Passenger Boarding Research Articles

Passenger Origin-Destination Estimation in Public Transit Using Boarding Count Data

Passenger boarding and alighting data is critical for understanding travel patterns in transit systems, yet many systems only record boarding, making it difficult to track alighting. This study introduces a method to estimate alighting counts using boarding data using transit symmetry at the cluster level. Bus stops are grouped into clusters, assuming equal inflow and outflow, and a gravity model is calibrated to estimate an Origin-Destination (OD) matrix. Using data from three bus routes in Regina, Canada (2017), the method achieved a symmetry ratio of 60% across clusters and a Mean Structural Similarity Measure score of 0.84, validating the accuracy of the OD estimates. This approach provides a practical solution to the challenge of missing alighting data. While effective for single-mode transit systems, future research will expand its application to multi-modal systems and address factors like seasonal and land-use variations, offering a cost-effective alternative for OD estimation.

Quantitative and Qualitative Analysis of Aircraft Round-Trip Times Using Phase Type Distributions

One of the major issues facing commercial airlines is the time that it takes to board passengers. Further, most airlines wish to increase the number of trips that an aircraft can make between two or more cities. Thus, reducing the overall boarding times by a few minutes will have a significant impact on the number of trips made by an aircraft, as well as enabling improvements in key measures such as the median and 75th and 95th percentiles. Looking at such measures other than the mean is critical as it is well known that the mean can under- or overestimate the performance of any model. While there is considerable literature on the study of strategies to decrease boarding times, the same cannot be said about the study of the boarding time given a particular strategy for boarding. Thus, the focus of this paper is to study analytically (using suitable stochastic models) and numerically the impact of reducing the average time on the key measures to help the system to plan accordingly. This is achieved using a well-known probability distribution, namely the phase type distribution, to model various events involved in the boarding process. Illustrative numerical results show a reduction in the percentile values when the average boarding times are decreased. Understanding the percentiles of the boarding times, as opposed to relying only on the average boarding times, will help management to adopt a better boarding strategy that in turn will lead to an increase in the number of trips that an aircraft can make.

An approach for two-dimensional convolutional neural networks for hourly passenger boarding demand prediction based on uneven smart-card data

An invaluable resource for understanding passenger boarding patterns and forecasting future travel demand is the tap-on smart-card data. Positive instances, on the other hand—boarding at a given bus stop at a certain time—are less common than negative instances when looking at the smart-card data (or instances) by boarding stops and by time of day. Machine learning algorithms that are used to estimate hourly boarding numbers at a certain location have been shown to be much less accurate when the data is imbalanced. Before using the smart-card data to forecast bus boarding demand, this research tackles the problem of data imbalance in the data. To create fake traveling instances to add into a synthetic training dataset containing more evenly distributed traveling and non-traveling examples, we suggest using deep generative adversarial networks (Deep-GAN). Next, a deep neural network, or DNN, is trained on the synthetic dataset to predict which instances from a given stop in a certain time frame will travel and which ones won't. According to the findings, resolving the data imbalance problem may greatly enhance the predictive model's functionality and make it more accurate in predicting ridership profiles. The suggested strategy may create a synthetic training set with a better similarity so diversity and, therefore, a stronger prediction capability, according to a comparison of the Deep-GAN's performance with other conventional resampling techniques. The study emphasizes the importance of the issue and offers helpful recommendations for enhancing the quality of the data and model performance for individual travel behavior analysis and travel behavior prediction.

Feeder bus service design under spatially heterogeneous demand

In rapidly sprawling urban areas and booming intercity express rail networks, efficiently designed feeder bus systems are more essential than ever to transport passengers to and from trunk-line rail terminals. When the feeder service region is sufficiently large, the spatial heterogeneity in demand distribution must be considered. This paper develops continuum approximation models for optimizing a heterogeneous fixed-route feeder network in a rectangular service region next to a rail terminal. Our work enhances previous studies by: (i) optimizing heterogeneous stop spacings along with line spacings and headways; (ii) accounting for passenger boarding and alighting numbers on bus dwell times and patron transfer delays at the rail terminal; and (iii) examining the advantages of asymmetric coordination between trunk and feeder schedules in both service directions. To tackle the increased modeling complexity, we introduce a semi-analytical method that combines analytically derived properties of the optimal solution with an iterative search algorithm. Local transit agencies can readily utilize this approach to design a real fixed-route feeder system.This paper reveals many findings and insights not previously reported. For instance, integrating the heterogeneous stop spacing optimization further reduces the system cost (by 4% under specific operating conditions). The cost savings increase with demand heterogeneity but decrease with the demand rate and service region size. Choosing the layout of feeder lines where buses pick up and drop off passengers along the service region’s shorter side also significantly lowers the system cost (by 6% when the service region’s aspect ratio is 1 to 2). Furthermore, coordinating trunk and feeder schedules in both service directions yields an additional cost saving of up to 20%.

