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>

scholarly journals Using Smart Card Data of Metro Passengers to Unveil the Urban Spatial Structure: A Case Study of Xi’an, China

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guohong Cheng ◽  
Shichao Sun ◽  
Linlin Zhou ◽  
Guanzhong Wu
Keyword(s):  
Smart Card ◽  
Policy Implications ◽  
Transport Sector ◽  
Urban Structure ◽  
Urban Spatial Structure ◽  
Smart Card Data ◽  
Western Area ◽  
Metro Systems ◽  
The City

This study adopted smart card data collected from metro systems to identify city centers and illustrate how city centers interacted with other regions. A case study of Xi’an, China, was given. Specifically, inflow and outflow patterns of metro passengers were characterized to measure the degree of population agglomeration of an area, i.e., the centricity of an area. On this basis, in order to overcome the problem of determining the boundaries of the city centers, Moran’s I was adopted to examine the spatial correlation between the inflow and outflow of ridership of adjacent areas. Three residential centers and two employee centers were identified, which demonstrated the polycentricity of urban structure of Xi’an. With the identified polycenters, the dominant spatial connections with each city center were investigated through a multiple linkage analysis method. The results indicated that there were significant connections between residential centers and employee centers. Moreover, metro passengers (commuters mostly) flowing into the identified employee centers during morning peak-hours mainly came from the northern and western area of Xi’an. This was consistent with the interpretation of current urban planning, which validated the effectiveness of the proposed methods. Policy implications were provided for the transport sector and public transport operators.

scholarly journals The Analysis of Potential Residential Area/Center Based on Residents’ Travel Behaviour and Planned Metro Stations

2021 ◽  
Vol 257 ◽  
pp. 03063
Author(s):  
Lu Liu ◽  
Yi Song ◽  
Peng Li
Keyword(s):  
Public Transportation ◽  
Housing Price ◽  
Travel Behaviour ◽  
Residential Center ◽  
Commuting Time ◽  
Public Transportation System ◽  
Metro Systems ◽  
The City ◽  
Travel Length

Metro is of vital importance in public transportation system. Recent studies have examined the influence of metro systems by various methodologies. However, few of them has focused on the stations which are planned to be built or still being built. Therefore, this study intends to evaluate the future metro stations and map the potential urban residential center, based on analyzing the metro card data of the existing metro systems. Based on a case study in Shenzhen, China, we identified 21 residential hot stations and 13 working hot stations. Also, the results indicate that most passengers have a travel length between 5-14 stops, while each residential center has its specific working center. Moreover, when the housing price decrease 1598.3 RMB per square meters, residents may be willing to move to a place with one more stop commuting time. Finally, based on two criteria established by the riding behavior, 67 new stations are found to have the chance to be new residential centers in the city. The strategy proposed in this study can help urban planners to understand the possible influences of new metro stations and assist them to do the planning work in a more appropriate way.

scholarly journals TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data

2021 ◽  
Vol 2 (3) ◽  
pp. 1-21
Author(s):  
Xiancai Tian ◽  
Baihua Zheng ◽  
Yazhe Wang ◽  
Hsiao-Ting Huang ◽  
Chih-Chieh Hung
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Total Duration ◽  
The Other ◽  
Smart Card Data ◽  
Additional Input ◽  
Metro Systems ◽  
The City ◽  
Metro System ◽  
Inference Task

In this article, we target at recovering the exact routes taken by commuters inside a metro system that are not captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategically propose two inference tasks to handle the recovering, one to infer the travel time of each travel link that contributes to the total duration of any trip inside a metro network and the other to infer the route preferences based on historical trip records and the travel time of each travel link inferred in the previous inference task. As these two inference tasks have interrelationship, most of existing works perform these two tasks simultaneously. However, our solution TripDecoder adopts a totally different approach. TripDecoder fully utilizes the fact that there are some trips inside a metro system with only one practical route available. It strategically decouples these two inference tasks by only taking those trip records with only one practical route as the input for the first inference task of travel time and feeding the inferred travel time to the second inference task as an additional input, which not only improves the accuracy but also effectively reduces the complexity of both inference tasks. Two case studies have been performed based on the city-scale real trip records captured by the AFC systems in Singapore and Taipei to compare the accuracy and efficiency of TripDecoder and its competitors. As expected, TripDecoder has achieved the best accuracy in both datasets, and it also demonstrates its superior efficiency and scalability.

