scholarly journals Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Hamed Faroqi ◽  
Mahmoud Mesbah ◽  
Jiwon Kim
Keyword(s):  
Similarity Measure ◽  
Smart Card ◽  
Public Transit ◽  
Pearson Correlation ◽  
Similarity Measures ◽  
Correlation Coefficients ◽  
The Public ◽  
Spatial Similarity ◽  
Smart Card Data ◽  
One Step

The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers’ spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.

scholarly journals Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Hamed Faroqi ◽  
Mahmoud Mesbah ◽  
Jiwon Kim
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Similarity Measures ◽  
Spatial And Temporal Patterns ◽  
Transit Network ◽  
The Public ◽  
Temporal Space ◽  
Smart Card Data ◽  
Transit Services ◽  
Spatiotemporal Clustering

Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

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>

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.

scholarly journals An Association Rule Based Method to Integrate Metro-Public Bicycle Smart Card Data for Trip Chain Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
De Zhao ◽  
Wei Wang ◽  
Ghim Ping Ong ◽  
Yanjie Ji
Keyword(s):  
Association Rules ◽  
Smart Card ◽  
Primary Objective ◽  
Smart Cards ◽  
The Public ◽  
Trip Chain ◽  
Chain Analysis ◽  
Smart Card Data ◽  
Transfer Behavior ◽  
Novel Method

Smart card data provide valuable insights and massive samples for enhancing the understanding of transfer behavior between metro and public bicycle. However, smart cards for metro and public bicycle are often issued and managed by independent companies and this results in the same commuter having different identity tags in the metro and public bicycle smart card systems. The primary objective of this study is to develop a data fusion methodology for matching metro and public bicycle smart cards for the same commuter using historical smart card data. A novel method with association rules to match the data derived from the two systems is proposed and validation was performed. The results showed that our proposed method successfully matched 573 pairs of smart cards with an accuracy of 100%. We also validated the association rules method through visualization of individual metro and public bicycle trips. Based on the matched cards, interesting findings of metro-bicycle transfer have been derived, including the spatial pattern of the public bicycle as first/last mile solution as well as the duration of a metro trip chain.

scholarly journals A New Semantic Similarity Measure Based On Ontology for Movie Rate Prediction

2019 ◽  
Vol 8 (3) ◽  
pp. 6756-6762
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Experimental Evaluation ◽  
Pearson Correlation ◽  
Similarity Measures ◽  
Similarity Score ◽  
Cosine Similarity ◽  
Semantic Similarity Measure ◽  
Rate Prediction ◽  
Target User

A recommendation algorithm comprises of two important steps: 1) Predicting rates, and 2) Recommendation. Rate prediction is a cumulative function of the similarity score between two movies and rate history of those movies by other users. There are various methods for rate prediction such as weighted sum method, regression, deviation based etc. All these methods rely on finding similar items to the items previously viewed/rated by target user, with assumption that user tends to have similar rating for similar items. Computing the similarities can be done using various similarity measures such as Euclidian Distance, Cosine Similarity, Adjusted Cosine Similarity, Pearson Correlation, Jaccard Similarity etc. All of these well-known approaches calculate similarity score between two movies using simple rating based data. Hence, such similarity measures could not accurately model rating behavior of user. In this paper, we will show that the accuracy in rate prediction can be enhanced by incorporating ontological domain knowledge in similarity computation. This paper introduces a new ontological semantic similarity measure between two movies. For experimental evaluation, the performance of proposed approach is compared with two existing approaches: 1) Adjusted Cosine Similarity (ACS), and 2) Weighted Slope One (WSO) algorithm, in terms of two performance measures: 1) Execution time and 2) Mean Absolute Error (MAE). The open-source Movielens (ml-1m) dataset is used for experimental evaluation. As our results show, the ontological semantic similarity measure enhances the performance of rate prediction as compared to the existing-well known approaches.

Public Transit Transfer Recognition Method Based on Vehicle GPS and Smart Card Data

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Min Fu ◽  
Wei Wang ◽  
Hao Wang ◽  
Yun Xiang ◽  
Wanbo Yang
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Recognition Method ◽  
Smart Card Data

Smart card data use in public transit: A literature review

2011 ◽  
Vol 19 (4) ◽  
pp. 557-568 ◽  
Author(s):  
Marie-Pier Pelletier ◽  
Martin Trépanier ◽  
Catherine Morency
Keyword(s):  
Literature Review ◽  
Smart Card ◽  
Public Transit ◽  
Data Use ◽  
Smart Card Data
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