Héctor Cogollos Adrián
ABSTRACT
The data collected by public transport tickets has become a valuable source of information for transportation analysis. There are numerous works that analyze them in case studies for subway, bus or train networks, but there are few studies referring to public transport in aquatic environments. In this paper, ticket validation data is used to analyze the movements of tourists in the centre of Venice where waterbuses are the principal public transport. The objective is to analyse the behavior of tourists and detect some relevant patterns. In order to attain this goal, first we build several complex networks which represent the flow of tourists between clusters of stops during different time periods of the day. This allows us to discover some common behaviours of tourists. In a second phase, we construct a set of trajectories by considering the sequences of validations for each user. By applying a hierarchical clustering algorithm, we detect the movement patterns of tourists, identifying which places they visit and in which order. For each cluster we define a representative, that illustrates visually the main routes followed by the tourists. This can represent a valuable information for the decision-maker of the local administration and public transport.
- J. Barry, R. Newhouser, A. Rahbee, and S. Sayeda. 2002. Origin and destination estimation in New York City with automated fare system data. Transportation Research Record 1817, 1 (2002), 183--187.Google Scholar
Cross Ref
- A.-S. Briand, E. Côme, M. Trépanier, and L. Oukhellou. 2017. Analyzing year-to-year changes in public transport passenger behaviour using smart card data. Transportation Research Part C: Emerging Technologies 79 (2017), 274--289.Google ScholarCross Ref
- Emre Celebi. 2015. Partitional Clustering Algorithms (1st ed. 2015. ed.). Springer, Cham. 79--98 pages.Google ScholarCross Ref
- Liang Liu, Anyang Hou, Assaf Biderman, Carlo Ratti, and Jun Chen. 2009. Understanding individual and collective mobility patterns from smart card records: A case study in Shenzhen. In 12th IEEE Conference on Intelligent Transportation Systems. IEEE, 1--6.Google ScholarCross Ref
- González M. C. Menezes R., Evsukoff A. 2013. Complex Networks. Springer.Google Scholar
- M. A. Ortega-Tong. 2013. Classification of London's public transport users using smart card data. Ph.D. Dissertation. Massachusetts Institute of Technology.Google Scholar
- M. Ouyang, W.J. Welsh, and P. Georgopoulos. 2004. Gaussian mixture clustering and imputation of microarray data. Bioinformatics 20, 6 (01 2004), 917--923.Google ScholarDigital Library
- R. Trasarti, A. Olteanu-Raimond, M. Nanni, T. Couronné, B. Furletti, F. Giannotti, Z. Smoreda, and C. Ziemlicki. 2015. Discovering urban and country dynamics from mobile phone data with spatial correlation patterns. Telecommunications Policy 39, 3--4 (2015), 347--362.Google ScholarDigital Library
- M. Xue, H. Wu, W. Chen, W. S. Ng, and G. H. Goh. 2014. Identifying tourists from public transport commuters. In Proc. of the 20th ACM SIGKDD intern. conference on Knowledge discovery and data mining. 1779--1788.Google Scholar
- C. Zhong, M. Batty, E. Manley, J. Wang, Z. Wang, F. Chen, and G. Schmitt. 2016. Variability in regularity: Mining temporal mobility patterns in London, Singapore and Beijing using smart-card data. PloS one 11, 2 (2016).Google Scholar
Index Terms
- Discovery of tourists' movement patterns in venice from public transport data
Recommendations
Identifying tourists from public transport commuters
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningTourism industry has become a key economic driver for Singapore. Understanding the behaviors of tourists is very important for the government and private sectors, e.g., restaurants, hotels and advertising companies, to improve their existing services or ...
Research on Urban Public Transport Transit System in Yishan Road Station, Shanghai
ICMTMA '11: Proceedings of the 2011 Third International Conference on Measuring Technology and Mechatronics Automation - Volume 03Urban public transport transit system plays an important role in the public transportation system in Shanghai. Based on the interchange station Yishan Road station, where subway line 3, 4 and 9 interchanges, the research on public transportation transit ...
Compacting Massive Public Transport Data
String Processing and Information RetrievalAbstractIn this work, we present a compact method for storing and indexing users’ trips across transport networks. This research is part of a larger project focused on providing transportation managers with the tools to analyze the need for improvements ...
Comments