Travel time prediction based on route links’ similarity

Accurate travel time prediction allows passengers to schedule their journeys efficiently. However, cyclical factors (time intervals of the day, weather conditions, and holidays), unpredictable factors (incidents, abnormal weather), and other complicated factors (dynamic traffic conditions, dwell tim...

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Bibliographic Details
Published in:Neural computing & applications 2023-02, Vol.35 (5), p.3991-4007
Main Authors: Alkilane, Khaled, Alfateh, M. Tag Elsir, Yanming, Shen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Buses
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Deep learning
Dwell time
Holidays & special occasions
Image Processing and Computer Vision
Intervals
Links
Machine learning
Neural networks
Original Article
Paradigms
Prediction models
Probability and Statistics in Computer Science
Public transportation
Similarity
Statistical methods
Traffic
Traffic congestion
Travel demand
Travel time
Weather
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Summary:Accurate travel time prediction allows passengers to schedule their journeys efficiently. However, cyclical factors (time intervals of the day, weather conditions, and holidays), unpredictable factors (incidents, abnormal weather), and other complicated factors (dynamic traffic conditions, dwell times, and variation in travel demand) make accurate bus travel time prediction complicated. This paper aims to achieve accurate travel time prediction. To do so, we propose a clustering method that identifies travel time paradigms of different route links and clusters them based on their similarity using the nonnegative matrix factorization algorithm. Additionally, we propose a deep learning model based on CNN with spatial–temporal attention and gating mechanisms to select the most relevant features and capture their dependencies and correlations. For each defined cluster, we train a separate model to predict the travel time at various time intervals over the day. As a result, the travel times of all journey links from related prediction models are aggregated to predict the total journey time. Extensive experiments using data collected from four different bus lines in Beijing show that our method outperforms the compared baselines.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07926-7