Passenger Travel Patterns Research Articles

Optimisation models for estimating public transport OD matrices using different data types

Efficient public transport systems rely on origin–destination matrices (ODMs) estimation to accurately capture passenger travel patterns, enabling adjustments to frequencies and lines as needed. In this study, we address the ODM estimation problem by employing multiple bi-level programs that consider an outdated ODM and observed passenger flows on specific transit line arcs. Additionally, we consider various optional data types, including boarding and alighting data, as well as the structure of the outdated ODM and passenger flows, either all, separately, in combination, or none. In our study, we reformulate these bi-level programs into single-level models, and we use a commercial solver to address the problem in benchmark instances. In our analysis, we focus on the impact of incorporating different types of information into the estimation process, leading to valuable insights. We find that considering all the data types leads to a higher accuracy than only a subset of these data types. In particular, focussing only on boarding and alighting data leads to improvements in the estimation process, whereas considering only the structure of the outdated ODM and passenger flows leads to reduced accuracy compared to not incorporating either. The latter highlights the significance of data selection in ODM estimations.

Travel pattern recognition of urban rail passengers based on spatiotemporal sequence similarity

Understanding passenger travel patterns is helpful for the allocation of passenger resources in urban rail transit. Based on rail transit smart card data, this paper proposes a method to identify travel patterns by modeling individual spatiotemporal sequences. First, all the stations visited by individual passengers are extracted, and the similarity of the stations is calculated by the frequency of inter-station travel, the distance between stations and the activity duration of the stations. The main spatial activity area of the individual is divided using a hierarchical clustering algorithm. Then, the spatiotemporal sequence is inferred based on the individual's travel order. The sequence is a set of discrete values that characterize the spatiotemporal state. PCA-KL and K-Means++ are used to extract the similarity sequence structure to identify the passenger travel pattern. Finally, the rail transit smart card data of Xi'an in a certain month is taken as an example to identify its passenger travel pattern. The results show that complex passenger flow has 5 travel modes, of which 3 typical modes are commuting travel in a macro sense, accounting for 79% of the passenger flow. It can be seen that the pattern recognition based on the similarity of individual spatiotemporal sequences in this paper fully reflects the particularity and versatility of the research method, and is highly operational for different cities.

Spnaf: An R package for analyzing and mapping the hotspots of flow datasets

This paper introduces {spnaf} (spatial network autocorrelation for flows), an R package designed for the hotspot analysis of flow (e.g., human mobility, transportation, and animal movement) datasets based on Berglund and Karlström’s G index. We demonstrate the utility of the {spnaf} package through two example analyses by data forms: 1) bike-sharing trip patterns in Columbus, Ohio, USA, using polygon data, and 2) U.S. airports’ passenger travel patterns, using point data. The {spnaf} is available for download from the Comprehensive R Archive Network (CRAN), which contains a vignette and sample data/code for immediate use. This package addresses limitations in existing spatial analysis packages and emphasizes its efficiency in detecting flow hotspots. It is highly applicable in various urban and geographic data science applications. {spnaf} is still in its early stages and we hope that interested readers can contribute to the development and enhancement of the package.

Comparing micro-level and macro-level models for epidemic diffusion in the metro system

ABSTRACT Few studies focus on how the ground transport system has increased COVID-19 transmission; the details of its spread remain unclear. The absence of station-level data obstructs healthcare professionals from effectively targeting anti-epidemic measures. This study employs agent-based modeling through GAMA software to identify Taipei metro stations implicated in initial transmission. In addition, a macro-level estimator is applied as a baseline model to compare COVID-19 arrival sequences at each station. Utilizing electronic metro ticket data, passenger travel patterns are discerned. We found (1) the average infection order of all stations, according to both models were not significantly different; (2) however, this difference between two model results became significant when the sample size was decreased. (3) Of all the stations, Taipei Main Station was the first because it has the highest passenger volume and the most connections. These early infected stations are near Taipei Main station and commercial or hub stations.

Portraying passenger travel patterns for Beijing public transit system with user profiling method

Purpose The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network. Design/methodology/approach To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week. Findings In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system. Originality/value This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China

The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018.

Reassignment Algorithm of the Ride-Sourcing Market Based on Reinforcement Learning

Reassignment strategies are of great significance to improve the dispatching efficiency of the ride-sourcing market by reassigning drivers and passengers. However, due to the focus on the feasibility of the reassignment strategy in the short period, previous studies ignore possible reassignment opportunities in the future and inevitably make short-sighted reassignment decisions. To fully exploit the effect of the reassignment strategy, this study proposes a two-stage reassignment framework, which integrates a reinforcement learning algorithm and the bilateral matching reassignment model. The Markov decision process is adopted to dynamically model the reassignment problem. In the framework, the reinforcement learning algorithm is utilized to first learn the randomness and dynamics of travel patterns from historical data and select vehicles participating in the reassignment process. Then, the bilateral matching reassignment model formulates the matching relationship after reassignment for passengers (drivers). Furthermore, for the cases where reassignment may increase individual matching distance, a personalized bilateral matching reassignment model is developed to avoid that. Experiments based on real data in Beijing found that learning passenger travel patterns and adjusting vehicle reassignment moments can greatly improve the passenger experience and reduce driving costs. The results also suggest that the efficiency of the reassignment strategy is influenced by the supply and demand conditions of the ride-sourcing system. This justifies that the framework can be applied to optimize the dispatching process, reduce carbon emissions, and build an eco-friendly travel system.