Identifying city bus passenger ridership patterns: a mixed-method analysis

PurposeThis study uses big data analysis aimed at discovering city bus passenger ridership patterns. Hence, marketing managers can get sufficient insights to formulate effective business plans and make timely decisions about company operations.Design/methodology/approachThis study uses a mixed-method analysis to analyze the results. First uses the RFM (recency, frequency, and monetary) model combined with a big data technique (K-means) to analyze bus passenger boarding behavior. In order to improve the validity and quality of the research, this study also conducted interviews with senior managers of the bus company from which the data was obtained.FindingsThe study identifies six distinct groups of passengers with different boarding behaviors, ranging from “general passengers” to “most valuable passengers”. General passengers constituted the largest group. As such, they should be the main target for municipal governments when promoting bus ridership as part of energy conservation and carbon-reduction activities. This group of passengers should be encouraged to take public transport vehicles more, instead of relying on personal vehicles. The fourth group identified included elderly passengers with hospitals as their destinations. Bus companies can cooperate with municipal government to provide morning “medical bus” services for the elderly. Interviews with bus company managers confirmed that the analytical results of this study correspond with the observations, experiences, and actual business operating plans of bus companies.Originality/valueOnly few studies have analyzed passengers' boarding behavior applying a mixed-method analysis.

Comprehensive examination of regional railway passenger behavior and dwell time components: Insights from video-based observations in Victoria, Australia

In understudied regional railway operations, this study explores passenger boarding and alighting patterns and how station design impacts them, particularly dwell times. Despite extensive metropolitan and suburban train research, regional railways have been overlooked. This study investigates regional rail passenger flow and dwell time to bridge this gap. This article studies dwell times and passenger boarding and alighting at two regional stations in Victoria, Australia, using CCTV data. The objective is to identify insights that might improve regional railway services' efficiency and user experience and advocate for sector-specific solutions. Analysis indicates distinct station boarding and alighting features, highlighting the discovery of the ‘blinded phenomenon’ for train conductors particularly in the afternoon peak (PMP). The results of the study showed that PMP services, which prioritise alighting passengers, had higher dwell times than the morning peak (AMP) services, which emphasise boarding passengers. Obstructed views make it difficult for train conductors to monitor passenger alighting, prolonging dwell times. Better human resource strategies, artificial intelligence for crowd surveillance, and strategic CCTV system deployment to streamline operations and improve passenger experience on regional railways are proposed in the paper, laying the groundwork for future research and operational changes in this vital transportation sector.

Bus Pass with Barcode Card Scan Code

Abstract: The "Bus Pass with Barcode Scan Project" aims to enhance the efficiency and security of bus transportation services by implementing a barcode-based pass system. This theoretical project focuses on the conceptual framework and key components required for the successful implementation of such a system. Implementing a secure and convenient bus pass system using barcode technology. Improves the accuracy and speed of passenger boarding by automating the pass verification process. Enhancing data management and analytics for better service planning and passenger experience. Developing a system for generating unique barcodesfor each bus pass holder. Ensures that the generated barcodes are tamper-proof and secure against duplication. Installing barcode scanners on buses to read the passenger's bus pass barcode. Ensures compatibility with various barcode types (e.g., QR codes, traditional barcodes). Creates a centralized database that store passenger information, including name, pass details, and barcode data. Implementing data encryption and security protocols to protect passenger information. Developing a mobile application for passengers to purchase, renew, or display their bus pass on their smartphones. Enabling easy scanning of the mobile app's barcode at the bus entry point.

Logistic model for pattern inference of subway passenger flows based on fare collection and vehicle location data

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods.