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 Evaluation of the Relationship between Personality and Conservatism of Investors in the Stock Exchange (Case Study: Insurance Companies)

2021 ◽  
Vol 8 (SPE3) ◽  
Author(s):  
Mahsa Mohseni
Keyword(s):  
Significant Relationship ◽  
Stock Exchange ◽  
Pearson Correlation ◽  
Simple Random Sampling ◽  
Insurance Companies ◽  
Statistical Population ◽  
Pearson Correlation Test ◽  
Correlational Method ◽  
The Relationship

The purpose of this research to "investigate the relationship between personality and conservatism of investors of insurance companies listed on the Tehran Stock Exchange." The present study was applied research in terms of purpose, which has employed a descriptive and correlational method. The statistical population of this research included all people who buy and sell shares of insurance companies listed on the Tehran Stock Exchange. According to Cochran's formula, the sample size was determined as much as 384 people collected by a simple random sampling method. The research instruments were the Conservative Questionnaire based on the Gribel and Leighton (1999) and McCrae and Costa (1985) five-factor personality questionnaire. The questionnaire’s validity was confirmed by 20 experts, and the reliability of all three questionnaires was acceptable for all three questionnaires due to Cronbach's alpha above 0.79. The data analysis was conducted using the Pearson correlation test and regression analysis. The results indicated a significant relationship between the investors’ personality and their conservatism in the Tehran Stock Exchange. There was also a significant relationship between all personality components except for extraversion with the investor’s conservatism in the Tehran Stock Exchange.

scholarly journals A Research on the effect of social media on tendency to entrepreneurship and business establishment (Case Study: Active Iranian Entrepreneurs in Social Media)

2017 ◽  
Vol 1 (1) ◽  
pp. 43 ◽  
Author(s):  
Masoud Tosifyan ◽  
Saeed Tosifyan
Keyword(s):  
Social Media ◽  
Structural Equation ◽  
Pearson Correlation ◽  
Pearson Correlation Test ◽  
Kolmogorov Smirnov ◽  
Business Establishment ◽  
Descriptive Survey ◽  
Inferential Comprehension ◽  
Smirnov Test

This research was done with the aim to investigate the effect of social media on tendency to entrepreneurship and business establishment. The aim of applied research and methods used in this survey was a descriptive survey research. A standard questionnaire was used to collect relevant data in this study. The reliability of each questionnaire was estimated 0.779, 0.806 and 0.798. The population study is Iranian entrepreneurs who are active in social media and number of them is uncertain; A sample of 120 active Iranian entrepreneurs were selected as target and a questionnaire was distributed among these individuals. To collect the information and necessary data to evaluate the hypotheses of the research, a questionnaire and SPSS and LISREL software were evaluated.  At inferential comprehension level, techniques of Kolmogorov-Smirnov test for being normal, Pearson correlation test and structural equation modelling were used to test the hypotheses. Based on the results, the hypotheses were accepted.

scholarly journals Methodology for Extracting Potential Customized Bus Routes Based on Bus Smart Card Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2224 ◽  
Author(s):  
Jing Li ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Qi Ouyang
Keyword(s):  
Smart Card ◽  
Traffic Congestion ◽  
Spatial Clustering ◽  
Extraction Process ◽  
Methodological Framework ◽  
Transit Service ◽  
Dbscan Algorithm ◽  
Smart Card Data ◽  
Division Process

To alleviate traffic congestion and traffic-related environmental pollution caused by the increasing numbers of private cars, public transport (PT) is highly recommended to travelers. However, there is an obvious contradiction between the diversification of travel demands and simplification of PT service. Customized bus (CB), as an innovative supplementary mode of PT service, aims to provide demand-responsive and direct transit service to travelers with similar travel demands. But how to obtain accurate travel demands? It is passive and limited to conducting online surveys, additionally inefficient and costly to investigate all the origin-destinations (ODs) aimlessly. This paper proposes a methodological framework of extracting potential CB routes from bus smart card data to provide references for CB planners to conduct purposeful and effective investigations. The framework consists of three processes: trip reconstruction, OD area division and CB route extraction. In the OD area division process, a novel two-step division model is built to divide bus stops into different areas. In the CB route extraction process, two spatial-temporal clustering procedures and one length constraint are implemented to cluster similar trips together. An improved density-based spatial clustering of application with noise (DBSCAN) algorithm is used to complete these procedures. In addition, a case study in Beijing is conducted to demonstrate the effectiveness of the proposed methodological framework and the resulting analysis provides useful references to CB planners in Beijing.

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.

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