bus stands and the misplacement of fruit and vegetable markets play a considerable role in Congestion. The level of service of the four routes is almost intolerable, and the sole P.O.F. road that offered comparable better services to city commuters is an acceptable illustration. The study has also identified the amount of fuel consumption on each route, focused on the environmental impact of Congestion due to emissions of green gasses, and provided feasible solutions in the form of alternatives.

AN ASSESSMENT OF TRAFFIC CONGESTION IN TAMAN SRI SERDANG, SELANGOR, MALAYSIA

The issue of traffic congestion has emerged as a significant and multifaceted challenge in numerous cities worldwide, encompassing substantial economic and environmental implications. Numerous experts have proposed that the foremost measure to address the congestion issue is to identify the characteristics of traffic congestion. This study aimed to assess the traffic congestion in urban road networks. Thus, the main methodological approach for conducting the study was the traffic volume study in Taman Sri Serdang, which was observed to examine the traffic flow during weekdays and weekends to examine the traffic flow patterns. Accordingly, conducting a traffic volume and Level of Service (LOS) analysis is necessary to enhance accessibility and road capacity. A comparison of the traffic trends and LOS during weekdays and weekends was also discussed. The findings concerning peak-hour traffic patterns are highlighted, and the conclusions are derived regarding urban traffic flow patterns.

Характеристики транспортного затора на основе данных системы фото- и видеофиксации

The relevance of the present paper is justified by the need to search for a scientifically based criterion for the formation, development and elimination of traffic congestion using modern highly efficient mathematical methods of information processing. It seems necessary to study the results of using the rescaled range, statistical, fractal, harmonic, and wavelet analysis of time series formed by the time (duration) of the movement of cars in a stream along a road section in the absence and presence of traffic congestion. The object of study is a section of the road network equipped with a software and hardware complex that allows measuring the characteristics of any car in the flow of transport. The subject of the study is the patterns of changes in the duration of movement of individual cars in the absence and presence of a congestion situation. The purpose of this study is a quantitative indicator of the formation, evolution, and elimination of traffic congestion. The basis of the theoretical and methodological approach is the use of modern highly efficient mathematical methods for processing information about traffic flows received from stationary complexes of photo and video recording of traffic violations. As a result of the study of time series, decompositions of the analyzed time series by harmonics and wavelets are constructed, statistical and fractal characteristics of the time series under consideration are determined. The analysis of the obtained results is carried out, the features of the obtained dependencies are revealed. The theoretical and practical significance of the work lies in the scientific substantiation of the quantitative characteristics of traffic congestion, which is of practical interest from the point of view of predicting anomalies in the movement of vehicles on the road network, making decisions on managing the operation of traffic lights, etc. The direction of further research is an in-depth analysis of the identified indicator and determination on its basis of a criterion for determining the stages of formation, evolution, and elimination of traffic congestion at intersections of the street and road network of a large industrial city.

Aviation and Airspace Management under Rough Set Theory

With the development of aviation industry, a series of problems have appeared in aviation and airspace, among which the most prominent problem is the congestion of aviation and airspace. Airspace congestion has become a major problem in the development of civil aviation in China. Especially in the central and eastern regions of China, airspace congestion is becoming more and more serious. To better solve the problem of airspace congestion, rough set theory and the Fuzzy C-means (FCM) model are first analyzed. By analyzing the temporal and spatial characteristics of traffic congestion in the control sector, a multisector traffic congestion identification model is established based on radar track data. Four multisector congestion characteristics including equivalent traffic volume, proximity, saturation, and traffic density are established. FCM and rough set theory are used to classify and identify sector congestion. Finally, the model based on FCM-rough set theory is compared with other methods based on the data of the regional control sector in northwest China. The experimental results show that the congestion recognition rate of the model is 92.6%, 93.5%, and 94.2%, and the congestion misjudgment rate is 1.5%, 1.2%, and 1.3%, respectively. Hence, the multisector congestion recognition model has a high recognition rate and a low misjudgment rate, and the overall discrimination result is relatively stable. By comparing the proposed method with other methods, it is concluded that the recognition accuracy of the model based on FCM theory is superior to other methods. In summary, the congestion situation of the sector is affected by a variety of macro- and micro-characteristics of the sector, and the congestion identification model is feasible and efficient. Multisector traffic congestion identification has certain application value for airspace planning, air traffic control-assisted decision making, and air traffic flow management. This work can optimize the aviation and airspace management system and provide relevant suggestions for the study of aviation and airspace congestion.

Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach

Understanding the spatiotemporal characteristics of traffic congestion is the cornerstone of generating traffic management and control strategies. Based on the large-scale taxi trajectory data in Shenzhen, China, this study designs an effective framework to explore the spatiotemporal patterns of traffic congestions. To bridge trajectory data with urban road networks, we develop a two-stage map-matching method from the aspects of distance and angle. Then, the free-flow speed of each road segment is extracted and employed to identify traffic congestion. In this way, a novel complex network method, named chronological network (chronnet), is utilized for traffic congestion modeling, and we employ an overlapping community detection algorithm to identify region-level bottlenecks. According to the network properties, we explore the influence scope of traffic congestions and uncover the role of each road segment in the propagation process. Meanwhile, community detection results indicate that there are typical local clustering structures in traffic congestions, and each community also has its unique traffic characteristics. Overall, these findings reveal that the complex network can effectively mine the consecutive patterns of traffic congestion.

Modeling and Analyzing for a Novel Continuum Model Considering Self-Stabilizing Control on Curved Road with Slope

It is essential to fully understand master the traffic characteristics of the self-stabilizing control effect and road characteristics to ensure the regular operation of transportation. Traffic flow on curved roads and slopes is irregular and more complicated than that on the straight road. However, most of the research only considers the effect of self-stabilizing in the straight road. This study attempts to bridge this deficiency from the following three aspects. First, we review the potential influencing factors of traffic flow stability, which are related to the vehicle's steady velocity, history velocity, and the turn radius of the road and the slope of the road. Based on the above review, an extended continuum model accounting for the self-stabilizing effect on a curved road with slope is proposed. Second, the linear stability criterion of the new model is derived by applying linear stability theory, and the neutral stability curve is obtained in detail. The modified KdV equation describing the evolution characteristics of traffic congestion is derived by using the nonlinear analysis method. Upon the theoretical analysis, the third aspect focuses on simulating the self-stabilizing effect under different slopes and radius, which demonstrates that the self-stabilizing effect is conducive to reducing congestion of the curved road with slope.

Spatial heterogeneity and migration characteristics of traffic congestion—A quantitative identification method based on taxi trajectory data

It is of great reference significance to exploring spatial dependence of urban traffic activities and researching internal causes of regional traffic state changes for road network optimization and residents’ travel behavior analysis. Based on trajectory data of taxis in Ningbo city of China, this study calculates average driving speed of taxis in different blocks during characteristic period and generates the global Moran’s I and the LISA clustering diagram. On this basis, the spatial clustering characteristics of congestion on working days and non-working days are analyzed. Furthermore, in order to further characterize the changes of congestion from the perspective of spatial migration, a method of measuring geometric displacement is adopted to describe spatio-temporal migration trend of traffic states, four indicators designed to identify urban frequently congested areas, including migration direction, angle, distance, and low-value area. The results show that the high-clustering area are located urban fringe and the low-clustering area are located at geometric center of major urban areas. Spatial–temporal migration law of low-value areas in city-center is obvious. Difference between trend is compared with non-working days, the offset and azimuth of low-value area in downtown on working days are even bigger. The accurate capture of the characteristics of congestion space migration at the urban scale will help to formulate more targeted congestion management strategies.

A review paper on traffic pollution

The most common sources of air pollution in cities are traffic congestion and traffic-related pollutants. Rapid urbanization in emerging nations has resulted in a large-scale expansion of motor vehicle usage, causing cities to become more crowded and polluting. There is a growing recognition that the current state of air quality in East African cities is unacceptably poor. This study examines existing traffic pollution issues in Nairobi, Kenya, and how they may be solved. The article starts with an overview of urbanization and its impact on pollution. It then goes on to look at the particular example of Nairobi as it seeks answers to the harmful consequences of pollution via a lens that views traffic congestion as critical to reducing traffic-related air pollution. It argues that the most efficient approach to reduce traffic congestion and, therefore, traffic pollution is to combine infrastructural, policy, regulatory, and softer measures. Furthermore, the study emphasizes the need for further research into the lived experience of navigating everyday life in Nairobi, as well as deeper investigation of the social, economic, and environmental viability of possible city solutions. While Nairobi is utilized as a case study city, the lessons gained are applicable to cities across the East African area, which share many of the same characteristics of traffic congestion and pollution.

