2014 TravelTimeEstimationofaPathUsin

• (Wang et al., 2014) ⇒ Yilun Wang, Yu Zheng, and Yexiang Xue. (2014). “Travel Time Estimation of a Path Using Sparse Trajectories.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623656

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Travel+Time+Estimation+of+a+Path+Using+Sparse+Trajectories
• http://dl.acm.org/citation.cfm?id=2623330.2623656&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; spatial databases and gis; spatial trajectories; spatio-temporal data mining; tensor decomposition; travel time estimation; urban computing

Abstract

In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 TravelTimeEstimationofaPathUsin	Yu Zheng Yilun Wang Yexiang Xue			Travel Time Estimation of a Path Using Sparse Trajectories				10.1145/2623330.2623656		2014