Width Design of Urban Rail Transit Station Walkway: A Novel Simulation-Based Optimization Approach

Abstract

The optimal design of the walkway at an urban rail transit station is a vital issue. The Transit Capacity and Quality of Service Manual (TCQSM) TCRP-100 report for the design of urban rail transit station walkway and the existing design models neglect the important factors such as randomness in the passenger arrival rate, randomness and state-dependent service time of the walkway and blocking phenomenon when the passenger flow demand exceeds the walkway capacity. There obviously exists a need to develop a design approach that overcomes these shortcomings. For this purpose, this paper details a simulation-based optimization approach that provides width design through automatic reconfiguration of walkway width during the simulation-optimization process based on phase-type (PH) distribution. The integrated PH/PH(n)/C/C discrete-event simulation (DES) model and optimization method that uses the genetic algorithm (GA) work together concurrently to obtain optimized (design) widths for different passenger flow and level of service (LOS) The numerical experiments are conducted to compare the proposed model with the existing design methods. It reveals that: (1) The width obtained by our proposed model is higher than the existing width design models; (2) when squared coefficient of variation of passenger arrival interval increases, the walkway width increases more for our proposed model than the existing design models; (3) when the arrival rate increases, the walkway width of our proposed model increases faster than the existing design models; (4) the increase in the length of walkway has no significant effect on the walkway width.

Multiagent Modelling and Simulation of a Physical Internet Enabled Rail-Road Intermodal Transport System

“…A queue model was developed for optimisation of walkways, a facility design issue inside the transit terminal [7]. Tang and Hu developed an agent-based model for understanding pedestrian dynamics and interactions in the public space, and the associated transition from micro-level organisations to the global phenomena [8] that might be critical for the infrastructure. The model projected the crowd flow, total travel time, density, and public accessibility that support facility design of transit terminals.…”

VR Panoramic Technology in Urban Rail Transit Vehicle Engineering Simulation System

“…Traffic congestion in large and medium-sized cities [1] continues to increase, and priority is given to the development of public transport to relieve the pressure of ground traffic, which has gradually become the preferred mode of urban planning and construction [2]. Among them, urban rail transit [3], as the transport tool with the largest carrying capacity in the public transport mode, has the characteristics of high efficiency and energy saving, and is the preferred mode for the development of transportation in large and medium-sized cities in recent years. Urban rail transit construction activities are mainly underground, the construction environment and construction technology are complex, and there are many different kinds of potential disaster risks.…”

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

“…The initiatives in the transportation system consist of various management strategies and effective restrictive measures which aim to reduce congestion and avoid accidents. The analysis of congestion in road networks includes macroscopic [1,2], medium [3,4] and microscopic [5,6] levels. The rearrangement of demand in the network once an element fails is a particular focus of some studies [7][8][9].…”

“…Furthermore, quantitative research in the congestion propagation process is very limited [3,4]. This paper aims to provide extensive analyses and simple formulations to help understand the behavior of the congestion propagation in urban rail networks.…”

“…The method for calculating the congestion propagation rate is described in Sect. 4. Numerical simulation on a real-world network with the field measurement data is presented in Sect.…”