A study of influence zone for railway stations of different hierarchy in the Indian Railways: analyzing passenger travel patterns

ABSTRACT The transportation planning of Indian Railways has become increasingly complex due to the growing population and increasing demand for mobility. To develop an efficient and effective last-mile transportation system, it is important to identify the extent of the influence zone of railway stations. This can guide policy decisions and infrastructure development. This research paper focuses on mapping the influence zones of 40 railway stations across the Northern Railway Division of India. It uses the travel length frequency distribution to analyze the distribution of passengers’ travel distances. The study shows that influence zones for railway stations vary significantly based on their hierarchy level. Higher-hierarchy stations tend to have larger and more dispersed influence zones, while those of lower-hierarchy stations tend to be smaller and more localized. The findings can be used to guide the expansion of existing stations and the improvement of transportation services. Overall, this study provides a valuable contribution to the field of transportation planning for Indian Railways. It demonstrates the importance of influence zone mapping in developing an efficient and effective transportation system.

Identification and Classification of Bus and Subway Passenger Travel Patterns in Beijing Using Transit Smart Card Data

Urban public transit has been rapidly developed in recent years. However, given increases in travel volume, the level of service still needs to be improved to meet the satisfaction of passengers. Transit service providers and researchers have focused on improving transit devices, but the service level of public transit has not yet been effectively improved, so more and more research is interested in analyzing the travel patterns of passengers. Compared with traditional survey methods, smart card collection systems—which can collect spatial-temporal information about passengers’ trips—are convenient for the study of bus and subway passengers’ travel patterns. However, the data provided by smart cards have not yet been fully explored. Therefore, this paper proposed a multistep methodology to gather information on the travel patterns of bus and subway passengers in Beijing, China. We conducted statistical analyses and used an unsupervised clustering method to study and classify passengers based on travel patterns. Four groups have been identified: standard commuters, flexible commuters, and two types of low-frequency passengers. Then, a comprehensive analysis was conducted. We also discussed the changes of passengers’ travel time consumption before and after the implementation of customized bus for high-frequency passengers. The analyses indicated that passengers’ travel patterns can provide useful information for transit service providers and can help improve the level of service of urban public transit by promoting the promulgation of local public transport policies and the implementation of customized services.

Modeling Limited-Stop Bus Corridor Services with Fare Payment Mode Choice and Trip Purpose Consideration.

This paper proposes a novel model for optimizing limited-stop bus corridor services with consideration of varied payment modes and different trip purposes. In the proposed model, the bus dwell time at a stop is dependent on the fare payment modes and the number of passengers getting off and waiting at the stop while those with the similar trip purpose are grouped into one user class. Given an origin-destination (OD) passenger trip matrix and a set of candidate bus lines serving a corridor, the proposed model is to minimize the total social cost that consists of the cost to the bus operator and the cost to the passengers. In the formulation of the optimization problem, a weighting parameter is adopted to balance the operator cost and the passenger cost. Numerical examples are presented to illustrate the importance of considering passenger flow impacts on bus (and passenger) travel times in the proposed model. We also investigate effects on the optimal limited-stop services (e.g., short-turn, skip-stop, and express) taking into account the choice of fare payment modes (e.g., on-board fare collection including payment by cash, magnetic strip or smart card, off-board fare collection) and different values of travel time due to passenger trip purposes. It is shown that the off-board payment mode would be more efficient in a high-demand corridor, that more passengers prefer to express and skip-stop services rather than normal regular services in the four collection systems, and that different limited-stop service plans should be used for different periods of the day in response to temporal variation in OD passenger travel patterns. The intellectual merit of this paper is not the seemingly obvious conclusions but that the proposed model can handle the problem of limited-stop bus corridor service design with the consideration of fare payment mode choice and trip purposes.

