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Accurate passenger flow forecasting in urban rail transit (URT) could provide a vital reference for operators' timely operation management. However, due to the enormous scale of the metro network, it is unwise to forecast passenger flows at the station scale individually. In this paper, a forecasting framework is proposed for network-scale forecasting tasks considering both accuracy and efficiency. There are mainly three stages in the forecasting framework. Firstly, three kinds of similarity measurements are presented regarding the adjacent similarity, geographic location similarity, and trend similarity. Secondly, three similarity graphs are formed by combining the three kinds of similarity measurements and flow time series. Thirdly, the multigraph network is applied to perform passenger flow forecasting. The experimental results indicated that the proposed method performs relatively accurately for the network-scale prediction with economic time costs. Specifically, the proposed model could acquire the feasible forecasts compared with the best disaggregate model using less than half of calculation costs, and above 10% reduction in root-mean squared error (RMSE) compared with the best aggregate model in the benchmark trials. Extensive contrast experiments were conducted to investigate the sensitivity and interpretability of the proposed model. [ABSTRACT FROM AUTHOR]

Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators' cost and passenger's service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions. Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first [ABSTRACT FROM AUTHOR]

The boarding/alighting process at railway platforms is an important determinant of the railway system performance and depends on the characteristics of passengers, the layout of the platform, and the rolling stock. This research aims to increase the understanding of the process, providing a methodological approach to model the passengers’ behaviour when boarding at railway platforms. Adequate criteria were selected to define the so called “boarding cluster” and an easy mechanism was developed to select the boarding clusters. Passenger flow data collected at Bern railway station in Switzerland was used to test the proposed approach. The results show that (a) the clusters near the doors grow in the longitudinal direction with a rate of 6:1 between the length and width of clusters, and that (b) the growth curves rise quickly when clusters are still small, i.e., at the beginning of the boarding/alighting activity. Further research is needed to extend the validation of the model, considering other variables, such as critical pedestrian densities which occur at specific hot spots near obstacles at platforms. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated discrete-event simulation (DES) using phase-type (PH) distribution and optimization method for the performance assessment, as well as optimal configuration of metro rail transit (MRT) station service facilities. The MRT facilities (including ticket facilities and elevator) are represented as a queuing network system with the passengers’ arrival flow and the service time based on the PH distribution, which considers the flow and service time fluctuation. The necessary performance measures include facility utilization, mean number of passengers and waiting time of passengers in the facilities are obtained by PH-based DES model of service facilities. The model is then coupled with a genetic algorithm that works simultaneously to give the optimal configuration of service facilities. The results of PH-based DES model are first verified by existing PH-based analytical models and then compared with other existing exponential-based and deterministic-based queuing models. The results reveal that increase in squared coefficient of variation of arrival interval causes an increase in the performance measure values and high number of ticket facilities compared to existing models. The existing models therefore underestimate the results. The squared coefficient of variation is an important parameter and cannot be ignored in analysis and design of service facilities. This research provides a more realistic and novel PH-based DES, as well as PH-based simulation-optimization method that will assist the planners and designers of MRT stations to make intelligent decisions regarding analysis and design. [ABSTRACT FROM AUTHOR]

Subway is an important transportation means for residents, since it is always on schedule. However, some temporal management policies or unpredicted events may change passenger flow and then affect passengers requirement for punctuality. Thus, detecting anomaly event, mining its propagation law, and revealing its potential impact are important and helpful for improving management strategy; e.g., subway emergency management can predict flow change under the condition of knowing specific policy and estimate traffic impact brought by some big events such as vocal concerts and ball games. In this paper, we propose a novel anomalies detection method of subway passenger flow. In this method, an improved robust principal component analysis model is presented to detect anomalies; then ST-DBSCAN algorithm is used to group the station-level anomaly data on space-time dimensions to reveal the propagation law and potential impact of different anomaly events. The real flow data of Beijing subway are used for experiments. The experimental results show that the proposed method is effective for detecting anomalies of subway passenger flow in practices. [ABSTRACT FROM AUTHOR]

In order to optimize the passenger flowlines and improve the operation capacity of Zhongchuan high-speed railway station, the simulation model of passenger flowlines is established based on the simulation software of Anylogic. The simulation of the passenger flowlines during peak and off-peak hours of the station are carried out respectively. The organization problems of passenger flowlines during peak hours are found out, and the optimization methods are put forward. The station capacity after optimization is compared with the original by employed the simulation model. The result shows, after optimization, although the walking distance of passengers who are entering the ticket hall to buy ticket is increased but the flowline separation effect is achieved, the flowlines become more clear, the cross interferences are reduced, and the station capacity is increased. [ABSTRACT FROM AUTHOR]

