The Multi-Type Demands Oriented Framework for Flex-Route Transit Design

Abstract

Flex-route transit is regarded as the feasible solution to provide flexible service for various demands. To improve the service of flex-route transit, this paper proposes a design framework with the input of multi-type demands. Firstly, according to the multi-feature-based classification method, static stations and dynamic stations are divided by hierarchical clustering algorithm based on historical demands. Secondly, in the two-stage planning method, an offline plan is generated by multi-route design model and route-design-oriented genetic algorithm based on the classified stations and the flexible combination of reserved demands and regular travel patterns. Then, an online plan is adjusted by route modification model and greedy algorithm based on the offline plan and real-time demands. Numerical experiments demonstrate the applicability of flex-route transit in the realistic road network and show that flex-route transit can transport demands more effectively and save nearly 40% of cost compared with traditional transit.

Optimizing Multi-Vehicle Demand-Responsive Bus Dispatching: A Real-Time Reservation-Based Approach

“…There are currently three main models of public transport systems in countries around the world, including the traditional Fixed-Route Transit System, the Customized Public Transport System, and the Flexible Public Transport System [9][10][11]. The fixed-route bus system is widely used in major cities in China.…”

A Layer-Based Relaxation Approach for Service Network Design

“…In addition, different network structures were addressed in the literature, such as multimodal networks [28], long-haul and local transportation structures [29], autonomous fleets [30], and heterogeneous fleets [31]. Furthermore, SND methodologies have attracted much scientific research attention in developing green logistics and green supply-chain systems: for example, the green closed-loop supply chain network design for ventilators during the COVID-19 epidemic [32], the multitier supply network design methodology in reducing cost and carbon emissions [33], the intermodal green p-hub median problem [34], an approach to evaluating the coal transportation chain to reduce carbon emissions and pollution [35], reducing the negative environmental impact in freight transportation by understanding service buyers [36], effective green transit network design methodologies under urban expansion [37,38], an electrical transportation network design method to reduce congestion and carbon emissions [39,40], and a collaborative fleet routing methodology for sustainable seaport transportation [41].…”