Iain Nicholas Smith
ABSTRACT
Traffic flow and speed differences between cars are important factors that indicate the likelihood and danger of collisions. A vital part of intelligent transportation systems is discovering important locations to monitor and ticket speeding vehicles. To find these locations, we study data from a low-density city. We identify three critical road groups that indicate risk levels based on car speed differences and weather conditions. We find that these groups have differing weekly trends, which allow traffic enforcement time to change locations to enforce them. We create an analysis that an intelligent transportation system could automate to reduce risk on these roads and save city resources on enforcement.
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Index Terms
- Analyzing Patterns of Car Speeding in an Urban Environment using Multivariate Functional Data Clustering
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