Optimising stopping patterns in railway schedules is a cost-effective way to reduce passengers’ generalised travel costs without increasing train operators’ costs. The challenge consists in striking a balance between an increase in waiting time for passengers at skipped stations and a decrease in travel time for through-going passengers, with possible consequent changes in the passenger demand and route choices. This study presents the formulation of the skip-stop problem as a bi-level optimisation problem where the lower level is a schedule-based transit assignment model that delivers passengers’ route choices to the skip-stop optimisation model at the upper level, and where the upper level in return provides an improved timetable to the lower level. A heuristic method for large-scale urban networks is presented to solve this extremely complex bi-level problem, where the skip-stop optimisation is a mixed-integer problem, whereas the route choice model is a non-linear non-continuous mapping of the timetable. The method was tested on the suburban railway network in the Greater Copenhagen Region (Denmark): the reduction in railway passengers’ in-vehicle travel time was 5.5%, the reduction in passengers’ generalised travel cost was 3.2% and, at the system level, the yearly consumer surplus amounted at 76.7 million DKK (about 10.3 million EUR or 12.7 million USD) when compared to the existing stopping patterns. [ABSTRACT FROM AUTHOR]

This study presents a fuzzy Bayesian network (FBN) method to analyze the influence on the safety risk of railway passenger transport applying different risk control strategies. Based on the fuzzy probability of the basic event determined by the expert group decision method, the proposed FBN method can reasonably predict the probability of railway passenger safety risk. It is also proven that control the risk in the safety management of railway passenger transport will be the most effective way to reduce the risk probability of the railway passenger transport safety. [ABSTRACT FROM AUTHOR]

The direction in research of the efficiency of decision-making units in this paper is an efficient→multi-inefficient→multi-efficient unit. So, the general purpose of this paper is twofold: (1) identification of «hidden» inefficient units within a multi-set, among efficient units of the basic set, and (2) achieving the efficiency in such identified inefficient units. This indicates (warns of!) a negative efficient→inefficient process, so as to provide a timely response and thereby prevent multi-inefficiency. The specific goal is to assess the efficiency of the Serbian railway passenger stations, first within the basic set of the Passenger Transport Section Belgrade, then in the multi-set of the Passenger Transport Sections, and finally in the superset, the Passenger Transport Sector. This is achieved by means of the multi-set DEA (Data Envelopment Analysis) method, which is a system for: (i) relative efficiency assessment, in the first iteration, through the basic set analysis, and (ii) decrease in efficiency of potentially inefficient units, in subsequent iterations, through the multi-set analysis. The result is that the efficient stations Požarevac and Pančevo Bridge are at the initial level, and the (newly) efficient Požarevac, Novi Sad and Inđija at the final level. The best practice station remains the Požarevac Station, which is multi-efficient, and therefore the role model to inefficient stations. The conclusion is drawn that the solution resulting from the multi-set DEA analysis is more realistic, and less relative, because it applies to a wider analysed set of decision-making units, i.e., a larger coverage when considering the issue. This is important for fitting into the new era of growing globalization, and therefore our recommendation is the integral multi-set, as opposed to the individual single set approach. [ABSTRACT FROM AUTHOR]

The use of double-unit trains is becoming a common means of increasing passenger capacity for a rail network. However, their expanded usage may create additional aerodynamic challenges. The present work obtains the characteristics of the slipstream caused by single and double unit trains using the detached eddy simulation (DES) method for 1/20th scaled models. The numerical results are verified by full-scale experiments. The slipstream velocities and pressures obtained by the two train models at different distances from the center of track (COT) and the top of rail (TOR) are compared. The coupling structure of the double-unit train model is found to produce a velocity peak that is much greater than that of the single-unit train model in the same position. At the area away from the COT and close to the TOR, the velocity of the far wake region is larger for the double-unit train model. The coupling structure also leads to a positive pressure change in the coupling region, and its value is comparable to or even much greater than that caused by the nose. It is considerable that the subsequent pressure criteria could take the positive pressure on the coupling region into account for the double-unit train. [ABSTRACT FROM AUTHOR]

Short-term forecasting of OD (origin to destination) passenger flow on high-speed rail (HSR) is one of the critical tasks in rail traffic management. This paper proposes a hybrid model to explore the impact of the train service frequency (TSF) of the HSR on the passenger flow. The model is composed of two parts. One is the Holt-Winters model, which takes advantage of time series characteristics of passenger flow. The other part considers the changes of TSF for the OD in different time during a day. The two models are integrated by the minimum absolute value method to generate the final hybrid model. The operational data of Beijing-Shanghai high-speed railway from 2012 to 2016 are used to verify the effectiveness of the model. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of the TSF. [ABSTRACT FROM AUTHOR]

