Abstract

1. Introduction

2. Methodology

3. Teleworker characteristics

4. Classification of teleworker types

5. Discussion

6. Conclusions and recommendations

Acknowledgements

Author contributions

References

Abstract

     With the arrival of COVID-19 in the Netherlands in Spring 2020 and the start of the “intelligent lockdown”, daily life changed drastically. The working population was urged to telework as much as possible. However, not everyone had a suitable job for teleworking or liked teleworking. From a mobility perspective, teleworking was considered a suitable means to alleviate travel. Even after the pandemic it can (continue to) reduce pressure on the mobility system during peak hours, thereby improving efficiency and level of service of transport services. Additionally, this could reduce transport externalities, such as emissions and unsafety. The structural impact from teleworking offers opportunities, but also challenges for the planning and operations of public transport. The aim of this study is to better understand teleworking during and after COVID-19 among train travellers, to support operators and authorities in their policy making and design. We study the telework behaviour, attitude towards teleworking, and future intentions through a longitudinal data collection. By applying a latent class cluster analysis, we identified six types of teleworkers, varying in their frequency of teleworking, attitude towards teleworking, intentions to the future, socio-demographics and employer policy. In terms of willingness-to-telework in the future, we distinguish three groups: the high willingness-to-telework group (71%), the low willingness-to-telework group (16%), and the least-impacted self-employed (12%). Those with high willingness are expected to have lasting changes in their travel patterns, where especially public transport is impacted.

Introduction

     COVID-19 first appeared in the Netherlands on the 27th of February 2020. On March 12th, the government drastically intervened and imposed an “intelligent lockdown” (Antonides and van Leeuwen, 2020). In this lockdown people were urged to stay at home as much as possible. This meant that among others schools were closed, people had to telework as much as possible (i.e. working from home), public transport operations were reduced (people were advised not to use public transport), and restaurants and bars were closed (de Haas et al., 2020). The large difference with lockdowns like imposed in e.g. France and Germany, is that people were still allowed to be outside and move around. All in all, these measures resulted in large changes in people’s daily life.

     For the working population, the intelligent lockdown resulted in a large adjustment: from working in office to teleworking as much as possible. Before the COVID-19 pandemic one in three employed people teleworked occasionally, with only 6% doing this almost full-time (Hamersma et al., 2020). With this, the Netherlands ranks second (after Sweden) in the European Union in terms of the share of employees that telework (Sostero et al., 2020). During the intelligent lockdown approximately 45–56% of the working population teleworked, where many did this full-time (Hamersma et al., 2020). Not every job is equally suitable for teleworking, as some professions require interaction with people (e.g. cashiers in supermarket, nurses in hospitals, and plumbers in customer’s houses) (Sostero et al., 2020). In the Netherlands, the largest switch towards teleworking was found among the working population that is highly educated and/or commuted by public transport (Hamersma et al., 2020). Due to this switch towards teleworking and the advice of the government against using public transport, public transport use decreased significantly (Antonides and van Leeuwen, 2020).

Conclusions and recommendations

     This paper presents the findings of an investigation into teleworking during COVID-19. The sudden changes and adaptations that were required of the working population to telework as much as possible, resulted in a suboptimal situation for several people. In this study, longitudinal data of train travelling teleworkers in times of COVID-19 is used to investigate differences and similarities among the population in terms of attitude towards teleworking, frequency of teleworking and intentions related to teleworking after the pandemic has ended. We apply a latent class cluster analysis to identify six different types of teleworkers: ‘enthusiastic and always’, ‘positive and partially’, ‘neutral, new, and frequently’, ‘forced and done with’, and ‘indifferent and occasional’. These six classes differ largely from one another. We can classify these based on their willingness-to-telework: the high willingness-to-telework group (71% of the teleworking train passenger population), the low willingness-to-telework group (16%) and the self-employed (12%). The first group largely work for organisations that are prepared for teleworking and have a job, personality, and home situation that fits teleworking. This group also experienced the biggest impact on their travel patterns, and due to their intentions to telework more often after the pandemic, it will also affect their future travel patterns most.

