Travel characteristics and vehicle type identification driven by trajectory data
DONG Chunjiao
ZHAO Tianyi
LU Lingyu
XIE Kun
CHEN Yuanduo
Abstract：To address the limitations of mixed-vehicle Global Positioning System(GPS)trajectory data in supporting fine-grained transportation demand analysis and modeling,this study develops a vehicle classification model based on the Gaussian Hidden Markov Model(Gaussian HMM).First,travel characteristic indicators are extracted from trajectory data in both spatial and temporal dimensions.A comparative analysis of travel behaviors between trucks and private cars is conducted to identify dis-tinctive classification features.Then,the model is trained and tested using the Baum-Welch(BW)and Viterbi algorithms,and a classification algorithm based on travel characteristics is designed.Finally,an empirical study is conducted using travel trajectory data from trucks and private cars in Beijing.The results indicate significant differences between trucks and private cars across seven indicators:travel start time,travel end time,total travel duration,average dwell time,average trip time,trip fre-quency,and travel distance.The proposed Gaussian HMM-based vehicle classification model achieves an accuracy rate of 83%for private cars,a recall rate of 82%for trucks,and an overall model accuracy of 79%,demonstrating its effectiveness in vehicle type identification.The research results of-fer valuable support for refined carbon emission estimation,differentiated demand management policy development,and fine-grained traffic management.
Keywords：traffic engineeringvehicle type identificationGaussian HMMtravel characteristic indi-catorstrucks and private cars
Publication Date：2025-04-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 78-85 )