Modelling and analysis of urban vehicle traffic congestion characteristics based on vehicle-borne network theory

In order to solve the problems of pollution and traffic safety caused by vehicle traffic congestion, this paper establishes an analysis model of urban vehicle traffic congestion characteristics based on vehicle network theory. Through the application of vehicular network, the extended mobility model of vehicular network is established, and the extended motion model of vehicular network is simulated with simulation tools and middleware tools to obtain the trajectory data of urban traffic vehicles. Based on the trajectory data, the survival analysis of urban vehicle traffic congestion is carried out. Kaplan-Meyer non-parametric regression model was used to estimate the duration of urban vehicle traffic congestion, and its distribution characteristics were quantitatively analysed. The experimental results show that the traffic congestion characteristics of urban vehicles are significantly different under different influencing factors, and the error of the trajectory data of urban traffic vehicles obtained by the proposed model is less than 1%.

Modelling and analysis of urban vehicle traffic congestion characteristics based on vehicle-borne network theory

In order to solve the problems of pollution and traffic safety caused by vehicle traffic congestion, this paper establishes an analysis model of urban vehicle traffic congestion characteristics based on vehicle network theory. Through the application of vehicular network, the extended mobility model of vehicular network is established, and the extended motion model of vehicular network is simulated with simulation tools and middleware tools to obtain the trajectory data of urban traffic vehicles. Based on the trajectory data, the survival analysis of urban vehicle traffic congestion is carried out. Kaplan-Meyer non-parametric regression model was used to estimate the duration of urban vehicle traffic congestion, and its distribution characteristics were quantitatively analysed. The experimental results show that the traffic congestion characteristics of urban vehicles are significantly different under different influencing factors, and the error of the trajectory data of urban traffic vehicles obtained by the proposed model is less than 1%.

Research on traffic congestion characteristics of city business circles based on TPI data: The case of Qingdao, China

This study aims to investigate the congestion of the urban business circle based on traffic performance index (TPI) data. A hierarchical clustering algorithm is adopted for the data analysis. A dataset of nearly 64,260 pieces of TPI data from July to October 2017 in Qingdao is collected, and its features, such as interval characteristics, mean time distribution and spatiotemporal correlation, are analyzed. The results show that the southern coastal commercial circle of Qingdao is more congested than the other circles; there exists a morning and afternoon peak, with two peaks on workdays; otherwise, weekend and vacation periods do not show congestion. The congestion level toward the end of the vacation week (October 4th–8th) is lower than that during the beginning (October 1st–3rd). Considering the temporal and spatial dimensions, the causes of the two different congestion states during the holidays are speculated upon The characteristics of traffic congestion in Qingdao business circle and its possible causes are proposed, and a new design and overall plan for Qingdao is promoted.

A new method for real-time evaluation of urban traffic congestion: a case study in Xi’an, China

Clarifying the spatiotemporal characteristics of urban traffic congestion can help to manage and alleviate such congestion more effectively. In this study, we used real-time traffic data provided by the Gaode LBS open platform to model and analyze urban traffic congestion in Xi’an, China, focusing on the spatiotemporal characteristics of single roads, different road grades, and the whole road network at different moments and time periods. Weekday real-time traffic data were collected from 23 April to 11 May, 2018, and the congestion index for different types of roads within Xi’an’s Third Ring Road was calculated. The results allowed us to thoroughly analyze the spatiotemporal characteristics of traffic congestion in Xi'an and suggest methods for congestion control and improvement, including enhanced traffic control during special periods and more balanced urban development. Our results show the feasibility of this evaluation approach, which can provide timely decision-making support for urban traffic congestion management.

