A review of Ride-Matching strategies for Ridesourcing and other similar services

ABSTRACT

Ridesourcing services have emerged as a popular alternative for commuters in metropolitan areas. There is a significant spatio-temporal variation of demand and supply for such services, which requires efficient ride-matching strategies to ensure optimal allocation of trips to drivers and users. This paper reviews different ride-matching techniques/strategies that highlight the outlook of different stakeholders, such as, drivers, users, and service providers and summarises the impacts of the matching process on the ideologies of the stakeholders. The review found that searching techniques guide the primary stakeholders like riders and drivers, and the assignment techniques ensure trip allotment. We also observed that fleet size is an important attribute to ensure availability as well as the assignment of ridesourcing services in an urban area.

KEYWORDS:

• Ridesourcing
• Ride-matching
• stakeholders’ perspectives
• search
• assignment

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1 For-hire services include pre-arranged reservations as well as on demand booking for hiring a cab.

2 Services where passengers searching for a vehicle via an online application (mostly using a smartphone) are assigned a suitable and available driver by a centralised operator.

3 The time taken to pick-up/drop-off passengers in a shared ride while other passengers are still on-board.

4 While rider-driver pairings are conducted through algorithms, strategies are deployed to monitor ride execution and vehicle movements.

5 “Long-term demand” refers to the demand generated due to the change in policies or changes in socio-economic characteristics of the population.

6 “Short-term demand” refers to the impact on existing demand due to factors such as lack of information, spatio-temporal variations, and weather.

7 Aggregate models can be defined as the models formulated by utilising two or more algorithms.

Additional information

Funding

This work was supported by the Curtin University of Technology (CIPRS) and Indian Institute of Technology Kharagpur (MHRD Scholarship).