Transit oriented development (TOD) has been an important topic for urban transportation planning research and practice. This paper is aimed at empirically examining the effect of rail transit station-based TOD on daily station passenger volume. Using integrated circuit (IC) card data on metro passenger volumes and cellular signaling data on the spatial distribution of human activities in Shanghai, the research identifies variations in ridership among rail transit stations. Then, regression analysis is performed using passenger volume in each station as the dependent variable. Explanatory variables include station area employment and population, residents’ commuting distances, metro network accessibility, status as interchange station, and coupling with commercial activity centers. The main findings are: (1) Passenger volume is positively associated with employment density and residents’ commuting distance around station; (2) stations with earlier opening dates and serving as transfer nodes tend to have positive association with passenger volumes; (3) metro stations better integrated with nearby commercial development tend to have larger passenger volumes. Several implications are drawn for TOD planning: (1) TOD planning should be integrated with rail transit network planning; (2) location of metro stations should be coupled with commercial development; (3) high employment densities should be especially encouraged as a key TOD feature; and (4) interchange stations should be more strategically positioned in the planning for rail transit network. [ABSTRACT FROM AUTHOR]

An urban subway network with a number of service lines forms the backbone of the public transport system for a large city of high population, such as Singapore, Hong Kong and Beijing. Passengers in these large cities heavily rely on urban subway networks for their daily life. The departure times of the last trains running on different lines of an urban subway network should be well coordinated in order to serve more passengers who can successfully transfer from one line to another, which is referred to as the last train departure time choice problem. This study aims to develop a global optimization method that can solve the last train departure time choice problem for large-scale urban subway networks. To do so, it first formulates a mixed-integer linear programming (MILP) model by introducing auxiliary binary and integer decision variables. For the real-life and large-scale instances, however, the formulated MILP model cannot be solved directly by the global optimization methods such as branch-and-bound algorithm invoked by CPLEX – one of the powerful optimization solvers because of the instance sizes. An effective two-phase decomposition method is thus proposed to globally solve the large-scale problems by decomposing the original MILP into two MILP models with small sizes. Finally, a real case study from the Beijing subway network is conducted to assess the efficiency and applicability of the two-phase decomposition method and perform the necessary sensitivity analysis of the operational parameters involved in the last train departure time choice problem. [ABSTRACT FROM AUTHOR]

Tram drivers have a difficult task in controlling one of the heaviest vehicles on the road whilst negotiating a complex road environment with multiple road users. Like all public transport drivers, tram drivers need to ensure passenger safety and to run on time. However, very little research has been conducted evaluating tram driving tasks and even less on evaluating tram drivers opinion on how other road users are affecting tram road safety. Therefore, the aim of this study is to investigate the key tram driving challenges, to identify the key road user factors affecting tram road safety as well as to explore the potential safety improvement initiatives on tram routes from the tram drivers’ viewpoint. The study incorporated five focus groups involving thirty tram drivers in Melbourne. The key themes emerged inductively from focus groups were identified through a data coding process. Outcomes of the focus groups revealed seven major challenges in tram driving: ensuring safety for all people in and around the tram, pressure for running on-time, maintaining constant concentration on roads, predicting other road users’ behavior in advance to avoid any crash incident, preventing passenger falls on board, accepting the operational constraints of trams and managing fatigue workloads. Tram drivers identified that other road users are unaware of safety issues around trams, have a poor understanding of road rules about driving with trams and often violate road rules around trams, and they mentioned this road user behaviors as the key challenges for safe tram driving. Tram drivers proposed rendering greater law enforcement on the tram network to penalize road users who are violating road rules around trams, introducing more safety campaigns and safety education to increase awareness among road users to improve tram road safety. Findings of this research enhance understanding of tram driving challenges, provide an in-depth knowledge of road user factors affecting tram road safety and suggest effective planning strategies for transit agencies to improve road safety. [ABSTRACT FROM AUTHOR]

Railways are required to operate with a high level of reliability and safety for transporting passengers within a certain budget. One important way of meeting these requirements is to implement proper maintenance activities. However, maintenance cost is always under pressure for operating company. Considering the main maintenance activities for rail, this paper discusses a model for systematic and holistic cost analysis using a Petri nets (PN) methodology. By developing sub-models including: deterioration, inspection, lubrication, maintenance and renewal, a whole model of rail maintenance process is developed based on PN. The Monte Carlo method is applied to the model to simulate the maintenance process and to evaluate the related cost. The application of the model is demonstrated by an example. The model is solved, and the maintenance cost during the rail’s life cycle is presented. The costs resulting from different maintenance activities are also simulated and discussed. [ABSTRACT FROM AUTHOR]

