scholarly journals Passenger-to-Car Assignment Optimization Model for High-Speed Railway with Risk of COVID-19 Transmission Consideration

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiaqi Zeng ◽  
Dianhai Wang ◽  
Guozheng Zhang ◽  
Yi Yu ◽  
Zhengyi Cai
Keyword(s):  
Disease Transmission ◽  
High Speed ◽  
Infection Risk ◽  
Low Flow ◽  
Base Case ◽  
High Speed Railway ◽  
Contagious Diseases ◽  
Proposed Model ◽  
Integer Quadratic Programming ◽  
Risk Areas

Narrow and closed spaces like high-speed train cabins are at great risk for airborne infectious disease transmission. With the threat of COVID-19 as well as other potential contagious diseases, it is necessary to protect passengers from infection. Except for the traditional preventions such as increasing ventilation or wearing masks, this paper proposes a novel measurement that optimizes passenger-to-car assignment schemes to reduce the infection risk for high-speed railway passengers. First, we estimated the probability of an infected person boarding the train at any station. Once infectors occur, the non-steady-state Wells–Riley equation is used to model the airborne transmission intercar cabin. The expected number of susceptible passengers infected on the train can be calculated, which is the so-called overall infection risk. The model to minimize overall infection risk, as a pure integer quadratic programming problem, is solved by LINGO software and tested on several scenarios compared with the classical sequential and discrete assignment strategies used in China. The results show that the proposed model can reduce 67.6% and 56.8% of the infection risk in the base case compared to the sequential and discrete assignment, respectively. In other scenarios, the reduction lies mostly between 10% and 90%. The optimized assignment scheme suggests that the cotravel itinerary among passengers from high-risk and low-risk areas should be reduced, as well as passengers with long- and short-distance trips. Sensitivity analysis shows that our model works better when the incidence is higher at downstream or low-flow stations. Increasing the number of cars and car service capacity can also improve the optimization effect. Moreover, the model is applicable to other epidemics since it is insensitive to the Wells–Riley equation parameters. The results can provide a guideline for railway operators during the post-COVID-19 and other epidemic periods.

scholarly journals A Discrete-Space Train Movement Model for a High-Speed Train under Temporary Speed Restriction

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Sihui Long ◽  
Lingyun Meng ◽  
Yihui Wang ◽  
Jianrui Miao ◽  
Xuan Li
Keyword(s):  
High Speed ◽  
Discrete Space ◽  
High Speed Train ◽  
Time Duration ◽  
Movement Model ◽  
High Speed Railway ◽  
Running Time ◽  
Proposed Model ◽  
The Impact ◽  
Block Section

This paper constructs a discrete-space train movement model to evaluate the impact of a temporary speed restriction (TSR) for a high-speed railway train. The established model can demonstrate train movement under different TSR conditions. The proposed model can reveal whether a train is affected by the block section influenced by the TSR within a time duration. Moreover, the model can output detailed train trajectories and the minimal train running time between two adjacent stations to analyse the impact of the TSR. Based on the experimental results, we carry out a comprehensive analysis of the impact of several factors on the running time and train trajectories, including the length of the affected area (i.e., number of affected block sections), the location of the TSR, the limited speed value, and the stopping patterns of the train at two adjacent stations. The experiments show that the proposed discrete-space train movement model can be used to analyse the impact of the TSR on a high-speed railway train under various considered TSR conditions.

scholarly journals Track Allocation Optimization in Multi-Direction High-Speed Railway Stations

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 459 ◽  
Author(s):  
Qin Zhang ◽  
Xiaoning Zhu ◽  
Li Wang
Keyword(s):  
High Speed ◽  
Occupation Time ◽  
Rolling Stock ◽  
High Speed Rail ◽  
High Speed Railway ◽  
Railway Stations ◽  
Proposed Model ◽  
Flexible Track ◽  
Integer Linear Model ◽  
Commercial Solver

