Abnormal Spatial-Temporal Pattern Analysis for Niagara Frontier Border Wait Times
Zhenhua Zhang, Lei Lin

TL;DR
This paper applies a dictionary-based compression algorithm to analyze historical border wait times at Niagara Frontier, identifying abnormal patterns and their contributing factors to improve border crossing management.
Contribution
It introduces a novel anomaly detection method for border wait times using compression algorithms, providing quantitative scores and insights into factors affecting delays.
Findings
Weekend and holiday congestion causes simultaneous anomalies across bridges.
Uneven freight demand contributes to high anomaly scores at specific bridges.
Peak abnormal patterns occur during noon for US-Canada cars and in the afternoon for trucks.
Abstract
Border crossing delays cause problems like huge economics loss and heavy environmental pollutions. To understand more about the nature of border crossing delay, this study applies a dictionary-based compression algorithm to process the historical Niagara Frontier border wait times data. It can identify the abnormal spatial-temporal patterns for both passenger vehicles and trucks at three bridges connecting US and Canada. Furthermore, it provides a quantitate anomaly score to rank the wait times patterns across the three bridges for each vehicle type and each direction. By analyzing the top three most abnormal patterns, we find that there are at least two factors contributing the anomaly of the patterns. The weekends and holidays may cause unusual heave congestions at the three bridges at the same time, and the freight transportation demand may be uneven from Canada to the USA at Peace…
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Taxonomy
TopicsTraffic Prediction and Management Techniques · Transportation Planning and Optimization · Traffic control and management
