scholarly journals Visualization and Analysis of Traffic Flow and Congestion in India

2021 ◽  
Vol 6 (3) ◽  
pp. 38
Author(s):  
Tsutomu Tsuboi
Keyword(s):  
Developing Countries ◽  
Traffic Flow ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Flow Analysis ◽  
Road Construction ◽  
Observation Data ◽  
Traffic Data ◽  
Congested Area ◽  
Growing Economy

The paper takes an analysis of traffic conditions in a developing country, namely, India. India is a country with a rapidly growing economy and a large market, and it has the second largest population in the world, which was 1.3 billion in 2018. India also suffers from environmental problems, such as air pollution and global warming that is contributed by traffic CO2 emissions from transportation. In order to analyze this problem, a particularly challenging issue in developing countries like India, is the collection of traffic data. In general, developing countries do not often have well established infrastructure such as installations of small traffic signals, they lack new road construction and public transportation, etc. This study is the first real traffic congestion analysis in India and introduces unique traffic flow analysis such as: (1) Collecting over a month of recent traffic data in a major city in India, (2) defining traffic congestion from occupancy parameter based on traffic flow theory and observation data, and (3) using geographical special analysis (GIS) for identifying traffic congestion location. These three combination analysis enables one to identify the most congested area in the city with quantitative congestion condition. This study becomes useful to other countries that have similar issues.

scholarly journals Traffic Flow Analysis and Management

2021 ◽  
Author(s):  
Tsutomu Tsuboi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Flow Analysis ◽  
Traffic Monitoring ◽  
Diving Behavior ◽  
Low Carbon ◽  
Gujarat State ◽  
Traffic Condition ◽  
Transportation Demand ◽  
The City

This research is about joint government founded program between Japan and India or Science and Technology Research Partnership for Sustainable development (SATREPS). The purpose of this research is to establish Low Carbon Transportation in developing countries and we choose one of major city in India, where it is Ahmedabad city of Gujarat state—west cost of India. In order to approach the target, we need to understand the current situation of traffic condition in the city. The current traffic condition in India is some chaotic because of their different driving behavior compared with the advanced countries. It is becoming the chaotic traffic condition in India by not only diving behavior during investigation of this research. The main reason of the traffic congestion comes from the unbalance between growing transportation demand and its insufficient infrastructure preparation. In this chapter, it introduces the current traffic condition based on four years monitoring of the traffic by the traffic monitoring cameras and comparison by the traffic flow theory at first. Then it introduces the new traffic analysis method especially for its traffic congestion analysis and its parameters. After the traffic congestion analysis, it summarizes conclusion and our next step from the experience.

scholarly journals Assessment of Changes in Toll Fares in Conditions of Increased Traffic Load

2021 ◽  
Vol 18 (5) ◽  
pp. 4-13
Author(s):  
A. U. Talavirya ◽  
M. B. Laskin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
High Speed ◽  
Discrete Event ◽  
Flow Analysis ◽  
Traffic Load ◽  
Intelligent Transport System ◽  
Toll Road ◽  
Road Projects ◽  
Toll Collection

The purpose of the study is to assess changes of toll fares used on toll collection points. When toll road is operating in an urban environment, the operator is inevitably faced with the need to adapt intelligent transport system to the conditions of an ever-increasing volume and changing composition of the traffic. Such changes have a direct impact on the capacity of the toll road, in particular at toll collection points. The conducted research is aimed at analyzing the method of reducing the traffic load at the toll collection points during peak hours by making changes to the toll fares aimed at uniform distribution of traffic flow throughout the day. As an example, a toll collection point on the main road direction of the Western High-Speed Diameter toll road was chosen.Materials and methods. A discrete-event simulation model developed in the AnyLogic software was used to assess the quality of operation and throughput of the toll collection point. The software has a sufficient level of detail to reproduce the operation of the toll collection system, and allows managing all the necessary parameters of the system and traffic flow. Analysis of the data obtained from the simulated model experiments was carried out using the statistical package R. Results. As part of the study, the operational characteristics of the toll collection point, in particular its threshold capacity, were determined, and, if it was exceeded, the length of the emerging queue was determined. Taking into account the operator’s risks arising from the formation of a traffic congestion, an approach was proposed to change the toll fare policy of the toll road by using a more flexible fares based on dividing the day time fare into several time intervals and using increasing coefficients for toll fare when paying for travel during peak hours. Using the example of toll booth of the Western High-Speed Diameter, the increasing coefficients of the cost of travel at a rush hour were considered, and the risks of reducing the operator’s income from toll collection were assessed.Conclusion. Based on the results obtained, an assessment of the effectiveness of the application of measures to change the existing toll fare policy, aimed at optimizing the traffic flow of a toll road, can be carried out. In addition, such an analysis can be used to assess the investment attractiveness of a project, develop a toll fare policy, increase income and other similar tasks. Further research can be aimed at increasing the economic indicators of toll road projects, and developing additional mathematical tools used in the formation of toll fare policy.