Track allocation optimization in railway stations is one of the most fundamental problems for scheduling trains, especially in multi-direction high-speed railway stations. With the construction of high-speed rail networks, this kind of station has become increasingly common. However, the track allocation depends not only on the station tracks, train timetable, and rolling stock plan, but also on the resources in the station throat area. As a result, an effective track allocation plan becomes significant but also difficult. In this paper, we consider all these factors to make the results more practicable and an integer linear model that minimizes the total occupation time of resources in the throat area is presented. A flexible track utilization rule is also adopted to this model to fit the characteristics of the multi-direction station. Meanwhile, a detailed explanation of resources’ occupation time is illustrated to facilitate the representation of the conflicting constraints. To resolve these issues, we use a commercial solver with its default parameters. A computational experiment of a station is conducted to verify the effectiveness of the proposed model. The resources utilization plan indicates that the capacity of a station is limited by the throat area, rather than by the station tracks.

scholarly journals An Alternative Approach for High Speed Railway Carrying Capacity Calculation Based on Multiagent Simulation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Mo Gao ◽  
Leishan Zhou ◽  
Yongjun Chen
Keyword(s):  
Carrying Capacity ◽  
High Speed ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Railway Network ◽  
High Speed Railway ◽  
Operating Process ◽  
Proposed Model ◽  
Train Operation ◽  
Capacity Calculation

It is a multiobjective mixed integer programming problem that calculates the carrying capacity of high speed railway based on mathematical programming method. The model is complex and difficult to solve, and it is difficult to comprehensively consider the various influencing factors on the train operation. The multiagent theory is employed to calculate high speed railway carrying capacity. In accordance with real operations of high speed railway, a three-layer agent model is developed to simulate the operating process of high speed railway. In the proposed model, railway network agent, line agent, station agent, and train agent are designed, respectively. To validate the proposed model, a case study is performed for Beijing–Shanghai high speed railway by using NetLogo software. The results are consistent with the actual data, which implies that the proposed multiagent method is feasible to calculate the carrying capacity of high speed railway.

scholarly journals Revisiting the Effects of High-Speed Railway Transfers in the Early COVID-19 Cross-Province Transmission in Mainland China

2021 ◽  
Vol 18 (12) ◽  
pp. 6394
Author(s):  
Chun-Hsiang Chan ◽  
Tzai-Hung Wen
Keyword(s):  
Disease Transmission ◽  
High Speed ◽  
Large Scale ◽  
Structural Changes ◽  
Transportation Network ◽  
Mainland China ◽  
Spillover Effects ◽  
Wuhan City ◽  
High Speed Railway ◽  
Spatial Transmission

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was reported at the end of 2019 in Wuhan, China, and was rapidly disseminated to all provinces in around one month. The study aims to assess the changes in intercity railway passenger transport on the early spatial transmission of COVID-19 in mainland China. Examining the role of railway transport properties in disease transmission could help quantify the spatial spillover effects of large-scale travel restriction interventions. This study used daily high-speed railway schedule data to compare the differences in city-level network properties (destination arrival and transfer service) before and after the Wuhan city lockdown in the early stages of the spatial transmission of COVID-19 in mainland China. Bayesian multivariate regression was used to examine the association between structural changes in the railway origin-destination network and the incidence of COVID-19 cases. Our results show that the provinces with rising transfer activities after the Wuhan city lockdown had more confirmed COVID-19 cases, but changes in destination arrival did not have significant effects. The regions with increasing transfer activities were located in provinces neighboring Hubei in the widthwise and longitudinal directions. These results indicate that transfer activities enhance interpersonal transmission probability and could be a crucial risk factor for increasing epidemic severity after the Wuhan city lockdown. The destinations of railway passengers might not be affected by the Wuhan city lockdown, but their itinerary routes could be changed due to the replacement of an important transfer hub (Wuhan city) in the Chinese railway transportation network. As a result, transfer services in the high-speed rail network could explain why the provinces surrounded by Hubei had a higher number of confirmed COVID-19 cases than other provinces.