As train passengers expect a high degree of reliability from a railway network with minimal delays, during the timetabling process planners need to balance the goals of maximizing the offered capacity and delay resistance. This is often done in a two-step process where first a feasible timetable is found for a given line structure, and consecutively the stability of this timetable is evaluated and local modifications are performed to the timetable. This paper describes an optimization method to find a feasible periodic timetable that also ensures maximum stability for heterogeneous railway networks. The model is capable to handle flexible train orders, running and dwell times, and overtaking locations. We use the minimum cycle time of the periodic timetable as an indicator for stability, and define an optimization problem with this minimum cycle time as the objective function to be minimized. We also present dimension reduction methods and an iterative optimization approach to improve the mathematical optimization process. We show the applicability of the approach with case studies on the central part of the Dutch railway network. [ABSTRACT FROM AUTHOR]

We have compared Swedish national forecasts for passenger transport produced from 1975 to 2009 with the actual outcomes, and we found substantial differences between forecasts of passenger kilometers by mode and actual outcomes. In forecasts produced since the early 1990 s, road and air traffic growth rates have generally been overpredicted. Aggregate railway growth has been fairly accurate, but commercial long-distance railway growth has been overpredicted, and the growth of subsidized intra-regional railway travel has been underpredicted (following vast unanticipated supply increases). Focusing on car traffic forecasts, we show that a very large share of forecast errors can be explained by input variables turning out to be different than what was assumed in the forecasts. Even the original forecasts are much closer to actual outcomes than simple trendlines would have been, and once the input assumptions are corrected, the forecasts vastly outperform simple trendlines. The potential problems of using cross-sectional models for forecasting intertemporal changes thus seem to be limited. This tentative conclusion is also supported by the finding that elasticities from the cross-sectional models are consistent with those from a time-series model. [ABSTRACT FROM AUTHOR]

Freight railroads contribute to the national economy by moving over 40% of intercity ton-miles of freight. Meanwhile, train accidents can damage infrastructure and rolling stock, disrupt operations, and possibly cause casualties and harm the environment. Understanding major accident causes is the first step in developing and prioritizing effective accident prevention strategies. The literature has predominantly focused on nationwide train accident cause analysis, without accounting for possible variation in accident cause distributions by railroad and season. This research develops a log-linear statistical model that can estimate the number of freight-train derailments accounting for railroad, accident cause, season, and traffic volume. The analysis shows that broken rails and track geometry defects are the two leading freight-train derailment causes on four major U.S. freight railroads. Fall and winter appear to have a higher likelihood of a broken-rail-caused derailment than spring and summer, given the same railroad and traffic level. By contrast, track-geometry-defect-caused derailments occur more frequently in spring and summer than in fall and winter, given all else being equal. The statistical modeling techniques in this paper can be adapted to other types of train accidents or accident causes, ultimately leading to the prioritization of train safety improvement resources on various spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Among today’s vast data availability, traffic probe data in particular offer exceptional new opportunities for examining multistate mobility. Invaluable resources such as the National Performance Management Research Data Set (NPMRDS) allow users to assess mobility performance toward improving transportation operations and reliability at a megaregion scale. In this study performance measures are developed for interstate freight mobility consistent across Mid-America, followed by an anomaly scanner that detects significant impacts to mobility such as those from major incidents, work zones, or winter weather. The scanner functions through a process control algorithm developed to scan tens of thousands of interstate kilometers each month to identify major disruptions to passenger and freight mobility. The scanner was validated against several known disruptions, and now provides monthly feedback to coalitions collaborating on multistate mobility performance improvement. [ABSTRACT FROM AUTHOR]

In the last decade the number of rail passenger journeys in Great Britain has increased by half and car trips per person are down by a tenth. Meanwhile there has been significant growth in internet use and ownership of smartphones. Travel patterns are changing in tandem with adoption of digital age innovations. At a time when Britain is also poised to invest tens of billions of pounds in high speed rail, this paper examines how the experience of rail passengers has changed from 2004 to 2014. It draws upon questions concerning travel time use designed by its first two authors that have been included in the National Rail Passenger Survey waves conducted in Autumn 2004, 2010 and 2014, yielding over 80,000 survey responses in total. The data reveal an ongoing decline of paper-based materials accompanying travellers in the face of increasing adoption of digital alternatives. The latter can, for many, make their journey time experience better. However, the significant increase from 2004 to 2010 in the proportion of passengers considering their time use to have been very worthwhile has not continued on to 2014. This appears to be attributable (in part) to increased crowding and reduced passenger satisfaction associated with rising demand for rail travel. The paper sets out empirical insights, drawing them together in the form of a diagrammatic depiction of the interplay of factors involved in rail passenger experience. This depiction is then used to consider the implications for the future of rail travel. While mobile technologies appear to be placing more control of passenger experience in the hands of the passengers themselves, there remain important imperatives for the rail industry to support positive use of travel time. [ABSTRACT FROM AUTHOR]

