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This study aims to investigate the effect of time-varying passenger flow on the wheel wear of metro vehicles to provide a more accurate model for predicting wheel wear and a new idea for reducing wheel wear.

Sectional passage flow data were collected from an operational metro line. A wheel wear simulation based on time-varying passenger flow was performed via the SIMPACK software to obtain the worn wheel profile and wear distribution. The simulation involves the following models: vehicle system dynamics model, wheel-track rolling contact model, wheel wear model and variable load application model. Later, the simulation results were compared with those obtained under the traditional constant load condition and the measured wear data.

For different distances traveled by the metro vehicle, the simulated wheel profile and wear distribution under the variable load remained closer to the measurements than those obtained under the constant load. As the distance traveled increased, the depth and position of maximum wear and wear growth rate under the variable load tended to approach the corresponding measured values. In contrast, the simulation results under the constant load differed greatly from the measured values. This suggests that the model accuracy under the variable load was significantly improved and the simulation results can offer a more accurate basis for wear prediction.

These results will help to predict wheel wear more accurately and provide a new idea for simulating wheel wear of metro vehicles. At the same time, measures for reducing wheel wear were discussed from the perspective of passenger flow changes.

Existing research on the wheel wear of metro vehicles is mainly based on the constant load condition, which is quite different from the variable load condition where the passenger flow in real vehicles varies over time. A method of simulating wheel wear based on time-varying load is proposed in this paper. The proposed method shows a great improvement in simulation accuracy compared to traditional methods and can provide a more accurate basis for wear prediction and wheel repair.

Under the constraints of given passenger service level and coupling travel demand with train departure time, this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.

The authors optimize the train operational plan in a special network layout, i.e. an urban rail corridor with dead-end terminal yard, by decomposing it into two sub-problems: train timetable optimization and rolling stock circulation optimization. As for train timetable optimization, the authors propose a schedule-based passenger flow assignment method, construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm. For the optimization of rolling stock circulation, the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.

The case study shows that the train operational plan developed by the study's approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.

The example verifies the efficiency of the model and algorithm.

This study aims to optimize the traffic capacity allocation to solve the problem of low share of public transit in the landside system so as to get rid of the congestion trouble in landside traffic. The optimal timetable for airport buses can be searched by changing the departure interval of each line and evaluating the corresponding performance continuously.

This paper constructs a simulation model based on the real-world situation in Beijing Capital International Airport (BCIA), which simulates the whole process of airport bus schedules and analyzes the connections among multiple steps for transferring. The evaluation system is constructed by considering the benefits of passengers, airports and companies comprehensively. The optimal timetable for airport buses can be searched by changing the departure interval of each line and evaluating the corresponding performance continuously.

According to the experimental results, an excellent evacuation effect can only be achieved when the majority of departure intervals of airport buses are shortened to 50% of their original values, and some busy routes such as the Beijing Station line are supposed to be reduced to one-third of their original fixed intervals. As the airport bus passenger flow presents an obviously periodic variation over days, the timetable of the airport bus is supposed to be redesigned every day. A flexible bus timetable can not only meet the dynamic passenger flow but also enhance the attractiveness of public transit.

This paper constructs a simulation model based on the real-world situation in BCIA, which can not only model the complex scenes in the whole process of airport bus schedules but also reflect the intricate interaction between transferring passengers and vehicles caused by dense streamlines.

To evacuate passengers arriving at intercity railway stations efficiently, metros and intercity railways usually share the same station or have stations close to each other. When intercity trains arrive intensively, a great number of passengers will burst into the metro station connecting with the intercity railway station within a short period, while the number of passengers will decrease substantially when intercity trains arrive sparsely. The metro timetables with regular headway currently adopted in real-world operations cannot handle the injected passenger demand properly. Timetable optimization of metro lines connecting with intercity railway stations is essential to improve service quality.

Based on arrival times of intercity trains and the entire process for passengers transferring from railway to metro, this paper develops a mathematical model to characterize the time-varying demand of passengers arriving at the platform of a metro station connecting with an intercity railway station. Provided the time-varying passenger demand and capacity of metro trains, a timetable model to optimize train departure time of a bi-direction metro line where an intermediate station connects with an intercity railway station is proposed. The objective is to minimize waiting time of passengers at the connecting station. The proposed timetable model is solved by an adaptive large neighborhood search algorithm.

Real-world case studies show that the prediction accuracy of the proposed model on passenger demand at the connecting station is higher than 90%, and the timetable model can reduce waiting time of passengers at the connecting station by 28.47% which is increased by 5% approximately than the calculation results of the generic algorithm.

This paper puts forward a model to predict the number of passengers arriving at the platform of connection stations via analyzing the entire process for passengers transferring from intercity trains to metros. Also, a timetable optimization model aiming at minimizing passenger waiting time of a metro line where an intermediate station is connected to an intercity railway station is proposed.

Tourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of inappropriate model selection for analyzing decisions. This paper investigated the effects of a time-varying weighting strategy on the performance of linear and nonlinear forecast combinations in the context of tourism.

This study used grey prediction models, which did not require that the available data satisfy statistical assumptions, to generate forecasts. A quality-control technique was applied to determine when to change the combination weights to generate combined forecasts by using linear and nonlinear methods.

The empirical results showed that except for when the Choquet fuzzy integral was used, forecast combination with time-varying weights did not significantly outperform that with fixed weights. The Choquet integral with time-varying weights significantly outperformed that with fixed weights for all model combinations, and had a superior forecasting accuracy to those of other combination methods.

The tourism sector can benefit from the use of the Choquet integral with time-varying weights, by using it to formulate suitable strategies for tourist destinations.

Combining forecasts with time-varying weights may improve the accuracy of the predictions. This study investigated incorporating a time-varying weighting strategy into combination forecasting by using CUSUM. The results verified the effectiveness of the time-varying Choquet integral for tourism forecast combination.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.