Mining missing train logs from Smart Card data

This paper shows how to recover the arrival times of trains from the gate times of metro passengers from Smart Card data. Such technique is essential when a log, the set of records indicating the actual arrival and departure time of each bus or train at each station and also a critical component in reliability analysis of a transportation system, is missing partially or entirely. The procedure reconstructs each train as a sequence of the earliest exit times, called S-epochs, among its alighting passengers at each stations. The procedure first constructs a set of passengers, also known as reference passengers, whose routing choices are easily identifiable. The procedure then computes, from the exit times of the reference passengers, a set of tentative S-epochs based on a detection measure whose validity relies on an extreme-value characteristic of the platform-to-gate movement of alighting passengers. The tentative S-epochs are then finalized to be a true one, or rejected, based on their consistencies with bounds and/or interpolation from prescribed S-epochs of adjacent trains and stations. Tested on 12 daily sets of trains, with varying degrees of missing logs, from three entire metro lines, the method restored the arrival times of 95% of trains within the error of 24 s even when 100% of logs was missing. The mining procedure can also be applied to trains operating under special strategies such as short-turning and skip-stop. The recovered log seems precise enough for the current reliability analysis performed by the city of Seoul.     

Characteristics of particulate matter (PM) concentrations influenced by piston wind and train door opening in the Shanghai subway system

More than 9 million passengers take Shanghai’s subway system every work day. The system’s air quality has caused widespread concern because of the potential harm to passengers’ health. We measured the particulate matter (PM) concentrations at three kinds of typical underground platform (side-type, island-type, and stacked-type platforms) and inside the trains in Shanghai’s metro during 7 days of measurements in April and July 2015. Our results demonstrated that the patterns of air quality variation and PM concentrations were similar at the side-type and island-type platforms. We also found that the PM concentrations were higher on the platforms than inside the train and that the PM concentrations in the subway system were positively correlated with those in the ambient air. Piston wind generated by vehicle motion pushes air from the tunnel to the platform, so platform PM concentrations increase when trains approach the platform. However, the piston wind effect varies greatly between locations on the platform. In general, the effect of the piston wind is weaker at the middle of the platform than at both ends. PM concentrations inside the train increase after the doors open, during which time dirty platform air floods into the compartments. PM_1.0 and PM_2.5 were significantly correlated both inside the train and on the platforms. PM_1.0 accounted for 71.9% of PM_2.5 inside the train, which is higher than the corresponding platform values. Based on these results, we propose some practical suggestions to minimize air pollution damage to passengers and staff from the subway system.     

An investigation of passenger interchange and train standing time at LRT stations: (i) Alighting,boarding and platform distribution of passengers

The train standing-time at a station is a determinant of the line capacity and the necessary fleet-size. Its determination is usually based on the assumption that boarding and alighting is uniform at all doors of a train. Uniform boarding and alighting is conceivable if passengers distribute themselves uniformly on station platforms while waiting for trains. The validity of the uniformity assumptions is tested using data from two stations (one CBD, one suburban) of the Calgary, Alberta LRT system. It is shown that passenger distribution on the platform, alighting and boarding is not uniform and is closely related to the location of platform access points. Some strategies that will encourage uniformity are discussed. However, procedures that can estimate the standing time for non-uniform boarding and alighting need to be developed.     

Integrated optimization on train scheduling and preventive maintenance time slots planning

