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Black Background

Disruptive Innovation


Team: Andre Alho, Cheng Cheng, Moshe Ben-Akiva, GiacomoMoshe E. Ben-Akiva, Chris Zegras, Vittorio Marzano (University of Napoli Federico II), Fang Zhao (Singapore-MIT Alliance for Research and Technology), Kyungsoo Jeong, Jinping (Jenna) Guan, Peiyu Jing, William Wong,  Andre Alho, Dao Trung,  Dao Hieu Trung, Linlin You. Dalla Chiara, Lynette Cheah

This project developed an integrated approach for future freight and logistics surveys including all relevant freight entities: establishments (including logistics operators and 3PL), carriers/drivers/vehicles, and shipments. The proposed approach leverages a coherent and holistic survey methodology and fully integrated survey instruments. It is based on innovative and scalable technologies with considerable time and geographical coverage (national, regional/urban and rural areas).

The key concept underlying the research is the extension to freight of the Future Mobility Survey (FMS) tool, which has already been proven effective in passenger surveys. FMS makes use of smartphones/tablets and GPS loggers, advanced sensing and communication technologies and machine learning algorithms to collect data reflecting what all relevant freight agents do, not what they say they do. State-of-the-art sensing devices enhance the quality and quantity of data, especially when combined with information from agents themselves.

The framework is, and it aims to ameliorate inherent limitations in current freight data collection methods, obtain unprecedented freight data for statistical purposes, and enable the implementation of a new generation of freight models, including agent-based models.

The project which piloted the technologies in the U.S. and Singapore, articulated into four main project tasks: 1. Innovative tracking of vehicles and shipments; 2. Development of innovative sensing-based survey and visualization tools; 3. Integration of FMS with data and modelling needs in freight; 4. Case studies.

The vision of this project was to create a new paradigm for freight surveys that leverages state-of-the-art technologies. This project utilized 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 was 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 were embedded into the FMS technology.

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