Recently, the explosive growth of ridesourcing, or on-demand ridesharing, has attracted a great deal of attention from researchers and planners. Despite its transformative impacts on mobility, limited studies have examined how built environment affects its use. In this study, we investigate the impacts of built environment on ridesourcing demand. We employ structural equation modelling to account for the complex relationships among study variables, and investigate the impacts at census block group level by using RideAustin data in Austin, Texas. Findings reveal strong impacts of built environment on ridesourcing demand and significant temporal heterogeneity. The models show that greater population/employment/service job densities, road density, pavement completeness, land use mix and job accessibility by transit produce more ridesourcing demand. Access to the commuter rail (MetroRail) also leads to greater demand. Furthermore, time-of-day (TOD) models demonstrate that these effects vary significantly according to the time of day. Our research has implications for policy making and for travel demand modelling of ridesourcing.

This study investigates the impact of the Austin MetroRail on commercial property values. Spatial Durbin models (SDM) suggest a modest price premium associated with rail transit across the study area. A premium gradient is also presented at different distance tiers from stations. When it comes to transit-oriented development (TOD), a synergistic effect in enlarging the premium is found: with additional $9.0/ft², $8.6/ft² and $5.3/ft² at 0.25 mi, 0.25–0.5 mi and 0.5–0.75 mi, respectively. Furthermore, geographically weighted regression shows the premium is significantly higher in central business district (CBD) and TOD areas. Such recognition of the heterogeneous price effect is of use in the design of project financing and TOD strategies.

In China, the roles played by high speed rail (HSR) in improving regional accessibility and promoting urban growth have recently generated significant research interest. However, the question as to how HSR could shape urban development seems rather complex. This paper aims to discuss HSR's role in shaping urban economic development by tracing links from HSR to manufacturing agglomeration with panel data from the Beijing–Guangzhou HSR network in China from 2000 to 2015. On the basis of the new economic geography model, market potential and manufacturing wages are incorporated into the analysis framework, and a treatment effect model is used to account for the endogeneity of HSR in the study. Model results provide three conclusions. First, in general, HSR significantly increases manufacturing agglomeration for HSR cities. Second, an inverted-U relationship between the agglomeration impact of HSR and market potential is confirmed. That is, while the agglomeration effect by HSR is significantly within a threshold level of market potential, HSR in fact has a negative effect, or the dispersion effect, on manufacturing agglomeration beyond the threshold level. Third, the agglomeration impact of HSR differs across regions. For example, the agglomeration impact of HSR is more significant in the second-tier HSR cities than in major HSR cities, indicating the overall trend of regional integration and economic convergence among different regions of HSR through time. Results from this study are useful for policy makers as well as for researchers for analyzing the economic implications of HSR in the future.