Surface street traffic estimation

By Jungkeun Yoon, Brian D. Noble, M. Liu. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2007

310 citations recorded by the source corpus.

In this paper, we propose a simple yet effective method of identifying traffic conditions on surface streets given location traces collected from on-road vehicles---this requires only GPS location data, plus infrequent low-bandwidth cellular updates. Unlike other systems, which simply display vehicle speeds on the road, our system characterizes unique traffic patterns on each road segment and identifies unusual traffic states on a segment-by-segment basis. We developed and evaluated the system by applying it to two sets of location traces. Evaluation results show that higher than 90% accuracy in characterization can be achieved after ten or more traversals are collected on a given road segment. We also show that traffic patterns on a road are very consistent over time, provided that the underlying road conditions do not change. This allows us to use a longer history in identifying traffic conditions with higher accuracy.

Subfields: Biomedical Engineering, Building and Construction, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ac31aa4ecc5ccdfd41377ae16a070aefdcbf1dac

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Citations in the corpus

Traffic Estimation And Prediction Based On Real Time Floating Car Data (2008, background): “technique provides is average travel times and speeds along road links or paths [8], [13], [14].”
Improving energy efficiency of location sensing on smartphones (2010, background): “Some instances of current ly popular LBAs include mobile social networking [2, 3, 9, 10], hea lthcare [1], local traffic [7,22,23,29,36], and local restaura nts [6].”
ParkNet: drive-by sensing of road-side parking statistics (2010, background): “thors in [2] propose techniques to mine location trace data reported by vehicles on streets reporting their locations infrequently over a cellular uplink, to characterize traffic patterns on road segments.”
Micro-Blog: sharing and querying content through mobile phones and social participation (2008, background): “In [13, 24], authors demonstrated the possibility of estimating traffic congestion using accelerometer readings and GPS traces from mobile phones.”
Virtual trip lines for distributed privacy-preserving traffic monitoring (2008, methodology): “[58] propose to use cellular network as a delivery method of GPS-based sensing information from probe vehicles.”
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones (2009, methodology): “[25] use GPS data to classify road conditions as “good” or “bad,” both spatially and temporally (which reflect the steadiness and speed of traffic, respectively).”