Parallel and Collaborative Passenger Flow Control of Urban Rail Transit Under Comprehensive Emergency Situation

Zhu, Guangyu; Sun, Ranran; Sun, XinNi; Wei, Yun; Wu, Bo

doi:10.1109/tiv.2023.3235109

Cited by 9 publications

(2 citation statements)

References 43 publications

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“…To sum up, in this problem, the multi-objective model can be described as single objective programming models with constraints [9] : (7)…”

Section: Minimum Tracking Interval (4)mentioning

confidence: 99%

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Urban Train Timetable Optimization Based on Multi-Objective Optimization

Huang,

Ming,

Wang

et al. 2024

Advances in Transdisciplinary Engineering

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The urban rail transit industry in modern society is growing at an alarming rate, and in the rail transit of the network is the schedule is the basis of the train operation. Current research focuses on designing and optimizing train schedules to better meet passenger demands. In this paper, a multi-objective programming model is established on the basis of objective functions that are coupled and aimed at minimizing operating costs and passenger travel efficiency in order to optimize the train timetable, while considering meeting the demand of passenger flow. One objective function is kept in the original problem according to the characteristics of the model, and other objective functions are transformed into constraint conditions by adding restricted domains, thus turning them into single-objective programming models. Genetic algorithm is used to obtain results and train operation is simulated through the dynamic programming algorithm to carry out dynamic search. The CSMA/CD (Carrier Sense Multiple Access/Collision Detection) protocol is introduced to optimize the constraint conditions. The waiting time is transformed into the minimum tracking time interval by sending the carrier monitoring code. As such, the departure time data of large and small routes are calculated dynamically, and equal interval parallel operation diagrams are drawn. The calculation results indicate that the multi-objective optimization model improved by genetic algorithm can effectively solve practical cases, and its train timetable can highly match the spatiotemporal distribution of passenger flow demand and obtain satisfactory feasible solutions within a reasonable time.

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“…To sum up, in this problem, the multi-objective model can be described as single objective programming models with constraints [9] : (7)…”

Section: Minimum Tracking Interval (4)mentioning

confidence: 99%

“…Under the above constraints, the train time sequence state transition equation of train O is updated as follows: (9) The trains passing through a station can be divided into docking trains and departing trains. [i/2] represents the station where the train is located.…”

Section: Dynamic Programming Simulationmentioning

confidence: 99%