Traffic Congestion Evaluation Research Articles

Economic Evaluation of Traffic Congestion & Design of Traffic Signal with Simulation at Ottapalam

Abstract: Traffic congestion is the most outstanding performance of the negative externalities of urban road traffic, which is characterized by the negative effects of time delay, energy waste, air pollution and many other impacts. It affects people’s daily life and could cause stress other than time and monetary loss. Traffic congestion causes negative impacts to transport sector and cause a massive increase in the transportation cost. Time is a precious commodity that we all have an equal amount of, and it is something that should not be wasted. A lot of our time is being wasted due to traffic congestion. Therefore, the study of impact of delay at important and busy road stretches in monetary terms is important. This study evaluates the traffic congestion cost at Ottapalam city which prevail heterogeneous traffic conditions. Congestion cost estimations are done by considering the factors including delay, traffic volume, passenger occupancy, and value of travel time of different vehicle types. The results obtained indicated that there was a considerable monetary loss due to traffic delay in the form of opportunity costs. So, there was a need to reduce this monetary loss by bringing suitable measures. The traffic condition of the selected road is found out using simulation by PTV Vissim software after implementing a traffic signal at a busy intersection between the road stretch. The reduction in traffic congestion shows the rightful implementation of the traffic signal.

Users’ Evaluation of Traffic Congestion in LTE Networks using Machine Learning Techniques

Over time, higher demand for data speed and quality of service by an increasing number of mobile network subscribers has been the major challenge in the telecommunication industry. This challenge is the result of an increasing population of human race and the continuous advancement in mobile communication industry, which has led to network traffic congestion. In an effort to solve this problem, the telecommunication companies released the Fourth Generation Long Term Evolution (4G LTE) network and afterwards the Fifth Generation Long Term Evolution (5G LTE) network that laid claims to have addressed the problem. However, machine learning techniques, which are very effective in prediction, have proven to be capable of great importance in the extraction and processing of information from the subscriber’s perceptions about the network. The objective of this work is to use machine learning models to predict the existence of traffic congestion in LTE networks as users perceived it. The dataset used for this study was gathered from some students over a period of two months using Google form and thereafter, analysed using the Anaconda machine learning platform. This work compares the results obtained from the four machine learning techniques employed that are k-Nearest Neighbour, Support Vector Machine, Decision Tree and Logistic Regression. The performance evaluation of the ML techniques was done using standard metrics to ascertain the real existence of congestion. The result shows that k-Nearest Neighbour outperforms all other techniques in predicting the existence of traffic congestion. This study therefore has shown that the majority of LTE network users experience traffic congestion.

Developing a fuzzy-based decision-making procedure for traffic control in expressway congestion management

This paper presents part of a multi-stage fuzzy logic controller (MS-FLC) that is developed for traffic control in congestion management on expressways. The decision-making process of traffic control for expressway congestion management using the MS-FLC consists of three tasks: (1) evaluation of current traffic congestion; (2) prediction of traffic congestion tendency; and (3) recommendation of control strategies and control actions to alleviate the congestion. This paper presents the 3rd stage of the MS-FLC that develops a fuzzy-based decision-making procedure (FDMP) for management of recurring and non-recurring congestion. Using fuzzy rules, the FDMP evaluates the current and anticipated traffic data and incident information to recommend control strategies at the strategic level, and control actions at the operational level. Results from this research show that: (i) the FDMP offers a comprehensive procedure in deriving control strategies and actions; (ii) FDMP control actions are derived from a systematic decision-making logic where the design of control rules is consistently oriented toward achieving desirable control objectives; (iii) the FDMP targets a proper balance in congestion management between the mainline and the ramp using compromise rule design; (iv) the FDMP facilitates using various forms of available traffic and incident data on an extended expressway segment to derive at control actions, making the system-wide gains possible; and (v) the FDMP could be applied for management of both recurring and non-recurring congestion.