A two-and-a-half-dimensional finite element method (2.5D FEM) is applied to investigate the dynamic response of an unsaturated ground subjected to moving loads caused by high-speed train. The partial differential equations of unsaturated porous medium in frequency domain are deduced based on the equations of motion and mass conservation of three phases, with consideration of the compressibility of solid grain and pore fluid. Governing equations of unsaturated soil in 2.5D FE form are derived by using the Fourier Transform with respect to the load moving direction. The track structure is simplified as an Euler beam resting on the unsaturated porous half-space and the viscous-elastic artificial boundaries are used to avoid the energy reflection from the boundary. Numerical simulations demonstrate effects of the degree of water saturation and train speed to the ground vibration and the excess pore water pressure. It is concluded that the degree of water saturation has a different influence on the ground displacement and acceleration. The gas phase has varied influence to the ground displacement amplitude at different train speed level at the track center. A very small amount of gas in the saturated ground largely increases the ground acceleration amplitude at a given train speed. Ground displacements attenuate rapidly with almost the same rate for both high and low train speeds near the track center. The maximum amplitude of excess pore water pressure is located at 1.5–2.0 m beneath the ground surface and decreases significantly as the degree of water saturation decreases.

Introduction

In recent years, the high-speed-railway has been developed as a quick and convenient means of mass transportation in China. Evaluating the train-caused ground vibration and its impact on the adjacent environment is hence an important design consideration. Investigations concerning the ground vibration induced by moving loads dated back to 1950s, which have been partly reviewed by Beskou and Theodorakopoulos [1]. Many investigations were conducted by analytical means assuming a homogeneous elastic half-space [2,3] or by using semi-analytical models for a multi-layered ground [4]. The FEM, the BEM or the FEM/BEM hybrid schemes [5–8] were also widely used considering their advantages at dealing with the irregularities of the geometry and material. By assuming the material and geometric properties to be constant along the load-moving direction, only the profile normal to the load-moving direction needs be considered, which is two-dimensional in nature. However, if the effect of the moving loads is to be considered, then the problem is somewhat between two- and three-dimensional (2.5D) [9,10]. Such an idea was firstly proposed by Hwang and Lysmer [11] in studying the response of an underground structure to traveling seismic waves, and was also used by Barros and Luco [12] to obtain the steady-state displacements and stresses within a multi-layered viscoelastic half-space generated by a buried or surface point load moving with constant speed. Yang and Hung [9,10] used the 2.5D FEM to study the 3D ground dynamic response under train loads, considering the discrete sleeper supports of the train tracks. The same concept was also adopted by Takemiya [13] and Bian et al. [14,15] in the study of the environmental vibration caused by the train loads. These studies simplified the subgrade soil as single-phase elastic or visco-elastic soil.

In this paper we present a stochastic model for predicting the propagation of train delays based on Bayesian networks. This method can efficiently represent and compute the complex stochastic inference between random variables. Moreover, it allows updating the probability distributions and reducing the uncertainty of future train delays in real time under the assumption that more information continuously becomes available from the monitoring system. The dynamics of a train delay over time and space is presented as a stochastic process that describes the evolution of the time-dependent random variable. This approach is further extended by modelling the interdependence between trains that share the same infrastructure or have a scheduled passenger transfer. The model is applied on a set of historical traffic realisation data from the part of a busy corridor in Sweden. We present the results and analyse the accuracy of predictions as well as the evolution of probability distributions of event delays over time. The presented method is important for making better predictions for train traffic, that are not only based on static, offline collected data, but are able to positively include the dynamic characteristics of the continuously changing delays.

