OD Count Estimation Based on Link Count Data

Jin, Yi; Jiang, Dongchen; Yuan, Shuai; Cao, Jianting; Wang, Lili; Zhou, Gang

doi:10.1007/978-3-540-88623-5_22

Cited by 1 publication

(1 citation statement)

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“…Predicting destination from partial trajectories is a problem that has tremendous potential of real world applications. Several solutions of this problem from different perspectives has been proposed in the literature using Origin-Destination (OD) matrix (Barceló et al, 2010, Jin et al, 2008, Moreira-Matias et al, 2016b, Park et al, 2014, Yue et al, 2009, Bayesian inference (Hazelton, 2008, Parry andHazelton, 2012), Support Vector Machine (SVM) (Li et al, 2011), Clustering (Chang et al, 2010, Mobility markov chains (Gambs and Killijian, 2012), Optimization (Miao et al, 2015), ensemble learning (Lam et al, 2015) and many more. In this section, we have given a brief literature review of some of the works done in this field.…”

Section: Related Workmentioning

confidence: 99%

Real time location prediction with taxi-GPS data streams

Laha

Putatunda

2018

Transportation Research Part C: Emerging Technologies

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The prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a Damped window model namely, Multivariate multiple regression, Spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. The Multivariate multiple regression method and the Ensemble of the three methods are found to be the two best performers. The next pickup location problem is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here also we find that the Multivariate multiple regression method and the Ensemble of the three methods gives better performance than the rest.

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