Capped Robust Optimal Control Method to Improve Tram Operation Reliability Considering Random Number of Passengers at Station Serving Multiple Lines

Tram signal priority control is a crucial approach for enhancing the reliability of tram operations and has been implemented in various cities. Nevertheless, unpredictable tram operations influenced by tram dwell time during station stops can cause signal priority control failure at intersections. It is challenging to precisely predict tram dwell time at stations that offer multiple lines. To address this issue, the proposed research presents a capped robust optimal control (CRC) technique for tram signal priority. This method entails considering the stochastic number of passengers boarding and alighting at stations with multiple lines. Furthermore, tram delay calculation models at intersections are established and integrated into an objective function. The main objective of this strategy is to enhance tram operation reliability and maximise tram operation efficiency while reducing the adverse impact of tram priority on other vehicles at the intersection. A case study was conducted to evaluate the effectiveness of the CRC method. The results indicate that the CRC technique significantly improves tram operation reliability and efficiency.

The Effect of Seat Layout on the Interaction of Passengers Inside the Train Carriage: An Experimental Approach for Urban Services

The platform–train interface (PTI) is one of the most complex spaces in metro stations. At the PTI, the interaction of passengers boarding and alighting reaches high density, affecting the boarding and alighting time, among other variables related to safety and efficiency. Developing research was performed to study the effect of seat layout on the interaction of passengers by means of experiments in a controlled environment. The laboratory facility included a mock-up of a train carriage and its adjacent platform. The train was representative of urban services in the Valparaiso Metro (Chile). The results showed that seat layout changed the patterns of interaction of passengers inside the train carriage. If seats were parallel to the movement of the train, then wider corridors inside the train were generated, and therefore, the number of passengers using this space could increase up to three times. However, in urban services, passengers were located closer to the train doors to be prepared for alighting, and therefore, the passenger numbers at the central hall remained the same with the seat layout. In addition, most passengers always used seats even if they were in a different position due to the aforementioned reasons. Further research will include passengers with reduced mobility and remaining inside the train while others are alighting to identify the effect of the space used on the interaction of passengers inside the train.

Impact of Non Scheduled Public Mini Bus Stops on Transit Trip Time: A Case Study Amman-Jordan

Public transport services face many challenges around the world. Due to the rise in demand for public transport, authorities desperately need to develop the systems, and it provides a service to be the same level for private cars in terms of service reliability measures. This research aims to study the influence of the non-scheduled random public mini bus stops on travel time for a specific mini bus route. These non-scheduled stops became a habitual behavior for Public mini buses, which affects the daily users of this transportation service in Jordan. In this study, a comprehensive plan was designed to collect data regarding the mini bus behavior along a selected bus route, using a transit on- board survey, GPS and questionnaires. The data collection involved locations of non-scheduled stops, number of stops, distance and speed. The data was then used to develop a trip time model. The developed model detects the delay time due to the non- scheduled mini bus stops. The analysis of the data indicated that passengers mainly depend on random mini bus stops because there aren’t scheduled stops, thus a large impact on traffic jam. Furthermore, it was found that the main variables that affect the trip travel time are the total delay time due to stopping randomly to board and alight passengers. The study concluded that eliminating the non-scheduled mini bus stops will reduce the trip time, thus get improve he service reliability.

A flexible train composition strategy with extra-long trains for high-speed railway corridors with time-varying demand

To accommodate the uneven spatio-temporal distribution of passenger demand and improve the maximum transportation capacity for high-speed railway (HSR) corridors, this study proposes a demand-oriented flexible train composition strategy. The proposed strategy allows flexibility in selecting the number of train composition units (e.g., train carriages) for each train to accommodate the demand variations, where extra-long trains (longer than the length of the train station platform) might be utilized. Under such a strategy, a seat allocation method coupled with a train stopping position control strategy is proposed to avoid the need for cross-carriage walking during passenger boarding or alighting (for those extra-long trains). The train stopping position control strategy specifies the carriages that dock at or extend beyond the station platform, while the seat allocation method allocates seats within each carriage to specific origin-destination pairs. The studied problem can be formulated as an integer nonlinear programming model, where the weighted sum of the HSR operating cost and passenger travel time cost is minimized. The proposed model is then reformulated into an integer linear programming model via a series of linearization techniques. A tailored heuristic algorithm based on the variable neighborhood search (VNS) and GUROBI solver is designed to produce high-quality solutions for large-scale problems. Two numerical examples, i.e., a small example and a real-world Shanghai-Hangzhou HSR line example, are presented to illustrate the effectiveness of the proposed approach. The computation results show that, in comparison to traditional fixed and flexible train composition strategies without extra-long trains, the proposed strategy can significantly reduce the total operating cost and total travel time cost.