Introduction

Accurate prediction of train delays (deviations from timetable) is an important requirement for proactive and anticipative realtime control of railway traffic. Traffic controllers need to predict the arrival times of the trains within (or heading towards) their area in order to control the feasibility of timetable realisation. Similarly, the transport controllers on behalf of train operating companies may use the predictions to estimate the feasibility of planned passenger transfers, as well as rolling-stock and crew circulation plans. Valid estimates of arrival and departure times are therefore important for preventing or reducing delay propagation, managing connections, and providing reliable passenger information. The difficulty for predicting the train event times comes from the uncertainty and unpredictability of process times in railway traffic. The models for real-time traffic control have so far mostly focused on overcoming the great combinatorial complexity of train rescheduling (Corman et al., 2014b; Meng and Zhou, 2014; Törnquist and Persson, 2007), delay management (Dollevoet et al., 2014) and rolling-stock and crew rescheduling (Nielsen et al., 2012; Potthoff et al., 2010). The developed approaches are able to solve complex instances in real-time, however they typically assume perfect deterministic knowledge of the input traffic state and subsequent traffic evolution. In recent years, the uncertainty of train event times has been recognised as one of the major obstacles for computing feasible and implementable solutions for rescheduling problems in railway traffic (Corman and Meng, 2014; Quaglietta et al., 2013). The uncertainty of an event is usually represented by the probability distribution of its realisation. However, most of the existing approaches assume fixed probability distributions for train delays and do not consider the effect that real-time information on train positions and delays may have on (the parameters of) the corresponding distributions. In order to create realistic online tools for real-time traffic management, the dynamics of uncertainty of delays needs to be considered. When new information about train positions and delays becomes available, the uncertainty for predicting subsequent events is typically reduced. The main objective of this paper is to examine the effect that the prediction horizon and incoming information about a running train may have on the predictability of subsequent arrival and departure times of all trains. In other words, we try to give an answer to the question: how does the probability distribution of delay of an event change over time?

1- Introduction

2- Train delay prediction problem: the Italian case

3- Train delay prediction systems

4- Description of data and custom KPIs

5- Results

6- Conclusions

References

Abstract

Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (TDPS) for large-scale railway networks which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.

Introduction

Big Data Analytics is one of the current trending research interests in the context of railway transportation systems. Indeed, many aspects of the railway world can greatly benefit from new technologies and methodologies able to collect, store, process, analyze and visualize large amounts of data [1,2] as well as new methodologies coming from machine learning, artificial intelligence, and computational intelligence to analyze that data in order to extract actionable information [3]. Examples are: condition based maintenance of railway assets [4–6], automatic visual inspection systems [7,8], risk analysis [9], network capacity estimation [10], optimization for energy-efficient railway operations [11], marketing analysis for rail freight transportation [12], usage of ontologies and linked data in railways [13], big data for rail inspection systems [14], complex event processing over train data streams [15], fault diagnosis of vehicle on-board equipment for high speed railways [16–18] and for conventional ones [19], research on storage and retrieval of large amounts of data for high-speed trains [20], development of an online geospatial safety risk model for railway networks [21], train marshaling optimization through genetic algorithms [22], research on new technologies for the railway ticketing systems [23]. In particular, this paper focuses on building a Train Delay Prediction System (TDPS) in order to provide useful information to traffic management and dispatching processes through the usage of state-of-the-art tools and techniques, able to extract useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Delays can be due to various causes: disruptions in the operations flow, accidents, malfunctioning or damaged equipment, construction work, repair work, and severe weather conditions like snow and ice, floods, and landslides, to name just a few. Although trains should respect a fixed schedule called Nominal Timetable (NT), Train Delays (TDs) occur daily and can negatively affect railway operations, causing service disruptions and losses in the worst cases. Rail Traffic Management Systems (TMSs) [24] have been developed to support the management of the inherent complexity of rail services and networks by providing an integrated and holistic view of operational performance, enabling high levels of rail operations efficiency.

