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.     

Train boarding and alighting rates at high passenger loads

Line capacity in metro and high‐frequency suburban railways is as much determined by station stop times as by factors such as line speed or train acceleration. This paper applies the method developed by London Underground to estimate the time that trains spend at stations, as a function of the physical characteristics of the situation (e.g. train door width) and the numbers of passengers involved. Analysis was carried out on a number of alternative designs for refurbishment of South West Trains' Class 455 inner‐suburban rolling stock. Whilst there is indeed an interaction between boarding and alighting passengers, this paper demonstrates that the LUL relationship breaks down at the highest passenger loads. Moreover, results indicate that passenger flow is not equal between different parts of the same group of boarders or alighters.     

Coordination of last train transfers using automated fare collection (AFC) system data

This paper studies last train coordination problem for metro networks, aiming to maximize the total number of passengers who can reach their destinations by metro prior to the end of operation. The concept of last boarding time is defined as the latest time that passengers can board the last trains and reach final destinations. The corresponding method for calculating last boarding time is also put forward. With automatic fare collection system data, an optimization model for coordinating last trains is proposed. The objective function optimizes the number of passengers who can reach their final destinations during the train period using departure times and headways of last trains for each line as decision variables. Afterwards, an adaptive genetic algorithm is put forward to solve this model and is applied to a case study of the Shanghai metro system. Copyright © 2017 John Wiley & Sons, Ltd.     

Bus arrival time calculation model based on smart card data

Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations.     

The train platforming problem: The infrastructure management company perspective

If railway companies ask for station capacity numbers, their underlying question is in fact one about the platformability of extra trains. Train platformability depends not only on the infrastructure, buffer times, and the desired departure and arrival times of the trains, but also on route durations, which depend on train speeds and lengths, as well as on conflicts between routes at any given time. We consider all these factors in this paper. We assume a current train set and a future one, where the second is based on the expected traffic increase through the station considered. The platforming problem is about assigning a platform to each train, together with suitable in- and out-routes. Route choices lead to different route durations and imply different in-route-begin and out-route-end times. Our module platforms the maximum possible weighted sum of trains in the current and future train set. The resulting number of trains can be seen as the realistic capacity consumption of the schedule. Our goal function allows for current trains to be preferably allocated to their current platforms.Our module is able to deal with real stations and train sets in a few seconds and has been fully integrated by Infrabel, the Belgian Infrastructure Management Company, in their application called Ocapi, which is now used to platform existing and projected train sets and to determine the capacity consumption.     

A bi-level model for single-line rail timetable design with consideration of demand and capacity

This paper proposes a bi-level model to solve the timetable design problem for an urban rail line. The upper level model aims at determining the headways between trains to minimize total passenger cost, which includes not only the usual perceived travel time cost, but also penalties during travel. With the headways given by the upper level model, passengers’ arrival times at their origin stops are determined by the lower level model, in which the cost-minimizing behavior of each passenger is taken into account. To make the model more realistic, explicit capacity constraints of individual trains are considered. With these constraints, passengers cannot board a full train, but wait in queues for the next coming train. A two-stage genetic algorithm incorporating the method of successive averages is introduced to solve the bi-level model. Two hypothetical examples and a real world case are employed to evaluate the effectiveness of the proposed bi-level model and algorithm. Results show that the bi-level model performs well in reducing total passenger cost, especially in reducing waiting time cost and penalties. And the section loading-rates of trains in the optimized timetable are more balanced than the even-headway timetable. The sensitivity analyses show that passenger’s desired arrival time interval at destination and crowding penalty factor have a high influence on the optimal solution. And with the dispersing of passengers' desired arrival time intervals or the increase of crowding penalty factor, the section loading-rates of trains become more balanced.     

An investigation of passenger interchange and train standing time at LRT stations: (ii) Estimation of standing time

The standing-time of trains at urban rail stations is pertinent to determining the line capacity and fleet size. The assumption of uniform boarding and alighting leads to under-estimation of the standing time. It is shown that the train standing-time is related to the fraction of boarders and the maximum demand for boarding and alighting at a door. It is further shown that the probability distribution of passengers at a door depends on the platform entrance locations. A methodology that takes into account the above factors is proposed for estimating the train standing-time.     

