The bus stop layout and route deployment may influence the efficiency of bus services. Evaluating the supply of bus service requires the consideration of demand from various urban activities, such as residential and job-related activities. Although various evaluation methods have been explored from different perspectives, it remains a challenging issue. This study proposes a spatial statistical approach by comparing the density of the potential demand and supply of bus services at bus stops. The potential demand takes jobs-housing locations into account, and the supply of bus services considers bus stops and their associated total number of daily bus arrivals. The kernel density estimation (KDE) and spatial autocorrelation analyses are employed to investigate the coupling relationship between the demand and supply densities at global and local scales. A coupling degree index (CDI) is constructed to standardize the measurement of demand-supply balance. A case study in Wuhan, China demonstrated that: (1) the spatial distribution of bus stops is reasonable at global level, (2) Seriously unbalanced locations for bus services have been detected at several stops. Related adjustments that can improve these defects are highly recommended.

Automated fare collection (AFC) systems can provide tap-in and tap-out records of passengers, allowing us to conduct a comprehensive analysis of spatiotemporal patterns for urban mobility. These temporal and spatial patterns, especially those observed over long periods, provide a better understanding of urban transportation planning and community historical development. In this paper, we explored spatiotemporal evolution of travel patterns using the smart card data of subway traveling from 2011 to 2017 in Shenzhen. To this end, a Gaussian mixture model with expectation–maximization (EM) algorithm clusters the travel patterns according to the frequency characteristics of passengers’ trips. In particular, we proposed the Pareto principle to negotiate diversified evaluation criteria on model parameters. Seven typical travel patterns are obtained using the proposed algorithm. Our findings highlighted that the proportion of each pattern remains relatively stable from 2011 to 2017, but the regular commuting passengers play an increasingly important position in the passenger flow. Additionally, focusing on the busiest commuting passengers, we depicted the spatial variations over years and identified the characters in different periods. Their cross-year usage of smart cards was finally examined to understand the migration of travel patterns over years. With reference to these methods and insights, transportation planners and policymakers can intuitively understand the historical variations of passengers’ travel patterns, which lays the foundation for improving the service of the subway system.

Community detection can reveal specific urban spatial structures related to human activities, and is achieved using mobility data from various sources. In the existing research, less attention has been devoted to communities related to urban transit travel. As public transit is a key component of the urban transport system, it is important to understand how transit communities are organized and how they evolve. This research proposes an approach to urban transit travel community detection using transit travel data and examines how the communities have evolved over time. The results in Shenzhen from 2015 to 2017 showed that the transit travel network had an obvious community structure, and the components (TAZs in this case) of the communities changed over time. During the three years, the western part of Shenzhen experienced more component changes on weekdays, and the central part of the city underwent more component changes on weekdays. In addition, the transit travel communities had a significant coupling relationship with urban administrative divisions. Exploring transit travel communities provides insight for improving public transit systems and enriches the research genealogy of urban spatial structure.

Exposure to inhalable particulate matter pollution is a hazard to human health. Many studies have examined the in-transit particulate matter pollution across multiple travel modes. However, limited information is available on the comparison of in-transit exposure among cities that experience different climates and weather patterns. This study aimed to examine the variations in in-cabin particle concentrations during taxi, bus, and metro commutes among four megacities located in the inland and coastal areas of China. To this end, we employed a portable monitoring approach to measure in-transit particle concentrations and the corresponding transit conditions using spatiotemporal information. The results highlighted significant differences in in-cabin particle concentrations among the four cities, indicating that PM concentrations varied in an ascending order of, and the ratios of different-sized particle concentrations varied in a descending order of CS, SZ, GZ, and WH. Variations in in-cabin particle concentrations during bus and metro transits between cities were mainly positively associated with urban background particle concentrations. Unlike those in bus and metro transit, in-cabin PM concentrations in taxi transit were negatively associated with urban precipitation and wind speed. The variations in particle concentrations during the trip were significantly associated with passenger density, posture, the in-cabin location of investigators, and window condition, some of which showed interactive effects. Our findings suggest that improving the urban background environment is essential for reducing particulate pollution in public transport microenvironments. Moreover, optimizing the scheduling of buses and the distribution of bus stops might contribute to mitigating the in-cabin exposure levels in transit. With reference to our methods and insights, policymakers and other researchers may further explore in-transit exposure to particle pollution in different cities.

Inhalable particulate pollution has adverse effects on human health. Many studies have been conducted to investigate levels and risks of exposure to particulate pollution in public transit, but scant attention has been paid to variations in the pollution levels among different positions inside the cabin. Differences can be observed among different positions inside transit vehicles, like passenger density and activities, which may lead to differentiated exposure risks between different positions. Therefore, this study investigates the level of PM_2.5 concentrations and the spatiotemporal variations among three different positions (front, middle, and rear) inside the cabin during bus travel. The three positions represent the three areas inside the bus cabin, i.e., front area (FA), middle area (MA), and rear area (RA). We developed a portable monitoring system for in-cabin measurements, which consists of a mobile device with the designated app, a PM_2.5 collecting device, and a temperature-relative humidity monitor. Surveys were carried out on buses of a route in Shenzhen, China, which include inbound and outbound trips during both morning and evening peaks of two working days. Based on spatial and temporal information embedded in data collection devices, PM_2.5 concentration data were spatially and temporally referenced. Comparative and descriptive statistical analyses were employed to examine the differences and variations in PM_2.5 concentration among the areas inside the bus cabin. There are three major findings. First, for all of the surveyed trips, FA showed the highest and RA showed the lowest PM_2.5 concentration. Second, concerning the average pollution level of route segments along the routes, the PM_2.5 concentrations in the three cabin areas were higher during inbound trips than during outbound trips. Third, within route segments, PM_2.5 concentrations in FA and MA increased rapidly after the bus stopped at stops, and then gradually decreased, while in RA they were relatively stable. In general, a higher level of passenger density increased in-cabin PM_2.5 concentration. The findings of our study could provide insight into improving the bus in-cabin microenvironment, such as the seating arrangement for vulnerable passengers.