Exploring trade-offs in frequency allocation in a transit network using bus route patterns: Methodology and application to large-scale urban systems

Verbas, Ömer; Mahmassani, Hani S.

doi:10.1016/j.trb.2015.06.018

Cited by 33 publications

(19 citation statements)

References 23 publications

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“…This is the only reference we have found related to the setting of the user's behavioral rules (38). In a real-world application of these rules, a specific survey on the future user population of the network should be conducted in order to provide reliable values for ∆ L and ∆ U parameters.…”

Section: Resultsmentioning

confidence: 99%

“…Leiva et al [37] also determines the type of vehicle to be used and considers operation costs. Verbas & Mahmassani [38] integrate all the service policies.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…If a new subpath (Z) linking the intermediate node P k−1 i to the final node P k−1 t is found, the algorithm verifies whether Z satisfies rule (38).…”

Section: The Corridor Generation Algorithmmentioning

confidence: 99%

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Integrated approach to network design and frequency setting problem in railway rapid transit systems

López-Ramos

Codina

Marı́n

et al. 2017

Computers & Operations Research

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© <2017>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This work presents an optimization-based approach to simultaneously solve the Network Design and the Frequency Setting phases on the context of railway rapid transit networks. The Network Design phase allows expanding existing networks as well as building new ones from scratch, considering infrastructure costs. In the Frequency Setting phase, local and/or express services are established considering transportation resources capacities and operation costs. Integrated approaches to these phases improve the transit planning process. Nevertheless, this integration is challenging both at modeling and computational effort to obtain solutions. In this work, a Lexicographic Goal Programming problem modeling this integration is introduced, together with a solving strategy. A solution to the problem is obtained by first applying a Corridor Generation Algorithm and then a Line Splitting Algorithm to deal with multiple line construction. Two case studies are used for validation, including the Seville and Santiago de Chile rapid transit networks. Detailed solution reports are shown and discussed. Conclusions and future research directions are given.Peer ReviewedPostprint (author's final draft

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Section: Resultsmentioning

confidence: 99%

“…Leiva et al [37] also determines the type of vehicle to be used and considers operation costs. Verbas & Mahmassani [38] integrate all the service policies.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Integrated approach to network design and frequency setting problem in railway rapid transit systems

López-Ramos

Codina

Marı́n

et al. 2017

Computers & Operations Research

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“…Herbon [22] considered the passenger demand elasticity of the stop, and used the generalized newsvendor model to establish the bus frequency and vehicle capacity optimization model. Verbas [23] analyzed the effect of the elasticity of passenger flow on the bus frequency, and proposed a method for calculating the space-time elasticity of passenger flow, also introducing space-time elasticity into the frequency allocation model. Tian [24] extended Verbas's model and established a frequency optimization model based on stop demand elasticity, the objective functions of which are the minimum of a bus single trip time and the maximum profit of bus operators.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The problem to be solved in this paper is to find the optimal headway of the line under passenger demand elasticity, the randomness of the bus travel time between bus stops, and the abandoned passengers flow conditions. The model below is mainly based on study [15] and refers to some results of the literature [22][23][24] stop (k = 1) to the last stop (k = K) within a given time period. The problem to be solved in this paper is to find the optimal headway of the line under passenger demand elasticity, the randomness of the bus travel time between bus stops, and the abandoned passengers flow conditions.…”

Section: Description Of the Problemmentioning

confidence: 99%

Setting Headways on a Bus Route under Uncertain Conditions

et al. 2019

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Urban public transport is a sustainable transportation strategy. Promoting public transport is an important means of urban transport sustainable development. Reasonable operation scheduling can increase the attractiveness of public transit systems and be conducive to the sustainability of transportation systems. Setting headways on a bus route is the key work of bus scheduling. For the refined management requirements of bus scheduling, this paper comprehensively considers the influence of three uncertainties on the bus route headway: passenger demand elasticity, which is an elasticity with respect to the supply of service, the randomness of the bus travel time between bus stops, and the abandoned passengers flow. A bus route headway allocation model is established. Considering the attractiveness of bus traveling and the interests of passengers, the objective functions are the maximum number of passengers waiting for the bus at each stop and the minimum number of passengers who fail to board. An enumeration combining a recursive algorithm under Monte Carlo random simulation conditions is designed to solve the problem. A comparison of the optimal bus departure interval (headway of the origin stop) under different conditions is studied using a numerical case. Under the same conditions, the results show that the model proposed in this paper can reduce the passengers waiting time and attract more passengers traveling by bus and it also meets the goal of sustainable public transport well. These findings are significant references for optimizing bus operations considering these uncertain factors.

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A method for solving the multi‐objective transit frequency optimization problem

Giesen¹,

Martínez

Mauttone

et al. 2016

J of Advced Transportation

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Frequency setting takes place at the strategic and tactical planning stages of public transportation systems. The problem consists in determining the time interval between subsequent vehicles for a given set of lines, taking into account interests of users and operators. The result of this stage is considered as input at the operational level. In general, the problem faced by planners is how to distribute a given fleet of buses among a set of given lines. The corresponding decisions determine the frequency of each line, which impacts directly on the waiting time of the users and operator costs. In this work, we consider frequency setting as the problem of minimizing simultaneously users' total travel time and fleet size, which represents the interest of operators. There is a trade-off between these two measures; therefore, we face a multiobjective problem. We extend an existing single-objective formulation to account explicitly for this tradeoff, and propose a Tabu Search solving method to handle efficiently this multi-objective variant of the problem. The proposed methodology is then applied to a real medium-sized problem instance, using data of Puerto Montt, Chile. We consider two data sets corresponding to morning-peak and off-peak periods. The results obtained show that the proposed methodology is able to improve the current solution in terms of total travel time and fleet size. In addition, the proposed method is able to efficiently suggest (in computational terms) different trade-off solutions regarding the conflicting objectives of users and operators.to the users and minimize the level of required resources delivered by the operators. Problem data is given by the itinerary of each line and origin-destination (OD) demand within a specific time horizon. An important component of the model is the assignment sub-model, which represents the behavior of the users with respect to a set of lines and frequencies. This sub-model is needed to measure the performance of the system with respect to the users, that is, the level of service.The literature concerning transit frequency optimization can be classified into (i) analytical models that admit closed-form solutions and (ii) mathematical programming formulations either explicit or not, with associated solution algorithms. In the first group, there are formulations that characterize the system in terms of few variables and allow getting a full description of the optimal solution. Although these models make considerable simplifications of the real system, they allow obtaining practical guidelines that are theoretically well founded [3,4], for example, the well-known rule of the square root [5,6]. The other important stream of work is based on a detailed characterization of the transit system, in terms of the route network and the demand that should be transported over it. These studies formulate the optimization problem in terms of a graph model, where decision variables are the capacities of the arcs (represented by the frequencies) and the flows that represent the...

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Exploring trade-offs in frequency allocation in a transit network using bus route patterns: Methodology and application to large-scale urban systems

Cited by 33 publications

References 23 publications

Integrated approach to network design and frequency setting problem in railway rapid transit systems

Integrated approach to network design and frequency setting problem in railway rapid transit systems

Setting Headways on a Bus Route under Uncertain Conditions

A method for solving the multi‐objective transit frequency optimization problem

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