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.     

Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station

Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so‐called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off‐peak hours separately. The peak‐hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off‐peak‐hour dwell times are estimated using a non‐parametric regression model, in particular, the k‐nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%–88.5% during peak hours and 80.1% during off‐peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

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.     

Train design features affecting boarding and alighting of passengers

Accurately predicting train dwell time is critical to running an effective and efficient service. With high‐density passenger services, large numbers of passengers must be able to board and alight the train quickly – and within scheduled dwell times. Using a specially constructed train mock‐up in a pedestrian movement laboratory, the experiments outlined in this paper examine the impact of train carriage design factors such as door width, seat type, platform edge doors and horizontal gap on the time taken by passengers to board and alight. The findings illustrate that the effectiveness of design features depends on whether there are a majority of passengers boarding or alighting. An optimum door width should be between 1.7 and 1.8 m. The use of a central pole and platform edge doors produced no major effects, but a 200 mm horizontal gap could increase the movement of passengers. There is no clear effect of the type of seats and neither the standbacks between 50, 300 and 500 mm. Further research will look for the relationship between the dwell time and the characteristics of passengers such as personal space. Copyright © 2017 John Wiley & Sons, Ltd.     

Pedestrian traffic management of boarding and alighting in metro stations

The objective of this work is to determine, by means of simulation and experiments, the effect of pedestrian traffic management in the boarding and alighting time of passengers at metro stations. Studies were made by means of a pedestrian traffic microsimulator (LEGION Studio) and experiments at the Human Dynamic Laboratory (HDL) of Universidad de los Andes in Santiago de Chile, to obtain criteria for the pedestrian traffic management on the platform and doors of metro cars. The methodology consists of building a boarding/alighting hall of a metro car and the relevant portion of the platform in front of the hall. The simulation scenarios included the location of the vertical handrail in the hall of the car, delimitation of a keep out zone in front of the doors and the use of differentiated doors for boarding and alighting. The results of the simulation and laboratory experiments are expressed in Pedestrian Level of Service (LOS), Passenger Service Time (PST), passenger density on the vehicle and platform, and passenger dissatisfaction. Both, the simulation results and laboratory experiments allow us to give some recommendations for the pedestrian traffic management in metro systems.     

Passenger-demands-oriented train scheduling for an urban rail transit network

This paper considers the train scheduling problem for an urban rail transit network. We propose an event-driven model that involves three types of events, i.e., departure events, arrival events, and passenger arrival rates change events. The routing of the arriving passengers at transfer stations is also included in the train scheduling model. Moreover, the passenger transfer behavior (i.e., walking times and transfer times of passengers) is also taken into account in the model formulation. The resulting optimization problem is a real-valued nonlinear nonconvex problem. Nonlinear programming approaches (e.g., sequential quadratic programming) and evolutionary algorithms (e.g., genetic algorithms) can be used to solve this train scheduling problem. The effectiveness of the event-driven model is evaluated through a case study.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

An investigation into passenger preference for express trains during peak hours

Seoul South Korea metro line 9 has a relatively short (27 km) length and dispatches express and local trains alternately with a 3-min headway. Despite a minimal time saving and more crowded conditions with regard to express trains, the passenger dependence on them is growing. The present study investigated the potential reasons for this seemingly irrational phenomenon during peak hours. A multinomial logit model was set up based only on route-specific variables, such as travel times and the proportion of express-train-involved travel time. Furthermore, the influence of trip-related properties, individual-specific characteristics, and latent factors on the preference for express trains was identified by adopting an interaction variable model. As a result, passenger transferrals from, or to, other lines, had a positive influence on the choice of express trains, but those who regularly used line 9 were less dependent on express trains. Passengers with pre-trip plans tended to prefer the use of express trains. Regarding the latent variables, passengers who were well aware of the line 9 system and sensitive to sanitation tended to choose express trains, while those who were sensitive to comfort tended to avoid express trains.     

