[Paper Review] Dial-a-ride problem with modular platooning and en-route transfers

Zhexi Fu, Joseph Y.J. Chow|arXiv (Cornell University)|Dec 1, 2022

Transportation and Mobility Innovations4 citations

TL;DR

This paper introduces the Modular Dial-a-Ride Problem (MDARP), a novel framework for on-demand mobility using modular vehicles that can dynamically form platoons and enable en-route transfers. It proposes a mixed-integer linear programming model and a Steiner-tree-inspired large neighborhood search heuristic, demonstrating up to 52.0% reduction in vehicle travel cost, 35.6% in passenger service time, and 29.4% in total cost compared to conventional on-demand services in tested scenarios.

ABSTRACT

Modular vehicles (MV) possess the ability to physically connect/disconnect with each other and travel in platoon with less energy consumption. A fleet of demand-responsive transit vehicles with such technology can serve passengers door to door or have vehicles deviate to platoon with each other to travel at lower cost and allow for en-route passenger transfers before splitting. A mixed integer linear programming (MILP) model is formulated to solve this "modular dial-a-ride problem" (MDARP). A heuristic algorithm based on Steiner-tree-inspired large neighborhood search is developed to solve the MDARP for practical scenarios. A set of small-scale synthetic numerical experiments are tested to evaluate the optimality gap and computation time between exact solutions of the MDARP using commercial software and the proposed heuristic. Large-scale experiments are conducted on the Anaheim network with 378 candidate join/split nodes to further explore the potentials and identify the ideal operation scenarios of MVs. The results show that MV technology can save up to 52.0% in vehicle travel cost, 35.6% in passenger service time, and 29.4% in total cost against existing on-demand mobility services in the scenarios tested. Results suggest that MVs best benefit from platooning by serving "enclave pairs" as a hub-and-spoke service.

Motivation & Objective

• To address inefficiencies in on-demand mobility services by integrating modular vehicles capable of dynamic platooning and en-route passenger transfers.
• To develop a mixed-integer linear programming (MILP) model that captures the complexities of modular vehicle operations, including joining, splitting, and transfers.
• To design a heuristic algorithm based on large neighborhood search inspired by Steiner trees to solve large-scale MDARP instances efficiently.
• To evaluate the cost and time savings of modular vehicles compared to traditional on-demand transit in realistic urban networks.
• To identify ideal operational scenarios where modular vehicles deliver maximum benefits, particularly through hub-and-spoke service patterns.

Proposed method

• Formulates a mixed-integer linear programming (MILP) model to represent the MDARP, incorporating vehicle joining/splitting decisions, passenger routing, and platooning constraints.
• Develops a heuristic algorithm based on large neighborhood search (LNS) with a Steiner-tree-inspired construction phase to generate high-quality initial solutions.
• Implements a local search procedure that explores neighborhood solutions by modifying vehicle routes, platoon formations, and transfer points.
• Uses a decomposition strategy to handle large-scale instances by focusing on candidate join/split nodes, reducing computational complexity.
• Validates the heuristic against exact MILP solutions on small-scale instances to assess optimality gap and computation time.
• Employs the Anaheim network with 378 candidate join/split nodes as a large-scale testbed to evaluate performance and scalability.

Experimental results

Research questions

• RQ1 How does modular platooning with en-route transfers affect vehicle travel cost, passenger service time, and total cost in on-demand mobility?
• RQ2 What is the optimality gap and computation time of the proposed heuristic compared to exact MILP solutions on small-scale instances?
• RQ3 In what operational scenarios do modular vehicles deliver the greatest cost and time savings?
• RQ4 How does the performance of modular vehicles vary with different network topologies and demand patterns?
• RQ5 What role do 'enclave pairs' and hub-and-spoke structures play in maximizing the benefits of modular vehicle operations?

Key findings

• Modular vehicles reduce vehicle travel cost by up to 52.0% compared to conventional on-demand mobility services in tested scenarios.
• Passenger service time is reduced by up to 35.6% due to optimized platooning and transfer coordination.
• Total system cost is reduced by up to 29.4% through combined savings in travel and operational costs.
• The proposed heuristic achieves near-optimal solutions with significantly reduced computation time compared to exact MILP solvers on small instances.
• The greatest benefits are realized when modular vehicles operate as a hub-and-spoke system serving 'enclave pairs'.
• Large-scale experiments on the Anaheim network confirm the scalability and practical viability of the MDARP framework.