Abstract
This study is designed to leverage ubiquitous mobile computing techniques on exploring app-based taxi movement patterns in large cities. To study patterns at different scales, we comprehensively explore both occupied and unoccupied vehicle movement characteristics through not only individual trips but also their aggregations. Moran’s I and its variations are applied to explore spatial autocorrelations among different rides. PageRank centrality is applied for a functional network representing traffic flows to discover places of interest. Gyration radius measures the scope of passenger mobility and driver passenger searching. Moreover, cumulative distribution and data visualization techniques are adopted for trip level characteristics and features analysis. The results indicate that the app-based taxi services are serving more neighborhoods other than downtown areas by taking large proportion of relatively shorter trips and contributing to net increase in total taxi ridership although net decrease in downtown areas. The spatial autocorrelations are significant not only within each service but also among services. With the smartphone-based applications, app-based taxi services are able to search passengers in a larger area and move more efficiently during both occupied and unoccupied periods. Mining from huge empty trip trajectory by app-based taxis, we also identify the existence of stationary/stops state and circulations.
Highlights
The smartphone application-based taxi services, such as Uber and Lyft, have grown exponentially in recent years and are challenging the predominance of traditional street-hailing taxicabs. Both app-based and traditional taxi services provide similar door-to-door urban mobility, appbased taxi services have a few extraordinary features that make them competitive: First, the smartphone applications adopted by app-based taxi services connect passengers with driver partners and provide real time feedback that can help learn patterns surrounding demand or supply; second, the free supply model allows driver partners to begin or end services anywhere and anytime, attracting considerable part-time driver partners; third, pricing can be adjusted dynamically by a multiplier that is developed based on local demand and supply; and fourth, various services are included in one single application, which can meet requirements by different groups, for instance, family trips with children, disabled, luxury trips, and economic trips
App-based taxi ridership doubled annually over the last three years to 133 million passengers in 2016 and is approaching traditional taxicab ridership level in New York City (NYC), where is the largest taxi market in North America operating more than 13,000 yellow taxicabs and experiencing severe declines in ridership, almost reduced by half in the last few years [1]
Beyond the spatial distributions of ridership that are shifting outside of downtown areas, we identify the significant local spatial autocorrelations of intra- and interservice rides
Summary
The smartphone application-based taxi services ( called e-hailing or transportation network companies), such as Uber and Lyft, have grown exponentially in recent years and are challenging the predominance of traditional street-hailing taxicabs Both app-based and traditional taxi services provide similar door-to-door urban mobility, appbased taxi services have a few extraordinary features that make them competitive: First, the smartphone applications adopted by app-based taxi services connect passengers with driver partners and provide real time feedback that can help learn patterns surrounding demand or supply; second, the free supply model allows driver partners to begin or end services anywhere and anytime, attracting considerable part-time driver partners; third, pricing can be adjusted dynamically by a multiplier that is developed based on local demand and supply; and fourth, various services are included in one single application, which can meet requirements by different groups, for instance, family trips with children, disabled, luxury trips, and economic trips. The remaining sections are organized as follows: the second section summarizes current literature on taxi movement patterns; the third section presents real datasets and preprocessing; the fourth section explores occupied trip movement patterns; the fifth section discusses unoccupied trip movement and special analyses on Uber passenger searching behaviors; and the last section concludes empirical findings
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