Individualized passenger travel pattern multi-clustering based on graph regularized tensor latent dirichlet allocation

Individual passenger travel patterns have significant value in understanding passenger’s behavior, such as learning the hidden clusters of locations, time, and passengers. The learned clusters further enable commercially beneficial actions such as customized services, promotions, data-driven urban-use planning, peak hour discovery, and so on. However, the individualized passenger modeling is very challenging for the following reasons: 1) The individual passenger travel data are multi-dimensional spatiotemporal big data, including at least the origin, destination, and time dimensions; 2) Moreover, individualized passenger travel patterns usually depend on the external environment, such as the distances and functions of locations, which are ignored in most current works. This work proposes a multi-clustering model to learn the latent clusters along the multiple dimensions of Origin, Destination, Time, and eventually, Passenger (ODT-P). We develop a graph-regularized tensor Latent Dirichlet Allocation (LDA) model by first extending the traditional LDA model into a tensor version and then applies to individual travel data. Then, the external information of stations is formulated as semantic graphs and incorporated as the Laplacian regularizations; Furthermore, to improve the model scalability when dealing with massive data, an online stochastic learning method based on tensorized variational Expectation-Maximization algorithm is developed. Finally, a case study based on passengers in the Hong Kong metro system is conducted and demonstrates that a better clustering performance is achieved compared to state-of-the-arts with the improvement in point-wise mutual information index and algorithm convergence speed by a factor of two.

The impact of the COVID-19 pandemic on the behaviour of bike sharing users.

Globally most governments implemented a ‘Working from Home’ (home office) strategy to contain the spread of the coronavirus in 2020 in order to ensure public safety and minimize the transmission of the virus. Unsurprisingly studies have found that COVID-19 has had a detrimental impact on urban transportation systems; however, the number of shared bicycle riders is progressively growing compared to other modes of public transit. The aim of this study is to investigate the influence of COVID-19 on the usage of shared bicycle systems in order to identify passenger travel patterns and habits. In addition, bicycle rentals are becoming more popular in some locations. This demonstrates that bike sharing as a transport option has a high level of social adaptability and is progressively being adopted by the general population in a fashion that promotes the resilience of transport systems.

COVid-19 influenced households’ Interrupted Travel Schedules (COVHITS) survey: Lessons from the fall 2020 cycle

The spread of the novel coronavirus disease-2019 (COVID-19) since early in 2020 has affected every aspect of daily life, including urban passenger travel patterns. Lockdowns to control the spread of COVID-19 created unprecedented travel demand contexts that have never been seen in modern history. So, it is essential to benchmark trends of travel behaviour, especially people's daily travel patterns that are necessary to develop a comprehensive understanding of the impacts of COVID-19. A multi-cycle benchmarking household travel study: the COVid-19 influenced Households' Interrupted Travel Schedules (COVHITS) Survey was implemented in the Greater Toronto Area with a random sample of over 4000 households. The results indicated a stark alteration in people's daily activity-travel patterns due to COVID-19. The pandemic caused a substantial decline in mobility in the study area. The average weekday household trip rate dropped from 5.2 to 2.0 trips. Transit modal shares suffered severely during the paramedic, while private car dependency was enhanced. Overall, transit modal share dropped from 17.3% to 8.1% in the study area, while the modal share of private cars increased from 70.8% to 74.1%. Factors such as having to work from home, ownership of private cars, and household incomes influenced mobility levels of the people in the study area during the pandemic. While overlooked, travel demand analysis can reveal effective strategies to curb the spread of such contagious diseases. An econometric model and analysis of sample data reveal several potential strategies. These include: (1) working/learning from home should be implemented until the end of the pandemic; (2) transit agencies should provide as much transit frequency as possible (particularly for bus routes) during peak hours to avoid crowding inside transit vehicles and project a positive image of public transit; and (3) strict restrictions should be implemented in regions with lower confirmed COVID-19 cases, as they became attractive destinations during the pandemic.

Mitigating unfairness in urban rail transit operation: A mixed-integer linear programming approach

In oversaturated urban rail transit systems, passengers departing from downstream stations often experience long waiting times due to unbalanced space-time demand and limited transit capacity. This is often prevalent during morning and evening peak periods in transit systems. This paper aims to mitigate the unfairness of waiting time among a time-varying number of passengers through train timetable's adjustment by optimizing the train skip-stopping pattern. We develop an approximate general model by clustering passengers into groups and introducing an aggregation granularity parameter. To characterize feasible passenger travel patterns, both rigid first-in-first-out rule and capacity constraints are incorporated in the proposed model. Preprocessing is proposed to reduce the space of solutions. Some small-scale case studies show that the proposed method outperforms the original timetable and the preprocessing is effective to reduce computation time. Case studies based on the Batong line of Beijing rail transit network are conducted, in which a variable neighborhood search algorithm is applied to obtain high-quality solutions in short computing times. The results show that the proposed approach not only mitigates the unfairness of waiting time among passengers but also improves other efficiency evaluation indexes, including the average waiting time and the maximum number of missed trains. We also investigate the impact of the aggregation granularity parameter on the computational effort and solution accuracy.

