Abstract
Trajectory movement labelling is an important pre-stage for predicting connected vehicle (CV) movement at intersections. Drivers’ movement prediction and warning at intersections ensure advanced transportation safety and researchers use machine learning-based data-driven approaches to implement these technologies. However, prediction of drivers’ movements at intersections requires labelling the train and test dataset accurately with different vehicle movements at intersections to evaluate the performance of the prediction model by comparing the actual and predicted intersection movements. Moreover, due to GPS detection error or missing co-operative awareness messages (CAM), the data resides with many abnormal trajectories which are unable to be matched with regular straight or any turning movements. Especially for big data with million trajectories, it is tedious to label the movements manually. To solve this problem, we have created an automated trajectory movement classification technique using a dual approach of map matching technique and deep transfer learning modelling. Data of connected vehicle trajectory information is taken from the Ipswich Connected Vehicle Pilot (ICVP) Project, which is one of the largest connected vehicle pilots within a naturalistic driving environment in Australia. Map matching approach is performed as initial labelling by analysing the origin and destination of the vehicle CAM messages at intersections and then was converted as image datasets of 19202 samples. The map matching error and abnormal trajectories are identified by visual inspection. With properly labelled 9496 training images, 10 transfer learning models are built and tested through the remaining 9706 testing images. The maximum testing accuracy (99.73%) is achieved from the Densenet169 model, and the result shows satisfactory accuracy for individual classes: straight (99.85%), turn left (99.59), turn right (99.25), u-turn (100%), abnormal (98.63%). This model becomes a routine tool that is used daily to automatically classify thousands of trajectory movements of the C-ITS data in the ICVP project.
Highlights
The connected vehicle is an emerging technology of Intelligent Transportation System (ITS) that has potential road safety applications and advanced transportation facilities [1]
Some common and renowned connected vehicle pilots are New York City DOT Pilot [12], Tampa-Hillsborough Expressway Authority Pilot [13], Wyoming DOT Pilot [7], Ipswich Connected vehicle Pilot [14, 15], Safe and Intelligent Mobility Project [16] etc. They are working on implementing connected vehicle technology and applications practically on-road as field operation test (FOT) over large urban areas
During the data collection in such extensive field operation test procedure, errors are occurred by communication devices like GPS and cause inaccurate co-operative awareness messages (CAM) location information, which raises the difficulty in properly classifying vehicle trajectories
Summary
The connected vehicle is an emerging technology of Intelligent Transportation System (ITS) that has potential road safety applications and advanced transportation facilities [1]. They are working on implementing connected vehicle technology and applications practically on-road as field operation test (FOT) over large urban areas Such largescale pilot studies confront major challenges with accurate data collections, management, processing and analysis. During the data collection in such extensive field operation test procedure, errors are occurred by communication devices like GPS and cause inaccurate CAM location information, which raises the difficulty in properly classifying vehicle trajectories At intersections, this inaccurate location information leads to obscurity in labelling the vehicle trajectory movements. This procedure helps classifying trajectory movement accurately (near 100%), including abnormal trajectories, and it does not bear any expense like commercial tools This model has potential application for connected vehicle pilot studies to: Preparing pure small dataset. This study compares many pre-trained networks of different sizes to annotate trajectories at intersections so that hardwires with different computational power can use the best performing model based on its capacity
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