Users’ Evaluation of Traffic Congestion in LTE Networks using Deep Learning Techniques

Users’ Evaluation of Traffic Congestion in LTE Networks using Deep Learning Techniques

Comparison of Logistics Time of R3A and R3B for Transportation of Lychee Product

This research presents the logistics management information system (LMIS) for the supply chain of lychee products of Phayao Province, Thailand. The main aim of this research is to develop a management application for Phayao’s agricultures to improve their competitive abilities on Chinese markets by utilizing a prediction method for traffic congestion based on both real-time and anticipated road traffic. The loss of productivity caused by traffic congestion has become a huge and increasingly heavy burden on Phayao farmers. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion is of great significance to solve this problem. By using traffic data obtained by distance, road conditions, transportation safety, traffic density, and customs clearance, the local farmers in Phayao can deliver lychee products on time and reduce the loss of high emissions and environmental pollution caused by traffic congestion effectively.

Modelling road traffic congestion at urban merge section under mixed traffic conditions

Identification and evaluation of traffic congestion are critical for better management of urban traffic, especially at merge sections where two different traffic streams converge and generate shockwaves. This study evaluates the travel time reliability measure (travel time index (TTI)) as a reliable congestion measure by analysing the impact of traffic flow parameters such as traffic volume, capacity, road geometrics and conflicting traffic flows at merge sections on TTI under mixed traffic conditions. The data required for congestion analysis were collected through video recording. Statistical models were developed to: (a) evaluate the relationship between road geometric characteristics and capacity of merge sections; (b) estimate traffic density and its evolution with time and compare it against occupancy values; and (c) correlate the relationship between TTI and traffic behaviour at merge sections and determine the variations in TTI for different levels of service of merge sections. The models developed are statistically validated using secondary data sets from the same study section, as well as from data collected from other multi-lane merge sections. The developed model successfully estimated recurring congestion by considering both space and time. This study highlights the potential usage of TTIs in capturing travel time variations more accurately at merge sections under mixed traffic conditions.

Traffic Congestion Evaluation at the Unsymmetrical Signalized Intersection of Sentul, Yogyakarta

Sentul intersection is the unsymmetrical signalized intersection located nearby the Sentul Market of Yogyakarta, which has a potential causing traffic congestion. The study aims to analyze the performance of an unsymmetrical intersection based on Indonesian’s regulation and to determine the congestion cost occurred at the Sentul signalized intersection. This study utilizes a quantitative method to evaluate the intersection performance by considering delay, vehicle queue, congestion cost, and level of service (LOS). The study included primary data collection from a traffic survey, a traffic modelling to assess the intersection performance, and a congestion cost analysis using software by considering three different conditions: Existing Condition, Alternative 1 and Alternative 2. The results showed that the performance of the existing condition was very poor with average delay of 106.71 second/vehicle, queue time of 134.42 seconds, LOS of F, and congestion cost of intersection was IDR 3,722,627 per hour. The 1st alternative was re-designing phase time of traffic light and the 2nd alternative involved re-designing green time, widening of approach area, applying left turn on red (LTOR) for the west-arm of intersection, and removing on-street parking area. The 2nd alternative is the best solution since it will reduce delay by 30 %, vehicle queue by 54 %, congestion cost decrease by 50 %, the LOS increase from F to E, and then improve intersection performance significantly.

Economic Evaluation of Traffic Congestion at Intersection: Case study from an Indian City

Traffic congestion causes negative impacts to transport sector and cause a massive increase in the transportation cost. Congestion cost evaluation helps wider aspects of the policy and planning and thus by providing potential solutions to the traffic congestion problem. This study evaluates the traffic congestion cost at signalized intersections located in the state of Kerala, India which prevail heterogeneous traffic conditions. Congestion cost estimations are done by considering the factors including delay, traffic volume, passenger occupancy, and value of travel time of different vehicle types. The annual congestion cost of traffic congestion at each approach of signalized intersections is quantified. The mode share of the present study reveals that private vehicles such as two wheeler and car constitute an average share of 75% of total traffic and which has the maximum share of traffic compared to public vehicles. Therefore, this study considers a candid relief measure proposal as mode shift to public transport from private vehicles. The result shows that marginal change in the two wheeler shift to public transport in Indian city reduce the congestion cost by 38% and thus can promote usage of public transport in the Indian cities to reduce congestion.