At present time the pile foundations widely are used on problematic soil grounds, such as a problematic soil grounds in North Kazakhstan or Astana city. In this direction in our country the foreign investors used their techniques and technology in mega projects and constructions – New Railway Station, Expo-2017 site, high-rise multifunctional complex “Abu-Dhabi Plaza” and Project “Light Railways Transport” (LRT). 1.1. Project New Railway Station The new dead-end station “Astana Passenger” has a capacity of 40 trains and 12000 passengers per day. The carriage rolling stock “Astana Passenger” station consist six dead-end tracks, a local VIP platform and three center platforms. The building of the railway station is a six-story and in the plan dimensions of 630 x116 m. The South (right) part of the building intended for the departure of passengers, the northern (left) part of the building intended for the arrival of the passengers. At the level +0.100 buildings are covered parking, including office and industrial facilities. Also in the pass space that links with each other parks can find shops, catering areas, the main green area. To communicate with the platform and concourse provided escalators, elevators and stairs. At the level +3.850 are car parks, technical and office facilities. On platforms (level +9.750) is the pavilion with escalators, elevators and stairs. Car access is provided to a center platform for the meeting of the official delegations. On the concourse (a level +19.600) is placed the main distributional room with sitting areas, service centers, offices and administrative offices of railway station. The station complex includes activities for people with limited mobility service (disabled persons). Railway station complex will be equipped with modern visual, acoustic and tactile information. Today, on the railway station monolithic concrete constructions has been established; more than 150.000 m³ of concrete has been laid. Around 26.000 tons of metal structures (88%) have been assembled. The glass mounted front system has been made installed for 15% (7200 m²). Approximately 20% of the roof (11 000 m²) has been already constructed. The construction of railroad overpass to the railway station is underway, about 26.7 thousand m³ (96%) concrete out of the total volume of 27.8 thousand m³ has been filled, more than 24.0 thousand tons (92%) of metal structures has been installed. Rails, concrete, metal, fittings, cement are used from domestic production. Mounted roof made by «VEMO» company from Germany, supporting parts under the railroad overpass – «MAGEVA» Switzerland. For the convenience of passengers a new building will be equipped with energy-efficient elevators and escalators. The underground heat-exchangers will be installed; they will heat the station with the help of thermal springs. This technology will allow the room to warm in winter and cool in summer. The project includes the construction of six receiving and departure tracks, the seventh path taking into account in the long-term expansion of the railway station. For this project were used the bored piles of 1500 mm diameter and 8.60 m in length and driving piles 300×300 mm, 12.0 m in length are designed to install in water saturated argillite (clayed soil).

With the rapid development of urban rail transit, huge energy consumption has attracted widespread attention of scholars. The energy consumption accounts for nearly half of the total energy consumption of the subway system. Therefore, the reduction in energy consumption of the subway makes a lot of sense. Domestic and foreign scholars have done a lot of research on the optimization of urban rail transit trains and achieved many results. Generally, research methods are divided into two categories [1]: analytical algorithms and numerical algorithms. The analytical algorithm is based on the factors of trains and lines and solves the problem of train energy saving in the form of mathematical expressions. Howlett [2] used maximum principle to search for the key operating points and then got the optimal solution of the problem. Khmelnitsky [3] used the maximum value principle to optimum the control of train with different road conditions; Liu [4] used the maximum value principle to optimize the train running curve combining the optimal control strategy with the train motion model; In order to minimize energy consumption, Liang[5] made the analysis of the optimal control under different road conditions, taking slopes and speed limits into consideration. In general, it is more likely to get the best results using analytical method, but it requires complex derivation, which appears to be the difficulty of solving the problem. On the other hand, the numerical method is also based on the train and road condition, but it is a method to complete the solution within a specified time in an iterative way. Compared with the analytical method, the numerical method is easier to deal with complex objective functions, so it has attracted the attention of many scholars. Wong [6] applied genetic algorithm to determine the number of coasting point; Jin [7] applied the neural network combined with genetic algorithm to optimize the speed curve of the train. However, the use of neural network for optimization may cause the problem of long training time and slow calculation speed; In 2016, Mohammad [8] established a multi-mass train motion model of the train and compared the optimization performance of the ant colony algorithm and the genetic algorithm. And it turned out to be better when using genetic algorithm to solve the problem of energy-saving of train; Li [9] proposed the principle of handle-level variation of the dichotomy of slopes, and used genetic algorithm to solve the relationship between train handle level and train position; Wang [10] used an adaptive genetic algorithm based on a phaseadapted adaptive strategy to search for the operating point.. However, this method has a good effect at the later stage of evolution, but is not favorable to evolution in the early stage of evolution which is more likely to fall into a local optimum during evolution. For these purposes, this paper aims at finding the optimal solution of energy-saving problem. The main organization of this paper is as follows: the motion model of the train is established and control strategies are analyzed in sectionĊ. The golden ratio genetic algorithm is introduced to solve the problem of optimal controlling of the train in section ċ. The method is verified under actual subway conditions in section Č and conclusions are shown in sectionč.

