  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.
