How Built Environment Impacts Online Car-Hailing Ridership

Abstract

Extensive research has shown that unilateral optimization of transit systems is not effective enough to significantly increase its transport efficiency. Considering that urban land-use characteristics, including residential, work, consumption, transit, and so forth, are significantly interrelated with travel demands and travel behaviors, this paper provides a way to optimize transit system by raising awareness of the relation between ridership and built environment. This paper adopted point of interest (POI) data to investigate the effect of physical built environment on online car-hailing ridership in Chengdu, China. The study area was tessellated with several Voronoi cells; these cells were further clustered into three ridership patterns based on the time-varying characteristic of ridership. Given that some differences existed in the three ridership patterns, a separate spatial ridership model was developed to understand the factors that influence ridership patterns using geographic weighted regression (GWR) analysis. The data and results verified that the built environment had various influences on online car-hailing alighting ridership in spatial and temporal dimensions, of which the significant POI factors for determining the ridership pattern during different periods were detected. Remarkably, this study took the ridership dataset from the online car-hailing transit system, mainly because the pick-up (PU) and drop-off (DO) locations generated by this service are closest to the origin and destination of the trip, except that it is more popular recently. Therefore, the analysis of the impact of built environment on travel based on the online car-hailing dataset can be captured in greater detail, with a higher degree of accuracy.

Jointly modeling bus and taxi ridership: A Bayesian multivariate spatial analysis accounting for cross‐ridership correlation

“…One limitation of this research is that it only considers built environment variables. Although the legitimacy of variable selection mirrors that of previous studies (Bi et al, 2020;Ma et al, 2018), incorporating other finescale demographic and policy factors might improve model performance and provide further practical planning guidance. In addition, although factors that influence personal choices on bus and taxi are beyond the scope of this study, it is worth investigating this aspect and further unravelling complicated interactions between bus and taxi ridership.…”

“…Previous studies have indicated that bus stations are often located in areas where people are densely distributed and that the built environment around a bus station is often relatively homogeneous. Therefore, bus stop‐based Thiessen polygons are appropriate spatial analysis units for travel behavior studies (Bi et al., 2020). Bus stations located within 500 m were first aggregated to avoid potential biases that might be propagated to the analysis from the following two aspects.…”

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

“…Connection from the metro station to the trip destination is required, which is the critical component of a multimodal journey; it is traditionally achieved by combining other travel modes, like walking, cycling, and the use of bus and taxi ( 10 , 20 ). Benefitting from the rapid development of internet technology and mobile payment, many cities witnessed the explosive growth in emerging transport services in recent years, including bike-sharing and ridesourcing programs ( 21 , 22 ). Besides being a stand-alone travel mode, they have another essential function: serving as the feeder mode to the metro.…”

“…Ridesourcing schemes are another typical example of a sharing economy service pertaining to transportation ( 21 , 28 , 29 ). Prearranged and on-demand ride services that match drivers and passengers using smartphone applications have rapidly spread worldwide because of their convenience.…”

Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

“…Xiao et al found that Shanghai presents a two-level hierarchical polycentric urban structure feature by applying a spatial embedded network model [40]. To build the original destination (OD) matrix, the common types of traffic analysis unit, include regular grids [41][42][43][44][45], hexagons [46], and irregular polygons (usually obtained by dividing the road network or district boundary). In particular, the regular grid is the most common due to its advantages, such as ease of use and visualization.…”