We propose a Mixed Integer Non-Linear Programming (MINLP) model in order to determine optimal line frequencies and capacities in dense railway rapid transit (RRT) networks in which typically several lines can run over the same open tracks. Given a certain demand matrix, the model determines the most appropriate frequency and train capacity for each line taking into account infrastructure capacity constraints, allocating lines to tracks while assigning passengers to lines. The service provider and the user points of view are simultaneously taken into account. The first one is considered by selecting the most convenient set of frequencies and capacities and routing passengers from their origins to their destinations while minimizing the average trip time. The second one by minimizing operation, maintenance and fleet acquisition costs. Due to the huge number of variables and constraints appearing in real size instances, a preprocessing phase determining the best k-paths linking origin and destination stations is followed. Then, the best paths are used to define sparse index sets in order to drastically reduce the size of the problem. As illustration, the model is applied to a simplified version of the Madrid Metropolitan Railway network. [ABSTRACT FROM AUTHOR]

Analysis of the state of technical equipment and railway infrastructure in the example section of the Tashkent-Andijan shows the possibility of increasing the speed of passenger trains. The results can be used in the reconstruction of railways operating under similar conditions, in order to increase train speed. [ABSTRACT FROM AUTHOR]

We present the literature's first mixed-integer linear programming model of a cyclic, combined train timetabling and platforming problem. The model's objectives are to minimize (1) the cycle length and (2) the total journey time of all trains dispatched during one cycle. The model falls outside the framework of the well-known periodic event scheduling problem and explicitly considers the minimization of cycle length using linear constraints and a linear objective function. We define the model, propose methods for obtaining bounds on the optimal objective value, and describe preprocessing techniques for reducing the number of variables and constraints. Numerous life-size problem instances are solved to optimality using IBM ILOG CPLEX. Results show the model's effectiveness in pursuing objectives 1 and 2, the benefits of deciding the cyclic train order versus assuming a given order, and the model's ability to calculate railway capacity without bias. [ABSTRACT FROM AUTHOR]

This paper proposes a highly integrated solution approach for rolling stock planning problems in the context of long distance passenger traffic between cities. The main contributions are a generic hypergraph-based mixed-integer programming model for the considered rolling stock rotation problem and an integrated algorithm for its solution. The newly developed algorithm is able to handle a large spectrum of industrial railway requirements, such as vehicle composition, maintenance constraints, infrastructure capacities, and regularity aspects. We show that our approach has the power to produce rolling stock rotations that can be implemented in practice. In this way, the rolling stock rotations at the largest German long distance operator Deutsche Bahn Fernverkehr AG could be optimized by an automated system utilizing advanced mathematical programming techniques. [ABSTRACT FROM AUTHOR]

Among different modes of transportation, travel through train is very comfortable and enjoyable especially for people travelling long distances. Population of people increases day by day and hence the requirements also increases in par with the population. Due to increase in population, most of them are not getting the seats at times. In order to overcome this, number of seats could be increased without modifying the outer dimensions of the coach which will be helpful and beneficial for the required people. The proposed idea is to increase the passenger carrying capacity of coaches of Indian Railways from 72 to 84 per coach. 12 seats per coach are increased when compared to existing non-AC 3-tier sleeper coach. Separate ladder provisions for climbing on to the upper compartments, additional windows, power socket for each seat are also provided. [ABSTRACT FROM AUTHOR]

This paper proposes a schedule-based passenger assignment method for high-speed rail networks considering the ticket-booking process. Passengers book tickets to reserve seats during the presale period in high-speed rail systems and passengers on trains are determined during the ticket-booking process. The ticket-booking process is modeled as a continuous and deterministic predecision process. A solution algorithm is designed using the discretization of the continuous process by partitioning the ticket-booking time and the optimal paths remain constant in any partition interval. Finally, an application to the Chinese high-speed rail network is presented. A comparison of the numerical results with the reality is conducted to validate the efficiency and precision of the method and algorithm. Based on the results, the operating efficiency of the current train schedule is evaluated and some specific improvement measures are proposed. [ABSTRACT FROM AUTHOR]

