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Safety issues associated with transporting crude oil by rail have been a concern since the boom of US domestic shale oil production in 2012. During the last decade, over 300 crude oil by rail incidents have occurred in the US. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on the routes of crude-on-rail and their associated risks is available to the public. To this end, this study proposes an unconventional way to reconstruct the crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method links the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments those crude oil trains were traveling on. A shortest path-based method was then applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their movement direction information. Validation of the inferred routes was performed using a public map and official crude oil incident data. Results suggested that the inferred routes based on geotagged photos have high coverage, with approximately 96% of the documented crude oil incidents aligned with the reconstructed crude-on-rail network. The inferred crude oil train routes were found to pass through many metropolitan areas with dense populations, who are exposed to potential risk. This finding could improve situation awareness for policymakers and transportation planners. In addition, with the inferred routes, this study establishes a good foundation for future crude oil train risk analysis along the rail route.

The US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.