OCT: A Novel Opportunistic Compression and Transmission Approach for Private Car Trajectory Data

摘要

This paper proposes a novel Opportunistic Compression and Transmission approach, namely OCT, with aims of reducing trajectory transmission overhead and storage cost. As show in Fig. 1, OCT consists of two components: the GPS & OBD terminal in private car and the cloud platform. The main operation for trajectory compression runs in the terminal. First, we obtain the raw trajectory {p₁, p₂, ..., p_n} of the vehicle through the GPS module, where p = (lon, lat, t) represent longitude, latitude and time. Then we align raw trajectory with the road network by map-matching method and divide it into path id sequence {e₁, e₂, ..., e_n} and time-distance sequence {ρ₁, ρ₂, ..., ρ_n}, where ρ = (d, t), d is the distance from trajectory Start Point, as shown in Fig. 2. We implement the same prediction model runs synchronously on both the terminal and the cloud side, which can predict the trajectory of the next moment of the vehicle. The opportunistic transmission only happens when path id sequence changed or there is a large deviation θ between the predicted value ρ' and the real value ρ. In this paper, we use θ = λ·Max dis(ρ, ρ')+(1-λ)Max time(ρ, ρ'), where λ = 0.5. In this case, the opportunistic compression strategy realize the reduction of the transmission overhead and storage cost. To validate the performance of the OCT, we collect a large-scale private car trajectory data from real urban environments. Experiments verify the effectiveness and superiority of the proposed method.