goods movement

This project proposes to improve vehicle utilization and energy efficiency by modeling and evaluating the innovative shared mobility services for freight, with a specific focus on on-demand food delivery. Overall, the shared mobility service has great potential to reduce freight transportation VMT cost and emission. With well-designed delivery policy, the on-demand food delivery can mitigate traffic in the urban city and bring a greener transportation system.

In this paper, focusing on meal delivery, the researchers proposed an eco-friendly on-demand meal delivery (ODMD) system to maximize the utilities of electric vehicles (EVs) to mitigate greenhouse gas (GHG) emissions and maintain low operational cost and delay cost.

In this research, the researchers propose a comprehensive framework to quantify the VMT and emissions incurred by ODFD with three main components: (i) a daily activity generation tool, Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns, to create a simulation scenario of ODFD behaviors based on a real-world roadway network and population demographics in the City of Riverside, California; (ii) an efficient order dispatching and routing algorithm, adaptive large neighborhood search, to obtain a high quality order dispatching and routing plan; (iii) an emission evaluation model, emission factor (EMFAC), to evaluate pollutant emissions from all dining-related trips.

In this research, the team studied a dynamic on-demand food delivery system and proposed a rolling horizon-based optimization approach integrated with adaptive large neighborhood search (ALNS) to efficiently obtain high-quality solutions. The system-level evaluation shows that on-demand food delivery has great potential to reduce dining-related VMT, resulting in significant reductions of fuel consumption and emissions, especially with Multi-R delivery policy.