Predicting the Time Required to Pass Congested Road Based on Neural Network Algorithm

In order to predict the duration of traffic congestion, this paper established a traffic congestion evaluation model based on cumulative ratio Logistic regression and a traffic congestion time prediction model based on BP neural network. Combining Pearson test, numerical combination, standard deviation method and other methods to solve the problem. Based on the measured data of Jinshui Road in Zhengzhou, the average error is 0.019m/ s and the prediction error rate is 0.15%, both within a reasonable range. The model can improve the accuracy of congestion time prediction and provide some help to real life.

Performance Evaluation of Unsignalized Intersection and Anticipated Alternative Solutions for Designated Corridors in Wolaita Sodo Town

The growth of traffic congestion on many streets, intersections and highways is a major concern to travelers, administrators, merchants, developers and the community at large. Its detrimental impacts in terms of longer journey times, higher fuel consumption, increased emissions of air pollutants, greater transport and other affected costs and changing investment decisions are increasingly recognized and felt across the country. Congestion reduces the effective accessibility of residents, activities and jobs resulting in lost opportunities for both the public and business. This issue is now days a great concern for Ethiopia and major cities in the country. Due to this evaluation of traffic congestion and determination of level of service with the cause of traffic congestion is the first step. From these major cities in Ethiopia, Wolaita Sodo city is now highly observed by traffic congestion especially at intersections. In this paper, first data of traffic volume count, entry time, and departure time and road width collected. Then average traffic speed ,traffic volume and traffic headway in terms of level of service and function were analyzed by using HCM-2000 and Microsoft excel sheets for only three most congested intersections. Keywords: Capacity, Congestion, Intersections, Performance Evaluation DOI : 10.7176/CER/11-10-02 Publication date: November 30 th 2019

Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods. The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions. This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data. Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method.

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.

Analysis and Prediction of Urban Traffic Congestion Based on Big Data

With the rapid development of big data technology, its application has become more and more extensive. The application of big data technology in intelligent transportation systems is the best way to solve traffic congestion in big cities. The paper analyses in detail the main causes of traffic congestion in big cities and the classification and evaluation of traffic congestion. Utilizing the Internet of Things and modern communication technologies, large-scale traffic data and related data based on GPS are acquired, and data analysis is carried out to construct a traffic prediction vehicle prediction model. The forecasting model is used to predict the traffic flow in each direction of traffic intersections at a certain time, predict the possibility of congestion at a certain time at a certain intersection, the traffic flow and congestion probability of a certain section at a certain time, and the travel trajectory and travel habit forecast of pedestrians. At the same time, consider the impact of non-motorized vehicles and pedestrians on traffic congestion. Use forecasting results and real-time traffic information monitoring to solve traffic congestion problems. Combined with traffic control and optimization strategy control for traffic collaborative management, it provides valuable reference for decision-making in metropolitan traffic congestion solutions.

Evaluation of traffic congestion degree: An integrated approach

Intersection traffic congestion evaluation is essential for effective intelligent transportation system planning, and an objective and precise assessment of traffic congestion is vital to ensure the smooth circulation of traffic. Multiple criteria decision-making is a method for evaluating the degree of traffic congestion. A hybrid multiple criteria decision-making method integrating the fuzzy analytic hierarchy process, techniques for order preference by similarity to an ideal solution, and gray correlation techniques are presented in this work. The proposed method applied fuzzy analytic hierarchy process to determine the weight of the evaluation index; subsequently, gray correlation techniques for order preference by similarity to an ideal solution were integrated to construct the hybrid decision-making method. A case study of traffic congestion at intersections with five evaluation indexes verified the effectiveness of the hybrid method. The evaluation results of the different methods show that the proposed method overcomes the one-sidedness of analytical hierarchy process–techniques for order preference by similarity to an ideal solution and analytical hierarchy process–gray correlation. Thus, the proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the degree of traffic congestion.

