A new transit assignment model based on line and node strategies. (August 2021)

• Record Type: Journal Article
• Authors: Ren, Hualing; Song, Yingjie; Long, Jiancheng; Si, Bingfeng
• Is Part Of: Transportation research. Volume 150(2021)
• Page Start: 121
• Page End: 142
• Publication Date: 2021-08
• Subjects: Transit assignment -- Line and node strategy -- Transfer constraint -- Variational inequality problem -- Projection algorithm; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2021.05.018
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: Propose the concepts of line strategy (LS) and node strategy (NS) and a novel methodology adopting the decision variables of LS and NS proportions. Impose transfer constraints explicitly and mix all the passengers with the same maximal number of transfers together. Establish the LNS (line and node strategy) -based model on the original transit networks and reduce the scope of the feasible transfer nodes by the maximal transfer constraints. Adopt both the method of successive averages (MSA) and a projection algorithm to solve the LNS-based model. The performance of the LNS-based model and algorithm on a transit subnetwork of Beijing showcases its huge advantage. Abstract: Passengers traveling on transit networks involve two kinds of decision-making strategies: deciding which lines are attractive at an origin or transfer node (denoted line strategy, LS), or deciding which node to transfer at when riding a line (denoted node strategy, NS). Combining these two strategies, this paper proposes a novel variational inequality formulation for the user equilibrium passenger assignment problem. The inclusion of the NS eliminates the need for passenger assignment on a large augmented graph, reducing the modeling complexity and making it easier to track all passengers' travel routes. Moreover, constraints on the maximal number of transfers—which are crucial in practical decision-making on transit networks—are explicitly included, further drastically reducing the set of passengers' feasible strategies. Furthermore, some extant strategy-based transit assignment models are shown to be a special case of the proposed model when the transfer constraint is removed. Finally, the properties of the proposed model are illustrated on a small network, and the model and algorithm exhibit huge advantages on the chosen transit subnetwork of Beijing.