The vision of this project is to create a new paradigm for freight surveys that leverages state-of-the-art technologies. This project will utilize vehicle tracking devices such as GPS loggers and smartphone/tablet applications to collect raw data regarding freight activities in both urban and intercity environments.

FMS technology will be utilized to combine the valuable information from state-of-the-art sensing devices with prompted-recall surveys from respondents. While tracking and sensing techniques provide accurate information on routes and stops, such data do not provide enough information on explanatory factors such as decision-maker characteristics and transportation attributes, e.g., shipment value and cost. However, these factors are necessary for behavioral modeling. Thus such additional information will be collected in daily surveys to gather further details regarding driver decisions. This is being done through web-based verification of tracking data. In addition, user-specific functionalities and visualizations will be embedded into the FMS technology.

Two pilot case studies will be conducted with the developed data collection techniques. The case studies will include integrated analysis of urban and intercity freight in the US. A proof of concept will illustrate FMS capabilities in addressing challenges existing in traditional freight data collection. The case study will also look to provide a better understanding of truck driver behavior. The final result of the case studies will be analyzed and findings will be documented.

This developed FMS tool will be used to enhance freight survey and modeling techniques in the US. This includes analyzing opportunities and challenges in surveying freight and logistics with the FMS tool, analyzing new opportunities for survey recruitment and collaboration, and developing guidelines on how FMS will enable the implementation of a new generation of freight models, e.g., agent-based behavioral models.