In this research, we study 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. We then use a daily activity generation tosimulationol, CEMDAP, to create a simulation scenario of on-demand food delivery behaviors based on real-world roadway network, restaurant locations, and population demographics in the City of Riverside, California. Two delivery policies are proposed: One-R and Multi-R, which allow orders from one or multiple restaurants to be bundled in one driver’s delivery trip, respectively. 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. Under 14%, 21% and 40% delivery penetration rate, the total dining-related VMT can be reduced by 5%, 10%, and 25%, respectively, compared to the baseline with no on-demand delivery, and the corresponding environmental impacts were also reduced significantly.

The data are collected from numerical simulations They are archived in two folders. 1. The data in Riverside_network.csv include Riverside network extract from BEAM, an open-sourced traffic simulation software, and link energy consumption and emissions calculated from emission models. 2. In this eco-friendly on-demand food delivery study, we created three scenarios considering different On-Demand Food Delivery (ODFD) penetration rate (14%, 21%, 40%), which are defined as ODFD_1, ODFC_2 and ODFD_3 in the results files. The results of this dynamic ODFD problems are generated in Python. 

All the files are in CSV format, which can be opened by any table or text editor.