Combining choice and response time data to analyse the ride-acceptance behavior of ride-sourcing drivers

This paper investigates the ride-acceptance behavior of drivers on ride-sourcing platforms, considering drivers’ freedom to accept or reject ride requests. Understanding drivers’ preferences is vital for ride-sourcing services to improve the matching of requests to drivers. To this end, we obtained a unique dataset from a major ride-sourcing platform in Iran. This dataset provides comprehensive details of driver and ride characteristics for both successful and unsuccessful matchings. We investigate the ride-acceptance behavior of drivers using a hierarchical drift–diffusion model, which captures the dependency between drivers’ choices and response times. This dependency implies that response time, in addition to the request acceptance or rejection decision, contains valuable information about drivers’ preferences which allows us to better comprehend drivers’ ride-acceptance behaviors. Furthermore, we conduct a thorough comparison between the drift–diffusion model and the logit model, considering their predictive ability, parameter estimates, and elasticities. Within the drift–diffusion model framework, we also derive time-dependent elasticities of acceptance probability and elasticity of drivers’ response times. Our results demonstrate that ride fare, ride duration to request origin, and rainfall volume have the most impact on drivers’ ride-acceptance decisions. The insights derived from this study can be utilized to enhance platform matching algorithms and strategies, thereby improving the efficiency of ride-sourcing platforms.