Railway Capacity Allocation Modeling Using a Genetic Algorithm

2017 ◽  
Vol 2608 (1) ◽  
pp. 115-124
Author(s):  
Hyunseung Kim ◽  
In-Jae Jeong ◽  
Dongjoo Park
Keyword(s):  
Genetic Algorithm ◽  
South Korea ◽  
High Speed ◽  
Rational Solution ◽  
Capacity Allocation ◽  
High Speed Railway ◽  
South Korean ◽  
Transport Services ◽  
Proposed Model ◽  
Railway Capacity

The South Korean government has established guidelines for railway capacity allocation. Railway transport services are provided by a monopoly company, which together with the guidelines, has hampered research into railway capacity allocation in South Korea. Recently, a new high-speed railway company has been established. Therefore, there is a pressing need for a fair and objective railway capacity allocation procedure. A model was developed to be applicable to South Korean high-speed railway capacity allocation, which is optimized by viewing the railway network as a location–time network. Because railway capacity allocation in South Korea is an administered process, various requirements must be followed; the model uses a genetic algorithm for such requirements. Two test scenarios were used to validate the proposed model, the solution to which resolves more than 70% of conflicts within 20 iterations (148 min). When an attempt is made to schedule infeasible trains compulsively, it is impossible to do so without relaxing one or more constraints. The average headway among real operating trains is very close to the results of the analysis. The proposed model with a genetic algorithm is a rational solution.

scholarly journals Optimization of Multiperiod Mixed Train Schedule on High-Speed Railway

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Wenliang Zhou ◽  
Junli Tian ◽  
Jin Qin ◽  
Lianbo Deng ◽  
TangJian Wei
Keyword(s):  
Time Distribution ◽  
High Speed ◽  
Periodic Operation ◽  
High Speed Railway ◽  
Arrival Times ◽  
Travel Costs ◽  
Proposed Model ◽  
Different Types ◽  
Iteration Number ◽  
Objective Value

For providing passengers with periodic operation trains and making trains’ time distribution better fit that of passengers, the multiperiod mixed train schedule is first proposed in this paper. It makes each type of train having same origin, destination, route, and stop stations operate based on a periodic basis and allows different types of train to have various operation periods. Then a model of optimizing multiperiod mixed train schedule is built to minimize passengers generalized travel costs with the constraints of trains of same type operating periodically, safe interval requirements of trains’ departure, and arrival times, and so forth. And its heuristic algorithm is designed to optimize the multiperiod mixed train schedule beginning with generating an initial solution by scheduling all types of train type by type and then repeatedly improving their periodic schedules until the objective value cannot be reduced or the iteration number reaches its maximum. Finally, example results illustrate that the proposed model and algorithm can effectively gain a better multiperiod mixed train schedule. However, its passengers deferred times and advanced times are a little higher than these of an aperiodic train schedule.

A Bi-Level Passenger Preference-Oriented Line Planning Model for High-Speed Railway Operations

2018 ◽  
Vol 2672 (10) ◽  
pp. 224-235 ◽  
Author(s):  
Yuxiang Yang ◽  
Jie Li ◽  
Chao Wen ◽  
Ping Huang ◽  
Qiyuan Peng ◽  
...  
Keyword(s):  
Travel Behavior ◽  
High Speed ◽  
Operating Costs ◽  
Line Planning ◽  
High Speed Railway ◽  
Planning Stage ◽  
Solution Approach ◽  
Planning Optimization ◽  
Proposed Model

To solve bottlenecks and capacity shortages in railway corridors, it is necessary to consider passengers’ temporal-spatial preferences in conjunction with train dispatching decisions at the line planning stage. A design of the departure and stop plans which is based on the passengers’ preferences would guarantee a high quality of service. This paper presents a line planning optimization model for high-speed railway (HSR) operations that addresses both the operating costs of lines and passengers’ preferences through a bi-level integer programming (IP) problem. Using actual travel data from the Shenzhen–Changsha HSR in China, we investigate passengers’ travel behavior during different time periods and between several origin and destination (OD) pairs to characterize their spatial-temporal preferences. An IP model is then developed that maximizes the degree of passenger departure time satisfaction (DTS) at the higher level and the operating costs of lines at the lower level. Due to the complexity of the problem, a methodology is proposed to find a conflict-free solution for the proposed model to find train frequencies in each block and their stop plans at each station between the upper- and lower-level models. It is shown that a given passenger trip demand between an origin and a destination could become more flexible by analyzing its time characteristics and the DTS. Finally, a case study is presented to show the effectiveness of the model and the solution approach.

