Many people criticize the inequity of traditional taxi (TT) services and believe the entry of ride-hailing taxis (RT) can address the issue. However, this has been understudied in the literature. This paper aims to estimate the equity of TT  and RT services during peak hours and to study how the entry of RT affects equity by analyzing trip data of TT and RT in New York City in 2010 and 2017 (before and after the entry of RT). First, we used the Lorenz curve and the Gini coefficient to estimate the equity of taxi services against population and employment. The results show that the equity of RT in 2017 is higher than that of TT  and the equity of TT + RT in 2017 is higher than that of 2010. Mixed geographically weighted regression (MGWR) was applied to determine whether the relationships between taxi trips and population/employment would vary across different taxi zones. The coefficient of variation (CV) of local coefficients of population/employment is used as an indicator of equity. Results show that RT services were more equitable than TT services in 2017 and that the overall taxi service in 2017 was more equitable than that of 2010.

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An accurate understanding of the relationship between subway trips and the built environment is crucial to meet people’s travel demands and promote the coordinated development of urban land. However, existing literature mainly examines this relationship at the station level, ignoring the variations between the surrounding areas of the station. Therefore, the present study aims to explore the influencing factors and spatial variations of subway trips at the grid level. First, a method was proposed to extract the subway trips using mobile positioning data in Chengdu, China. Then, two geographically weighted regression (GWR) models were adopted to examine the spatially varying relationships between the subway trip origin and destination and selected explanatory variables. The results show that the hotel, company, residential, tourist, bus, subway, road density, and transfer stations variables positively affect trip origin and destination. However, distance to the nearest subway station has a negative impact. Besides, the goodness of fit of the GWR model is better than that of the global regression model, indicating that the influence of the built environment on trip origin and destination varies across space. This study can guide planning departments and transportation agencies to implement target policies and create a convenient travel environment at the micro-level.

The distance from the origin or destination to or from the subway station is defined as the access or egress distance, which determines the service coverage of the subway station. However, little literature studies the distances at the station level, and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variation of the distances at the station level by using the mobile phone positioning data of more than 1.2 million anonymous users in Chengdu, China. First, this study proposes a method to extract the access and egress trips of the subway. Next, the ordinary least squares (OLS) regression models are carried out to select the significant explanatory variables. Finally, the geographically weighted regression (GWR) models are used to model the spatial variation relationship between the 85th percentile access/egress distances and the selected explanatory variables. The results show that different stations’ access/egress distances vary significantly in space. Hotel, residence, life, finance, road density, and mixed land use are found to be negatively correlated with distances, while education, 36–45 years old, male, and high education are positively correlated. In addition, the GWR model reveals that the influence of explanatory variables on access/egress distance varies from space to space. The results further promote the understanding of the existing system and provide a relevant reference for planners and transportation departments to optimize land use and public transportation planning.

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