We study a tactical level crew capacity planning problem in railways which determines the minimum required crew size in a region while both feasibility and connectivity of schedules are maintained. We present alternative mathematical formulations which depend on network representations of the problem. A path-based formulation in the form of a set-covering problem along with a column-and-row generation algorithm is proposed. An arc-based formulation of the problem is solved with a commercial linear programming solver. The computational study illustrates the effect of schedule connectivity on crew capacity decisions and shows that arc-based formulation is a viable approach. [ABSTRACT FROM AUTHOR]

The operating scheme of urban rail traffic is closely related to the service level and operating costs. In particular, a study on the optimization of the operating scheme under express-slow mode is of great value. Hidden problems were revealed when an attempt was made to improve the service level. Consequently, how to promote a better service and how to solve operating safety issues are discussed in this paper. The method aims firstly at providing a better service; the entire travel time of passengers was analyzed, and a time-cost algorithm model was established, to minimize the travel time. Then, an optimized model was built and the ant colony algorithm was applied to seek the best solutions for both operators and passengers. Meanwhile, a new problem arose: there was not enough braking distance for the trains to stop in an emergency situation. Finally, the relationship between braking distance and velocity was analyzed, and we were able to assure velocity before braking. The results were that the service level under express-slow mode can be promoted through the optimized model, which also saved passengers' time. It was found that locating the critical point of the braking distance and speed was of great importance. The study has a vital significance and can assist decisions made in support of urban rail transit operations. [ABSTRACT FROM AUTHOR]

This paper discusses a proposal of methodology of revitalisation of regional railway tracks and regional railway traffic, liberalization and competition in railway transport, quality requirements in regional traffic, principles of the contract on transport services in the public interest and with the current state of regional passenger transport in Slovakia and Serbia. This is Part 2 which is dedicated to study the current situation of regional railway transport in Slovakia and Serbia, the investment plans, the potential changes on the railway transport market and the quality of transport service provided in these countries. [ABSTRACT FROM AUTHOR]

The frequent incidents caused by metro passengers in China suggest that it is necessary to explore the classification and effects of passenger behaviors and their relations to incident involvement. A metro passenger behavior questionnaire (MPBQ) and a metro station staff questionnaire (MSSQ), both comprising 32 behavior items, were developed and surveyed on a sample of metro passengers ( N = 579) and metro staff ( N = 99). Using the MPBQ, the self-reported frequency of each aberrant behavior was measured and subjected to explanatory factor analysis, which revealed a three-factor solution on the 28 retained behavior items: transgressions, self-willed inattentions and abrupt violations. ANOVA was used to examine the effects of demographic and riding profile variables on different types of behaviors. The MSSQ was used to collect metro staff opinions on behavior frequency, severity and entities that might be affected, given that a specific behavior occurred. An importance hierarchy was established over the 32 identified behaviors to determine the most important riding behaviors. Finally, logistic regression showed that riding time, number of stops experienced by a passenger and, more importantly, transgressions and abrupt violations, were significant predictors of incident involvement. The possible explanations and implications of the findings might help in understanding passenger behaviors and targeting metro safety interventions in ways that promote safer operations. [ABSTRACT FROM AUTHOR]

European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the transport planners. The model was built using common Machine Learning techniques that learn from the past experience. In the model preparation, world development indicators defined by the World Bank were used as input parameters. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transit, the phenomenon of outburst passenger flows flocking to stations is occurring much more frequently. Passenger flow control is one of the main methods used to ensure passengers’ safety. While most previous studies have only focused on control measures inside the target station, ignoring the collaboration between stops, this paper puts emphasis on joint passenger control methods during the occurrence of large passenger flows. To provide a theoretic description for the problem under consideration, an integer programming model is built, based on the analysis of passenger delay and the processes by which passengers alight and board. Taking average passenger delay as the objective, the proposed model aims to disperse the pressure of oversaturated stations into others, achieving the optimal state for the entire line. The model is verified using a case study and the results show that restricted access measures taken collaboratively by stations produce less delay and faster evacuation. Finally, a sensitivity analysis is conducted, from which we find that the departure interval and maximum conveying capacity of the train affect passenger delay markedly in the process of passenger control and infer that control measures should be taken at stations near to the one experiencing an emergency. [ABSTRACT FROM AUTHOR]

