On Predicting and Compressing Vehicular GPS Traces

Abstract

Abstract-Many vehicular safety applications rely on vehicles periodically broadcasting their position information and location trace. In very dense networks, such safety messaging can lead to offered traffic loads that saturate the shared wireless medium. One approach to address this problem is to reduce the frequency of location update messages when the movements of a vehicle can be predicted by nearby vehicles. In this paper, we study how predictable vehicular locations are, given a Global Positioning System trace of a vehicles recent path. We empirically evaluate the performance of linear and higher degree polynomial prediction algorithms using about 2500 vehicle traces collected under urban and highway driving conditions. We find that linear polynomial prediction using the two most recent known locations performs best. Also, traces with a time granularity of 0.2s are highly predictable in low speed urban environments, and a location update rate of 1Hz may suffice to represent urban vehicular movements. Lastly, the paper also evaluates compression of different time-granularity traces using line simplification and polynomial interpolation techniques to reduce message sizes.

One-pass error bounded trajectory simplification

“…Various mobile devices, such as smart-phones, on-board diagnostics, and wearable smart devices, have been widely using their sensors to collect massive trajectory data of moving objects at a certain sampling rate (e.g., 5 seconds), and transmit it to cloud servers for location based services, trajectory mining and many other applications. It is known that transmitting and storing raw trajectory data consumes too much network bandwidth and storage capacity [2][3][4][10][11][12][13]15,[18][19][20][21][22]. Further, we find that the online transmitting of raw trajectories also seriously aggravates several other issues such as out-of-order and duplicate data points in our experiences when implementing an online vehicleto-cloud data transmission system.…”

Dealing with noise in crowdsourced GPS human trajectory logging data

“…28 This approach can be used in conjunction with the map-matching method to identify the road and the exact location while the object is moving. 28,29 Offline compression on the other hand, reduces a full trajectory series using a batched compression algorithm to produce a subset trajectory T ′ from T where the number of points in new trajectory T ′ is less than T. 30 A well-known trajectory reduction method is Douglas-Peucker. 25 The idea underpinning in this method is to substitute a subset of trajectory paths into a line segment where the line segment is similar to the original trajectory.…”