Assessment of Traffic Congestion by Traffic Flow Analysis in Pabna Town

In recent time, traffic congestion problem has become one of the most costly, both monetary and environmentally, problems in many cities all over the world. It also causes valuable time loss of citizens. Many experts suggested that the foremost step to mitigate the congestion problem is to identify traffic congestion characteristics. This study has been conducted to measure the intensity of traffic congestion of Pabna town. Traffic volume survey and spot speed study were the main methodological approaches to conduct the study. The data collected has been analyzed through the use of different statistical computer packages. Different traffic flow theories and models have also been used to calculate traffic flow parameters that expose traffic characteristics. The study has explored that auto-rickshaw, cycle rickshaw, bicycle, and motorcycle are the popular traffic mode and traffic flow becomes so intensive during the morning, noon and evening hours. It has also revealed that Ataikula road is the most congestion-prone route than A.H road and the level of services of all intersections is F except traffic morr which provided a comparatively better level of services to the travelers of the town. The study has also identified the major problems associated with different road sections having poor level of services.

Prediction of Urban Road Congestion Using a Bayesian Network Approach

A reasonable prediction of the possibility of traffic congestion is able to help traffic managers to make efficient decisions to reduce the negative effect of traffic congestion. Previous research has made valuable analyses in this field. However, they rarely consider the dependency and uncertainty of traffic jams. In consideration of the characteristics of traffic congestion from different perspectives, this research proposes a Bayesian Network (BN) analysis approach to predict the possibility of urban traffic congestion. The built-up area of Beijing is taken as the study area of this research. A comprehensive set of variables are utilized to reflect the characteristics of traffic congestion from various viewpoints. The BN method is used to analyze the uncertainty and probability of traffic congestion, and is proved to be fully capable of representing the stochastic nature of traffic congestion. Furthermore, the difference of the congestion probabilities because of applying different urban transport development policies is analyzed in comparison. The study results show that the both road construction and bus system development at the same time can obviously mitigate traffic congestion for the built-up area of Beijing. In the future research, the impact of the comprehensive development of various travel modes on urban traffic congestion needs further studies.

Spatial and Temporal Characteristics for Traffic Congestion Based on Microwave Data

Based on empirical microwave data on the second Ring Road in Beijing, spatial and temporal characteristics of traffic congestion were analyzed. Three specific indexs for the congestion have been analyzed, i.e., congestion duration, time percentage of traffic congestion and key congestion sections. The hazard-based traffic congestion duration model was established through survival method. Temporal-spatial distribution of traffic congestion was studied using non-parametric method. The discrepancies of traffic congestion on different time and different links were considered. Results show that there has a big difference on congestion duration and congestion number between different links. Hazard rate will be less than 10% when congestion lasting more than 20 minutes. Three characteristics of the key congested points are confirmed. The judgment for key congestion sections show that key congestion segments have original affect to other sections.

Potential Freeway Congestion Severity Measure: Impact of Continuous Congestion Patterns

To better understand the characteristics of traffic congestion and efficiently employ congestion measures for interstate highway systems, this study examined the limitations of the congestion measures currently in use such as the percent of congestion and the duration of continuous congestion. Based on this study, these congestion measures provided different results of the congestion severity for even the same interstate highway segment, which means that the use of a single measure might not give a reliable evaluation result. In order to minimize the potential bias and enhance the advantage of each congestion measure, this study proposed a composite congestion severity measure by combining congestion information of both percent of congestion and duration of continuous congestion measures. The writers found that the proposed composite congestion severity measure efficiently identified the levels of the congestion severity of interstate highway segments and that it could be instantly accomplished with the existing loop detector count systems in most states. This proposed measure may be a potential candidate for describing levels of congestion severity and can provide a proper guideline for transportation engineers or decision makers to efficiently allocate available budget and resources to correct applicable interstate segments.

Demand Analysis considering Traffic Congestion

This paper first introduces characteristics of traffic congestion which causes various social problems, and asserts the importance of dynamic analysis on time-dependent congestion phenomena. Next, the conventional static marginal cost analysis is extended to the dynamic one using a queueing model. As the result, the dynamic marginal cost is shown to be more closely related to the duration of congestion period than the personal cost. Finally, applications of congestion pricing and system optimum control are discussed based on the dynamic marginal cost.