Travel Time Prediction Using a LSTM Model Reflecting Traffic State Transition in Interurban Expressway

Yusuke Tago; Masakazu NAKANISHI; Yoshiki Suga; Tadashi NISHINO; Kazuhiko YAMASHITA; Takahiko Kusakabe

doi:10.2208/jscejj.25-00086

January 1, 2026Japanese Journal of JSCE

Travel Time Prediction Using a LSTM Model Reflecting Traffic State Transition in Interurban Expressway

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Key Points

The study aims to predict travel time based on traffic volume and speed data using deep learning techniques.
Developed a travel time prediction model combining traffic flow dynamics and LSTM deep learning.
Validated the model over a 35km stretch of highway during traffic bottlenecks and peak holiday travel.
Measured prediction accuracy using Mean Absolute Error (MAE).
Achieved 14 minutes MAE for 3-hour future travel time predictions during congested conditions.
Demonstrated effective modeling that integrates current traffic data with predictive analytics.
Validated the ability of the model to maintain accuracy despite variations in traffic conditions.

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Implication

This research demonstrates accurate travel time prediction in interurban expressways, suggesting effective modeling techniques for traffic management.

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Cite This Study

Tago et al. (2026) studied this question.

synapsesocial.com/papers/69e864866e0dea528dde9530 https://doi.org/10.2208/jscejj.25-00086

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Travel Time Prediction Using a LSTM Model Reflecting Traffic State Transition in Interurban Expressway

Key Points

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Abstract

Discussion

Cite This Study

Travel Time Prediction Using a LSTM Model Reflecting Traffic State Transition in Interurban Expressway

Key Points

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Implication

Abstract

Discussion

Cite This Study

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