Optimization for Customized Bus Stop Planning, Order Schedule, and Routing Design in On-Demand Urban Mobility

Detours are inevitable in on-demand customized bus (CB) systems. Previous studies alleviate the impact of detours by predefining high spatial-temporal similarity of travel orders for CB. However, this assumption is clearly inconsistent with orders’ distribution at the urban level and leads to low bus occupancy rate in practical use. In this paper, we propose a novel service policy to achieve cost-effective customized bus, which consists of dynamically deployed bus stops and a spatial-temporal heterogeneous-order service. Then, to address the challenge of computational complexity, we provide an order-oriented graphic model named order correlation network (OCN) to formulate the CB design problem. By introducing OCN, we propose a near-optimal computationally efficient solution to the problem, which is scalable and suitable for real-time implementation. Finally, comparative experiments based on the real-world taxi trajectory dataset in San Francisco are implemented to verify the performance of our proposed CB in terms of service coverage and travel efficiency.