Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
For peer-to-peer ride-hailing providers, it is a crucial competitive advantage to cost-efficiently dispatch passenger requests and to communicate accurate waiting times. To determine waiting times and dispatch decisions, transport network companies need precise information about the location of all available drivers. Due to technical limitations and outdated data (e.g., low sample rates, continuous movement of drivers), however, existing systems, which typically use the last observed locations of drivers, regularly suffer from dispatches with critical delays. In this paper, we present an approach to predict probability distributions for drivers' future locations, which are calculated based on patterns observed in past trajectories. We evaluate the applicability and accuracy of the proposed algorithm on a large real-world trajectory dataset of a transportation network company. Our results allow quantifying the risk of critical delays and thus enable risk considerations in improved dispatching strategies.
A Probabilistic Location Prediction Approach to Optimize Dispatch Processes in the Ride-Hailing Industry
Online
For peer-to-peer ride-hailing providers, it is a crucial competitive advantage to cost-efficiently dispatch passenger requests and to communicate accurate waiting times. To determine waiting times and dispatch decisions, transport network companies need precise information about the location of all available drivers. Due to technical limitations and outdated data (e.g., low sample rates, continuous movement of drivers), however, existing systems, which typically use the last observed locations of drivers, regularly suffer from dispatches with critical delays. In this paper, we present an approach to predict probability distributions for drivers' future locations, which are calculated based on patterns observed in past trajectories. We evaluate the applicability and accuracy of the proposed algorithm on a large real-world trajectory dataset of a transportation network company. Our results allow quantifying the risk of critical delays and thus enable risk considerations in improved dispatching strategies.
https://aisel.aisnet.org/hicss-54/da/smart_mobility/2