Using crowdsourced data to analyze transport crime
Journal article, 2018

Anecdotal evidence suggests that harsh social conditions in the road haulage industry are having an impact on transport crime. This paper analyses transport crime, and demonstrates how to use a combination of official statistics and crowdsourced data in the process. A hierarchical regression analysis was applied to investigate the relations among different factors in order to predict transport crime threats. A secondary data set on transport crime from the Swedish Police was combined with primary crowdsourced data from volunteer observations of trucks in Sweden from both high-wage and low-wage countries. The findings imply that transportation is more vulnerable to antagonistic threats in geographical areas where the low-wage hauliers operate more frequently. For policymakers and practitioners, these findings provide useful guidance for the planning of security measures. To the authors' knowledge, this paper is the first exploratory study of its kind that uses a combination of official statistics and crowdsourced data.

crowdsourcing data

social sustainability

statistical analysis

Transport crime



Henrik Sternberg

Lund University

Björn Lantz

Chalmers, Technology Management and Economics, Innovation and R&D Management

International Journal of Logistics Research and Applications

1367-5567 (ISSN) 1469-848X (eISSN)

Vol. 21 2 133-147

Subject Categories

Transport Systems and Logistics


Environmental Analysis and Construction Information Technology



More information

Latest update

7/3/2018 6