TY - JOUR

T1 - Traffic control recognition with speed-profiles

T2 - A deep learning approach

AU - Cheng, Hao

AU - Zourlidou, Stefania

AU - Sester, Monika

N1 - Funding Information: Funding: This research was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG)) with grant number 227198829/GRK1931.

PY - 2020/11

Y1 - 2020/11

N2 - Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes.

AB - Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes.

KW - Classification

KW - Conditional generative model

KW - Crowdsensing

KW - Crowdsourcing

KW - GPS tracks

KW - Intersections

KW - Sequence-to-sequence

KW - Speed-profiles

KW - Traffic regulators

KW - Traffic-signs

KW - Trajectories

UR - http://www.scopus.com/inward/record.url?scp=85112473805&partnerID=8YFLogxK

U2 - 10.3390/ijgi9110652

DO - 10.3390/ijgi9110652

M3 - Article

AN - SCOPUS:85112473805

VL - 9

JO - ISPRS International Journal of Geo-Information

JF - ISPRS International Journal of Geo-Information

SN - 2220-9964

IS - 11

M1 - 652

ER -