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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
27.08.2024
DOI
https://doi.org/10.7307/ptt.v36i4.592
ISSUE
2024 (Vol 36), Issue 4
LICENSE
Copyright (c) 2024 Yongpeng ZHAO, Yongcang LI, Changxi MA, Ke WANG, Xuecai XU
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Optimised LSTM Neural Network for Traffic Speed Prediction with Multi-Source Data Fusion

Authors:

Yongpeng ZHAO
Lanzhou Jiaotong University, School of Traffic and Transportation; Gansu Provincial Highway and Transportation Construction Group Co., Ltd

Yongcang LI
Gansu Road and Bridge Construction Group Co., Ltd

Changxi MA
Lanzhou Jiaotong University, School of Traffic and Transportation

Ke WANG
Lanzhou Jiaotong University, School of Traffic and Transportation

Xuecai XU
Huazhong University of Science and Technology, School of Civil and Hydraulic Engineering

Keywords:traffic speed prediction, multi-source data fusion, improved particle swarm optimisation, long short-term memory network

Abstract

Predicting traffic speed accurately and in real-time is crucial for the development of smart transportation systems. Given the nonlinear and stochastic nature of vehicle data, integrating diverse spatio-temporal data sources with the Improved Particle Swarm Optimisation (IPSO) offers a promising approach to optimise the Long Short-Term Memory Neural Network (LSTM). Firstly, we enhance the optimisation capabilities of PSO by implementing nonlinear inertial weight and adaptive variation. Secondly, addressing the challenge of selecting the LSTM hyperparameters, the PSO algorithm effectively identifies global optimal solutions for hyperparameter optimisation, ensuring appropriate settings through iterative training. Subsequently, we conduct a case study using multi-source spatio-temporal traffic speed data, comparing our proposed IPSO-LSTM model with traditional neural network prediction models and advanced models. Results from the experiment demonstrate that the IPSO-LSTM model presented in this study addresses issues of parameter selection and inaccurate prediction encountered by traditional LSTM models in traffic state prediction. Moreover, it enhances the model’s ability to capture speed time series dynamics. Notably, in processing complex speed data, our model exhibits superior accuracy and stability in prediction.

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How to Cite
ZHAO, Y. (et al.) 2024. Optimised LSTM Neural Network for Traffic Speed Prediction with Multi-Source Data Fusion. Traffic&Transportation Journal. 36, 4 (Aug. 2024), 765-778. DOI: https://doi.org/10.7307/ptt.v36i4.592.

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