Abstract
AbstractIndividual passenger travel patterns have significant value in understanding passenger’s behavior, such as learning the hidden clusters of locations, time, and passengers. The learned clusters further enable commercially beneficial actions such as customized services, promotions, data-driven urban-use planning, peak hour discovery, and so on. However, the individualized passenger modeling is very challenging for the following reasons: 1) The individual passenger travel data are multi-dimensional spatiotemporal big data, including at least the origin, destination, and time dimensions; 2) Moreover, individualized passenger travel patterns usually depend on the external environment, such as the distances and functions of locations, which are ignored in most current works. This work proposes a multi-clustering model to learn the latent clusters along the multiple dimensions of Origin, Destination, Time, and eventually, Passenger (ODT-P). We develop a graph-regularized tensor Latent Dirichlet Allocation (LDA) model by first extending the traditional LDA model into a tensor version and then applies to individual travel data. Then, the external information of stations is formulated as semantic graphs and incorporated as the Laplacian regularizations; Furthermore, to improve the model scalability when dealing with massive data, an online stochastic learning method based on tensorized variational Expectation-Maximization algorithm is developed. Finally, a case study based on passengers in the Hong Kong metro system is conducted and demonstrates that a better clustering performance is achieved compared to state-of-the-arts with the improvement in point-wise mutual information index and algorithm convergence speed by a factor of two.
Funder
Research Grants Council, University Grants Committee
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Computer Science Applications,Information Systems
Reference44 articles.
1. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022
2. Briand AS, Côme E, Trépanier M et al (2017) Analyzing year-to-year changes in public transport passenger behaviour using smart card data. Transp Res Part C: Emerg Technol 79:274–289
3. Chang J, Gerrish S, Wang C, et al (2009) Reading tea leaves: How humans interpret topic models. In: Advances in neural information processing systems, pp 288–296
4. Chen L, Jose JM, Yu H, et al (2016) A semantic graph based topic model for question retrieval in community question answering. In: Proceedings of the ninth ACM international conference on web search and data mining, pp 287–296
5. Cheng Z, Trépanier M, Sun L (2020) Probabilistic model for destination inference and travel pattern mining from smart card data. Transportation 48(4):2035–2053
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献