Simulation data for on-demand food delivery in Riverside, CA

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%, respec..., 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.