A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass

The integrated timetable and speed profile optimization model has recently attracted more attention because of its good achievements on energy conservation in metro systems. However, most previous studies often ignore the spatial and temporal uncertainties of train mass, and the variabilities of tractive force, braking force and basic running resistance on energy consumption in order to simplify the model formulation and solution algorithm. In this paper, we develop an integrated metro timetable and speed profile optimization model to minimize the total tractive energy consumption, where these real-world operating conditions are explicitly considered in the model formulation and solution algorithm. Firstly, we formulate a two-phase stochastic programming model to determine the timetable and speed profile. Given the speed profile, the first phase determines the timetable by scheduling the arrival and departure times for each station, and the second phase determines the speed profile for each inter-station with the scheduled arrival and departure times. Secondly, we design a simulation-based genetic algorithm procedure incorporated with the optimal train control algorithm to find the optimal solution. Finally, we present a simple example and a real-world example based on the operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results of the real-world example show that, during peak hours, off-peak hours and night hours, the total tractive energy consumptions can be reduced by: (1) 10.66%, 9.94% and 9.13% in comparison with the current timetable and speed profile; and (2) 3.35%, 3.12% and 3.04% in comparison with the deterministic model.     

Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

In a heavily congested metro line, unexpected disturbances often occur to cause the delay of the traveling passengers, infeasibility of the current timetable and reduction of the operational efficiency. Due to the uncertain and dynamic characteristics of passenger demands, the commonly used method to recover from disturbances in practice is to change the timetable and rolling stock manually based on the experiences and professional judgements. In this paper, we develop a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time and operational costs of trains. To capture the complexity of passenger traveling characteristics, the arriving ratio of passengers at each station is modeled as a non-homogeneous poisson distribution, in which the intensity function is treated as time-varying origin-to-destination passenger demand matrices. By considering the number of on-board passengers, the total energy usage is modeled as the difference between the tractive energy consumption and the regenerative energy. Then, we design an approximate dynamic programming based algorithm to solve the proposed model, which can obtain a high-quality solution in a short time. Finally, numerical examples with real-world data sets are implemented to verify the effectiveness and robustness of the proposed approaches.     

Analytical model for calibrating delay at congested bus stops

Abstract

A model is proposed to calculate the overall operating and delay times spent at bus stops due to passenger boarding and alighting and the time lost to queuing caused by bus stop saturation. A formula for line demand at each stop and the interaction between the buses themselves is proposed and applied to different bus stops depending on the number of available berths. The application of this model has quantified significant operational delays suffered by users and operator due to consecutive bus arrival at stops, even with flows below bus stop capacity.     

Models of bus boarding and alighting dynamics

Understanding the dynamics of boarding/alighting activities and its impact on bus dwell times is crucial to improving bus service levels. However, research is limited as conventional data collection methods are both time and labour intensive. In this paper, we present the first use of smart card data to study passenger boarding/alighting behaviour and its impact on bus dwell time. Given the nature of these data, we focus on passenger activity time and do not account for the time necessary to open and close doors. We study single decker, double decker and articulated buses and identify the specific effects of floor/entrance type, number of activities and occupancy on both boarding and alighting dynamics. A linear relationship between average boarding and alighting times and their respective standard deviations is also found, whereas the variability of boarding and alighting time decreases with the number of passengers boarding and alighting. After observing the cumulative boarding/alighting processes under different occupancy conditions, we propose a new model to estimate passenger activity time, by introducing critical occupancy – a parameter incorporating the friction between boarding/alighting and on-board passengers. We conduct regression analyses with the proposed and another popular model for simultaneous boarding/alighting processes, finding that the critical occupancy plays a significant role in determining the regime of boarding and alighting processes and the overall activity time. Our results provide potential implications for practice and policy, such as identifying optimal vehicle type for a particular route and modelling transit service reliability.