Ensuring adequate public transport-based accessibility to health facilities in different regions is a major concern of social equity and public health for government. However, the imbalanced spatial distribution of health facilities may lead to an inaccurate evaluation of the accessibility, which is shaped by both land use and transportation. To address this problem, this study proposed a new approach to evaluate the adequacy of public transport-based accessibility to health facilities considering the spatial heterogeneity. First, we obtained the spatial distribution of health facilities based on POI data, calculated the population centroids of census tract-based mobile phone positioning data, and estimated travel times from population centroids to every health facility based on web map services. Second, the public transport-based accessibility to health facilities was measured by the isochrone approach. Then, the spatial heterogeneity of the health facilities was quantified by a spatial proximity index based on the gravity model. At last, a benchmark curve of accessibility vs. spatial proximity was established to evaluate the public transport-based accessibility to health facilities in different areas with spatial heterogeneity. A case study of 218 census tracts in Shanghai was conducted to verify this method. Consequently, we successfully identified the census tracts where the public transport-based accessibility to health facilities is insufficient. It shows that even some census tracts within the central city areas are still short of public transport-based accessibility to health facilities, whereas some tracts in the urban periphery may have adequate public transport-based accessibility even though there are limited health facilities nearby.

Congestion pricing is one effective demand management strategy to alleviate traffic congestion. This work investigates pricing schemes for mixed traffic flow systems where the human-driven vehicles (HVs) and autonomous vehicles (AVs) coexist. The emerging and integration of autonomous vehicles can help improve the overall transportation efficiency and safety. Given the coexistence of HVs and AVs in the near future, there is need to adjust the existing traffic management strategies to adapt to the mixed traffic conditions. In this study, congestion pricing is imposed on the HVs and the AVs differently, that is, a distance-based toll to the HVs while a delay-based toll to the AVs. We consider six user groups based on the value of time (VOT) and the vehicle types. Compared with the unified distance-based toll, the advantage of delay-based toll is demonstrated first. Then, a surrogate-based optimization framework, namely the regressing Kriging (RK) model, is formulated. Three pricing schemes are investigated and compared: equity-oriented (EQ), environment friendliness-oriented (EN), and revenue-oriented (RE) schemes. Results show that the RE scheme collects the highest revenues; however, its cost-efficiency is weakened. The EQ scheme reduces the variance in the average travel costs among user groups, thus solving the equity issue.

A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability.