Using data analytics for smart loading zones management in cities

Purpose and goal

Implementing novel solutions in the design and management of (un)loading zones (LZ) for freight vehicles can lead to a smarter use of urban space for the public sector and to more sustainable logistics for urban freight companies. This feasibility study aims at connecting 5 different actors (a parking application provider with international experience in LZ management, urban freight strategists from the public sector, a transport and logistics company, a truck manufacturer and academia) to explore potential solutions for LZ data-driven management under budget constraints.

Expected results and effects

1. Produce a compilation of existing technologies and practices to manage LZ worldwide 2. Produce a guideline describing the type of decisions to be made when designing a LZ and data needs. 3. Develop a model that uses historical data to anticipate LZ use at different times of day. 4. Develop a routing model to assess potential savings from forecasting LZ use. 5. Connect the different actors involved in the design, management and use of LZ. 6. Produce a roadmap to implement digitalization for smart LZ system. 7. Evaluate the potential for a pilot test.

Planned approach and implementation

1. Administration and project meetings: Start: January 2019 End: September 2019 2. State of the art in LZ management (WP1) Start: January 2019 End: March 2019 3. Smart Loading Zones Design and Operation (WP2) Start: February 2019 End: April 2019 4. Forecasting LZ demand using machine learning techniques (WP3) Start: February 2019 End: July 2019 5. Integrating smart LZ information to enhance freight distribution routing efficiency and sustainability (WP4) Start: July 2019 End: September 2019 6. Evaluating pilot test in Swedish cities (WP5) Start: February 2019 End: September 2019