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Border crossing delays cause problems like huge economics loss and heavyenvironmental pollutions. To understand more about the nature of bordercrossing delay, this study applies a dictionary-based compression algorithm toprocess the historical Niagara Frontier border wait times data. It can identifythe abnormal spatial-temporal patterns for both passenger vehicles and trucksat three bridges connecting US and Canada. Furthermore, it provides aquantitate anomaly score to rank the wait times patterns across the threebridges for each vehicle type and each direction. By analyzing the top threemost abnormal patterns, we find that there are at least two factorscontributing the anomaly of the patterns. The weekends and holidays may causeunusual heave congestions at the three bridges at the same time, and thefreight transportation demand may be uneven from Canada to the USA at PeaceBridge and Lewiston-Queenston Bridge, which may lead to a high anomaly score.By calculating the frequency of the top 5% abnormal patterns by hour of theday, the results show that for cars from the USA to Canada, the frequency ofabnormal waiting time patterns is the highest during noon while for trucks inthe same direction, it is the highest during the afternoon peak hours. ForCanada to US direction, the frequency of abnormal border wait time patterns forboth cars and trucks reaches to the peak during the afternoon. The analysis ofabnormal spatial-temporal wait times patterns is promising to improve theborder crossing management

Abnormal Spatial-Temporal Pattern Analysis for Niagara Frontier Border Wait Times