Abstract:A station closure is an abnormal operational situation in which the entrances or exits of a rail transit station have to be closed for some time due to an unexpected incident. A novel approach is developed to estimate the impacts of the alternative station closure scenarios on both passenger behavioral choices at the individual level and passenger demand at the disaggregate level in a rail transit network. Therefore, the contributions of this study are two-fold: (1) A basic passenger behavior optimization mode… Show more
“…The multimodal transport resources contain bridging-bus, taxi, shared bicycle, and walking, which are labeled as m1, m2, m3, and m4, respectively. Accordingly, the general cost of each behavior is 1 , 2 , and 3 , respectively, for events 1 …”
Section: Three-layer Discrete Choice Behavior Model For Alternativementioning
confidence: 99%
“…The general simulation procedure can be attributed to Yin et al [1]. A new behavior model solver in the simulation algorithm is developed for the three-layer discrete choice behavior model, which is summarized as shown in Algorithm 1:…”
Section: Solution Algorithm Of Passenger Behavior Modelmentioning
confidence: 99%
“…The genetic algorithm is used to solve the binary integer programing model and the simulation procedure is to generate different types of the passenger flow demand. And the basic simulation procedure is attributed to Yin et al [1]. The general algorithm framework for solving the problem of station disruption controlling optimization is described as follows.…”
Section: The General Solution Algorithmmentioning
confidence: 99%
“…Station disruption, also called station closure, is an abnormal and usually unplanned operational situation in which operators must close the entrances or exits of a metro station for various reasons, such as unexpected incidents or taking steps to avoid overcrowding [1]. In these situations, passengers cannot use the closed station as their departure station or destination stations during the closure time.…”
Section: Introductionmentioning
confidence: 99%
“…This approach was proved to be effective for identifying the impact of a station disruption, but it is not applicable for predicting the impact of a station disruption that does not actually occur or is about to occur. Another effective mathematical model that could capture the behavior of passengers affected by a station disruption was proposed for analyzing alternative disruption scenarios and their likely outcomes in our previous study [1]. But this model ignored the randomness of the passengers' choice behaviors and the influence of the controlling strategies 2 Journal of Advanced Transportation on passenger behaviors.…”
A station disruption is an abnormal operational situation that the entrance or exit gates of a metro station have to be closed for a certain of time due to an unexpected incident. The passengers' travel behavioral responses to the alternative station disruption scenarios and the corresponding controlling strategies are complex and hard to capture. This can lead to the hardness of estimating the changes of the network-wide passenger demand, which is the basis of carrying out a response plan. This paper will establish a model to solve the metro station disruption problem by providing optimal additional bus-bridging services. Two main contributions are made: (1) a three-layer discrete choice behavior model is developed to analyze the dynamic passenger flow demand under station disruption; and (2) an integrated algorithm is designed to manage and control the station disruption crisis by providing additional bus-bridging services with the objective of minimizing the total travel time of affected passengers and the operating cost of bridging-buses. Besides, the multimodal transport modes, including metro, bridging-bus, shared-bike, and taxi, are considered as passengers' alternative choices in face of the station disruption. A numerical study based on the Beijing metro network shows that additional bus-bridging services can significantly eliminate the negative impact of the station disruption.
“…The multimodal transport resources contain bridging-bus, taxi, shared bicycle, and walking, which are labeled as m1, m2, m3, and m4, respectively. Accordingly, the general cost of each behavior is 1 , 2 , and 3 , respectively, for events 1 …”
Section: Three-layer Discrete Choice Behavior Model For Alternativementioning
confidence: 99%
“…The general simulation procedure can be attributed to Yin et al [1]. A new behavior model solver in the simulation algorithm is developed for the three-layer discrete choice behavior model, which is summarized as shown in Algorithm 1:…”
Section: Solution Algorithm Of Passenger Behavior Modelmentioning
confidence: 99%
“…The genetic algorithm is used to solve the binary integer programing model and the simulation procedure is to generate different types of the passenger flow demand. And the basic simulation procedure is attributed to Yin et al [1]. The general algorithm framework for solving the problem of station disruption controlling optimization is described as follows.…”
Section: The General Solution Algorithmmentioning
confidence: 99%
“…Station disruption, also called station closure, is an abnormal and usually unplanned operational situation in which operators must close the entrances or exits of a metro station for various reasons, such as unexpected incidents or taking steps to avoid overcrowding [1]. In these situations, passengers cannot use the closed station as their departure station or destination stations during the closure time.…”
Section: Introductionmentioning
confidence: 99%
“…This approach was proved to be effective for identifying the impact of a station disruption, but it is not applicable for predicting the impact of a station disruption that does not actually occur or is about to occur. Another effective mathematical model that could capture the behavior of passengers affected by a station disruption was proposed for analyzing alternative disruption scenarios and their likely outcomes in our previous study [1]. But this model ignored the randomness of the passengers' choice behaviors and the influence of the controlling strategies 2 Journal of Advanced Transportation on passenger behaviors.…”
A station disruption is an abnormal operational situation that the entrance or exit gates of a metro station have to be closed for a certain of time due to an unexpected incident. The passengers' travel behavioral responses to the alternative station disruption scenarios and the corresponding controlling strategies are complex and hard to capture. This can lead to the hardness of estimating the changes of the network-wide passenger demand, which is the basis of carrying out a response plan. This paper will establish a model to solve the metro station disruption problem by providing optimal additional bus-bridging services. Two main contributions are made: (1) a three-layer discrete choice behavior model is developed to analyze the dynamic passenger flow demand under station disruption; and (2) an integrated algorithm is designed to manage and control the station disruption crisis by providing additional bus-bridging services with the objective of minimizing the total travel time of affected passengers and the operating cost of bridging-buses. Besides, the multimodal transport modes, including metro, bridging-bus, shared-bike, and taxi, are considered as passengers' alternative choices in face of the station disruption. A numerical study based on the Beijing metro network shows that additional bus-bridging services can significantly eliminate the negative impact of the station disruption.
We present a simulation-based approach to capture the interactions between train operations and passenger behavior during disruptions in urban rail transit systems. The simulation models the full disruption and recovery cycle. It is based on a discrete-event simulation framework to model the network vehicles movement. It is paired with an agent-based model to replicate passenger route choices and decisions during both the undisrupted and disrupted state of the system. We demonstrate that optimizing and flexibly changing the train dispatch schedules on specific routes reduces the impact of disruptions. Moreover, we show that demand uncertainty considerably changes the measures of performance during the disruption. However, the optimized schedule still outperforms the non-optimized schedule even under demand uncertainty. This work ties into our ongoing project to find flexible strategies to enhance the system resilience by explicitly incorporating uncertainties into the design of rail system architectures and operational strategies.
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