We address the problem of simultaneously scheduling trains and planning preventive maintenance time slots (PMTSs) on a general railway network. Based on network cumulative flow variables, a novel integrated mixed-integer linear programming (MILP) model is proposed to simultaneously optimize train routes, orders and passing times at each station, as well as work-time of preventive maintenance tasks (PMTSs). In order to provide an easy decomposition mechanism, the limited capacity of complex tracks is modelled as side constraints and a PMTS is modelled as a virtual train. A Lagrangian relaxation solution framework is proposed, in which the difficult track capacity constraints are relaxed, to decompose the original complex integrated train scheduling and PMTSs planning problem into a sequence of single train-based sub-problems. For each sub-problem, a standard label correcting algorithm is employed for finding the time-dependent least cost path on a time-space network. The resulting dual solutions can be transformed to feasible solutions through priority rules. Numerical experiments are conducted on a small artificial network and a real-world network adapted from a Chinese railway network, to evaluate the effectiveness and computational efficiency of the integrated optimization model and the proposed Lagrangian relaxation solution framework. The benefits of simultaneously scheduling trains and planning PMTSs are demonstrated, compared with a commonly-used sequential scheduling method.     

Non-discriminatory train dispatching in a rail transport market with multiple competing and collaborative train operating companies

Train dispatching is vital for the punctuality of train services, which is critical for a train operating company (TOC) to maintain its competitiveness. Due to the introduction of competition in the railway transport market, the issue of discrimination is attracting more and more attention. This paper focuses on delivering non-discriminatory train dispatching solutions while multiple TOCs are competing in a rail transport market, and investigating impacting factors of the inequity of train dispatching solutions. A mixed integer linear programming (MILP) model is first proposed, in which the inequity of competitors (i.e., trains and TOCs) is formalized by a set of constraints. In order to provide a more flexible framework, a model is further reformulated where the inequity of competitors is formalized as the maximum individual deviation of competitors’ delay cost from average delay cost in the objective function. Complex infrastructure capacity constraints are considered and modelled through a big M-based approach. The proposed models are solved by a standard MILP solver. A set of comprehensive experiments is conducted on a real-world dataset adapted from the Dutch railway network to test the efficiency, effectiveness, and applicability of the proposed models, as well as determine the trade-off between train delays and delay equity.     

基于指定到达追踪间隔的高铁车站接车进路长度研究

确定合理的高铁车站接车进路长度对压缩到达追踪间隔时间有重要意义。本文首先通过构建满足到达追踪间隔时间的高铁车站接车进路长度计算模型,提出了接车进路长度的主要影响因素为由线路限制速度、站前坡坡度、制动力使用系数三因素(简称三因素)所确定的车载设备监控制动距离内列车运行时间。然后,通过对常见的线路限制速度、站前坡坡度、制动力使用系数取值下的车载设备监控制动距离内列车运行时间进行牵引计算仿真,并运用三因素方差分析法分析了三因素的影响显著度,得到了线路限制速度、站前坡坡度对高铁车站接车进路长度影响显著的结论。最后,基于高铁车站接车进路长度计算模型,得到了一组指定到达追踪间隔下的高铁车站接车进路长度表,为高铁车站设计提供思路。     

On particulate emissions from moving trains in a tunnel environment

Increasing attention is being paid to airborne particles in railway environments because of their potential to adversely affect health. In this study, we investigate the contribution of moving trains to both the concentration and size distribution of particles in tunnel environments. Real-time measurements were taken with high time-resolution instruments at a railway station platform in a tunnel in Stockholm in January 2013. The results show that individual trains stopping and starting at the platform substantially elevate the particulate concentrations with a mobility diameter greater than 100 nm. Two size modes of the particulate number concentrations were obtained. A mode of around 170 nm occurs when a train moves, while the other mode peaks at about 30 nm when there is no train in the station. By using principal component analysis (PCA), three contributing sources were identified on the basis of the classification of the sizes of the particles, namely railway-related mechanical wear, suspension due to the movement of trains and sparking of electric-powered components. It is concluded that the particulate matter released by individual moving trains is a key contributor to fine particles (100–500 nm) on the railway platform in a tunnel.     

