Spatio-Temporal Data Mining Analysis to Extract Association Rules Among Traffic Accidents and Environmental Reports

The 311 non-emergency service (also known as environmental reports) which is developed based on the public participatory geospatial information system (PPGIS) has considered as a big data source including a huge number of reports with a large variance in the form of location, time and types of reports. This study aims to explore the spatial and temporal relationships between traffic accident reports, land use diversity and 311 service reports. For this purpose, the Apriori algorithm is a data mining method utilized to extract association rules from accident data, land uses and environmental reports. The dataset includes crash reports, the 311 reports, land-use parcels and road network for Boston, gathered during three years 2015 to 2018. The results prove that there is a highly significant correlation between traffic accidents and some citizen’s reports such as street cleaning, highway maintenance, street lights, sign and signal, sanitation and enforcement & abandoned Vehicles. Moreover, a significantly strong relation between crash characteristics and land use types could be demonstrated. For instance, Motor Vehicle accidents generally happened in the vicinity of residential and commercial areas, and road intersections. Furthermore, most pedestrian crashes often happened in the street, during the weekday, and in the proximity of the residential areas. This kind of results could be used to predict the locations that are prone to crash events after receiving some related environmental reports. The potential of the presented association rule mining results could be used in the decision support systems for traffic safety management.

Spatio-Temporal Data Mining Analysis to Extract Association Rules Among Traffic Accidents and Environmental Reports Keywords:

Big Data , Spatio-Temporal Analysis , Data Mining , 311 Non-Emergency Service , PPGIS , Apriori