Visualization and Assessment of the Effect of Roadworks on Traffic Congestion Using AVL Data of Public Transit

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2022

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Springer Nature

Abstract

Congestion-free movement of traffic during peak hours in urban areas is rarely witnessed nowadays. Several factors are responsible for traffic congestion, and a large amount of reliable data is necessary to investigate them. In this study, we investigated the effectiveness of automated vehicle location (AVL) data of public transit in evaluating the effect of route diversion due to roadworks on traffic congestion. The public transit vehicle data from Mysore intelligent transport system were used for the purpose. In the preliminary analysis, the spatiotemporal variations in the speed data of public transit were visualized using spatiotemporal speed plots. A comparison study of traffic states in an urban street and an arterial road was conducted using a visualization tool. The data from Inner Ring Road of Mysore city were used to evaluate the effect of roadworks on traffic congestion. The road links of Inner Ring Road were evaluated for two scenarios: normal scenario and route diversion scenario. The results revealed that the spatiotemporal visualization technique can be used to diagnose the changes in traffic congestion, especially near intersections and bus stops. It is concluded that the AVL data from public transit buses proves to be a potential data source for traffic state prediction and evaluation of traffic congestion. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

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Keywords

Bus transportation, Buses, Data visualization, Intelligent systems, Intelligent vehicle highway systems, Roads and streets, Urban transportation, Visualization, Automated vehicle locations, Automatic vehicle locations, Intelligent transport, Intelligent transport system, Public transit, Roadwork, Spatiotemporal visualization, Traffic state, Transport systems, Vehicle location data, Traffic congestion, assessment method, comparative study, public transport, spatiotemporal analysis, traffic congestion, visualization

Citation

Journal of Geovisualization and Spatial Analysis, 2022, 6, 2, pp. -

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