Service-Oriented Load Balancing Approach to Alleviating Peak-Hour Congestion in a Metro Network Based on Multi-Path Accessibility

Abstract

To further improve the service quality and reduce safety risks in current congested metro systems during peak hours, this paper presents a load balancing (LB) approach so that available capacity can be utilized more effectively in order to alleviate peak hour congestion. A set of under-utilized yet effective alternative routes were searched using a deletion algorithm (DA) in order to share the passenger loads on overcrowded metro line segments. An optimization model was constructed based on an improved route generalized time utility function considering the penalties of both in-vehicle congestion and transfers. A detailed load balancing solution was generated based on the proposed algorithm. A real-world example of three overloaded metro line segments in the Shanghai metro network were selected and used to verify the feasibility and validity of the developed load balancing method. The results show that the load balancing method can effectively reduce the overcrowding situation to a great extent. Finally, two prospective inducing schemes are discussed to help implement the load balancing solution in the actual metro system in an efficient and effective manner.

A Comprehensive Model to Study the Dynamic Accessibility of the Park & Ride System

“…Thus, it is more reliable to measure accessibility for each transport mode as a separate unit [26]. Deep analyses have also been done on what aspects of accessibility people have via a given means of transport, such as the train, the bus, the automobile, and the taxi [27][28][29][30]. A technique that incorporates data from current geographic information systems for the purpose of offering a real-time measurement of the accessibility and connectivity of transport modes is described in [31,32].…”

Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model

“…The model determined the service offset and turn-back stations. Huang et al [11] constructed a load balancing approach to optimize metro train operation. The model was based on an improved route which generalized the time utility function, considering the penalties of both in-vehicle congestion and transfers, thereby improving the service quality and reducing safety risks in metro systems, at the same time.…”

Analysis of Crowded Propagation on the Metro Network

“…Different paths will bring flow pressure on different stations, and thus the inter-OD traffic is often allocated to efficient paths by means of passenger flow allocation. Huang found that the crowd can be relieved by sharing the passengers to the alternative routes [15]. Peng et al divided the passenger flow into different types and created a dynamic assignment model to reflect the real passenger flow state [16].…”