A First Look at Predictability and Explainability of Pre-request Passenger Waiting Time in Ridesharing Systems

TL;DR

This paper explores the predictability and explainability of pre-request passenger waiting time in ridesharing, introducing a new model that leverages demand and supply factors for accurate predictions without driver info.

Contribution

It presents FiXGBoost, a novel feature interaction-based model for pre-request waiting time prediction, and provides an in-depth analysis of demand-supply impacts.

Findings

FiXGBoost achieves high prediction accuracy.

Demand and supply dynamics significantly influence waiting times.

The model offers high explainability of key factors.

Abstract

Passenger waiting time prediction plays a critical role in enhancing both ridesharing user experience and platform efficiency. While most existing research focuses on post-request waiting time prediction with knowing the matched driver information, pre-request waiting time prediction (i.e., before submitting a ride request and without matching a driver) is also important, as it enables passengers to plan their trips more effectively and enhance the experience of both passengers and drivers. However, it has not been fully studied by existing works. In this paper, we take the first step toward understanding the predictability and explainability of pre-request passenger waiting time in ridesharing systems. Particularly, we conduct an in-depth data-driven study to investigate the impact of demand&supply dynamics on passenger waiting time. Based on this analysis and feature engineering, we propose FiXGBoost, a novel feature interaction-based XGBoost model designed to predict waiting time without knowing the assigned driver information. We further perform an importance analysis to quantify the contribution of each factor. Experiments on a large-scale real-world ridesharing dataset including over 30 million trip records show that our FiXGBoost can achieve a good performance for pre-request passenger waiting time prediction with high explainability.