A Spatiotemporal Thermo Guidance Based Real-Time Online Ride-Hailing Dispatch Framework

Abstract

Online ride-hailing platforms can gather travel requests and allocate service vehicles to balance transportation demands and supplies, which may result in an increase in the utilization rate of service resources and improve the transportation efficiency and social welfare. An effective and flexible dispatching strategy can significantly reduce the waiting time of passengers and increase the profit of service vehicles. In this paper, we propose a real-time service vehicle dispatching framework in the context of large-scale online ride-hailing, which considers all the main issues involved in the problem in a unified and integrated way. Firstly, we formally define the problem and introduce a novel mathematical model based on the spatiotemporal thermo to guide the dispatching process so as to achieve a better balance of demands and supplies. Secondly, we propose an effective machine learning method to predict the spatiotemporal thermo based on the historical data of multi-dimensional factors. Afterward, decision rules to dynamically segment timeframes and a Kuhn-Munkres based algorithm are developed to solve the dispatching problem. Finally, extensive experiments on large-scale real-world instances are conducted to verify the effectiveness of the proposed framework. INDEX TERMS transportation, vehicle dispatching, online ride-hailing, spatiotemporal thermo, dynamic timeframe = , and is on duty during time range [ = , = ]. We use = to denote the vehicle's current service status. = equals 1 when the vehicle is assigned to serve a request, and otherwise equals 0. Definition 4 (Request): A request of a passenger, represented by = < B , B , B >, submitted at time B , indicates a transportation need from the origin node B to the destination node B. A request will be canceled by the passenger if it is not served after a fixed time D. Definition 5 (Service-pair): When a service vehicle is assigned to answer a request , a service-pair < , > is created. Note that we consider the circumstance that a vehicle only serves one request at a time.

A Pricing Mechanism for Ride-Hailing Systems in the Presence of Driver Acceptance Uncertainty

“…maximizing profit is one of the important objectives of ridehailing platforms, the quality of the service they provide is usually measured by matching rates, rider wait times and other criteria derived from both drivers and riders. Various optimization models have been proposed for assigning drivers to riders with the objective of maximizing service quality objective functions [11]- [17]. These models either do not take the impact of pricing into consideration as in [14], [15], [17] or they usually assume a static flat rate pricing scheme where drivers' compensation rate does not change across different regions or time periods as in [11], [12], [16].…”

“…Various optimization models have been proposed for assigning drivers to riders with the objective of maximizing service quality objective functions [11]- [17]. These models either do not take the impact of pricing into consideration as in [14], [15], [17] or they usually assume a static flat rate pricing scheme where drivers' compensation rate does not change across different regions or time periods as in [11], [12], [16].…”

Demand Forecasting of Online Car-Hailing With Stacking Ensemble Learning Approach and Large-Scale Datasets

“…GBDT is widely used because of its effectiveness, accuracy and interpretability. LightGBM model is a variant of GBDT model proposed by Microsoft Asia Research Institute in 2016 [20] . GBDT and XGBoost adopt level wise strategy.…”

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