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

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Yuan Ren ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Large Scale ◽  
Spatial Clustering ◽  
Reconstruction Algorithm ◽  
Travel Patterns ◽  
Node Importance ◽  
The People ◽  
Passenger Travel ◽  
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.

Algorithm of Estimating Alighting Bus Stops of Smart Card Passengers Based on Trip-Chain

2012 ◽  
Vol 253-255 ◽  
pp. 1918-1921
Author(s):  
Jun Chen ◽  
Zhao Fei Wang
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Large Scale ◽  
Estimation Algorithm ◽  
Transportation Systems ◽  
Trip Chain ◽  
Smart Card Data ◽  
Bus Stops ◽  
Public Transportation Planning ◽  
Public Transportation Systems

In order to apply smart card data in decision-making of public transportation planning and management, the paper researched estimating method of alighting bus stops of smart card passengers. Based on Trip-chain thought, the paper presented estimation algorithm applying the three hypotheses of “Next Trip”, “Last Trip” and “Return Trip”. Then, the algorithm was tested and analyzed using large-scale actual data of Advanced Public Transportation Systems of Nanning City in China. The results show that Trip-chain Method can estimate majority of alighting bus stops.

Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data

2017 ◽  
Vol 18 (11) ◽  
pp. 3135-3146 ◽  
Author(s):  
Juanjuan Zhao ◽  
Qiang Qu ◽  
Fan Zhang ◽  
Chengzhong Xu ◽  
Siyuan Liu
Keyword(s):  
Smart Card ◽  
Temporal Analysis ◽  
Travel Patterns ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Spatio Temporal

scholarly journals The Pattern of Non-Roundtrip Travel on Urban Rail and Its Application in Transit Improvement

2020 ◽  
Vol 12 (9) ◽  
pp. 3525
Author(s):  
Zijia Wang ◽  
Hao Tang ◽  
Wenjuan Wang ◽  
Yang Xi
Keyword(s):  
Smart Card ◽  
Distribution Patterns ◽  
Travel Patterns ◽  
Travel Information ◽  
Transit Ridership ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Urban Rail ◽  
In Transit ◽  
Abundant Data

Transit smart card records detail travel information of passengers, which provides abundant data for analyzing public travel patterns. Regular travelers’ information extracted from smart card data (SCD) have been extensively analyzed. However, rare studies have been devoted to non-roundtrips, which account for a relatively large portion of the overall transit ridership, especially in metropolises such as Beijing. This study aimed to reveal the non-roundtrip pattern using the passenger travel data obtained from SCD. Weekly non-roundtrip SCD were used to analyze the spatiotemporal distribution patterns of overall and typical non-roundtrips’ origins and destinations (ODs). Also, subway data and bus data were combined and visualized in geographic information system (GIS). The reasons for frequent non-roundtrips generated in the metropolitan city were inferred. The results demonstrate some detected spatiotemporal patterns of non-roundtrips. It is not surprising that a large proportion of non-roundtrips serve as a rail access to intercity, but there are still many trips of this kind showing a commuting pattern. Merging SCD with bus data, the results also reveal that passengers may choose other modes as a substitute return transportation option due to rail fare or overcrowding problem. This study focused on irregular trips normally neglected in the literature and found that the number of these trips is too large to be ignored in a diversified city like Beijing. Meanwhile, the travel patterns of non-roundtrips extracted can be used to direct the operation strategies for both rail and bus. The research framework raised here could be applied in other cities and comparative analysis could be done in the future.

scholarly journals Exploring Equity in Public Transportation Planning Using Smart Card Data

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3039
Author(s):  
Kiarash Ghasemlou ◽  
Murat Ergun ◽  
Nima Dadashzadeh
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Transportation Planning ◽  
Demand Characteristic ◽  
Monthly Data ◽  
Smart Card Data ◽  
Planning Methods ◽  
Public Transportation Planning ◽  
Daily Travel ◽  
A Share

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

Application of smart card data in validating a large-scale multi-modal transit assignment model

2017 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Ahmad Tavassoli ◽  
Mahmoud Mesbah ◽  
Mark Hickman
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Transit Assignment ◽  
Assignment Model ◽  
Smart Card Data

scholarly journals Understanding the integration of buses and metro systems using smart card data

2019 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Diao Lin ◽  
Ruoxin Zhu
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Pearson Correlation ◽  
Metro Station ◽  
Smart Card Data ◽  
Pearson Correlation Test ◽  
Metro Systems ◽  
The City ◽  
Afternoon Peak

<p><strong>Abstract.</strong> Buses are considered as an important type of feeder model for urban metro systems. It is important to understand the integration of buses and metro systems for promoting public transportation. Using smart card data generated by automatic fare collection systems, we aim at exploring the characteristics of bus-and-metro integration. Taking Shanghai as a case study, we first introduced a rule-based method to extract metro trips and bus-and-metro trips from the raw smart card records. Based on the identified trips, we conducted three analyses to explore the characteristics of bus-and-metro integration. The first analysis showed that 46% users have at least two times of using buses to access metro stations during five weekdays. By combining the ridership of metro and bus-and-metro, the second analysis examined how the share of buses as the feeder mode change across space and time. Results showed that the share of buses as the feeder mode in morning peak hours is much larger than in afternoon peak hours, and metro stations away from the city center tend to have a larger share. Pearson correlation test was employed in the third analysis to explore the factors associated with the ratios of bus-and-metro trips. The metro station density and access metro duration are positively associated with the ratios. The number of bus lines around 100&amp;thinsp;m to 400&amp;thinsp;m of metro stations all showed a negative association, and the coefficient for 200&amp;thinsp;m is the largest. In addition, the temporal differences of the coefficients also suggest the importance of a factor might change with respect to different times. These results enhanced our understanding of the integration of buses and metro systems.</p>

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