Transport mode identification by clustering travel time data

Abstract

Travel time data of road users collected by Bluetooth scanners are of great value in traffic monitoring and planning. To estimate the travel time of road users over a segment of road, discriminating between different types of travellers is essential, but often overlooked by researchers. This paper explores the feasibility of transport mode identification using clustering methods. The performance of the \(k\)-means clustering algorithm and the Gaussian mixture model is examined via an empirical study of travel time data collected from road segments in the north Brisbane region, Queensland, Australia. It is demonstrated that both clustering methods are able to detect multiple transport modes and produce travel time estimates that are close to reality. The methods and results provide a guideline for transport mode identification, and may contribute to further issues related to traffic monitoring such as forecasting and planning. References Banfield, J. and Raftery, A. (1993) Model-based Gaussian and non-Gaussian clustering. Biometrics, 49, 803–821. doi:10.2307/2532201. Bhaskar, A., and Chung, E. (2013) Fundamental understanding on the use of Bluetooth scanner as a complementary transport data. Transport Res C-Emer, 37, 42–72. doi:10.1016/j.trc.2013.09.013. Bhaskar, A., Kieu, L., Qu, M., Nantes, A., Miska, M. and Chung, E. (2014) Is bus overrepresented in Bluetooth mac scanner data? Is mac-ID really unique?. Int J ITS Res. doi:10.1007/s13177-014-0089-9. Bhaskar, A., Qu, M. and Chung, E. (2014) Bluetooth vehicle trajectories by fusing Bluetooth and loops: motorway travel time statistics. IEEE T Intell Transp, 16, 113–122. doi:10.1109/TITS.2014.2328373. Coretto, P. and Hennig, C. (2010) A simulation study to compare robust clustering methods based on mixtures. Adv Data Anal Classif, 4, 111–135. doi:10.1007/s11634-010-0065-4. D'Urso, P. and Maharaj, E. (2009) Autocorrelation-based fuzzy clustering of time series. Fuzzy Set Syst, 160, 3565–3589. doi:10.1016/j.fss.2009.04.013. Fraley, C. and Raftery, A. (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J, 41, 578–588. Hennig, C. (2004) Breakdown points for maximum likelihood estimators of location-scale mixtures. Ann Stat, 32(4), 1313–1340. doi:10.1214/009053604000000571 Hennig, C. (2010) Methods for merging Gaussian mixture components. Adv Data Anal Classif, 4, 3–34. doi:10.1007/s11634-010-0058-3 Li, L., Xiqun, C., Zhiheng, L. and Lei, Z. (2013) Freeway travel-time estimation based on temporal and spatial queueing model. IEEE T Intell Transp, 14, 1536–1541. doi:10.1109/TITS.2013.2256132 Liao, T. W. (2005) Clustering of time series data - a survey. Pattern Recogn, 38, 1857–1874. doi:10.1016/j.patcog.2005.01.025 Liu, S., Anh, V., McGree, J. M., Kozan, E. and Wolff, R. C. (2015) A new approach to spatial data interpolation using higher-order statistics. Stoch Environ Res Risk Assess, 29, 1679–1690. doi:10.1007/s00477-014-0985-1 Liu, S. and Maharaj, E. (2013) A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples. Comput Stat Data An, 60, 32–49. doi:10.1016/j.csda.2012.11.014 Liu, S., Maharaj, E. and Inder, B. (2014) Polarization of forecast densities: a new approach to time series classification. Comput Stat Data An, 70, 345–361. doi:10.1016/j.csda.2013.10.008 Liu, S., McGree, J. M., Ge, Z. and Xie, Y. (2015) Computational and Statistical Methods for Analysing Big Data with Applications. Academic Press, London. ISBN: 978-0-12-803732-4. MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. Paper presented at the the 5th Berkeley Symposium on Mathematical Statistics and Probability. Malinovskiy, Y., Saunier, N. and Wang, Y. (2012) Analysis of pedestrian travel with static Bluetooth sensors. Transport Res Rec, 2299, 137–149. doi:10.3141/2299-15 Martchouk, M., Mannering, F. and Bullock, D. (2011) Analysis of freeway travel time variability using Bluetooth detection. J Transp Eng, 137, 697–704. doi:10.1061/(ASCE)TE.1943-5436.0000253 Mei, Z., Wang, D. and Chen, J. (2012) Investigation with Bluetooth sensors of bicycle travel time estimation on a short corridor. Int J Distrib Sens N. doi:10.1155/2012/303521. Peel, D. and McLachlan, G. (2000) Robust mixture modelling using the t-distribution. Stat Comput, 10, 339–348. doi:10.1023/A:1008981510081 Rousseeuw, P. J. (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput Appl Math, 20, 53–65. doi:10.1016/0377-0427(87)90125-7 Schwarz, G. (1978) Estimating the dimension of a model. Ann Stat, 6, 461–464. Sun, L., Yang, J. and Mahmassani, H. (2008) Travel time estimation based on piecewise truncated quadratic speed trajectory. Transport Res A-Pol, 42, 173–186. doi:10.1016/j.tra.2007.08.004