The evolutionary mechanism of high-speed railway system based on hypernetwork theory

2018 ◽  
Vol 32 (15) ◽  
pp. 1850182 ◽  
Author(s):  
Qi Suo ◽  
JinLi Guo
Keyword(s):  
High Speed ◽  
Preferential Attachment ◽  
Power Law Distribution ◽  
High Speed Railway ◽  
Evolutionary Mechanism ◽  
Proposed Model ◽  
Railway System ◽  
Evolving Model ◽  
The Impact ◽  
Further Development

Railway system as an important transportation pattern can improve regional economic growth and accelerate the development of transportation structure. In this paper, we propose an evolving model to describe the evolutionary mechanism of high-speed railway system by using hypernetwork theory. The stations are represented as nodes while the lines are represented as hyperedges. The evolving process includes two ingredients: growing and linking which is driven by both random and preferential attachment. We analytically deduce the node hyperdegree distribution which is shown to follow a shifted power-law distribution. Furthermore, we test the impact of parameters on the model. Then the empirical investigations of China Railway High-speed (CRH) are demonstrated which can be explained by the proposed model. The model can reveal the macro characteristics of railway system and provide reference for the further development of high-speed railway.

scholarly journals Estimation of Disease Transmission in Multimodal Transportation Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yu Zheng
Keyword(s):  
Disease Transmission ◽  
High Speed ◽  
Transportation Network ◽  
Distribution Model ◽  
High Speed Railway ◽  
Traffic Distribution ◽  
Flight Mode ◽  
Transportation Modes ◽  
And Control

Mathematical models are important methods in estimating epidemiological patterns of diseases and predicting the consequences of the spread of diseases. Investigation of risk factors of transportation modes and control of transportation exposures will help prevent disease transmission in the transportation system and protect people’s health. In this paper, a multimodal traffic distribution model is established to estimate the spreading of virus. The analysis is based on the empirical evidence learned from the real transportation network which connects Wuhan with other cities. We consider five mainstream travel modes, namely, auto mode, high-speed railway mode, common railway mode, coach mode, and flight mode. Logit model of economics is used to predict the distribution of trips and the corresponding diseases. The effectiveness of the model is verified with big data of the distribution of COVID-19 virus. We also conduct model-based tests to analyze the role of lockdown on different travel modes. Furthermore, sensitivity analysis is implemented, the results of which assist in policy-making for containing infection transmission through traffic.

scholarly journals Modeling of Moisture Content of Subgrade Materials in High-Speed Railway Using a Deep Learning Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
LiLei Chen ◽  
Jing Chen ◽  
Chao Wang ◽  
Yanhua Dai ◽  
Rongyan Guo ◽  
...  
Keyword(s):  
Moisture Content ◽  
High Speed ◽  
Short Term Memory ◽  
High Speed Railway ◽  
Cold Regions ◽  
Proposed Model ◽  
Hydraulic Conditions ◽  
Novel Method ◽  
Long Short Term Memory

Moisture content of subgrade materials is an essential factor affecting frost heave deformation of high-speed railway subgrade in a seasonally frozen region. Modeling and predicting moisture transport play an important role in analyzing the subgrade thermal and hydraulic conditions in cold regions. In this study, a long short-term memory (LSTM) model was proposed based on subgrade material moisture in two sections during one winter and spring cycle from 2015 to 2016. The reliability of the model was verified by comparing the monitoring data with the model results. The results demonstrate that the LSTM model can be effectively used to forecast the dynamic characteristics of the moisture of subgrade materials. The data of simulated moisture content of subgrade materials have a root mean square error ranging from 0.17 to 0.47 in the training phase and from 0.20 to 10.5 in the testing phase. The proposed model provides a novel method for long-term moisture prediction in subgrade materials of high-speed railways in cold regions.

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