What question did this study set out to answer?

To evaluate the spatial and temporal distribution of high-quality, high-profit passengers and identify consumption imbalances across urban taxi and ride-hailing markets using trajectory data.

July 3, 2019Journal of Advanced TransportationOpen Access

Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data

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Authors

BHBeibei HuTianjin Normal University XXXuanxuan XiaBeijing Jiaotong University XSXinyi ShenSoochow University

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Implication

Trajectory data analysis reveals spatial and temporal imbalances in high-quality taxi customers, suggesting traffic congestion and commuting patterns drive ride-hailing profitability disparities.

Key Points

To evaluate the spatial and temporal distribution of high-quality, high-profit passengers and identify consumption imbalances across urban taxi and ride-hailing markets using trajectory data.
Constructed a passenger evaluation model utilizing taxi global positioning system (GPS) trajectory records.
Calculated profit margins for individual trips and implemented a grid-based spatial partition method to delineate high-quality customers across urban functional zones, administrative districts, and time periods.
High-quality customers concentrate primarily on the urban periphery rather than the city center, driven in part by downtown traffic congestion.
High-quality customer demand clusters more tightly on workdays than on nonworkdays and peaks during morning and evening rush hours.
Traditional taxis demonstrate greater temporal customer imbalance than ride-hailing services, with high-quality customer proportions across administrative zones showing no consistent positive correlation with total order volume.

Cite This Study

Hu et al. (2019) studied this question.

synapsesocial.com/papers/6a6fe51278a11c550e09abd7 https://doi.org/10.1155/2019/3689389

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Implication

Trajectory data analysis reveals spatial and temporal imbalances in high-quality taxi customers, suggesting traffic congestion and commuting patterns drive ride-hailing profitability disparities.

Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data

Discussion

Member takes

Implication

Key Points

Cite This Study

Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data

Discussion

Member takes

Implication

Key Points

Cite This Study

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