Evaluation of bus bridging scenarios for railway service disruption management: a users’ delay modelling tool

Srikukenthiran

Shalaby

2020

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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.

Section: Methodsmentioning

confidence: 99%

“…In addition, the tool computes other performance measures of the bus bridging scenario from the transit operator’s perspective, which are also used to assess the performance of the optimal bus bridging plan. The UDMT methodology and assumptions can be found in research undertaken by Aboudina et al ( 18 ).…”

Section: Methodsmentioning

confidence: 99%

Capacity-Constrained Bus Bridging Optimization Framework

Srikukenthiran

Shalaby

2020

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“…A UDMT is used in this study to conduct a sensitivity analysis of major factors that affect the performance of bus bridging policies. The tool was developed to estimate the delays (i.e., excess waiting times because of disruption) of all affected passengers in the system caused by any metro incident and bus bridging strategy of known characteristics ( 11 ). The affected passengers include metro passengers along the disrupted rail segment and bus riders on the bus routes from which some shuttle buses are dispatched.…”

Section: User Delay Modeling Toolmentioning

confidence: 99%

“…In an earlier phase of this research, and to address the aforementioned gap in the literature, the authors of this study developed a user delay modeling tool (UDMT) that employs an analytical approach to estimating the impacts of user-specified bus bridging scenarios on the total users’ delay, as well as other system performance measures ( 11 ). A better understanding is still needed, however, of the characteristics of effective bus bridging strategies and policy guidelines.…”

mentioning

confidence: 99%

Managing Unplanned Rail Disruptions: Policy Implications and Guidelines Towards an Effective Bus Bridging Strategy

Aboudina

Diab

et al. 2019

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Bus bridging is a key strategy used by transit agencies to handle rail service interruptions. In practice, buses are dispatched from scheduled services to act as temporary shuttles along the disrupted rail segment. This study provides a robust analysis of four factors affecting bus bridging policies: 1) initial dispatch direction of shuttle buses, 2) dispatch time (i.e., the response time for requesting shuttle buses), 3) uncertainty in predicting the incident duration, and 4) reduction of metro passengers demand because of disruption. A user delay modeling tool is used to assess various bus bridging policies based on their resulting users’ delays (for affected passengers) and other system performance measures. The tool was validated, and sensitivity analysis was conducted based on real disruption scenarios that suspended various segments of the metro service in the City of Toronto. The main results indicate that: 1) the initial dispatch direction of shuttle buses should take into consideration the demand at the disrupted segment while maintaining a moderate level of shuttle bus utilization; 2) a 1-min increase in the dispatch time causes about 0.4 min additional waiting time at disrupted metro stations per passenger; 3) incidents with high forecasting errors can cause excessive delays for metro passengers and significant wasted time of non-utilized shuttle buses; and, 4) significant users’ delay savings are observed at higher demand reduction levels. This paper provides transportation practitioners and planners with a better understanding of the different aspects of bus bridging policies based on users’ delays and shuttle buses’ performance metrics.

“…Data-driven analysis of bus bridging operations is key to understanding how this strategy affects the resilience of the subway service. A decision support tool was developed recently to help agencies manage unplanned service interruptions using optimized bus bridging strategies (7)(8)(9).…”

mentioning

confidence: 99%

Assessing the Bus Bridging Effectiveness on the Operational Resilience of the Subway Service in Toronto

Shalaby

2021

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Unplanned rail disruptions result in substantial delays to passengers and severe effects on the economy of a large city like Toronto. While bus bridging has been a widely adopted method to replace the subway service in such events, its effect on the operational resilience of the subway service is less often studied. This study assesses the resilience of the subway network of Toronto employing an optimal bus bridging strategy. First, subway incidents are categorized based on their characteristics using K-mean clustering analysis. The incidents are then grouped based on the performance of optimal bus bridging plans. Classification and regression tree analysis is used for this task, employing two metrics: the total user delay and total number of shuttle buses under the optimal bridging scenario. Queueing and optimization models developed previously by the University of Toronto are used to determine and simulate the optimal bus bridging plans of a sample of incidents. The severity of unplanned disruptions is finally demonstrated using a severity scale and the effect of incident duration uncertainty is analyzed. The results show that along the congested city alignments, where the capacity of the roads and stations is limited, the bus bridging service is often insufficient to replace the train service. However, it could be a good alternative in uncongested subway segments where the available street capacity is relatively high, allowing large bus volumes to serve the corridor. This model is easily applicable to different rail systems and it could assess other systems to produce better bridging plans.