Exploring the operational characteristics of ride-sourcing in an urban area

The emergence of ride-sourcing services has radically changed travel behavior and has important implications for future urban mobility. However, despite the growing market for mobility-on-demand (MOD), empirical studies on the operational characteristics of ride-sourcing systems are sparse. This paper provides an exploratory analysis of the operations of ride-sourcing using large-scale dis-aggregate mobility service data and documents the findings. From a case study in Singapore, we found that the demand follows reproducible patterns, which allows for realistic estimates of fleet operations over time and space. High travel demand during peak periods is observed along with a surge in prices (by 1.6 times on average). Shift behavior such as number of shifts, shift duration, and choices of shift start and end time affect fleet operations and fleet utilization rate. From the user metrics, we found that passengers may experience higher waiting and travel time due to the longer distance required to pickup in residential areas, the network congestion in commercial and business districts, and the detouring effects for sharing. Finally, the driver's total and marginal revenue were analyzed, which indicated a lower average revenue per unit time, distance, and service item in a shared ride compared to a single ride. These empirical observations can help understand the spatio-temporal characteristics of ride-sourcing operations in an urban area and provide insights for transportation planning and operations.

Automated summary

The emergence of ride-sourcing services has changed travel behavior and has implications for future urban mobility. However, there is a lack of empirical studies on the operational characteristics of ride-sourcing systems. This paper analyzes the operations of ride-sourcing using data from Singapore and finds reproducible patterns in demand, high demand during peak periods, and pricing surges. Shift behavior and user metrics also impact fleet operations and revenue.