Understanding crime and demographic influence on non-motorized trips: Macro-level analysis

Abstract

ABSTRACTInterest and emphasis on non-motorized modes of transportation have been increasing in the recent years. A better understanding of factors affecting non-motorized commuting trips would enable stakeholders in non-motorized transportation to come up with relevant and cost-effective strategies to promote it. This study aims to improve the understanding on the association between non-motorized commuting trips, crime, and related demographics. Moreover, this study demonstrates an innovative approach for using yearly crime dataset (Chicago) in conjunction with the American Community Survey (ACS) dataset. ACS dataset contains data pertaining to non-motorized commuting trips and important socioeconomic characteristics. An unsupervised data mining technique called Self-Organizing Map (SOM) was used to find association between different factors as it does not require any prior assumptions. The findings show that areas with lower income households are associated with high pedestrian and bicycle commuting. A negative association between crime and non-motorized commuting was also identified. The results also show that areas with larger youth populations are likely to have high non-motorized commuting trips. This study provides insights into the ongoing state-of-the-art study designs and analysis methods associated with non-motorized travel mode.