A dynamic stochastic model for evaluating congestion and crowding effects in transit systems

Abstract

One of the most common motivations for public transport investments is to reduce congestion and increase capacity. Public transport congestion leads to crowding discomfort, denied boardings and lower service reliability. However, transit assignment models and appraisal methodologies usually do not account for the dynamics of public transport congestion and crowding and thus potentially underestimate the related benefits. This study develops a method to capture the benefits of increased capacity by using a dynamic and stochastic transit assignment model. Using an agent-based public transport simulation model, we dynamically model the evolution of network reliability and on-board crowding. The model is embedded in a comprehensive framework for project appraisal. A case study of a metro extension that partially replaces an overloaded bus network in Stockholm demonstrates that congestion effects may account for a substantial share of the expected benefits. A cost-benefit analysis based on a conventional static model will miss more than a third of the benefits. This suggests that failure to represent dynamic congestion effects may substantially underestimate the benefits of projects, especially if they are primarily intended to increase capacity rather than to reduce travel times.

Reconciling transfer synchronization and service regularity: real-time control strategies using passenger data

“…A public transport simulation model, BusMezzo, was used for mimicking real-world operations as a testbed for testing the performance of the controllers and their consequences under different scenarios. BusMezzo is a dynamic public transport operation and assignment model (Cats, West, and Eliasson 2016;Toledo et al 2010), which simulates the progress of individual public transport vehicles and passengers using an agent-based approach. In this implementation, vehicle travel times between stops were simulated by sampling from distributions whose mean and standard deviation were specified, while the passenger arrivals at each stop follow the Poisson distribution.…”

“…Passengers that are left behind are retained in the flow of waiting passengers. Previous studies have demonstrated that BusMezzo can reproduce the bunching phenomenon ) and represent dynamic congestion effects including variations in on-board crowding and denied boarding (Cats, West, and Eliasson 2016). During a simulation run, each time a public transport vehicle enters a transfer stop, BusMezzo calls an instance of the control algorithm in MATLAB.…”

“…Despite these additions, no holding controller has yet taken into account the riding discomfort caused by the on-board crowding conditions. The interrelation between service reliability and passenger congestion has been shown to carry significant consequences for passenger benefits in saturated networks (Cats, West, and Eliasson 2016).…”

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

“…There are various influential factors of the propagation rate. In order to compute the propagation rate, these factors are classified into six classes: passenger flow characteristic, train departure interval, passenger transfer convenience, the time of congestion occurring, the initial congested station and station capacity [38]. We divided these parameters into two classes: parameter A associated with passenger transfers and parameter B associated with time.…”

Integrated Optimization of Bus Line Fare and Operational Strategies Using Elastic Demand

“…With improved level of life passengers are prone to increase the value of comfort items. For example, crowding in vehicles increases the value of time of passengers and hence their generalized travel cost [35]. In this paper, a linear demand function is developed to reflect the combined effects of attributes of strategies, crowding, and bus fare on demand.…”