Traffic Congestion Model in India by Shock Wave Theory

Mapping Intimacies ◽

10.3233/faia210250 ◽

2021 ◽

Author(s):

Tsutomu Tsuboi

Keyword(s):

Shock Wave ◽

Traffic Flow ◽

Traffic Congestion ◽

Flow Analysis ◽

Wave Theory ◽

Traffic Monitoring ◽

Flow Data ◽

Shock Wave Theory ◽

Unique Study ◽

Traffic Flow Analysis

This manuscript is a series of traffic flow analysis in India and it describes traffic congestion model by using shockwave theory from fluid mechanism. This is unique study for emerging country India traffic flow analysis during more than one month in October 2020. In order to chaotic traffic flow analysis in India, author observes one moth traffic flow data from the traffic monitoring cameras in 26 locations in a city where it is one of major city Ahmedabad in Gujarat states of India. In terms of traffic congestion, it is sued occupancy parameter of traffic flow data which indicates road occupancy percentage by vehicles. By using shock wave theory, author defines two traffic congestion model which are “forwarding traffic congestion” model and “stacking traffic congestion” model. These models explain traffic congestion condition and it is able to provide hint for solving traffic congestion problem in India.

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Evaluation of Traffic Flow Analysis Tools Applied to Work Zones Based on Flow Data Collected in the Field

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1811-07 ◽

2002 ◽

Vol 1811 (1) ◽

pp. 57-66 ◽

Cited By ~ 12

Author(s):

Thomas Schnell ◽

Jeffrey S. Mohror ◽

Fuat Aktan

Keyword(s):

Traffic Flow ◽

Flow Analysis ◽

Flow Data ◽

Work Zones ◽

Analysis Tools ◽

Traffic Flow Analysis

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Traffic Flow Analysis and Management

10.5772/intechopen.95087 ◽

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.

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URBAN TRAFFIC FLOW ANALYSIS BASED ON DEEP LEARNING CAR DETECTION FROM CCTV IMAGE SERIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-499-2018 ◽

2018 ◽

Vol XLII-4 ◽

pp. 499-506 ◽

Cited By ~ 6

Author(s):

M. V. Peppa ◽

D. Bell ◽

T. Komar ◽

W. Xiao

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Flow Analysis ◽

Weather Conditions ◽

Traffic Monitoring ◽

Harmonic Mean ◽

Fine Tuning ◽

Road Users ◽

Car Detection ◽

Traffic Flow Analysis

Abstract. Traffic flow analysis is fundamental for urban planning and management of road traffic infrastructure. Automatic number plate recognition (ANPR) systems are conventional methods for vehicle detection and travel times estimation. However, such systems are specifically focused on car plates, providing a limited extent of road users. The advance of open-source deep learning convolutional neural networks (CNN) in combination with freely-available closed-circuit television (CCTV) datasets have offered the opportunities for detection and classification of various road users. The research, presented here, aims to analyse traffic flow patterns through fine-tuning pre-trained CNN models on domain-specific low quality imagery, as captured in various weather conditions and seasons of the year 2018. Such imagery is collected from the North East Combined Authority (NECA) Travel and Transport Data, Newcastle upon Tyne, UK. Results show that the fine-tuned MobileNet model with 98.2% precision, 58.5% recall and 73.4% harmonic mean could potentially be used for a real time traffic monitoring application with big data, due to its fast performance. Compared to MobileNet, the fine-tuned Faster region proposal R-CNN model, providing a better harmonic mean (80.4%), recall (68.8%) and more accurate estimations of car units, could be used for traffic analysis applications that demand higher accuracy than speed. This research ultimately exploits machine learning alogrithms for a wider understanding of traffic congestion and disruption under social events and extreme weather conditions.

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