Regional Bus Timetabling Model with Synchronization

The timetabling subproblem of bus transit network planning determines the departure times for all trips of the lines along the entire day. Most of the public transport networks consider planning periods identical for all lines. In this study we drop this strong assumption by introducing specific periods for each line, which is more realistic. Thus, we propose the multiperiod synchronization bus timetabling (MSBT) problem, which specifies the departure times of the trips of all lines where each line has its own planning periods along the day, with the objective of optimizing synchronization events: maximize passenger transfers and minimize bus bunching along the network. We propose an integer linear programming formulation for the MSBT problem and analyze the structural properties of this formulation by a constraint propagation methodology. These properties are the basis for different operators that lead to the design of efficient metaheuristics for solving the problem. We empirically obtain high-quality feasible solutions for real size instances and show that by considering a multiperiod approach, synchronization events of trips belonging to different planning periods are not ignored, as it is the case when several single period timetables are merged.

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Section: Related Literaturementioning

confidence: 99%

Multiperiod Bus Timetabling

Ibarra-Rojas

Lopez-Irarragorri

Ríos-Solís

2016

Transportation Science

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“…[1] considered the problem of minimizing waiting times when travel time between stops is random and services have fixed headways. Several other works that study coordination have employed meta heuristic approaches to solve the general problem [12,6,5,11,10] using methods such as genetic algorithms, tabu search, and local search. The approach presented herein is similar in spirit to [2] who present a Lagrangian-based heuristic that seeks to optimize one line at a time.…”

Section: Schedule Optimizationmentioning

confidence: 99%

Missed connections

Coffey

Nair

Pinelli

et al. 2012

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science

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We present a methodology to measure multi-modal interconnectivity between different transportation modes that operate in a city. The interconnectivity of an urban network represents how well different services integrate to offer seamless transportation options to users. On the supply side, we leverage open data sources that cities provide to accurately model the services that they offer. On the demand side, we account for myopic user behavior through the use of journey planners and travel demand estimates. The reciprocal interaction between supply and demand is then used to characterize interconnectivity. In a multi-modal setting, we present different measures that can be employed to understand shortcomings in connectivity across the network. Using these metrics, improvements in service schedules are proposed using an optimization model that seeks to perturb existing schedules to improve transit connectivity. Realworld data from Washington, D.C. is used to demonstrate presented measures and optimal schedule perturbation for one route are presented which result in a 19% reduction in delays.

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“…Moreover, the authors considered criteria such as minimizing the fleet size, minimizing the number of lines that a bus is assigned to, minimizing the deadhead costs, and minimizing the idle times of buses waiting for the next trip. Liu and Shen [27] integrate the Transit Network Timetabling presented by Liu et al [28] and a multiple deport vehicle scheduling problem minimizing the number of buses and deadhead costs. To solve the problem, the authors develop a Bi-level Nesting Tabu Search which is implemented in a small example.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimal Formulations for Bus Fleet Size of Public Transit under Headway-Based Holding Control

Liang

2019

Journal of Advanced Transportation

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In recent years, with the development of advanced technologies for data collection, real-time bus control strategies have been implemented to improve the daily operation of transit systems, especially headway-based holding control which is a proven strategy to reduce bus bunching and improve service reliability for high-frequency bus routes, with the concept of regulating headways between successive buses. This hot topic has inspired the reconsideration of the traditional issue of fleet size optimization and the integrated bus holding control strategy. The traditional headway-based control method only focused on the regulation of bus headways, without considering the number of buses on the route. The number of buses is usually assumed as a given in advance and the task of the control method is to regulate the headways between successive buses. They did not consider the bus fleet size problem integrated with headway-based holding control method. Therefore, this work has presented a set of optimal control formulations to minimize the costs for the passengers and the bus company through calculating the optimal number of buses and the dynamic holding time, taking into account the randomness of passenger arrivals. A set of equations were formulated to obtain the operation of the buses with headway-based holding control or the schedule-based control method. The objective was to minimize the total cost for the passengers and the bus company in the system, and a Monte Carlo simulation based solution method was subsequently designed to solve the optimization model. The effects of this optimization method were tested under different operational settings. A comparison of the total costs was conducted between the headway-based holding control and the schedule-based holding control. It was found that the model was capable of reducing the costs of the bus company and passengers through utilizing headway-based bus holding control combined with optimization of the bus fleet size. The proposed optimization model could minimize the number of buses on the route for a guaranteed service level, alleviating the problem of redundant bus fleet sizes caused by bus bunching in the traditional schedule-based control method.

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Regional Bus Timetabling Model with Synchronization

Cited by 29 publications

References 2 publications

Multiperiod Bus Timetabling

Multiperiod Bus Timetabling

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Multiobjective Optimal Formulations for Bus Fleet Size of Public Transit under Headway-Based Holding Control

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