Identifying hazardous driving pattern based on mobility tracks

Authors: JINZI WANG*, , SHENJUN YAO, Dr., JIANPING WU, Prof.
Topics: Transportation Geography
Keywords: Track identification, machine learning
Session Type: Paper
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Congressional B, Omni, West
Presentation File: No File Uploaded


Hazardous driving behavior is always the problem that people concern most. Previous research focused on the factors that might influence the traffic incidents happening. However, different traffic incidents might influence by various factors, such as weather, driver's mood, vehicle's type and so on. This paper focuses on the driving pattern itself, and tries to find out the spatial and temporal distribution to better understand the driving pattern itself. Study dataset is from Shanghai Transportation Department in machinery trucks. First, the GPS tracks should be identify by the PCS and GCS. Next, this paper used machine learning method to extract features in each track. Then, the similar tracks would be gathered together. And finally, each type of tracks would be tagged by the results.

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