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition.

Stochastic Traffic Congestion Evaluation of Korean Highway Traffic Information System with Structural Changes

Stochastic Traffic Congestion Evaluation of Korean Highway Traffic Information System with Structural Changes

Evaluation, Classification, and Influential Factors Analysis of Traffic Congestion in Chinese Cities Using the Online Map Data

This study proposes a new method to describe, compare, and classify the traffic congestion states in 23 Chinese cities using the online map data and further reveals the influential factors that may affect them. First, the real-time traffic congestion information is obtained from the online map of AutoNavi in a 15-minute interval. Next, a new measuring index is introduced to describe the overall characterization of congestion patterns in each city based on online map data, which is named as the congestion ratio. The next analysis is the cluster analysis based on the temporal distribution of the congestion ratio, which helps to identify groups of the selected cities with similar traffic congestion states. These cities are categorized as four groups according to the severity of traffic congestion: severely congested, less severely congested, amble, and smooth cities. Lastly, multiple linear regression models are developed to identify the primary factors that affect the congestion ratio. The result shows that the influences of per capita road area, car ownership, and vehicle miles traveled (VMT) on the congestion ratio are significant. Sensitivity analyses are also implemented in order to reveal more effective policy measures in mitigating traffic congestion in urban areas.

Traffic Congestion Identification and Analysis of Urban Road Network Based on Granular Computing

This paper proposes a generalized model based on the granular computing to recognize and analyze the traffic congestion of urban road network. Using the method of quotient space to reduce the attributes associating with traffic congestion, the identification of traffic congestion evaluation system is established including 3 first class indexes and second class indexes of 11. The weight of evaluation indexes are sorted by value in descending order, which are calculated based on rough set theory. In order to improve the efficiency of traffic congestion identification, the appropriate granular is determined by the model parameter μ. When μ is larger, the identification is more effective and the run time of model is longer conversely. Experiments show when the value of μ is between 0.8 and 0.98, the effect of traffic congestion identification is comprehensive optimal.

Traffic Congestion Evaluation and Signal Timing Optimization Based on Wireless Sensor Networks: Issues, Approaches and Simulation *

This paper proposed the model and algorithms for traffic data monitoring and signal timing optimization based on continuum traffic model and wireless sensor networks. Given the scenario that sensor nodes are sparsely installed along the segment between signalized intersections, an analytical model is built based on continuum traffic equations, and an adaptive interpolation method is proposed to estimate traffic parameters with scattered sensor data. Based on the principle of traffic congestion formation, a congestion factor is introduced which can be used to evaluate the real-time status of traffic congestion along the segment, and to predict the subcritical state of traffic jams. The result is expected to support the signal timing optimization of traffic light control for the purpose to avoid traffic jams before its formation. We simulated the traffic monitoring based on Mobile Century dataset, and analyzed the performance of signal control on VISSIM platform when congestion factor is introduced into the phase optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring, and it's helpful to alleviate urban traffic congestion that remarkably decreases the average delays and maximum queue length.

An Automatic Recognition Approach for Traffic Congestion States Based on Traffic Video

AbstractWith the increasing demand for traffic information services as well as the extensive deployment of traffic video surveillance, there is a critical need for realizing automatic identification of congestion state with traffic video. To this end, this study proposes a traffic congestion evaluation model with adaptive learning ability. The qualitative process of the proposed model has been previously analyzed. In this method, the video image feature sets are extracted initially, followed by the state classification model training and learning via support vector machine. Subsequently, genetic algorithm is used to realize the online adaptive optimization. The field experimental results indicate that this method has high recognition accuracy, fast processing speed, and strong adaptive ability, and it can provide an appropriate solution for solving the problem of all—day traffic congestion states recognition based on the traffic video information.