  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
Bridge 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 the
day, the results show that for cars from the USA to Canada, the frequency of
abnormal waiting time patterns is the highest during noon while for trucks in
the same direction, it is the highest during the afternoon peak hours. For
Canada to US direction, the frequency of abnormal border wait time patterns for
both cars and trucks reaches to the peak during the afternoon. The analysis of
abnormal spatial-temporal wait times patterns is promising to improve the
border crossing management
