Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data

Mo, Baichuan; Ma, Zhenliang; Koutsopoulos, Haris N.; Zhao, Jinhua

doi:10.1155/2021/5597130

Cited by 20 publications

(13 citation statements)

References 26 publications

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“…6 Applications to a metro simulation Urban metro systems are important components of urban transportation systems. The implementation of a metro system simulation provides a powerful instrument for system performance monitoring, which enables operators to characterize the level of service and make decisions accordingly (Mo, Ma, Koutsopoulos, et al 2021). Such a simulation is a typical stochastic simulation since a metro system contains a great deal of passengers' uncertainty, Here we consider a passenger flow simulation for a single metro route, and apply the proposed design and analysis methods to it.…”

Section: Numerical Experiments With Test Functionsmentioning

confidence: 99%

Design and analysis of computer experiments with both numeral and distribution inputs

Li¹,

Chen²,

Xiong³

2022

Preprint

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Nowadays stochastic computer simulations with both numeral and distribution inputs are widely used to mimic complex systems which contain a great deal of uncertainty.This paper studies the design and analysis issues of such computer experiments. First, we provide preliminary results concerning the Wasserstein distance in probability measure spaces. To handle the product space of the Euclidean space and the probability measure space, we prove that, through the mapping from a point in the Euclidean space to the mass probability measure at this point, the Euclidean space can be isomorphic to the subset of the probability measure space, which consists of all the mass measures, with respect to the Wasserstein distance. Therefore, the product space can be viewed as a product probability measure space. We derive formulas of the Wasserstein distance between two components of this product probability measure space. Second, we use the above results to construct Wasserstein distance-based space-filling criteria in the product space of the Euclidean space and the probability measure space. A class of optimal Latin hypercube-type designs in this product space are proposed. Third, we present a Wasserstein distance-based Gaussian process model to analyze data from computer experiments with both numeral and distribution inputs. Numerical examples and real applications to a metro simulation are presented to show the effectiveness of our methods.

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Section: Numerical Experiments With Test Functionsmentioning

confidence: 99%

Design and analysis of computer experiments with both numeral and distribution inputs

Li¹,

Chen²,

Xiong³

2022

Preprint

View full text Add to dashboard Cite

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“…Table 2 lists industrial applications in maritime industries [262,82], transportation [2,13,10,171,199,16], autonomous vehicles [263,228], health [2,178,10], behavior analysis [199,16], manufacturing [216,10,77] and indoor positioning [216,2].…”

Section: Industrial Applicationsmentioning

confidence: 99%

“…Comment: Transportation, behavior analysis, logistic optimization, manufacturing [171] Transportation, capacity management…”

Section: Visual Analytic Tool (Vat)mentioning

confidence: 99%

“…While Asakura and Hato [16] and Andrienko et al [10] worked on data handling and labeling part, Mo et al [171] studied the use of movement data to predict the demand of flow through the rail network. They propose a simulation-based optimization (SBO) framework to simultaneously calibrate origin-destination (OD) flow, passenger path choices and train capacity for urban rail systems using automated fare collection and automated vehicle location data to analyze performance and conduct performance retrospectives of urban rail systems.…”

Section: Manufacturingmentioning

confidence: 99%

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Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Baumgartner

Smith

Rana

et al. 2022

Preprint

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Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for futuredevelopment and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications.

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“…The automatic fare collection (AFC) system has been widely used in subway systems and provides a data-driven approach for analyzing the passengers’ choice behavior [ 15 ] and the passenger flow distribution in a subway system [ 16 , 17 ]. For example, Zhang [ 18 ] estimated the network-wide link travel time and station waiting time using AFC data in an urban rail transit system.…”

Section: Introductionmentioning

confidence: 99%

A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability

Zhi

et al. 2022

Entropy

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The ever-increasing travel demand has brought great challenges to the organization, operation, and management of the subway system. An accurate estimation of passenger flow distribution can help subway operators design corresponding operation plans and strategies scientifically. Although some literature has studied the problem of passenger flow distribution by analyzing the passengers’ path choice behaviors based on AFC (automated fare collection) data, few studies focus on the passenger flow distribution while considering the passenger–train matching probability, which is the key problem of passenger flow distribution. Specifically, the existing methods have not been applied to practical large-scale subway networks due to the computational complexity. To fill this research gap, this paper analyzes the relationship between passenger travel behavior and train operation in the space and time dimension and formulates the passenger–train matching probability by using multi-source data including AFC, train timetables, and network topology. Then, a reverse derivation method, which can reduce the scale of possible train combinations for passengers, is proposed to improve the computational efficiency. Simultaneously, an estimation method of passenger flow distribution is presented based on the passenger–train matching probability. Finally, two sets of experiments, including an accuracy verification experiment based on synthetic data and a comparison experiment based on real data from the Beijing subway, are conducted to verify the effectiveness of the proposed method. The calculation results show that the proposed method has a good accuracy and computational efficiency for a large-scale subway network.

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Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data

Cited by 20 publications

References 26 publications

Design and analysis of computer experiments with both numeral and distribution inputs

Design and analysis of computer experiments with both numeral and distribution inputs

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability

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