Trip-Vehicle Assignment Algorithms for Ride-Sharing

Li, Songhua; Li, Minming; Lee, Victor

doi:10.1007/978-3-030-64843-5_46

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…They also give a 5/2-approximation algorithm for the total distance minimization version of this special case (under the additional assumption that there are exactly twice as many requests as there are vehicles). Li, Li, and Lee [29] match the 5/2 approximation guarantee while relaxing the latter assumption. Namely, they only require there being at most twice as many requests as there are vehicles.…”

Section: Introductionmentioning

confidence: 93%

On the Request-Trip-Vehicle Assignment Problem

Mori¹,

Samaranayake²

2021

SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21)

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The request-trip-vehicle assignment problem is at the heart of a popular decomposition strategy for online vehicle routing. In this framework, assignments are done in batches in order to exploit any shareability among vehicles and incoming travel requests. We study a natural ILP formulation and its LP relaxation. Our main result is an LP-based randomized rounding algorithm that, whenever the instance is feasible, leverages mild assumptions to return an assignment whose: i) expected cost is at most that of an optimal solution, and ii) expected fraction of unassigned requests is at most 1/e. If trip-vehicle assignment costs are α-approximate, we pay an additional factor of α in the expected cost. We can relax the feasibility requirement by considering the penalty version of the problem, in which a penalty is paid for each unassigned request. We find that, whenever a request is repeatedly unassigned after a number of rounds, with high probability it is so in accordance with the sequence of LP solutions and not because of a rounding error. We additionally introduce a deterministic rounding heuristic inspired by our randomized technique. Our computational experiments show that our rounding algorithms achieve a performance similar to that of the ILP at a reduced computation time, far improving on our theoretical guarantee. The reason for this is that, although the assignment problem is hard in theory, the natural LP relaxation tends to be very tight in practice.

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Section: Introductionmentioning

confidence: 93%

On the Request-Trip-Vehicle Assignment Problem

Mori¹,

Samaranayake²

2021

SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21)

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“…Once a travel request has been made, a single CAV from a particular station s ∈ S must be assigned to the travel request. Although the travel-vehicle assignment is another interesting problem to explore [25], [26], in this letter, we consider the assignment to be given from a higherlevel decision layer and assume that CAVs always return to the same station where dispatched from.…”

Section: A Road Network and Travel Requestmentioning

confidence: 99%

Combined Optimal Routing and Coordination of Connected and Automated Vehicles

Bang,

Chalaki,

Malikopoulos

2022

Preprint

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In this letter, we consider a transportation network with a 100% penetration rate of connected and automated vehicles (CAVs), and present an optimal routing approach which takes into account the efficiency achieved in the network by coordinating the CAVs at specific traffic scenarios, e.g., intersections, merging roadways, roundabouts, etc. To derive the optimal route of a travel request, we use the information of the CAVs that have already received a routing solution. This enables each CAV to consider the traffic conditions on the roads. Given the trajectories of CAVs resulting by the routing solutions, the solution of any new travel request determines the optimal travel time at each traffic scenario while satisfying all state, control, and safety constraints. We validate the performance of our framework through numerical simulations. To the best of our knowledge, this is the first attempt to consider the coordination of CAVs in a routing problem.

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Combined Optimal Routing and Coordination of Connected and Automated Vehicles

Bang

Chalaki

Malikopoulos

2022

IEEE Control Syst. Lett.

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This paper addresses the challenge of generating optimal vehicle flow at the macroscopic level. Although several studies have focused on optimizing vehicle flow, little attention has been given to ensuring it can be practically achieved. To overcome this issue, we propose a route-recovery and ecodriving strategy for connected and automated vehicles (CAVs) that guarantees optimal flow generation. Our approach involves identifying the optimal vehicle flow that minimizes total travel time, given the constant travel demands in urban areas. We then develop a heuristic route-recovery algorithm to assign routes to CAVs that satisfy all travel demands while maintaining the optimal flow. Our method lets CAVs arrive at each road segment at their desired arrival time based on their assigned route and desired flow. In addition, we present an efficient coordination framework to minimize the energy consumption of CAVs and prevent collisions while crossing intersections. The proposed method can effectively generate optimal vehicle flow and potentially reduce travel time and energy consumption in urban areas.

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Trip-Vehicle Assignment Algorithms for Ride-Sharing

Cited by 4 publications

References 11 publications

On the Request-Trip-Vehicle Assignment Problem

On the Request-Trip-Vehicle Assignment Problem

Combined Optimal Routing and Coordination of Connected and Automated Vehicles

Combined Optimal Routing and Coordination of Connected and Automated Vehicles

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