Abstract
The rapid growth of transportation network companies (TNCs) has reshaped the traditional taxi market in many modern cities around the world. This study aims to explore the spatiotemporal variations of built environment on traditional taxis (TTs) and TNC. Considering the heterogeneity of ridership distribution in spatial and temporal aspects, we implemented a geographically and temporally weighted regression (GTWR) model, which was improved by parallel computing technology, to efficiently evaluate the effects of local influencing factors on the monthly ridership distribution for both modes at each taxi zone. A case study was implemented in New York City (NYC) using 659 million pick-up points recorded by TT and TNC from 2015 to 2017. Fourteen influencing factors from four groups, including weather, land use, socioeconomic and transportation, are selected as independent variables. The modeling results show that the improved parallel-based GTWR model can achieve better fitting results than the ordinary least squares (OLS) model, and it is more efficient for big datasets. The coefficients of the influencing variables further indicate that TNC has become more convenient for passengers in snowy weather, while TT is more concentrated at the locations close to public transportation. Moreover, the socioeconomic properties are the most important factors that caused the difference of spatiotemporal patterns. For example, passengers with higher education/income are more inclined to select TT in the western of NYC, while vehicle ownership promotes the utility of TNC in the middle of NYC. These findings can provide scientific insights and a basis for transportation departments and companies to make rational and effective use of existing resources.
Highlights
With the popularity of mobile phone usage, transportation network companies (TNCs) that offer app-based services, such as Uber, DiDi, and Lyft, claim to provide stability and convenience with peer-to-peer (p2p) processes that connect passengers and private drivers on-line and in real-time [1]
The relationship between the two modes will inevitably be mutually competitive, and this competitive relationship will demonstrate nonstationarity in time and space. In response to this problem, we select New York City (NYC) as a case study to illustrate that the geographically and temporally weighted regression (GTWR) model can be an effective tool for analyzing spatiotemporal heterogeneity
The effects of the influencing factors for the traditional taxis (TTs) and TNC can be quantitatively evaluated in the temporal term, and spatial variations can be analyzed by the coefficients at different spatial units
Summary
With the popularity of mobile phone usage, transportation network companies (TNCs) that offer app-based services, such as Uber, DiDi, and Lyft, claim to provide stability and convenience with peer-to-peer (p2p) processes that connect passengers and private drivers on-line and in real-time [1]. Much evidence has shown that the rapid development of TNC has had a huge impact on the traditional taxi (TT), leading the taxi industry to experience significant losses in terms of market share, revenue, labor power and facility [2]. This is obvious in large modern cities such as New York City (NYC), where the annual taxi load decreased from 145 million in 2015 to 113 million in 2017, decreasing nearly 23% in three years. In May 2013, the price of a yellow car’s license plate in NYC had been cut in half, the licenses of many taxi company vehicles were idle because of the lack of new drivers [3]
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