A hybrid simulation model of passenger emergency evacuation under disruption scenarios: A case study of a large transfer railway station

Hassannayebi, Erfan; Memarpour, Mehrdad; Mardani, Soheil; Shakibayifar, Masoud; Bakhshayeshi, Ivan; Espahbod, Shervin

doi:10.1080/17477778.2019.1664267

Cited by 32 publications

(6 citation statements)

References 56 publications

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“…The general formulation of AUGMECON2 is represented in Eqs. ( 23)- (25). The method and formulation are implemented in GAMS software to generate Pareto optimal solutions.…”

Section: Augmented E-constraint Methodsmentioning

confidence: 99%

“…In these networks, the pickup and delivery of demands on the hubs can be two-or one-way depending on demand volumes [53]. Hub location-allocation has various application areas in telecommunication networks [32], air transport [17,48], freight transportation [33], postal services [6], public and urban transportation [39], rail transport [24,25], and emergency services [4]. The problem is to locate hub and non-hub facilities in a distribution network and to allocate all the origin and destination points (non-hub) directly to the hub so that the network costs including costs of collection, distribution, and other related costs are minimized [27].…”

Section: Introductionmentioning

confidence: 99%

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Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window

Basirati

Jokar

Hassannayebi

2019

Neural Comput & Applic

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This study addresses a many-to-many hub location-routing problem where the best-found locations of hubs and the bestfound tours for each hub are determined with simultaneous pickup and delivery within the hard time window. To find practical solutions, the hubs and transportation fleet have constrained capacity, in which every node can be serviced by multiple allocations with the hard time window and limited tour length. First, a bi-objective optimization model is proposed to balance travel costs among different routes and to minimize the total sum of fixed costs of locating hubs, the costs of handling, traveling, assigning, and transportation costs. The problem is then solved using an augmented e-constraint technique for small to medium size instances of the problem. Due to the NP-hardness nature of the problem, the proposed multi-objective optimization model is solved by a multi-objective imperialist competitive algorithm (MOICA). To show the superior performance of the MOICA, the solutions are compared with those obtained by the non-dominated sorting genetic algorithm (NSGA-II). For the large-scale problem instances, the comparative results indicate that the MOICA can indeed provide better Pareto optimal solutions compared to NSGA-II for the large-scale problem instances.

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“…The general formulation of AUGMECON2 is represented in Eqs. ( 23)- (25). The method and formulation are implemented in GAMS software to generate Pareto optimal solutions.…”

Section: Augmented E-constraint Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window

Basirati

Jokar

Hassannayebi

2019

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…Chen et al (2017) implemented four methodologies including a queuing model and an agent-based simulation to calculate the evacuation time under a given number of evacuees in different emergency situations and evacuation plans. Hassannayebi et al (2020) used an agentbased and discrete-event simulation model to assess the service level performance and crowdedness in a metro station under various disruption scenarios, including train failure in the tunnel and fire at the station gallery. Zhou et al (2019) proposed a hybrid bi-level model to optimize the number and initial locations of leaders as well as the routes of leaders during the evacuation to guide passenger's evacuation in urban rail transit stations.…”

Section: Passenger Evacuation Under Emergencymentioning

confidence: 99%

Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertainty

Mo¹,

Koutsopoulos²,

Shen³

et al. 2022

Preprint

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When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate the congestion during public transit disruptions. Passengers with different origin-destination and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation as an optimal flow problem with uncertain demand information. To tackle the non-analytical formulation of travel times due to left behind, we propose a simulation-based first-order approximation to transform the original problem into linear programming. Uncertainties in demand are modeled with robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (-20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce the system travel time. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model.

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“…ABM amalgamated with other forms of transport modelling helps to capture entity level details, complex decision-making, and visualization of the behaviour of drivers and other agents [19,20]. e ABM has been effective in modelling the behaviour and interaction of the vehicle with the passengers, estimating passenger interaction with metros, and estimating exposures to air pollutions from transportation [21][22][23].…”

Section: Literature Reviewmentioning

confidence: 99%

Context-Aware Services Using MANETs for Long-Distance Vehicular Systems: A Cognitive Agent-Based Model

Raees

Khan

Abbasi

et al. 2021

Scientific Programming

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Long-distance transportation systems play an important role in economic growth. Yet, these systems are incurred with multifaceted delays and cost problems. The major incites affecting transportation systems are congestion, breakdowns, emergencies, and inclement weather. Scarcity of information about the environment also exacerbates travel problems. It is essential to employ monitoring and guidance that aid in making timely decisions through premediated information. This work aims to provide a flexible model for the long-distance transport system. The model is based on problems faced in long-distance transportation. Moreover, we examine the possible use of emerging Information and Communication Technologies (ICTs) for better transportation. The system dynamics study the problem at hand through cognitive agent-based modelling (ABM) concepts. The integrated model lays the rules to abate traffic delays. In this model, the distance travelled by vehicles is divided into sections using checkpoints. Every section is composed of different agents such as medical units, police stations, workshops, and petrol pumps. The vehicle shifts connection over the mobile ad hoc network (MANET) when enters or leaves a section. We used NetLogo for simulation of the model. A monitoring and guidance system is tested, and obtained results are analyzed by addressing problems causing delays. The guidance system helps vehicles to take optimal decisions for the time, congestion, and rests. The model can be used to improve decision-making for vehicles through premediated decisions. The proposed model can help to improve the efficiency of the transportation systems by reducing travel time.

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A hybrid simulation model of passenger emergency evacuation under disruption scenarios: A case study of a large transfer railway station

Cited by 32 publications

References 56 publications

Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window

Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window

Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertainty

Context-Aware Services Using MANETs for Long-Distance Vehicular Systems: A Cognitive Agent-Based Model

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