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 article, we propose a novel service policy to achieve cost-effective CB, 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 data set in San Francisco are implemented to verify the performance of our proposed CB in terms of service coverage and travel efficiency.