Empirical Investigation of Dwell Times in Ridepooling Systems

While ridepooling has garnered considerable attention in research, there remains a notable gap in understanding the intricacies of the dwelling process, particularly the boarding and alighting of passengers. Existing studies have predominantly focused on dwelling behavior within conventional transportation modes like buses or trams. This study seeks to fill this gap by providing insights into passengers’ boarding and alighting behavior within ridepooling systems and examining the factors influencing dwell time, such as group size and stop location. To achieve this, we collaborated with sprinti, an on-demand ridepooling service in the Hanover region, Germany. We collected data on dwell times and customer characteristics during several shifts in 2022 by employing a ‘silent observer’ approach. The findings reveal an average dwell time of 16.25 s and a median of 13 s. Additionally, the analysis demonstrates that several determinants impact dwell time, such as group size, vehicle type, and the presence of luggage.

Optimization of the Operation Plan of Airport Express Train with Consideration of Train Departure Time Window

This paper proposes an optimization model for the train operation scheme of the Airport Express Line (AEL) based on the expected arrival time of passengers by the introduction of the train departure time to cope with the time‐dependent passenger flow and provide better prompt train service according to passengers’ demand. Considering factors such as train sections, station arrangement, passenger capacity, departure time windows, passenger flow conservation, and boarding and disembarkation processes, this paper also aims to find the optimal combination of the passengers’ total travel time and the train operation cost. A set of alternative train options is introduced to simplify the model and convert integer variables related to train pairs into 0‐1 variables. The elaborately designed simulated annealing algorithm mainly focuses on the key elements of strategies like initial solution generation, neighborhood solution construction, and the allocation of passenger flows, tailored to the model’s unique features and the time‐dependent passenger flow. Neighborhood solution strategies include the increase or haut of train operations and the adjustment of the number of stops, which refines the solution space and boosts the process efficiency of the heuristic algorithm. Additionally, the model and algorithm proposed in this paper are practiced during the peak hour of Nanjing Metro Line S1 for empirical validation. The research findings demonstrate that the optimized train operation scheme is better synchronized with the fluctuating number of time‐dependent passenger flows and exhibits notable improvement in computational efficiency and convergence.

Моделирование объемов отправлений городского общественного транспорта

Organizing the effective operation of urban public transport and planning the development of transport infrastructure requires predicting the volume of passenger traffic. In particular, it needs to predict the volume of passenger departures from stopping points, which depends on a large number of different factors, such as the population density near the stopping point, the types of transport departing from it, the number of routes passing through it, etc. In this article, we propose modeling outgoing passenger traffic based on a regression approach. To test the approach and determine the most appropriate specifications for regression equations, data on passenger boarding volumes at bus stops in Ekaterinburg, as well as open data from the database of apartment buildings, were used.

Pengukuran Beban Kerja Psikologis Operator Di Bay 4 (Preparation) PT XYZ Unit Usaha Passenger Boarding Bridge Menggunakan Metode NASA-TLX

Psychological workload is a workload that is felt when doing work that is formed cognitively, so that the workload is felt by each worker. As a result of these work activities will have an impact on worker productivity so it is necessary for someone to know how much workload is felt on the work activities that are being carried out. Subjective workload measurement can be done using one method, namely the NASA-TLX method. This physical and mental activity has a consequence that is the emergence of workload. Bay 4 has a very important role in PT. XYZ Passenger Boarding Bridge Business Unit which has the task of making the initial components, for all workers. The psychological workload of operators here is expected to be low so that all processes can run smoothly so that production targets can be achieved. Based on the results of research conducted using the NASA-TLX method, it can be concluded that overall the workload of workers working in Bay 4 is in the high category with an average WWL value of 66.02. This is one that must be dealt with quickly so that the work in Bay 4 can go as expected.

Optimizing Transit Operations and Passenger Experience: A Simulation-based Study of Bus Interarrival and Passenger Load Scenarios at a Major Transfer Point

This study employs simulation-based analysis of passenger flow and bus operations at the Adam Malik transfer point within the TransJakarta BRT system. The findings from the analysis of the current system indicate that the average number of total passenger exits in corridors 13, 13B, 13C, and L13E were 618, 509, 1143, and 790, respectively. Additionally, the average time spent by passengers within the system for each corridor was 4.48 minutes, 6.12 minutes, 5.94 minutes, and 5.46 minutes, respectively. Through the development of 15 scenarios, the study demonstrates the impact of adjusting passenger boarding procedures and reducing interarrival time on waiting times and passenger processing efficiency, particularly during peak hours. The results emphasize the substantial influence of these variables on waiting times, total exits, and system duration for passengers and buses.