The current collection quality of pantograph–catenary directly determines the stable and safe operation of highspeed trains, which is one of the key factors restricting the highest driving speed of trains [1]. Due to the huge cost and difficulties of the field experiment in a real railway line, mathematical modeling has been a prevalent tool to study the dynamic performance of the pantograph–catenary system. Nowadays, the studies of modeling and simulation for railway catenary mainly focused on two points. The first one is the static solution for the initial equilibrium state, which is to calculate the static configuration of the catenary to make it meet the design requirements (such as the tension, arrangement of droppers and pre-sag). The other one is the dynamic solution for the pantograph–catenary interaction, including the dynamic modeling method, solution algorithm as well as some simulations for external disturbances, such as wind, iced line and irregularities of contact wire. In 2012, the Ministry of Railways of China proposed ‘High-speed Railway Power Supply Security Detection and Monitoring System,’ namely ‘6C’ system. The core idea is to use the captured pictures and video to realize the noncontact detection. However, the level of automatic image recognition needs to be improved. High-speed railway pantograph–catenary system is a random vibration system, and its dynamic characteristic evaluation is difficult. At present, the dynamic characteristic evaluation of pantograph–catenary system is mainly based on statistical parameters, such as the stationary mean value and variance in the European standard [2]. These evaluation indexes assume that the data of pantograph–catenary system is generalized stationary, which cannot satisfy the requirement for dynamic analysis of high-speed pantograph– catenary system; besides, it is difficult to evaluate the pantograph–catenary system performance efficiently using a single statistic. The overview in this paper mainly focuses on four aspects of the current research, which are the simulation, modeling, detection and evaluation of catenary. Firstly, the existing solution methods for the initial equilibrium state are summarized. The common solution methods for linear and nonlinear dynamics of catenary are classified and discussed. And, previous studies on the effect of environmental wind on catenary are reviewed. Then, non-contact detection techniques for catenary, especially various detection methods and strategies, are summarized. The state evaluation of catenary is reviewed, which mainly focuses on evaluation methods on the static and dynamic state of catenary and their validity. At last, addressing the current shortfalls, some suggestions are proposed for future research.