Scheduling and platforming trains at busy complex stations

We consider the problem of train planning or scheduling for large, busy, complex train stations, which are common in Europe and elsewhere, though not in North America. We develop the constraints and objectives for this problem, but these are too computationally complex to solve by standard combinatorial search or integer programming methods. Also, the problem is somewhat political in nature, that is, it does not have a clear objective function because it involves multiple train operators with conflicting interests. We therefore develop scheduling heuristics analogous to those successfully adopted by train planners using “manual” methods. We tested the model and algorithms by applying to a typical large station that exhibits most of the complexities found in practice. The results compare well with those found by traditional methods, and take account of cost and preference trade-offs not handled by those methods. With successive refinements, the algorithm eventually took only a few seconds to run, the time depending on the version of the algorithm and the scheduling problem. The scheduling models and algorithms developed and tested here can be used on their own, or as key components for a more general system for train scheduling for a rail line or network.Train scheduling for a busy station includes ensuring that there are no conflicts between several hundred trains per day going in and out of the station on intersecting paths from multiple in-lines and out-lines to multiple platforms, while ensuring that each train is allowed at least its minimum required headways, dwell time, turnaround time and trip time. This has to be done while minimizing (costs of) deviations from desired times, platforms or lines, allowing for conflicts due to through-platforms, dead-end platforms, multiple sub-platforms, and possible constraints due to infrastructure, safety or business policy.     

长途跨线高速列车直达与中转选择问题

我国快速客运网基本形成,长途跨线列车直达与中转选择对于高速列车开行方案设计至关重要。为提升长途跨线高速列车开行效益和服务品质,按长途跨线列车直达与中转方式对比的思路,以旅客出行需求、基础设施能力、移动设备能力等多方面因素对长途跨线列车开行方式的影响为约束条件,研究长途跨线列车直达与中转选择模型及求解方法。选取2025年15个长途跨线起讫点为对象进行案例研究,提出D站—E站、F站—D站等9个起迄点之间开行长途跨线直达列车的建议方案,研究表明直达与中转换乘选择模型能够解决长途跨线列车开行方案编制问题。     

Multiple-phase train trajectory optimization with signalling and operational constraints

The train trajectory optimization problem aims at finding the optimal speed profiles and control regimes for a safe, punctual, comfortable, and energy-efficient train operation. This paper studies the train trajectory optimization problem with consideration of general operational constraints as well as signalling constraints. Operational constraints refer to time and speed restrictions from the actual timetable, while signalling constraints refer to the influences of signal aspects and automatic train protection on train operation. A railway timetable provides each train with a train path envelope, which consists of a set of positions on the route with a specified target time and speed point or window. The train trajectory optimization problem is formulated as a multiple-phase optimal control model and solved by a pseudospectral method. This model is able to capture varying gradients and speed limits, as well as time and speed constraints from the train path envelope. Train trajectory calculation methods under delay and no-delay situations are discussed. When the train follows the planned timetable, the train trajectory calculation aims at minimizing energy consumption, whereas in the case of delays the train trajectory is re-calculated to track the possibly adjusted timetable with the aim of minimizing delays as well as energy consumption. Moreover, the train operation could be affected by yellow or red signals, which is taken into account in the train speed regulation. For this purpose, two optimization policies are developed with either limited or full information of the train ahead. A local signal response policy ensures that the train makes correct and quick responses to different signalling aspects, while a global green wave policy aims at avoiding yellow signals and thus proceed with all green signals. The method is applied in a case study of two successive trains running on a corridor with various delays showing the benefit of accurate predictive information of the leading train on energy consumption and train delay of the following train.     

A railway capacity determination model and rail access charging methodologies

This article discusses approaches to the determination of railway capacity and the significance of the following factors on capacity: mix of trains, length and weight of trains, direction of train travel, acceleration and deceleration, stopping protocols of trains, location and length of crossing loops, location of signals, length of sections, dwell times and sectional running times. A more accurate method to calculate railway capacity is developed using previously unaddressed aspects for capacity determination. Capacity and pricing are two key issues for organizations involved with open track access regimes. A train access charging methodology is therefore developed and incorporated into a railway capacity determination model.     