Metro Passenger Flow Prediction via Dynamic Hypergraph Convolution Networks

Metro passenger flow prediction is a strategically necessary demand in an intelligent transportation system to alleviate traffic pressure, coordinate operation schedules, and plan future constructions. Graph-based neural networks have been widely used in traffic flow prediction problems. Graph Convolutional Neural Networks (GCN) captures spatial features according to established connections but ignores the high-order relationships between stations and the travel patterns of passengers. In this paper, we utilize a novel representation to tackle this issue - hypergraph. A dynamic spatio-temporal hypergraph neural network to forecast passenger flow is proposed. In the prediction framework, the primary hypergraph is constructed from metro system topology and then extended with advanced hyperedges discovered from pedestrian travel patterns of multiple time spans. Furthermore, hypergraph convolution and spatio-temporal blocks are proposed to extract spatial and temporal features to achieve node-level prediction. Experiments on historical datasets of Beijing and Hangzhou validate the effectiveness of the proposed method, and superior performance of prediction accuracy is achieved compared with the state-of-the-arts.

Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data

Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system.

Big data driven urban railway planning: Shenzhen metro case study

Planning a successful metro railway system is challenging due to the fast development of urban cities and time-consuming construction. Recently big data reflecting city dynamics has become widely available, which enables us to solve this challenging problem from a data mining perspective. In this paper, we propose a framework to evaluate the traffic efficiency of metro railway systems from various factors such as the railway traffic flow, the structure of the traffic system and the spatial distribution of work-and-home. Based on the commute data (e.g., railway boarding) of Shenzhen rail transit reported by 28,000 passengers and electronic boarding card data provided by Shenzhen Railway Company, we assess the bottlenecks and congested areas of the system, understand passenger travel patterns, and observe organizational operations and deficiencies. The experimental results help us better understand how big data can help make Shenzhen metro railway more efficient and effective in terms of planning and management in the future.

PENGARUH RENCANA INDUK BANDAR UDARA DI PROVINSI KALIMANTAN SELATAN TERHADAP JUMLAH PENUMPANG ANGKUTAN UDARA PADA TAHUN 2020 DAN 2030

The Minister of Transportation Regulation No.69 of 2013 set the national master plan of the airport in 2020 and 2030 including the airports in the Province of South Kalimantan. This causes a change in the role of the airport in South Kalimantan so effected on travel pattern change and affect on passengers trip distribution. Therefore, an analysis is needed to predict passenger travel patterns in the future. Data of passenger movement from Syamsudin Noor Airport, Gusti Syamsir Alam Airport, and Bersujud Airport analyzed by Exponential Smoothing Forecasting Method. While Tanjung Warukin airport is analyzed by multiple regression analysis method. The result of this study is in 2020 there is a new route that is Tanjung-Kotabaru predicted as many as 1.773 passengers using the route, it gives influence on demand of airport in Kotabaru that is from 299.203 people will be 300,976 people. The newly operated Tanjung-Banjarmasin route has the potential to survive as indicated by the number of passengers in 2020 of 5942 people.

Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network

The “left-behind” phenomenon occurs frequently in Urban Rail Transit (URT) networks with booming travel demand, especially during peak hours in a complex URT network, which makes passenger travel patterns more complicated. This paper proposes a methodology to mine passenger travel patterns based on fare transaction records from automatic fare collection (AFC) systems and Automatic Vehicle Location (AVL) data from Communication Based Train Control (CBTC) Systems or tracking systems. By introducing the concept of a sequence, a space-time-sequence trajectory model is proposed to simulate a passenger’s travel activities, including when they are left-behind. The paper analyzes passenger travel trajectory links and estimates the weight of each feasible trajectory under tap-in/tap-out constraints. The station time parameters, including access/egress and transfer walking-time parameters, are important inputs for the model. The paper also presents a maximum-likelihood approach to estimate these parameters from AFC transaction data and AVL data. The methodology is applied to a case study using AFC and AVL data from the Beijing URT network during peak hours to test the proposed model and algorithm. The estimation results are consistent with the results obtained from the authorities, and this finding verifies the feasibility of our approach.

Passenger Travel Regularity Analysis Based on a Large Scale Smart Card Data

Analysis of passenger travel habits is always an important item in traffic field. However, passenger travel patterns can only be watched through a period time, and a lot of people travel by public transportation in big cities like Beijing daily, which leads to large-scale data and difficult operation. Using SPARK platform, this paper proposes a trip reconstruction algorithm and adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel patterns of each Smart Card (SC) user in Beijing. For the phenomenon that passengers swipe cards before arriving to avoid the crowd caused by the people of the same destination, the algorithm based on passenger travel frequent items is adopted to guarantee the accuracy of spatial regular patterns. At last, this paper puts forward a model based on density and node importance to gather bus stations. The transportation connection between areas formed by these bus stations can be seen with the help of SC data. We hope that this research will contribute to further studies.