scholarly journals Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

2014 ◽  
Vol 26 (5) ◽  
pp. 393-403 ◽  
Author(s):  
Seyed Hadi Hosseini ◽  
Behzad Moshiri ◽  
Ashkan Rahimi-Kian ◽  
Babak Nadjar Araabi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Weather Conditions ◽  
Traffic Data ◽  
Data Loss ◽  
Traffic Lights ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Flow Prediction ◽  
Forecasting Method

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

scholarly journals Analyzing Public Transportation Plans to Addressing Traffic Congestions in Developing Countries

2019 ◽  
Vol 18 (1) ◽  
pp. 44-57
Author(s):  
Hussaen Ali Hasan Kahachi
Keyword(s):  
Developing Countries ◽  
Public Transport ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Well Being ◽  
Transport Services ◽  
Greater Cairo ◽  
The City ◽  
Privately Owned

Traffic congestions is one of the main problems for many cities especially in newly urbanizing countries worldwide. The issue of traffic congestions has major impact not only on the planning of the city, but also on many aspects such as residence overall well-being. Governments often try to address this issue through a number of initiatives, most important of which is promoting public transport in order to reduce the dependency on private cars in the city. This research analyzed state-led public transportation initiatives impact on addressing traffic congestion in developing countries through a case study of the Greater Cairo Region in Egypt. The research specifically focused on two state-led public transportation programs, namely the Great Cairo Transport Authority (CTA) program to improve the existing public transport services in GCR and the GCR underground metro program during the 1990s to early 2010s. The research found that although these programs were successful in limiting the increase of privately owned cars and taxies in GCR, they did not decrease traffic congestions due to a number of issues including malpractice, political and administrative corruption, and rapid population growth and increased population densities that almost doubled in the period from early 1990s to early 2010s.

scholarly journals Performance Evaluation of a Hybrid PSO Enhanced ANFIS Model in Prediction of Traffic Flow of Vehicles on Freeways: Traffic Data Evidence from South Africa

2021 ◽  
Vol 7 (1) ◽  
pp. 2
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Lagouge Kwanda Tartibu ◽  
Fabio Arena ◽  
Ziya Cakici
Keyword(s):  
South Africa ◽  
Traffic Flow ◽  
Rural Areas ◽  
Traffic Congestion ◽  
Significant Rise ◽  
Traffic Density ◽  
Vehicular Traffic ◽  
Traffic Data ◽  
Inference System ◽  
Vehicular Traffic Flow

In the last few years, there has been a significant rise in the number of private vehicles ownership, migration of people from rural areas to urban cities, and the rise in the number of under-maintained freeways; all these have added to the perennial problem of traffic congestion. Traffic flow prediction has been recognized as the solution in alleviating and reducing the problem of traffic congestion. In this research, we developed an adaptive neuro-fuzzy inference system trained by particle swarm optimization (ANFIS-PSO) by performing an evaluative performance of the model through traffic flow modelling of vehicles on five freeways (N1,N3,N12,N14 and N17) using South Africa Transportation System as a case study. Six hundred and fifty (650) traffic data were collected using inductive loop detectors and video cameras from the five freeways. The traffic data used for developing these models comprises traffic volume, traffic density, speed of vehicles, time, and different types of vehicles. The traffic data were divided into 70% and 30% for the training and validation of the model. The model results show a positively correlated optimal performance between the inputs and the output with a regression value R2  of 0.9978 and 0.9860 for the training and testing. The result of this research shows that the soft computing model ANFIS-PSO used in this research can model vehicular traffic flow on freeways. Furthermore, the evidence from this research suggests that the on-peak and off-peak hours are significant determinants of vehicular traffic flow on freeways. The modelling approach developed in this research will assist urban planners in developing practical ways to tackle traffic congestion and assist motorists and pedestrians in travel behaviour decision-making. Finally, the approach used in this study will assist transportation engineers in making constructive and safety dependent guidelines for drivers and pedestrians on freeways.

