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.     

A study of train dwelling time at the hong kong mass transit railway system

This paper investigates the relationship between the dwelling time of trains and the crowding situations at Mass Transit Railway (MTR) stations in Hong Kong. Regression models were established for the dwelling delays of trains due to congestion at stations, and a simulation model making use of the Monte-Carlo technique is developed to assess the reliability of the estimated train dwelling time. Therefore, the distribution and the confidence interval of the train dwelling time can be predicted on the basis of observed boarding and alighting distributions.     

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.     

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.     

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.     

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.     

On passenger saturation flow in public transport doors

In previous studies the authors have shown passengers’ boarding and alighting times for the Transantiago system obtained at the Pedestrian Accessibility and Movement Environment Laboratory (PAMELA) of University College London. Following this line of research, the aim of this paper is to demonstrate the existence of pedestrian saturation flows in public transport doors and show some values of this variable under different conditions. The methodology to achieve this aim was real-scale experiments performed in both PAMELA and the Human Dynamics Laboratory at Universidad de los Andes in Santiago de Chile. Different groups of people getting off a mock-up of a public transport vehicle were recorded by means of video cameras. The videos were then visually processed to find values of passenger saturation flow according to door configurations. The variables studied were the vertical gap between the platform and the vehicle chassis and the width of the door. Results indicate that it is possible to define values of passenger saturation flows for different characteristics of public transport doors. These values proved to be statistically sensitive to both the vertical gap and the width of the door. In addition, results indicate that there seems to be both a vertical gap and a door width for which the flow of passenger reaches its optimum rate.     

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.     

Boarding and alighting times for elderly persons using special bus service

This technical note examines the boarding and alighting times for elderly persons with respect to buses. The study was conducted with the assistance of Special Transport Service, a small-scale operation consisting of two buses. The main findings of the research are estimates for the boarding and alighting times for one ambulatory person: 2.328 minutes for boarding the bus and 1.002 minutes for alighting from the bus.     

A congested and dwell time dependent transit corridor assignment model

This research proposes an equilibrium assignment model for congested public transport corridors in urban areas. In this model, journey times incorporate the effect of bus queuing on travel times and boarding and alighting passengers on dwell times at stops. The model also considers limited bus capacity leading to longer waiting times and more uncomfortable journeys. The proposed model is applied to an example network, and the results are compared with those obtained in a recent study. This is followed by the analysis and discussion of a real case application in Santiago de Chile. Finally, different boarding and alighting times and different vehicle types are evaluated. In all cases, demand on express services tends to be underestimated by using constant dwell time assignment models, leading to potential planning errors for these lines. The results demonstrate the importance of considering demand dependent dwell times in the assignment process, especially at high demand levels when the capacity constraint should also be considered. Copyright © 2017 John Wiley & Sons, Ltd.     

Examining determinants of rail ridership: a case study of the Orlando SunRail system

The current study contributes to the literature on transit ridership by considering daily boarding and alighting data from a recently launched commuter rail system in Orlando, Florida – SunRail. The analysis is conducted based on daily boarding and alighting data for 10 months for the year 2015. With the availability of repeated observations for every station, the potential impact of common unobserved factors affecting ridership variables are considered. The current study develops an estimation framework, for boarding and alighting separately, that accounts for these unobserved effects at multiple levels – station, station-week and station-day. In addition, the study examines the impact of various observed exogenous factors such as station level, transportation infrastructure, transit infrastructure, land use, built environment, sociodemographic and weather variables on ridership. The model system developed will allow us to predict ridership for existing stations in the future as well as potential ridership for future expansion sites.     

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.     

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.     

Bus journey times in medium size urban areas

The predictive accuracy of the models based on the fundamental relation between journey time and passenger demand can be improved through data disaggregation or route segmentation. Primary reason for this is the improvement in the estimates of stopping delays and delays due to passenger boarding and/or alighting (dwell time). Both Poisson and Negative Binomial model estimates of stoppings for passenger boarding and alighting are shown to improve with disaggregation. These improvements, however, contribute little to the overall predictability of the fundamental models which are useful for gaining insight into the significance and variability of the stopping delays and dwell time, or testing sensitivity to changes in the long term. Site or route specific models of journey times which have better predictive capability exist, and may be used for short-run planning. However, the interchangeability and performance over time of the latter, have to be evaluated before making definitive conclusions.     

Passenger travel characteristics and bus operational states: a study based on IC card and GPS data in Yinchuan,China

ABSTRACT

In recent years, public transport has been developing rapidly and producing large amounts of traffic data. Emerging big data-mining techniques enable the application of these data in a variety of ways. This study uses bus intelligent card (IC card) data and global positioning system (GPS) data to estimate passenger boarding and alighting stations. First, an estimation model for boarding stations is introduced to determine passenger boarding stations. Then, the authors propose an innovative uplink and downlink information identification model (UDI) to generate information for estimating alighting stations. Subsequently, the estimation model for the alighting stations is introduced. In addition, a transfer station identification model is also developed to determine transfer stations. These models are applied to Yinchuan, China to analyze passenger flow characteristics and bus operations. The authors obtain passenger flows based on stations (stops), bus lines, and traffic analysis zones (TAZ) during weekdays and weekends. Moreover, average bus operational speeds are obtained. These findings can be used in bus network planning and optimization as well as bus operation scheduling.     

A robust method for estimating transit passenger trajectories using automated data

Development of an origin-destination demand matrix is crucial for transit planning. The development process is facilitated by automated transit smart card data, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a novel trip chaining method which uses Automatic Fare Collection (AFC) and General Transit Feed Specification (GTFS) data to infer the most likely trajectory of individual transit passengers. The method relaxes the assumptions on various parameters used in the existing trip chaining algorithms such as transfer walking distance threshold, buffer distance for selecting the boarding location, time window for selecting the vehicle trip, etc. The method also resolves issues related to errors in GPS location recorded by AFC systems or selection of incorrect sub-route from GTFS data. The proposed trip chaining method generates a set of candidate trajectories for each AFC tag to reach the next tag, calculates the probability of each trajectory, and selects the most likely trajectory to infer the boarding and alighting stops. The method is applied to transit data from the Twin Cities, MN, which has an open transit system where passengers tap smart cards only once when boarding (or when alighting on pay-exit buses). Based on the consecutive tags of the passenger, the proposed algorithm is also modified for pay-exit cases. The method is compared to previous methods developed by the researchers and shows improvement in the number of inferred cases. Finally, results are visualized to understand the route ridership and geographical pattern of trips.     

GIS-based transit trip allocation methods converting stop-level boarding and alighting trips into TAZ trips

ABSTRACT

This study introduces a framework to improve the utilization of new data sources such as automated vehicle location (AVL) and automated passenger counting (APC) systems in transit ridership forecasting models. The direct application of AVL/APC data to travel forecasting requires an important intermediary step that links stops and activities – boarding and alighting – to the actual locations (at the traffic analysis zone (TAZ) level) that generated/attracted these trips. GIS-based transit trip allocation methods are developed with a focus on considering the case when the access shed spans multiple TAZs. The proposed methods improve practical applicability with easily obtained data. The performance of the proposed allocation methods is further evaluated using transit on-board survey data. The results show that the methods can effectively handle various conditions, particularly for major activity generators. The average errors between observed data and the proposed method are about 8% for alighting trips and 18% for boarding trips.     

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.     

Evaluation of a public transportation level of service concept

The level of service of a bus line is evaluated by its operational characteristics, particularly by the ratio between average bus travel time on a given route and the average passenger car travel time on the shortest distance between the origin and the destination of the bus in question. It is shown that the level-of-service measure may be predicted by such independent variables as route length, average distance between bus stations, number of signalized and unsignalized intersections, and the ratio between such intersections. It is hypothesized that use of other independent variables such as boarding and alighting passengers, or volume to capacity ratio on the route concerned, could improve the predictive power of the suggested models. Further research is recommended on the effect of these latter variables and other operational variables which might influence bus level of service, and also on the comparison between direct bus lines and lines which use transfer points.     

Frequency-based transit assignment considering seat capacities

This paper proposes a frequency-based assignment model that considers travellers probability of finding a seat in their perception of route cost and hence also their route choice. The model introduces a “fail-to-sit” probability at boarding points with travel costs based on the likelihood of travelling seated or standing. Priority rules are considered; in particular it is assumed that standing on-board passengers will occupy any available seats of alighting passengers before newly boarding passengers can fill any remaining seats. At the boarding point passengers are assumed to mingle, meaning that FIFO is not observed, as is the case for many crowded bus and metro stops, particularly in European countries. The route choice considers the common lines problem and an user equilibrium solution is sought through a Markov type network loading process and the method of successive averages. The model is first illustrated with a small example network before being applied to the inner zone of London’s underground network. The effect of different values passengers might attach to finding a seat are illustrated. Applications of the model for transit planning as well as for information provision at the journey planner stage are discussed.