01697523

Component

Probe vehicle data permit accurate inference of a road-network condition and drivers’ behavior. However, even high-precision GPS devices deliver noisy trajectories. Therefore, a map matching algorithm is needed to infer the true trajectory. Available methods become inadequate in urban canyons at low sampling rates. Firstly, the authors demonstrate a novel data-assisted cost-based map matching algorithm. Their contributions include: (i) Robustness to sparse-sampled GPS trajectories due to the novel transition probability distribution used in the Markov model to solve the matching problem. (ii) Robustness to GPS noise. Its parameters adapt based on the number of satellites visible at each location sample, and the altitude value which assists the map matching. (iii) Improvement of accuracy using the sampled true maximum vehicle speed to narrow down the entire road-network to an accurate trust-region of the possibly-visited routes within a sample interval. And, (iv) using a connected sliding window when finding the most probable path. (v) Unlike other algorithms, the method is road-network and map independent. The algorithm is tested on data provided by the New York City Department of Transportation. Because this data lacked ground-truth, qualitative assessment against other open-source map matching algorithms, such as Google Snap to Roads API and BMW car IT Barefoot, reveals an improvement of accuracy at noisy and low-speed locations. Secondly, the authors create a Driver Behavior Index system (DBI) for New York City. It provides safety-related metrics of city roads and identifies speeding, congestion, hard braking, and acceleration hotspots, which will be correlated later with crash data.

This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.

19-03327

English

6p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Maps; References

Algorithms; Digital mapping; Global Positioning System; Metropolitan areas; Safety analysis; Traffic safety; Trajectory

Map matching

New York (New York)

Data and Information Technology; Highways; Operations and Traffic Management

Transportation Research Board Annual Meeting 2019 Paper #19-03327

TRIS, TRB, ATRI

Dec 7 2018 9:30AM