Exploring the Built Environment Factors in the Metro That Influence the Ridership and the Market Share of the Elderly and Students

The metro has developed rapidly in the past two decades and has become one of the crucial patterns of transportation for urban residents in China. Many studies have explored the factors affecting metro ridership, but few have focused on the metro usage of specific groups, such as the elderly and students. This paper uses the negative binomial regression model to explore the relationship between the built environment and the metro ridership of three types of people (adults, the elderly, and students) by using the metro smart card data of Qingdao. We also used the fractional response model to discuss the factors that influence the ridership share for the elderly and students. The results show that most variables promote the metro usage of the three groups of people but have a significantly different effect on the market share of those groups. Specifically, the number of schools, hospitals, supermarkets, squares, parks, and scenic spots near metro stations significantly increases the proportion of the elderly metro usage. The number of bus stops and schools substantially increases the share of metro ridership by students. The research results can provide valuable insights for promoting the metro’s overall ridership and minimizing the gap in allocating public transport resources among different groups.

A Data-Based Bi-Objective Approach to Explore the Accessibility of Multimodal Public Transport Networks

One of the top concerns for travelers when choosing public transportation is whether they can reach their destination in limited time and monetary cost on the basis of ensured reliability. However, the existing literature shows no studies on how to evaluate bi-objective multimodal accessibility under travel time uncertainty. In order to fill this research gap, this paper creates a multimodal super network based on smart card data in which the transfers among taxi, bus, and subway modes are developed and applied. Next, we propose a two-stage opportunity accessibility model to calculate bi-objective multimodal accessibility under travel time uncertainty. Then we propose a multimodal reliability path finding model and a reliability boundary convergence algorithm to solve this problem. Finally, we conduct a large-scale real-world case study. It is found that the impedance heterogeneity between different modes is significant, and multimodal travel has better accessibility than a unimodal one. Although multimodal accessibility decreases as the reliability increases, the advantage of multimodal over unimodal accessibility increases with reliability, and it can be improved up to 14.61% by multimodal transfers. This model can effectively guide traffic management departments to improve traffic accessibility in terms of time and cost and advise commuters to choose living places.

Using Smart Card Data of Metro Passengers to Unveil the Urban Spatial Structure: A Case Study of Xi’an, China

This study adopted smart card data collected from metro systems to identify city centers and illustrate how city centers interacted with other regions. A case study of Xi’an, China, was given. Specifically, inflow and outflow patterns of metro passengers were characterized to measure the degree of population agglomeration of an area, i.e., the centricity of an area. On this basis, in order to overcome the problem of determining the boundaries of the city centers, Moran’s I was adopted to examine the spatial correlation between the inflow and outflow of ridership of adjacent areas. Three residential centers and two employee centers were identified, which demonstrated the polycentricity of urban structure of Xi’an. With the identified polycenters, the dominant spatial connections with each city center were investigated through a multiple linkage analysis method. The results indicated that there were significant connections between residential centers and employee centers. Moreover, metro passengers (commuters mostly) flowing into the identified employee centers during morning peak-hours mainly came from the northern and western area of Xi’an. This was consistent with the interpretation of current urban planning, which validated the effectiveness of the proposed methods. Policy implications were provided for the transport sector and public transport operators.

A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data

In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population.