Stochastic prediction of train delays in real-time using Bayesian networks

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.     

Reduced dwell times resulting from train–platform improvements: the costs and benefits of improving passenger accessibility to metro trains

Abstract

This paper examines whether a dwell time reduction on a high-intensity metro service, as a result of a series of accessibility enhancements, can contribute to an increased level of service and accessible public transport for passengers together with a reduction in costs for the operator. Actual train operation data were collected by on-site observations and from London Underground Ltd. A simple simulation is built to represent the effect on the overall cycle times of trains if certain parameters (e.g. dwell time) are changed. Four models are developed, concerning: (1) step height between train and platform, (2) an assumption of passenger service time to be no longer than 20 s, (3) door width and (4) the combination of step height and door width. From the application of the models it appears that the fourth model provides the highest reduction in dwell time and diminishes the overall cycle times of trains. However, it is the most expensive to implement as it requires work to raise platforms and the purchase of new rolling stock.     

Standardization of capacity unit for headway-based rail capacity analysis

Determining the required capacity upgrades to accommodate future demand is a critical process in assisting public and private financing of capacity investments. Conventional railway systems usually operate multiple types of trains on the same track. These different types of trains can exert substantially different capacity impact, and can cause serious operational conflicts. In the past, rail line capacity is commonly defined as the maximum number of trains that can be operated on a section of track within a given time period. However, a specific unit (trains/hr or trains/day) does not reflect the heterogeneity of train types. According to the concept of base train equivalents (BTE) and base train unit (BTU), this study developed headway-based models to determine BTE for transforming different train types into a standard unit (i.e., BTU). An approximate method for lines with three and more types of trains was also proposed to compute BTEs for non-base trains. Results from the case studies demonstrate that this method enables the standardization of rail capacity unit, facilitates assessment of the impact from heterogeneous trains, and allows comparison and evaluation of the capacity measurements from different lines and systems.     

A heuristic for the train pathing and timetabling problem

In a railroad system, train pathing is concerned with the assignment of trains to links and tracks, and train timetabling allocates time slots to trains. These important tasks were traditionally done manually, but there is an increasing move toward automated software based on mathematical models and algorithms. Most published models in the literature either focus on train timetabling only, or are too complicated to solve when facing large instances. In this paper, we present an optimization heuristic that includes both train pathing and train timetabling, and has the ability to solve real-sized instances. This heuristic allows the operation time of trains to depend on the assigned track, and also lets the minimum headway between the trains to depend on the trains’ relative status. It generates an initial solution with a simple rule, and then uses a four-step process to derive the solution iteratively. Each iteration starts by altering the order the trains travel between stations, then it assigns the services to the tracks in the stations with a binary integer program, determines the order they pass through the stations with a linear program, and uses another linear program to produce a timetable. After these four steps, the heuristic accepts or rejects the new solution according to a Threshold Accepting rule. By decomposing the original complex problem into four parts, and by attacking each part with simpler neighborhood-search processes or mathematical programs, the heuristic is able to solve realistic instances. When tested with two real-world examples, one from a 159.3 km, 29-station railroad that offers 44 daily services, and another from a 345 km, eight-station high-speed rail with 128 services, the heuristic obtained timetables that are at least as good as real schedules.     

Multi-objective optimization of train routing problem combined with train scheduling on a high-speed railway network

Based on train scheduling, this paper puts forward a multi-objective optimization model for train routing on high-speed railway network, which can offer an important reference for train plan to provide a better service. The model does not only consider the average travel time of trains, but also take the energy consumption and the user satisfaction into account. Based on this model, an improved GA is designed to solve the train routing problem. The simulation results demonstrate that the accurate algorithm is suitable for a small-scale network, while the improved genetic algorithm based on train control (GATC) applies to a large-scale network. Finally, a sensitivity analysis of the parameters is performed to obtain the ideal parameters; a perturbation analysis shows that the proposed method can quickly handle the train disturbance.     