Public transit has been widely recognized as a potential way to develop low-carbon transportation. In this paper, an optimal allocation model of public transit mode proportion (MPMP) has been built to achieve the low-carbon public transit. Optimal ratios of passenger traffic for rail, bus, and taxi are derived by running the model using typical data. With different values of traffic demand, construction cost, travel time, and accessibilities, MPMP can generate corresponding optimal ratios, benefiting decision impacts analysis and decision makers. Instead of considering public transit as a united system, it is separated into units in this paper. And Shanghai is used to test model validity and practicality. [ABSTRACT FROM AUTHOR]

The current paper focuses its study on the Southeast Europe (SEE) and tries to identify real passenger needs (demand) which can reveal rail dynamic and competitiveness in specific connections. Based on a dedicated examination of 11 SEE city hubs (Bologna, Venice, Trieste, Ljubljana, Vienna, Bratislava, Budapest, Thessaloniki, Sofia, Zagreb and Bucharest) examined within "Rail4SEE -- Rail Hub Cities for South East Europe" project, the paper aims to conclude in a quantitative analysis of demand data at regional and transnational level and on a comparative analysis based both on demand and supply data that will open the ground for the development of an accurate SEE modal split model. [ABSTRACT FROM AUTHOR]

Waiting time influences the overall perception of service quality. The passenger-perceived waiting time can determine their waiting experience. The concept of waiting time refers to the comparison between the passengers' inherent tolerance of waiting and the possible improvement scenarios. This study investigates the passengers' tolerance of waiting under various scenarios of train delays in order to improve their perceived waiting time. We propose the adoption of a modern psychometric method utilizing the Rasch model to measure a subjective latent construct known as ‘wait tolerance'. The Rasch measurement provides mathematical procedures for transforming scores from an ordinal to an interval scale to observe which scenarios can reduce certain passengers' perceived waiting time in the case of a delay. Empirical results show that ‘uncontrollable circumstances', ‘friendly staff attitudes', and ‘providing appropriate messages of apology' can improve the passenger-perceived waiting time during train delays. Likewise, distinct differences are found in the passengers' tolerance of waiting in terms of various personal characteristics, such as gender, age, and train riding frequency. The findings propose the implementation of strategies for improvement by rail system operators, as well as for regulators to define a reasonable service level in the case of train delays. The reviews show possible future innovative research orientations as well. [ABSTRACT FROM PUBLISHER]

This paper analyzes the morning peak-period commuting pattern in a one- to-many transit system. Commuters make their optimal time-of-use decision by trading off the travel time and crowding cost against the schedule delay cost. An equivalent mathematical programming model is proposed to characterize the equilibrium state, we deduce five equilibrium properties of the morning peak-period commuting in a one-to-many mass transit system. 1) Commuters of the same destination disperse in continuous train services and the number of commuters reaches its peak value in the train which arrives the destination at desired time. 2) Earlier trains which are choosed by short distance commuters carry long distance commuters simultaneously. 3) When earlier trains arrive at specific stations, the numbers of commuters in vehicle are constant independent of the train number. 4) When later trains leave specific stations, the number of commuters in vehicle are constant independent of the train number. 5) In specific region of trains, the number of commuters in later trains is less than earlier trains. Illustrated by two numerical examples, all equilibrium states accord with these properties. [ABSTRACT FROM AUTHOR]

In the present paper, hyper-Erlang distribution (HErD) which is a restricted class of phase-type distribution (PH) with good versatility and analyticity and seven common distributions are used to fit the distribution of time interval between arriving passengers. From the comparison of a large number of experiments, the fitting effect of HErD is proven to be the best and the applicability of all distributions is given. Then, the fitting parameters of HErD are extracted to analyze the law of their variations, and it demonstrates that when the coefficient of variation is smaller than 1, the distribution is mixed by one exponential distribution (ED) and one 4-order Erlang distribution (ErD), in which there's a power functional relationship between the mixing ratio of ED and the square of coefficient of variation. However, when the coefficient of variation is larger than 1, the distribution is mixed by two EDs, in which there's an S functional relationship between the mixing ratio of one ED and square of coefficient of variation. As a result, the parameters of HErD are simplified into 2 scale parameters determined by the given long-term peak traffic and super- peak coefficient, and thus the PH distribution and PH queuing model can be introduced to the systemic planning and design of urban rail transit station, which will improve the rationality of the design. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose an empirical study of railway revenue management using ticket reservation data. [•] The latent class choice model is used to account for passenger taste heterogeneity. [•] The proposed formulation allows for simultaneous optimization of pricing and seat allocation. [•] The approach results in significant revenue gain by accounting for passenger choice behavior. [•] The method can be transferred to other problems for which just internet booking data are available. [ABSTRACT FROM AUTHOR]

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-termpassenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway. [ABSTRACT FROM AUTHOR]

The article focuses on the demand for passenger rail in Spain after the extension of the Spanish high-speed rail (HSR) network. It is reported that a large increase in HSR travel demand occurred during the expansion Spanish HSR network in 2005-2009. It discusses reasons for the low impact of the HSR expansion on modal share as compared to other European countries.

