Spatiotemporal Characteristics of Ride-sourcing Operation in Urban Area

TL;DR

This study analyzes the spatiotemporal patterns of ride-sourcing services in Singapore using large-scale data, revealing demand cycles, fleet utilization, and user experience metrics to inform transportation planning.

Contribution

It provides empirical insights into ride-sourcing operations, demand patterns, and driver behaviors based on real-world data, which is novel for urban transportation analysis.

Findings

Reproducible demand patterns identified during peak hours.

Significant surge pricing correlates with increased service rates.

Driver shift behaviors explain daily utilization patterns.

Abstract

The emergence of ride-sourcing platforms has brought an innovative alternative in transportation, radically changed travel behaviors, and suggested new directions for transportation planners and operators. This paper provides an exploratory analysis on the operations of a ride-sourcing service using large-scale data on service performance. Observations over multiple days in Singapore suggest reproducible demand patterns and provide empirical estimates of fleet operations over time and space. During peak periods, we observe significant increases in the service rate along with surge price multipliers. We perform an in-depth analysis of fleet utilization rates and are able to explain daily patterns based on drivers' behavior by involving the number of shifts, shift duration, and shift start and end time choices. We also evaluate metrics of user experience, namely waiting and travel time distribution, and explain our empirical findings with distance metrics from driver trajectory analysis and congestion patterns. Our results of empirical observations on actual service in Singapore can help to understand the spatiotemporal characteristics of ride-sourcing services and provide important insights for transportation planning and operations.