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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
20.12.2024
DOI
https://doi.org/10.7307/ptt.v36i6.667
ISSUE
2024 (Vol 36), Issue 6
LICENSE
Copyright (c) 2024 Qinyang WANG, Jing CHEN, Ying SONG, Xiaodong LI, Wenqiang XU
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Fusing Visual Quantified Features for Heterogeneous Traffic Flow Prediction

Authors:Qinyang WANG, Jing CHEN, Ying SONG, Xiaodong LI, Wenqiang XU

Abstract

This paper presents a novel traffic flow prediction method emphasising heterogeneous vehicle characteristics and visual density features. Traditional models often overlook the variety of vehicles, resulting in inaccuracies. The proposed method utilises visual techniques to quantify traffic features, such as mixed flow and vehicle accumulation, enhancing dynamic density estimation and flow fluidity. We introduce a spatio-temporal prediction model that integrates various data types, capturing complex dependencies and improving accuracy. This research advances traffic flow prediction by considering the diverse nature of vehicles and leveraging visual data, offering valuable insights for intelligent transportation systems. Experimental results demonstrate the superiority of this approach over conventional methods, especially in capturing traffic flow fluctuations.

Keywords:heterogeneous traffic flow, spatio-temporal modelling, traffic flow prediction, visual traffic quantification

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