Price and Time Optimization for Utility-Aware Taxi Dispatching

Hikima, Yuya; Kohjima, Masahiro; Akagi, Yasunori; Kurashima, Toshio; Toda, Hiroyuki

doi:10.1007/978-3-030-89188-6_28

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The authors showed that the aforementioned indirect optimization task is valuable for real-world mobility-on-demand (MoD) services. Note that such a user-oriented aspect of optimization problems has attracted much attention (e.g., user-oriented routing [16,17] and on-demand transportation [18]). In particular, the adopted discrete choice model is connected to stated preferences, which are studied in the stable matching literature.…”

Section: Introductionmentioning

confidence: 99%

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Experimental study on the information disclosure problem: Branch-and-bound and QUBO solver

Otaki¹,

Okada²,

Yoshida³

2023

Front. Appl. Math. Stat.

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The aim of this study was to explore the information disclosure (ID) problem, which involves selecting pairs of two sides before matching toward user-oriented optimization. This problem is known to be useful for mobility-on-demand (MoD) platforms because drivers' choice behaviors are appropriately modeled, but solving the problem is still under development, although heuristic solvers have been proposed. We develop new branch-and-bound-based (BnB) solvers and a new heuristic solver based on a quadratic unconstrained binary optimization (QUBO) formulation. Our numerical experiments show that the QUBO-based solver indeed works within the limit of available bits, and the BnB solver performs slightly better than existing heuristic ones.

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Section: Introductionmentioning

confidence: 99%

“…In particular, these parameters β can be estimated from collected data by using maximum-likelihood estimation [20]. The use of this model is a standard approach in MoD research (e.g., Hikima et al [18]).…”

Section: Introductionmentioning

confidence: 99%

Experimental study on the information disclosure problem: Branch-and-bound and QUBO solver

Otaki¹,

Okada²,

Yoshida³

2023

Front. Appl. Math. Stat.

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Optimization-based Predictive Approach for On-Demand Transportation

Otaki

Nishi

Shiga

et al. 2022

Lecture Notes in Computer Science

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Price and Time Proposal Optimization for Ride-Hailing Services Based on Individual Utilities

Hikima

Akagi

Kohjima

et al. 2023

Transactions of the Japanese Society for Artificial Intelligenc

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The recent development of mobile applications offering ride-hailing services has made large-scale taxi usage data available, and dispatching and pricing technologies based on such data have advanced greatly. While existing methods have achieved some success, they ignore an important perspective: individual priorities in terms of time and money savings. For example, existing methods may dispatch a distant taxi to a customer who prioritizes promptness over cost, and a high-priced taxi to a customer who wants to save money more than time. These mismatches degrade the requester's utility and the service provider's profit. In this paper, we propose a new framework for determining price and time proposals based on the individual's preferences in ride-hailing services. First, we formulate a new optimization problem to yield better price and time proposals for requesters based on their priorities. We model the requester's taxi acceptance probabilities by the generalized cost model and the discrete choice model, both of which are widely used in transportation economics. The price and time proposals yielded by solving our problem achieve high requester satisfaction because price and time proposals are created to suit for each requester. They raise service provider profits because the proposals are less likely to be rejected. Although the discontinuity of the objective function and the difficulty of its evaluation make our problem difficult to solve, we propose a fast approximation algorithm. The proposed algorithm outputs an L-approximate solution in O(n • (n 3 + m 3 )) time, where L is a hyperparameter controlling the relative weighting of requester satisfaction and service provider profit, n is the number of requesters, and m is the number of taxis. Simulations on synthetic and real datasets show that our method increases the requester's satisfaction and the service provider's profit simultaneously with practical computation times.

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Price and Time Optimization for Utility-Aware Taxi Dispatching

Cited by 3 publications

References 25 publications

Experimental study on the information disclosure problem: Branch-and-bound and QUBO solver

Experimental study on the information disclosure problem: Branch-and-bound and QUBO solver

Optimization-based Predictive Approach for On-Demand Transportation

Price and Time Proposal Optimization for Ride-Hailing Services Based on Individual Utilities

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