Robust cooperative train trajectory optimization with stochastic delays under virtual coupling

A Human-Computer Interaction-based Method for Line Plan Adjustment of China High-Speed Railway

IGV Optimized Strategy for Smart Ports Based on Reinforcement Learning: Taking G Port as an Example

Scheduling wheel inspection for sustainable urban rail transit operation: A Bayesian approach

Cooperative Control of Multistation Passenger Inflows in Case of Irregular Large-Scale Passenger Flows

This study focuses on the large passenger flow control problem, after an operation interruption occurs, to develop a methodology that can efficiently control the passenger inflows of multiple stations and avoid overcrowding inside stations. An early-warning model for irregular large-scale passenger flows (ILSPF) and a dynamic ILSPF control model are proposed. The early-warning model is developed to predict passenger flows in the future with historical data and detect when to start control measures in actual time. The ILSPF cooperative control model focuses on cooperatively controlling the passenger inflows of multiple stations to ensure passenger safety in vehicles and stations, as well as maximize the number of passengers transported and minimize the passengers’ total waiting times. An improved particle swarm optimization algorithm was designed to determine an optimal solution, and a case study on the Chengdu metro in China was carried out to examine the performance of the model. The obtained results verify the effectiveness of the model and algorithm and prove that ILSPF control can regulate the passenger inflow demand, better match the passenger demand and capability on the line, increase the total number of passengers transported, and balance the proportion of passenger boarding at each station.

How subjective information with AI for digital revolution

This paper summarizes the relationship of subjective information with artificial intelligence (AI) technology and points out how the role of subjective information and its position in AI. Eventually, the characteristic of digital era is the “softening of the theories and hardening of the experiences”. Subjective information is widely used in digital revolution for transforming the qualitative estimations into quasi-quantitative solutions, such as the empirical methods in decision making for quantitative management, etc., it will be the transferor for realizing it. The theoretical formulation of how subjective information is digitized through “Fuzzy-AI Model” for digital revolution is presented in this paper; it has becoming a universal problem solver of utilizing AI technology for quantizing the degree uncertainties in decision-making and fuzzy estimation. Besides, the “Big Data” searching will heavily depend on the completeness of its source information, yet “subjective information” approach can directly predict human thinking or the internal law of complicated objective events into an explicit digital form, for the completeness of source information to make the correct and comprehensive “Big Data” prediction possible. Practical case studies are presented.

Complete Estimation Approach for Characterizing Passenger Travel Time Distributions at Rail Transit Stations

Data Fusion Approach for Evaluating Route Choice Models in Large-Scale Complex Urban Rail Transit Networks

Evaluating the Wheelset Health Status of Rail Transit Vehicles: Synthesis of Wear Mechanism and Data-Driven Analysis

Calibrating travel time thresholds with cluster analysis and AFC data for passenger reasonable route generation on an urban rail transit network

Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

An urban rail transit (URT) system is operated according to relatively punctual schedule, which is one of the most important constraints for a URT passenger’s travel. Thus, it is the key to estimate passengers’ train choices based on which passenger route choices as well as flow distribution on the URT network can be deduced. In this paper we propose a methodology that can estimate individual passenger’s train choices with real timetable and automatic fare collection (AFC) data. First, we formulate the addressed problem using Manski’s paradigm on modelling choice. Then, an integrated framework for estimating individual passenger’s train choices is developed through a data-driven approach. The approach links each passenger trip to the most feasible train itinerary. Initial case study on Shanghai metro shows that the proposed approach works well and can be further used for deducing other important operational indicators like route choices, passenger flows on section, load factor of train, and so forth.

A hybrid optimization strategy for the maintenance of the wheels of metro vehicles: Vehicle turning, wheel re-profiling, and multi-template use

The wheel–rail contact relationship has a great impact on the security and reliability of metro vehicles in service. In particular, wear modeling and maintenance optimization of the wheels play significant roles with regard to both safety and cost. However, it is difficult to provide a satisfactory model of wheel wear because of the open nature of real wheel–rail systems and the constantly varying environmental conditions in which they operate. Historically, re-profiling, which also has its limitation to some extent, was adopted as a common strategy to restore the original profiles of the worn wheels. Acknowledging that re-profiling is not the only strategy for dealing with wheel wear, the authors of this study have developed a more advanced optimization approach that includes two more strategies, namely, vehicle turning and multi-template use, to give as near an optimal solution as possible. Vehicle turning refers to the reversal of the vehicle’s orientation on the rail, whereas multi-template use refers to the situation where different re-profiling templates are used alternately. In this paper, re-profiling, vehicle turning, and multi-template use have been discussed separately. Then a hybrid optimization strategy for the maintenance of the wheels of metro vehicles has been proposed, with the aim of maximizing the wheel life while minimizing the relevant costs. An initial case study on the Shanghai Metro system shows that the proposed approach is able to provide a more reasonable solution for the optimization of the maintenance strategies.

