Calibration and validation of matching functions for ride-sourcing markets

In order to investigate which models are more applicable under different ride-sourcing markets, a paper published in Communications in Transportation Research  (COMMTR) builds a simulator to simulate the ride-sourcing markets, and calibrates and validates the application scope for several matching functions describing the ride-sourcing market.

Ride-sourcing services have become increasingly important in meeting people's transportation needs since their emergence. Compared to traditional street hailing services, online ride-sourcing has greatly reduced the matching friction in the market through online matching. This means that people can get a ride more efficiently and quickly, and drivers can find passengers more easily. Ride-sourcing services have improved the efficiency of the transportation system.

To further improve the matching efficiency of the online ride-sharing market, researchers have attempted to describe these innovative ride-sourcing markets through mathematical models, the core of which is the matching function that describes the matching friction. A good matching function is essential for the precise characterization of ride-sourcing markets, which can assist the platforms and policymakers in better designing operating and regulating strategies to maximize the platform profit and social welfare. Previous research has developed a variety of matching functions for ride-sharing markets, such as perfect matching functions, Cobb-Douglas type matching functions, queuing models, and some physical models.

Although researchers have proposed many matching functions, less is known about the applicability and performance of these matching functions, that is, under what situations each of these matching functions can well characterize the real market. The development of each city is different, and, therefore, the situation of the ride-sourcing market is also different. In particular, the supply and demand in the ride-sourcing markets have a crucial influence on the key performance metrics of the ride-sourcing market, such as the matching rate, the service quality, and so on.

To address this issue, the authors build a simulator to simulate a total of 420 ride-sourcing market scenarios under different combinations of supply and demand. The key performance metrics, including the matching rate in the market, the passengers’ average matching time, the passengers’ average pick-up time, and the passengers’ average total waiting time, are utilized to test and compare eight widely used matching functions under various market scenarios. Finally, the best-fit models for estimating the market metrics in different market scenarios with different supply and demand are summarized.

According to the data and code disclosure and sharing policy of Communications in Transportation Research (COMMTR), the code of this study is open sourced and available at https://github.com/hku-kejintao/simulator-matching-function-validation.

They published their study on March 21, 2022, in Communications in Transportation Research at DOI: https://doi.org/10.1016/j.commtr.2022.100058.

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About Communications in Transportation Research

Communications in Transportation Research publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. The mission is to provide fair, fast, and expert peer review to authors and insightful theories, impactful advances, and interesting discoveries to readers. We welcome submissions of significant and general topics, of inter-disciplinary nature (transport, civil, control, artificial intelligence, social science, psychological science, medical services, etc.), of complex and inter-related system of systems, of strong evidence of data strength, of visionary analysis and forecasts towards the way forward, and of potentially implementable and utilizable policies/practices. Communications in Transportation Research is a fully open access journal. It is co-published by Tsinghua University Press and Elsevier, and sponsored by the State Key Laboratory of Automotive Safety and Energy (Tsinghua University). At its discretion, Tsinghua University Press will pay the open access fee for all published papers from 2021 to 2025.

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