OPTIMISING TRAFFIC FLOW AT A SIGNALISED INTERSECTION USING SIMULATION

2019 ◽  
Vol 4 (2) ◽  
pp. 261
Author(s):  
Muhammad Rozi Malim ◽  
Faridah Abdul Halim ◽  
Sherey Sufreney Abd Rahman
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Simulation Software ◽  
Traffic Signal ◽  
Traffic Data ◽  
Traffic Light ◽  
Traffic Lights ◽  
Light Management ◽  
Optimal Setting ◽  
Pedestrian Crossing

Traffic signal lights system is a signalling device located an intersection or pedestrian crossing to control the movement of traffic. The timing of traffic signal lights has attracted many researchers to study the problems involving traffic light management and looking for an inexpensive and effective solution that requires inexpensive changes in the infrastructures. A simple traffic lights system uses a pre-timed control setting based on the latest traffic data, and the setting could be manually changed. It is a common type of signal control and sometimes the setting was not correctly configured with the traffic data, thus leading to congestion at an intersection. Many mathematical strategies were applied to get an optimal setting. This study aims to model the traffic flow at Persiaran Kayangan and Persiaran Permai Intersection, Section 7, Shah Alam, as the case study, by using AnyLogic simulation software. The model was used to determine the best timings of traffic green lights that minimise the average time at the intersection and reduce traffic congestion. The findings showed that the best timings of traffic green lights for four directions at the intersection are 120 seconds, 75 seconds, 130 seconds and 100 seconds, respectively. These timings of green lights produced the lowest average time at the intersection (55.65 seconds).

scholarly journals Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

2018 ◽  
Vol 218 ◽  
pp. 03021 ◽  
Author(s):  
Bagus Priambodo ◽  
Yuwan Jumaryadi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Historical Data ◽  
Time Series Prediction ◽  
Network Models ◽  
Traffic Data ◽  
Data Traffic ◽  
Neural Network Models

During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.

scholarly journals How to Mitigate Traffic Congestion Based on Improved Ant Colony Algorithm: A Case Study of a Congested Old Area of a Metropolis

2019 ◽  
Vol 11 (4) ◽  
pp. 1140 ◽  
Author(s):  
Zhichao Li ◽  
Jilin Huang
Keyword(s):  
Traffic Flow ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Road Transportation ◽  
Transit Systems ◽  
Simulation Based ◽  
Improved Model ◽  
Improved Ant Colony Algorithm

Old areas of metropolises play a crucial role in their development. The main factors restricting further progress are primitive road transportation planning, limited space, and dense population, among others. Mass transit systems and public transportation policies are thus being adopted to make an old area livable, achieve sustainable development, and solve transportation problems. Identifying old areas of metropolises as a research object, this paper puts forth an improved ant colony algorithm and combines it with virtual reality. This paper predicts traffic flow in Yangpu area on the basis of data obtained through Python, a programming language. On comparing the simulation outputs with reality, the results show that the improved model has a better simulation effect, and can take advantage of the allocation of traffic resources, enabling the transport system to achieve comprehensive optimization of time, cost, and accident rates. Subsequently, this paper conducted a robustness test, the results of which show that virtual traffic simulation based on the improved ant colony algorithm can effectively simulate real traffic flow, use vehicle road and signal resources, and alleviate overall traffic congestion. This paper offers suggestions to alleviate traffic congestion in old parts of metropolises.

scholarly journals Predicting traffic flow during peak commuting hours

2021 ◽  
Author(s):  
Z. Shen
Keyword(s):  
Traffic Flow ◽  
Public Transportation ◽  
Traffic Congestion ◽  
High Efficiency ◽  
Road Traffic ◽  
Low Cost ◽  
Survey Method ◽  
Rail Transit ◽  
Validity And Reliability ◽  
Peak Period

Abstract Traffic congestion during the peak commuting period is becoming more and more serious in many cities in China. Accurate prediction of traffic flow on urban roads during the peak commuting period is one of the key fundamental problems to reduce traffic congestion and build intelligent transportation. This paper proposes a low-cost and high-efficiency method to address the challenges of the existing ORIGIN-DESTINATION survey method, which is costly, inefficient and has limited accuracy of results. By using the city-wide traffic, social security, insurance, population and other digital city data, we group the residents' travel modes into four types: walking, public transportation, rail transit and self-driving based on the service radius and travel distance of transportation, and analyze and count the number of private car trips and rail transit passenger flow by using the rail transit priority principle and the shortest path algorithm, and predict the commuting peak period based on this method. The road traffic flow during the peak commuting period is also predicted. The validity and reliability of the method are demonstrated by using typical data of Wuhan city as an example.

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