High-speed railways benefit society by greatly reducing the time distance between cities and by transporting more people than alternative public transportation modes such as airplane and longdistance bus. The development of these railways promotes industry accumulation in urban areas, develops tourism in rural areas, and, in some cases, changes the residential distribution of people. However, such railways are expensive to build owing to the enormous sunk costs such as the cost of track and expenses for land acquisition. Moreover, the basic plans of high-speed rail construction are often cancelled because of budget limitations. Thus, policy-makers must be clear that the construction justifies the investment.1 A hedonic approach is often used in evaluating the overall economic benefit of railways. While many studies have evaluated the impact of transportation infrastructure development, there are few studies sufficiently considering some estimation problem from the viewpoint of causal inference (e.g., Imbens and Wooldridge 2009; Imbens and Rubin 2015). In the impact evaluation of transportation infrastructure development on land price, there are mainly three estimation problems; namely omitted variable bias, location selection bias, and the timing of the treatment. The definition of the treatment groups is also a major problem when evaluating the impact of large-scale transportation infrastructure. This study investigates whether the benefit of high-speed railways is capitalized in residential land prices applying a method of impact evaluation (e.g., Angrist and Pischke 2009, 2010; Gertler et al. 2016) to hedonic model.2 We focus on the case of the Linear Chuo Shinkansen train (LCS hereafter) in Japan, the construction of which was announced in 2011 and which is scheduled to open in 2027.3 An analysis of the relationship between the construction of LCS and the rise in land price has useful features for making clear the issues to be considered in the hedonic approach. This study aims to present one solution to the three estimation problems and definition problem of treatment group by using balanced panel data on residential land prices from 2008 to 2015 and information on the time distance to large cities in Japan. This research measures a partial benefit in meaning it does not include the synergy with urban development newly planned after the analysis period until the start of LCS service. However, the focus of this study allows us to measure the total benefits due to shortening the time distance in the sense that it can eliminate the influence of regional development, which is carried out together with the construction of a large-scale transportation infrastructure.

Urban systems are made up of city nodes and various kinds of interactions (social, economic, and political) that materialize to some extent through transportation and information flows (Meijers, 2005; Devriendt et al., 2010). Even though information and communication technologies (ICTs) overwhelmingly facilitate instant communication, face-to-face interactions are still important in the contemporary world (Bertolini and Dijst, 2003). High-speed physical means of transportation, such as airlines and high-speed railways (HSR), which can dramatically decrease the geographic and temporal constraints of commuting for business transactions, tourism, post-migratory travel to keep social links with friends and relatives, academic collaborations, and political activities, are all crucial in facilitating the formation of functional urban systems (Hall and Pain, 2006). Given their important role in linking urban areas,the development of airlines and HSR has been supported with substantial capital and infrastructure investment in China. The development of both systems has been very rapid. The global ranking of China’s airline transportation networks, based on scheduled seats, was 37th in 1978, but rose to second place after 2005. The number of civil-certificated schedule airports in mainland China increased from 94 in 1990 to 216 in 2016 and is expected to reach 260 in 2020, according to the 13th five-year plan of China’s contemporary transportation system (Fu et al., 2012; NDRC, 2016). Although China’s HSR networks entered the transportation market at a late stage in 2003, they have become the largest in the world (a total of 19,000 km by the end of 2015, accounting for over 60% of the global figure), even though HSR length per capita is less spectacular due to the size of the country (Delaplace and Dobruszkes, 2016). This network served > 70% of the population and the cities involved account for 80% of GDP (Wang et al., 2015; NDRC, 2016). It should be noticed that, in 2016 the mode shares of HSR and airlines are 6.4% and 2.6%, respectively, compared to the 81.2% and 1.4% of highways and waterways (NBSC, 2017). These investments have stimulated the integration of the national urban network (Ng and Wang, 2012) and are seen as part of its future integration with Euro-Asian urban systems via the by the Belt and Road Initiative (BRI) (Liu and Dunford, 2016).1 A great deal of the existing literature exploring the functional relationships within urban systems has relied upon scheduled seat airline data, measuring the capacity of aircraft movements across the world (Smith and Timberlake, 2001; Derudder and Witlox, 2005; Derudder and Witlox, 2009; Van Nuffel et al., 2010). However, HSR travel has received less attention, and also the few available studies on the functional relationships within urban systems in Europe (Hall and Pain, 2006) and China (Zhang et al., 2016) at the regional and sub-regional levels are based on time schedule data not a measure of capacity. In most of the research carried out to date, airline and HSR travel have been studied separately. One exception is the study by Xiao et al. (2013), in which passenger data of conventional railways and airlines are used to estimate a reversed gravity model to identify the attractiveness of a limited number of cities in China. Several studies have used the supply and demand side of airline flow data to understand HSR’s impacts on domestic aviation in China (Chen, 2017; Wang et al., 2017).

