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This study proposes an improved learning-and-optimization train fare design method to deal with the commuting congestion of train stations at the central business district (CBD). The conventional learning-and-optimization scheme needs accurate boarding/alighting demand to update the train fare in each trial. However, when congestion happens, the observed boarding/alighting demand will be larger than the actual boarding/alighting demand due to the delays and the longer dwelling time. Thus, the actual boarding/alighting demand is not available in practice. The improved algorithm deals with this issue by using inexact and less information to determine the new trial fare during the iteration. Namely, the improved method bypasses the conditions that may lead to biased results so as to significantly enhance the reliability of the learning-and-optimization method. The simplified algorithm also makes this method more practical. The convergence property of the proposed algorithm is rigorously proved and the convergence rate is demonstrated to be exponential. Numerical studies are performed to demonstrate the efficiency of the improved learning-and-optimization method.
Xinyuan Chen; Wei Zhang; Xiaomeng Guo; Zhiyuan Liu; Shuaian Wang. An improved learning-and-optimization train fare design method for addressing commuting congestion at CBD stations. Transportation Research Part E: Logistics and Transportation Review 2021, 153, 102427 .
AMA StyleXinyuan Chen, Wei Zhang, Xiaomeng Guo, Zhiyuan Liu, Shuaian Wang. An improved learning-and-optimization train fare design method for addressing commuting congestion at CBD stations. Transportation Research Part E: Logistics and Transportation Review. 2021; 153 ():102427.
Chicago/Turabian StyleXinyuan Chen; Wei Zhang; Xiaomeng Guo; Zhiyuan Liu; Shuaian Wang. 2021. "An improved learning-and-optimization train fare design method for addressing commuting congestion at CBD stations." Transportation Research Part E: Logistics and Transportation Review 153, no. : 102427.
In this study, we addresse traffic congestion on river-crossing channels in a megacity which is divided into several subareas by trunk rivers. With the development of urbanization, cross-river travel demand is continuously increasing. To deal with the increasing challenge, the urban transport authority may build more river-crossing channels and provide more high-volume public transport services to alleviate traffic congestion. However, it is widely accepted that even though these strategies can mitigate traffic congestion to a certain level, they are not essential approaches to address traffic congestion. In this study, we consider a channel toll scheme for addressing this issue. Additional fares are applied to private vehicles, that an appropriate number of private vehicle drivers are motivated to take public transport or switch to neighboring uncongested river-crossing channels. To minimize the toll surcharge on both neighboring channels, while alleviating the traffic flow to a certain level, in this study, we provide a bi-objective mathematical model. Some properties of this model are discussed, including the existence and uniqueness of the Pareto optimal solution. To address this problem, a trial-and-error method is applied. Numerical experiments are provided to validate the proposed solution method.
Xinyuan Chen; Yiran Wang; Yuan Zhang. A Trial-and-Error Toll Design Method for Traffic Congestion Mitigation on Large River-Crossing Channels in a Megacity. Sustainability 2021, 13, 2749 .
AMA StyleXinyuan Chen, Yiran Wang, Yuan Zhang. A Trial-and-Error Toll Design Method for Traffic Congestion Mitigation on Large River-Crossing Channels in a Megacity. Sustainability. 2021; 13 (5):2749.
Chicago/Turabian StyleXinyuan Chen; Yiran Wang; Yuan Zhang. 2021. "A Trial-and-Error Toll Design Method for Traffic Congestion Mitigation on Large River-Crossing Channels in a Megacity." Sustainability 13, no. 5: 2749.
This paper investigates a distance-based preferential fare scheme for park-and-ride (P&R) services in a multimodal transport network. P&R is a sustainable commuting approach in large urban areas where the service coverage rate of conventional public transport modes (e.g., train and bus) is poor/low. However, P&R services in many cities are less attractive compared to auto and other public transport modes, especially for P&R facilities sited far away from the city center. To address this issue, this paper proposes a distance-based preferential fare scheme for P&R services in which travelers who choose the P&R mode get a discount. The longer the distance they travel by train, the better the concessional price they get. A multimodal transport network equilibrium model with P&R services is developed to evaluate the impacts of the proposed distance-based fare scheme. The travelers’ mode choice behavior is modeled by the multinomial logit (MNL) discrete choice model, and their route choice behavior is depicted by the user equilibrium condition. A mathematical programming model is then built and subsequently solved by the outer approximation method. Numerical simulations demonstrate that the proposed distance-based preferential fare scheme can effectively motivate travelers to use a P&R service and significantly enhance the transport network’s performance.
Xinyuan Chen; Ruyang Yin; Qinhe An; Yuan Zhang. Modeling a Distance-Based Preferential Fare Scheme for Park-and-Ride Services in a Multimodal Transport Network. Sustainability 2021, 13, 2644 .
AMA StyleXinyuan Chen, Ruyang Yin, Qinhe An, Yuan Zhang. Modeling a Distance-Based Preferential Fare Scheme for Park-and-Ride Services in a Multimodal Transport Network. Sustainability. 2021; 13 (5):2644.
Chicago/Turabian StyleXinyuan Chen; Ruyang Yin; Qinhe An; Yuan Zhang. 2021. "Modeling a Distance-Based Preferential Fare Scheme for Park-and-Ride Services in a Multimodal Transport Network." Sustainability 13, no. 5: 2644.