T-drive

Abstract

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this paper, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. We build our system based on a realworld trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.

Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage

“…The location record of the T time period in BLD is generated into [T/ TP] = m probability distribution matrix {P 1 , P 2 ,…, P m } according to the cycle of TP. Then, the periodic behavior of moving objects can be analyzed by calculating their Kullback-Leibler (KL) divergence (Yuan et al 2013).…”

Delineating intra-urban spatial connectivity patterns by travel-activities: A case study of Beijing, China

“…This linear programming system can be solved by ordinary MINIMAX, MAD and GP methods 3 . The second approach is the algebraic method [12], which is an approximation algorithm by conducting series multiplication of Equation 1 as…”

“…Topics including human mobility [1], urban configuration [2], transport intelligence [3], energy and pollution [4] benefit substantially from the so-called "Big Data Revolution" [5]. Trajectory data with detailed spatiotemporal information is of particular interests to geographers [6].…”

Effective Following Patterns Mining Scheme for the Movements of Objects

“…The increasing use of location-aware devices has led to an increasing availability of mobility data, such as trajectory data of moving objects [1,2], traffic data [3], climate data [4], and animal migration data [5][6][7]. The availability of such data on devices will inevitably engender the study of patterns mining at an unprecedented scale both in terms of the areas covered by the moving objects and also the number of individuals involved in the study.…”