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April  2018, 14(2): 447-472. doi: 10.3934/jimo.2017055

## The multimodal and multiperiod urban transportation integrated timetable construction problem with demand uncertainty

 1 Av. Pedro de Alba, San Nicolás de los Garza, NL 66450, México, PhD Student in Program for Economy and Enterprise at the University of Málaga 2 Universidad Autónoma de Nuevo León, Av. Pedro de Alba, San Nicolás de los Garza, NL 66450, México 3 Universidad de Málaga, Campus El Ejido S/N, Málaga, 29071, España

* Corresponding author: Paulina Avila-Torres

Received  January 2016 Revised  November 2016 Published  June 2017

Fund Project: CONACyT, AUIP, DoA, Spanish MINECO and Andalucia Goverment.

The urban transport planning process has four main activities: Network design, Timetable construction, Vehicle scheduling and Crew scheduling; each activity has subactivities. In this paper the authors work with the subactivities of timetable construction: minimal frequency calculation and departure time scheduling. The authors propose to solve both subactivities in an integrated way. The developed mathematical model allows multi-period planning and it can also be used for multimodal urban transportation systems. The authors consider demand uncertainty and the authors employ fuzzy programming to solve the problem. The authors formulate the urban transportation timetabling construction problem as a bi-objective problem: to minimize the total operational cost and to maximize the number of multi-period synchronizations. Finally, the authors implemented the SAUGMECON method to solve the problem.

Citation: Paulina Ávila-Torres, Fernando López-Irarragorri, Rafael Caballero, Yasmín Ríos-Solís. The multimodal and multiperiod urban transportation integrated timetable construction problem with demand uncertainty. Journal of Industrial & Management Optimization, 2018, 14 (2) : 447-472. doi: 10.3934/jimo.2017055
##### References:

show all references

##### References:
Transport planning process [5]
Departure times[5]
Types of synchronization nodes [13]
Multiperiod Scheduling Urban Transportation Problem. $S_{h}$ is the scheduling horizon, $T^v$ are the time periods. In each $T^v$ demand is considered almost constant
Differences of how to represent departures
First departure
Consecutive departure
Last departure
Flowchart to determine the frequency [5]
Window time for synchronization
Correlation
Execution time effect
Cost vs. Synchronization (Instance 20)
Cost vs. Synchronization (Instance 24)
Cost behaviour in relation to instance parameters
Synchronizations behaviour in relation to instance parameters
Literature review
 Authors Frequency Transfer nodes Bunching nodes Cost Multimodal Uncertain Demand Multiperiod Integration Chen et al. x x x Chakroborty x x x Zhao & Zeng x x x Szeto & Wu x x Hadas & Shnaiderman x x x Baskaran & Krishnaiah x x x Tilahun & Ong x x Liu et al. x x Zhang et al. x x Wang et al. x x Eranki x Ibarra-Rojas et al. x x x Avila et al. x x x x x x x x
 Authors Frequency Transfer nodes Bunching nodes Cost Multimodal Uncertain Demand Multiperiod Integration Chen et al. x x x Chakroborty x x x Zhao & Zeng x x x Szeto & Wu x x Hadas & Shnaiderman x x x Baskaran & Krishnaiah x x x Tilahun & Ong x x Liu et al. x x Zhang et al. x x Wang et al. x x Eranki x Ibarra-Rojas et al. x x x Avila et al. x x x x x x x x
Characteristics of instances
 Parameter Low level High level Routes 8 20 Periods 2 12 Segments 10 150 Sync. nodes 2 12 Headways 5-10 5-20
 Parameter Low level High level Routes 8 20 Periods 2 12 Segments 10 150 Sync. nodes 2 12 Headways 5-10 5-20

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