Train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: Nonlinear integer programming models with linear constraints

This paper focuses on how to minimize the total passenger waiting time at stations by computing and adjusting train timetables for a rail corridor with given time-varying origin-to-destination passenger demand matrices. Given predetermined train skip-stop patterns, a unified quadratic integer programming model with linear constraints is developed to jointly synchronize effective passenger loading time windows and train arrival and departure times at each station. A set of quadratic and quasi-quadratic objective functions are proposed to precisely formulate the total waiting time under both minute-dependent demand and hour-dependent demand volumes from different origin–destination pairs. We construct mathematically rigorous and algorithmically tractable nonlinear mixed integer programming models for both real-time scheduling and medium-term planning applications. The proposed models are implemented using general purpose high-level optimization solvers, and the model effectiveness is further examined through numerical experiments of real-world rail train timetabling test cases.     

Energy consumption,pollutant production,and dollar costs of diesel suburban commuter trains

The results presented in this report are based on data obtained from Chicago's three largest diesel commuter railroads. Those aspects of their operations that relate to energy and pollution are described. Service characteristics, such as average occupancy and average trip distance, are presented. Energy consumption results are presented and discussed. With energy efficiency measured in passenger-miles per Btu, it is found that trips by diesel commuter train are 3.5 times more energy efficient than Chicago Central Area auto trips. The total trip from home to suburban station, then by train to a downtown terminal, is found to be 2.2 times more energy efficient than Chicago Central Area auto trips. Pollutant production rates are presented for five pollutants. For every pollutant except sulfur oxides, trains are found to be less polluting per passenger-mile than autos. Per passenger-mile pollutant emissions from trains are, overall, less damaging by a factor of 5.5 than the per passenger-mile emissions from autos. Travel on these diesel commuter trains is less costly to society than auto travel (1972 suburban-based autos). This is the case whether one compares the train trip alone with an auto trip or the home-to-suburb an-station-tlien-to-a-downtown-terminal trip with a home-to-downtown auto trip.     

Precise estimation of connections of metro passengers from Smart Card data

The aim of this study is to estimate both the physical and schedule-based connections of metro passengers from their entry and exit times at the gates and the stations, a data set available from Smart Card transactions in a majority of train networks. By examining the Smart Card data, we will observe a set of transit behaviors of metro passengers, which is manifested by the time intervals that identifies the boarding, transferring, or alighting train at a station. The authenticity of the time intervals is ensured by separating a set of passengers whose trip has a unique connection that is predominantly better by all respects than any alternative connection. Since the connections of such passengers, known as reference passengers, can be readily determined and hence their gate times and stations can be used to derive reliable time intervals. To detect an unknown path of a passenger, the proposed method checks, for each alternative connection, if it admits a sequence of boarding, middle train(s), and alighting trains, whose time intervals are all consistent with the gate times and stations of the passenger, a necessary condition of a true connection. Tested on weekly 32 million trips, the proposed method detected unique connections satisfying the necessary condition, which are, therefore, most likely true physical and schedule-based connections in 92.6 and 83.4 %, respectively, of the cases.     

Exploring passenger anxiety associated with train travel

Although people are often encouraged to use public transportation, the riding experience is not always comfortable. This study uses service items to measure passenger anxieties by applying a conceptual model based on the railway passenger service chain perspective. Passenger anxieties associated with train travel are measured using a modern psychometric method, the Rasch model. This study surveys 412 train passengers. Analytical results indicate that the following service items cause passenger anxiety during trains travel: crowding, delays, accessibility to a railway station, searching for the right train on a platform, and transferring trains. Empirical results obtained using the Rasch approach can be used to derive an effective strategy to reduce train passenger anxiety. This empirical study also demonstrates that anxiety differs based on passenger sex, age, riding frequency, and trip type. This information will also prove useful for transportation planners and policy-makers when considering the special travel needs of certain groups to create a user-friendly railway travel environment that promotes public use.