No abstract

In many large cities, public transit systems have been carrying an everincreasing burden of commuters. In such systems, service disruptions can negatively impact system performance and transit users well after they are resolved. Currently, transit agencies handle these disruption episodes in an ad-hoc fashion, largely due to the lack of adequate analytical tools to aid in analyzing and selecting appropriate response strategies. This paper presents a proof-of-concept case study of the Greater Toronto transit network using Nexus, a new crowd dynamics and transit network simulation platform. Nexus enables detailed simulation of all transit system actors using a novel method of linking together established simulators of surface transit, fully separated rail transit, and stations. Transit users, as agents in the model, move between the different simulators and have their routes determined by an external dynamic routing module. The case study focuses on interfacing Nexus with a commercial pedestrian simulator, MassMotion, to allow for detailed crowd simulation at key stations, and illustrating how the platform could be used for disruption management. To this end, the impact of disruptions of various lengths was analyzed, and a simple response strategy was implemented to provide an example of how the system could be used to test mitigating strategies.

Urban rail systems frequently suffer from unexpected service disruptions, which can result in severe delays and user dissatisfaction. “Bus bridging” is the strategy most commonly applied in responding to rail service interruptions in North America and Europe. Buses are pulled from regular routes and dispatched to serve as shuttles along the disrupted rail segment until regular train service is restored. In determining the required number of buses and source routes, most transit agencies rely on ad hoc approaches based on operational experience and constraints, which do not necessarily alleviate the extensive delays and queue build-ups at affected stations, nor do they minimize system-wide impacts in an optimal manner. This paper proposes a genetic algorithm-based optimization model to determine the optimal number of shuttle buses and route allocation to minimize overall subway- and bus rider delay for any given rail disruption incident. The generated optimal solutions were sensitive to bus-bay capacity constraints along the shuttle service corridor of any given disrupted subway segment, utilizing methods found in the Transit Capacity and Quality of Service Manual. The model was used in an analysis of real-world incident data obtained from the Toronto Transit Commission and supplemented by other passenger and travel time data. The bus bridging toolkit showed strong potential to produce efficient shuttle response plans that reduced the transit user delays by more than 50% while ensuring minimum queue formation at disrupted stations and maximizing the utilization of shuttle buses.

No abstract

Transit user behavioural response under disrupted service conditions, specifically how transit riders choose among available mode options to complete their trips, is not well understood. This study aimed to investigate transit user mode choice in response to rapid transit service disruption in the City of Toronto, incorporating such factors as the type of disruption, stage of the passenger’s trip (pre-trip or en-route), weather conditions, and uncertainty of delay duration. A joint revealed preference (RP) and stated preference (SP) survey was designed where the RP part gathered information on the respondent’s actual response to the most recent service disruption while the SP part solicited the respondent’s travel choices under a set of hypothetical service disruption scenarios. A transit trip planner tool was developed to generate alternative transit mode and path options to avoid the disrupted segment. An empirical model using RP data is presented to verify the survey design technique.