Data-Driven Wheel Wear Modeling and Reprofiling Strategy Optimization for Metro Systems

With the rapid developments in metro systems worldwide, more research concerning optimization of maintenance actions is needed, because the availability and service state of a metro system directly influences the daily activity of a city and its people. In particular, the prediction of wear and maintenance optimization of wheels is significant. Maintenance costs for a rail track subsystem represent more than half the total maintenance costs for a metro line. A hard rail–soft wheel compromise extends the life of the rails and increases the wheel replacement frequency with economic benefits. An improved strategy for predicting and maintaining wheel wear will allow agencies to improve reliability, enhance safety, and maximize wheel life while minimizing relevant costs. In this study, historical data are used to analyze wheel wear curves, and the flange thickness and wheel diameter are identified as the most important profile parameters. A new data-driven model of wheel wear trends is given for variations in wheel diameter and flange thickness. An approach for optimizing the wheel reprofiling strategy is based on this model and determines the optimum reprofiling point that maximizes wheel life while minimizing relevant costs. An initial case study on the Shanghai, China, metro network shows that the proposed approach can provide a reasonable solution for optimization of the reprofiling strategy.

Diagnosing urban rail transit vehicles with FMEA and fuzzy set

Purpose – The purpose of this paper is to develop an effective treatment to analyze and diagnose urban rail transit (URT) vehicle maintenance strategy. Design/methodology/approach – In this paper, the technique of Failure Mode and Effects Analysis (FMEA) is introduced into the examination of URT trains, first. Then the method of fuzzy-set-based assessment for FMEA is presented, which is the quantitative tool of Fuzzy-Set-based treatment for FMEA in analysis and diagnoses to URT maintenance strategy. Moreover, recommendations for further improvement of the proposed approach are also provided. Initial application into the vehicle maintenance of Shanghai URT System shows, that the proposed approach has a good performance and consequently is worth further development. Findings – The paper presents a FMEA and fuzzy-set-based theoretical approach for analyzing and diagnosing current methods in URT vehicle maintenance strategy. Practical implications – With rapid development of URT systems in the world especially in those highly populated areas, much more attentions are turning to researches on URT maintenance, nevertheless, few quantitative research achievement are mentioned or applied. This paper is a tentative attempt at introducing fuzzy-set theory into quantitative analysis and diagnoses of URT maintenance strategy. Originality/value – The study in this paper is helpful in theory and practice of URT maintenance and its methodology could be further applied into a broad family of facility group or system in other engineering fields.

Validating Rail Transit Assignment Models with Cluster Analysis and Automatic Fare Collection Data

Passenger flow data are necessary for making and coordinating operational plans for urban rail transit (URT) systems; the availability and the service state of those systems directly influence the activity of a city and its people. Although many transit assignment models have been developed, the results of passenger flows estimated by these models as well as assumptions made in the estimation process, especially for large-scale, complex, and dynamically changing URT networks, had not been validated. This paper proposes a methodology that can validate existing URT assignment models by using automatic fare collection data and a cluster analysis technique. Initial applications to the URT system of Shanghai, China, which is one of the largest in the world, show that the proposed approach works well and can efficiently find the origin–destination pairs in which passengers' route choices are misestimated by those assignment models. The analysis suggests that several factors result in errors (for the URT assignment model used in Shanghai). These factors include the threshold for the difference in travel costs, a misrepresentation of the transferring cost, and inadequate values for the standard deviation. This information is useful for detecting errors in existing URT assignment models, leading to improvements.

Calibrating Rail Transit Assignment Models with Genetic Algorithm and Automated Fare Collection Data

Modified stochastic user-equilibrium assignment algorithm for urban rail transit under network operation