The climate impact from a long-distance trip can easily vary by a factor of 10 per passenger depending on mode choice, vehicle efficiency, and occupancy. In this paper we compare the specific climate impact of long-distance car travel with coach, train, or air trips. We account for both, CO2 emissions and short-lived climate forcers. This particularly affects the ranking of aircraft's climate impact relative to other modes. We calculate the specific impact for the Global Warming Potential and the Global Temperature Change Potential, considering time horizons between 20 and 100 years, and compare with results accounting only for CO2 emissions. The car's fuel efficiency and occupancy are central whether the impact from a trip is as high as from air travel or as low as from train travel. These results can be used for carbon-offsetting schemes, mode choice and transportation planning for climate mitigation. [ABSTRACT FROM AUTHOR]

Purpose: The train dispatching is a crucial issue in the train operation adjustment when passenger flow outbursts. During holidays, the train dispatching is to meet passenger demand to the greatest extent, and ensure safety, speediness and punctuality of the train operation. In this paper, a fuzzy passenger demand forecasting model is put up, then a train dispatching optimization model is established based on passenger demand so as to evacuate stranded passengers effectively during holidays. Design/methodology/approach: First, the complex features and regularity of passenger flow during holidays are analyzed, and then a fuzzy passenger demand forecasting model is put forward based on the fuzzy set theory and time series theory. Next, the bi-objective of the train dispatching optimization model is to minimize the total operation cost of the train dispatching and unserved passenger volume during holidays. Finally, the validity of this model is illustrated with a case concerned with the Beijing-Shanghai high-speed railway in China. Findings: The case study shows that the fuzzy passenger demand forecasting model can predict outcomes more precisely than ARIMA model. Thus train dispatching optimization plan proves that a small number of trains are able to serve unserved passengers reasonably and effectively. Originality/value: On the basis of the passenger demand predictive values, the train dispatching optimization model is established, which enables train dispatching to meet passenger demand in condition that passenger flow outbursts, so as to maximize passenger demand by offering the optimal operation plan. [ABSTRACT FROM AUTHOR]

Purpose: Urban rail transit is a complex and dynamic system, which is difficult to be described in a global mathematical model for its scale and interaction. In order to analyze the spatial and temporal characteristics of passenger flow distribution and evaluate the effectiveness of transportation strategies, a new and comprehensive method depicted such dynamic system should be given. This study therefore aims at using simulation approach to solve this problem for subway network. Design/methodology/approach: In this thesis a simulation model based on multi-agent approach has been proposed, which is a well suited method to design complex systems. The model includes the specificities of passengers' travelling behaviors and takes into account of interactions between travelers and trains. Findings: Research limitations/implications: We developed an urban rail transit simulation tool for verification of the validity and accuracy of this model, using real passenger flow data of Beijing subway network to take a case study, results show that our simulation tool can be used to analyze the characteristic of passenger flow distribution and evaluate operation strategies well. Practical implications: The main implications of this work are to provide decision support for traffic management, making train operation plan and dispatching measures in emergency. Originality/value: A new and comprehensive method to analyze and evaluate subway network is presented, accuracy and computational efficiency of the model has been confirmed and meet with the actual needs for large-scale network. [ABSTRACT FROM AUTHOR]

Purpose: In recent years the problem of traffic congestion and its management has become increasingly prominent. It is a hot research about how to make full use of computer simulation technology to make transportation more rational and more organized. In this paper, we focus on traffic of Beijing West Railway Station north area, and try to find a way to reduce traffic congestion in this area. Approach: In this paper, we studied the traffic flow by survey. We also built a traffic simulation model with VISSIM software. Different types of vehicles and their speed are set in model according survey data. The simulation model provides different traffic scenarios of Beijing West Railway Station north area. Findings: We found the traffic of this area up is to 1800 vehicles/hour. Heavy traffic burden causes traffic congestion in two positions: the bus hub and car drop-off point. If we can extend bus interval departure time and park cars to south square of Beijing West Railway Station, the traffic condition will be improved. Originality: This paper gives a solution to reduce traffic congestion in Beijing West Railway Station north area. The bus hub and car parking lots are the key point of traffic problem in this area. [ABSTRACT FROM AUTHOR]