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

Abstract

The distribution of high-quality customers (hereafter HQC) for taxis (including ride-hailing cars) has a significant impact on drivers’ revenue and taxis’ operation efficiency. Based on the taxi global positioning system data, we construct an evaluation model of passengers to discuss the imbalance of consumption in taxi market. The profit margin for each taxi order is calculated, and then a grid-based method is used to distinguish the HQC and the regions with potential higher benefits. We analyze the HQC’s distribution of taxis (including ride-hailing cars) in different areas and in different time periods. We find that the HQC are distributed mainly on the periphery of the main urban area, which indicates that traffic condition is even worse in the urban center because of factors such as congestion. The HQC are more concentrated on workdays and more scattered on nonworkdays, which implies that the public have different travel habits and demands on workdays and nonworkdays. The proportion of HQC in each administrative district or functional zone is not always positively correlated to either the proportion of total orders or the total HQC. This indicates that the distribution of HQC in each administrative district or functional zone is imbalanced. The proportion of orders and that of HQC are roughly the same in the temporal dimension, being higher in the morning and evening rush hours. Compared with the distribution of the HQC of ride-hailing, that of taxis is more imbalanced in the temporal dimension. Relevant departments should further coordinate taxi pricing, strengthen market control, and promote sustainable development in the taxi and ride-hailing markets.

A Modified Latent Dirichlet Allocation Topic Approach for Driving Style Exploration Using Large-Scale Ride-Hailing GPS Data

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Research on the Coupling Coordination Relationships between Urban Function Mixing Degree and Urbanization Development Level Based on Information Entropy

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