01698048

Component

Intelligent transportation systems (ITS) provide new opportunities to collect data at a relatively low cost for the development of transportation models. However, data from ITS sources - such as cell phones, social media applications, GPS, and Bluetooth devices - may not contain the required activity-based travel patterns. For instance, cell phone data typically consist of locations and timestamps of calls made and received by travelers. From these data, the activity-based travel patterns, such as the activities of the callers, are not clear. In order to capture these latent patterns, the current state of the art focuses primarily on the use of parametric models, which make strong restrictive assumptions about a priori knowledge of the number of underlying activities. To address this limitation, a nonparametric Bayesian estimation approach is proposed to capture latent travel activities without prespecifying their cardinality. The proposed non-parametric model, a hierarchical segmented infinite hidden Markov model, overcomes the need to perform cross-validation arising from standard expectation maximization training or model selection to find the optimum cardinality of hidden activities. The proposed model can summarize, into a few meaningful activity groupings, large amounts of ITS data that are missing activity labels. Then, semantic meaning is added to these groupings by using other information, such as land use and time-of-day characteristics. The proposed model is general enough to be applied to any sequential ITS data.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

19-06060

English

0000-0002-1217-9808

14p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Data collection; Intelligent transportation systems; Markov processes; Travel behavior; Travel patterns

Data and Information Technology; Highways; Operations and Traffic Management

Transportation Research Board Annual Meeting 2019 Paper #19-06060

TRIS, TRB, ATRI

Dec 7 2018 9:44AM