مقاله سال 2016

 مجله امگا – Omega

برنامه ریزی توقف قطار، برنامه ریزی قطار، بهینه سازی مشارکتی، قطار سریع السیر

With the development of social economy, a large scale of highspeed railways have been put into operation or been being under construction in some countries to meet large passenger flow demands. Thus, effectively managing and operating the high-speed railways then becomes an important issue for the different railway companies. On the high-speed railway corridors, train stop planning and train scheduling, which are regarded as the most important parts of train operations and managements, have often been studied separately up to now due to the complexity of each involved problem. In practice, as a sub-problem of the train operating managements, the train stop plan is usually made on the basis of predictions of the potential passenger flow for different origin–destination pairs, and the generated stop plan needs to be adjusted repeatedly in order to satisfy the realistically changing requirements over the service time horizon. With the specified stop plan design, the scheduling process then aims to determine the arrival and departure times at each predetermined service station such that no operational conflicts occur between different trains, and expectedly, the resource utilization of the railway traffic system can be maximized. Typically, in comparison to the train stop plan, a train schedule can provide more detailed operational instructions for all the involved trains on the tactic level-based decision strategies.

In general, train stop planning and train scheduling are usually included in different pre-trip planning stages. For clarity, Lusby et al. [36] gave a detailed flowchart to show the railway planning process, as shown in Fig. 1. In this process, once a pre-specified train stop plan is changed according to the realistic requirements (for instance, busy transport during the Spring Festival in China), a new train schedule on the railway needs to be regenerated to satisfy the varied stop plan constraints. Obviously, this process essentially increases the complexity of railway operations. Aiming to produce a comprehensive operational plan on the tactic levels, we are particularly interested in how to design effective methods to collaboratively optimize these two problems, and then generate a system-optimization based planning strategy. Since this topic still has not attracted sufficient attention in the literature, we hereinafter shall address this issue formally.

1.1. Literature review Practically, the line planning and train scheduling are two significant parts for determining a detailed train operation plan. In the stage of line planning, one needs to specify the number of trains, type of trains, the stop plan for each train, etc., which is usually included in the strategic level-based decision making. In this process, the train stop planning is of particular importance for the real-world operations once the number and type of trains are provided. For this problem, the main task is to determine the stopping stations for each train on railway lines to satisfy the passenger demands with the minimum cost. In the literature, five kinds of train stop plans are often be considered in reality, including the all-stop operation, skip-stop operation, zonal operation, express/local operation and combined stop operation. Each type of these stop plans has its own advantages, disadvantages and applicable conditions. Typically, although the allstop operation is obviously the simplest stop planning for satisfying all passenger demands, it might probably enhance the total travel time of long-distance passengers. With this concern, Vuchic [45] further considered the skip-stop operation, zonal operation and express/local operation, and gave evaluation methods of using these stop strategies by summarizing, analyzing and comparing each of them. Zolfaghari et al. [56] pointed out that although stop skipping can effectively reduce the waiting times for passengers on boarding a vehicle and those at downstream stops, it still might increase the waiting time for passengers at skipped stops and those who are requested by the driver to alight at a given stop to wait for the next vehicle in service. According to the operating experience of Japanese Shinkansen, Lan [30] proposed that in designing operation plans of Beijing–Shanghai high-speed railway, staggered stop plan, direct and other forms of stops programs should be taken into account. In addition, Cheng and Peng [6] considered the combined stop plan with elastic demands. The computational results showed that the combined stop plan is more suitable for some special passenger flow.