Optimasi Pelayanan Kapal Penumpang melalui Clustering Penumpang dengan Metode Silhouette Coefficient

Improving the efficiency of passenger ship services is a big challenge for the shipping industry. This research aims to optimize ship passenger services at Muara angke port by segmenting passengers using the Silhouette Coefficient method. This research uses the k-means algorithm and the Silhouette Coefficient method in order to get passenger clusters based on boarding passengers and disembarking passengers. The Silhouette Coefficient method in this research is used to find the number of clusters. The dataset used is passenger data from January 1 - December 31, 2021. The results showed that 2 clusters were obtained, namely the highest cluster (C1) and the lowest cluster (C2) from the k-means algorithm with the results of the centroid value obtained for cluster 1 passengers up, namely 53,414 and passengers down, namely 54,585, the centroid value obtained for cluster 2 passengers up, namely 596,396 and passengers down, namely 532,455. The value with the Silhouette Coefficient method is obtained 0.704 in cluster 1 for the highest cluster there are 2091 data with a percentage value of 57.27% and 0.548 in cluster 2 for the lowest cluster there are 1560 data with a presentation value of 42.73%. The Silhouette Coefficient value is close to the value of 1, which means that the Muara angke port is good enough in optimizing passenger services.

Study of time indicators of public transport operation depending on the season of the year

Mobility problems in large cities of Ukraine and Eastern Europe are complicated by the fact that the increase of private transport volume significantly exceeds street and road network`s capacity. This is most noticeable during peak periods in terms of daylight hours and throughout the year. From the point of sustainable mobility view, this negative phenomenon significantly affects urban public transport, which does not have separate dedicated traffic lines. This article analyzes the issue regarding the deterioration of the transport situation in large cities. The reason for this is the increase in traffic on main streets during the day peaks, as well as the presence of seasonal traffic factors. If the issue of the occurrence and traffic jams duration and the increase in the correspondence time of private transport is sufficiently studied, then the problems of changing the schedules of public transport and taking into account the increase in the trip duration depending on the time of year need to be clarified. The routes of public transport, which do not have a separate infrastructure and move in the general flow together with private cars, were chosen for the study. According to the results of remote monitoring of public transport, a change in the trip duration and time lost due to the boarding and disembarking of passengers on similar trolleybus routes in different seasons was established. Based on the obtained data, a matrix of trip duration unevenness coefficients for public transport routes was formed, and a measure of the seasonality effect on these indicators was established. The obtained results make it possible to quantitatively determine the influence of the season and time of the day on the change in the trip duration, which can be applied in further studies using simulation tools and for practical use in drawing up seasonal traffic schedules. The results of the research complement the currently relevant scientific works, which concern the problems of seasonal mobility, as well as the influence of the social infrastructure objects functioning (schools, kindergartens, and other educational institutions) of cities on the peak load of the street and road network, which extends the duration of traffic not only for private but also public transport.

Analysis of Boarding Strategies on an Airbus A320 Using Discrete Event Simulation

Boarding time constitutes a critical element of turnaround time, which is used to measure the efficiency of airline operations. Therefore, to reduce boarding time, it is imperative to reconsider traditional passenger boarding strategies to make them more efficient. In this sense, this study seeks to analyze the impact of different strategies on boarding times using discrete event simulation on an Airbus 320. Seven boarding strategies have been identified and considered in our study, as follows: random, back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal. The impact of carrying hand luggage and the presence of priority passengers has been considered, as well as the impact of having a continuous arrival of passengers during the boarding process versus having all passengers available at boarding time. In general, simulation results have pointed out that the outside-in and reverse pyramid strategies are the most effective, improving boarding time by up to 15%, when compared to the random strategy. Moreover, the back-to-front strategy, which is generally implemented by airline companies, has been shown to be the most inefficient strategy. Efficient boarding strategies are expected to contribute to the sustainability of air travel by minimizing the turnaround time, improving operational efficiency, and reducing emissions.