A Study on the Monitoring of Urban Activity and Spatial Structure Changes using Public Transportation Big Data : Based on the Smart Card Data and the Prestige Centrality Index in Seoul, Korea

2017 ◽  
Vol 52 (6) ◽  
pp. 73-90 ◽  
Author(s):  
Jaehyun Ha ◽  
Sugie Lee
Keyword(s):  
Big Data ◽  
Spatial Structure ◽  
Smart Card ◽  
Public Transportation ◽  
Centrality Index ◽  
Structure Changes ◽  
Smart Card Data ◽  
Urban Activity ◽  
The Prestige

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.

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>

Predicting Passenger’s Public Transportation Travel Route Using Smart Card Data

2018 ◽  
pp. 199-213
Author(s):  
Chen Yang ◽  
Wei Chen ◽  
Bolong Zheng ◽  
Tieke He ◽  
Kai Zheng ◽  
...  
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Smart Card Data

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 33-45 ◽  
Author(s):  
Elodie Deschaintres ◽  
Catherine Morency ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Travel Behavior ◽  
Travel Demand ◽  
Public Transit ◽  
User Behavior ◽  
Individual Level ◽  
Smart Card Data ◽  
Demand Models ◽  
The Individual

A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

Public Transportation OD Calculation Based on Trip Chain

2013 ◽  
Vol 401-403 ◽  
pp. 2151-2154
Author(s):  
Lai Ping Luo ◽  
Jing Zhang
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Bus Stop ◽  
Trip Chain ◽  
Smart Card Data ◽  
Card System

Public transportation smart card system in China has been widely used in many cities recently. Large amounts of information implicit in the smart card, but it is not completely applied, because the information is incomplete, such as the information of getting-off bus stop. For this problem, a method is proposed to calculate OD (Origin-Destination) of smart card data. And it is well applied in digging the information of getting-off bus stop.

Generating Travel Patterns of Public Transportation Users Using a k-means Clustering Based on Smart Card Data

2020 ◽  
Vol 23 (3) ◽  
pp. 204-215
Author(s):  
Inmook Lee ◽  
Jaehong Min ◽  
Kyoungtae Kim ◽  
Seung-Young Kho
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Travel Patterns ◽  
Smart Card Data

scholarly journals Rationalizing Urban Transportation using Smart Card Data

2019 ◽  
Vol 8 (11S) ◽  
pp. 507-515
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Public Transportation ◽  
Urban Population ◽  
Transport Systems ◽  
Living Standard ◽  
Transit System ◽  
Advantages And Disadvantages ◽  
Smart Card Data ◽  
The Impact

The urban population in 2014 accounted for 54% of the total global population, up from 34% in 1960, and continues to grow. The global urban population is expected to grow approximately 1.84%, 1.63% and 1.44% between 2015 and 2020, 2020 and 2025, and 2025 and 2030 respectively. This growing population puts pressure on government not only to accommodate the current and potential citizens but also provide them facilities and services for a better living standard. Providing a sustainable growing environment for the citizens is the biggest challenge for the government. As the populations increase, complexity network of transportation, water and sanitation, emergency services, etc. will increase many folds. SMART CITY Mission is being implemented to resolve this issue. As the cities turn smart, so should the transportation facilities. India on June 2018 had only 20 cities with populations of over 500,000 have organized public transport systems, pointing to the large gap to be bridged in their journey to turn smart. The aim of this paper is to examine the impact of smart card data from public transport for improving the predictions and planning of public transport usage and congestions. The mobile apps like M-Indicator, Google Maps don’t interlink, do not have a real time tracking of vehicles, fare distribution, congestion-based route mapping for public transportation. These factors are addressed in the paper with its advantages and disadvantages. This paper also talks about how information from smart card is to be extracted, how Big Data is to be managed and finally come to a smart, sustainable Urban Transit System. This paper also brings into light the data security issues and measures to curb those issues. This paper proposes and emphasizes on a single smart card for all modes of public transport

scholarly journals Comparative Examination of Network Clustering Methods for Extracting Community Structures of a City From Public Transportation Smart Card Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 53377-53391 ◽  
Author(s):  
Takashi Nicholas Maeda ◽  
Junichiro Mori ◽  
Izumi Hayashi ◽  
Tetsuo Sakimoto ◽  
Ichiro Sakata
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Network Clustering ◽  
Clustering Methods ◽  
Community Structures ◽  
Smart Card Data ◽  
Comparative Examination
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