Abstract
Alleviating public traffic congestion is an efficient and effective way to improve the travel time reliability and quality of public transport services. The existing public network optimization models usually ignored the essential impact of public traffic congestion on the performance of public transport service. To address this problem, this study proposes a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs). The proposed methodology involves two steps: (1) Extracting three traffic indicators of the RSBs from smart card data and bus trajectory data; (2) The self-organizing map (SOM) is used to cluster and effectively recognize traffic patterns embedded in the RSBs. Furthermore, a congestion index for ranking the SOM clusters is developed to determine the congested RSBs. A case study using real-world datasets from a public transport system validates the proposed methodology. Based on the congested RSBs, an exploratory example of public transport network optimization is discussed and evaluated using a genetic algorithm. The clustering results showed that the SOM could suitably reflect the traffic characteristics and estimate traffic congestion of the RSBs. The results obtained in this study are expected to demonstrate the usefulness of the proposed methodology in sustainable public transport improvements.
Highlights
A well-developed public transport system is the key to achieving citizens’ mobility in an environmentally sustainable fashion
self-organizing map (SOM) Algorithm Results The three traffic indicators of an road segments between bus stops (RSBs) are presented as vectors in the input space of the SOM
The optimum map sizes of SOM were chosen after several trials with different sizes, and the topographic error (TE) and quantization error (QE) were used to measure the suitability and fitness of the optimized map size [44]
Summary
A well-developed public transport system is the key to achieving citizens’ mobility in an environmentally sustainable fashion. Due to demand fluctuations and traffic congestion, it is a challenging task for operators to ensure the efficiency of public transport service and improve travel time reliability [1]. There is a growing realization that the alleviation of traffic congestion through public transport network (PTN) optimization is a fundamental solution to reduce high traffic congestion costs, lower energy consumption, lessen air pollution, and improve mobility [2]. In-vehicle travel time, stop spacing, frequency of service, capacity, and congestion related to over-crowded vehicles are usually considered as variables in network optimization problems. Examples of these models can be seen in the studies of References [4,5,6].
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