A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System
Tanzina Afrin,Nita Yodo +1 more
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TLDR
This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.Abstract:
Traffic congestion is a perpetual problem for the sustainability of transportation development. Traffic congestion causes delays, inconvenience, and economic losses to drivers, as well as air pollution. Identification and quantification of traffic congestion are crucial for decision-makers to initiate mitigation strategies to improve the overall transportation system’s sustainability. In this paper, the currently available measures are detailed and compared by implementing them on a daily and weekly traffic historical dataset. The results showed each measure showed significant variations in congestion states while indicating a similar congestion trend. The advantages and disadvantages of each measure are identified from the data analysis. This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.read more
Citations
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Proceedings ArticleDOI
General Transit Feed Specification Assisted Effective Traffic Congestion Prediction Using Decision Trees and Recurrent Neural Networks
TL;DR: In this paper , the authors focus on using GTFS (General Transit Feed Specification) data and have constructed a meticulous and reflective dataset, and propose a novel approach that maps traffic congestion as a classification problem rather than a regression-prediction problem.
Journal ArticleDOI
Towards Unifying Resilience and Sustainability for Transportation Infrastructure Systems: Conceptual Framework, Critical Indicators, and Research Needs
TL;DR: In this paper , the authors proposed a forward-looking unification framework, where the sustainability and resilience of transportation infrastructure systems can be analyzed simultaneously, using seven critical indicators including emission, speed, temperature, energy consumption, delay, mobility, and accessibility.
Journal ArticleDOI
Measuring Traffic Congestion with Novel Metrics: A Case Study of Six U.S. Metropolitan Areas
TL;DR: In this article , new metrics are presented using the Hägerstrand's space-time cube that has been studied from time geography perspectives since the 1960s, and the product of distance and time, i.e., distanceTime, is proposed as a base metric to measure traffic congestion amounts.
Book ChapterDOI
Rule-based Recommendation System for Traffic Congestion Measures
TL;DR: In this paper , the authors developed a recommendation system able to generate the congestion measures in accordance with the context under study and the goal of this research is to assist researchers and traffic operators to choose the most suitable congestion measures to the studied area.
Journal ArticleDOI
Traffic Congestion Prediction Based on Multivariate Modelling and Neural Networks Regressions
TL;DR: In this paper , the authors proposed a new prediction approach to reduce the traffic congestion problem by studying a scheme for predicting traffic flow information using four machine learning techniques: feed forward neural networks (FFNN), Radial Basis Function Neural Networks (RBFNN), simple linear regression model, and polynomial linear regression models.
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