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.

本研究针对动态按需送餐系统展开研究，提出了一种结合自适应大邻域搜索（Adaptive Large Neighborhood Search，ALNS）的滚动时域优化方法，可高效获取高质量求解方案。随后，本研究采用日常活动生成仿真工具CEMDAP，基于美国加利福尼亚州河滨市的真实道路网络、餐厅点位与人口统计数据，构建按需送餐行为仿真场景。本研究提出两类送餐调度策略：One-R与Multi-R，分别支持将单家或多家餐厅的订单合并至同一配送员的配送行程中。系统级评估结果表明，按需送餐模式可有效降低与就餐出行相关的车辆行驶里程（Vehicle Miles Traveled，VMT），进而大幅减少燃油消耗与污染物排放，采用Multi-R调度策略时效果尤为显著。当送餐渗透率分别为14%、21%与40%时，与就餐相关的总车辆行驶里程可分别降低5%、10%与25%（原文此处表述截断）。本研究数据集来源于数值仿真实验，所有数据归档于两个文件夹中：
1. 数据集文件Riverside_network.csv包含从开源交通仿真软件BEAM中提取的河滨市道路网络数据，以及基于排放模型计算得到的路段能耗与污染物排放数据。
2. 在本次绿色按需送餐研究中，本研究设置了三类不同按需送餐（On-Demand Food Delivery，ODFD）渗透率的仿真场景，渗透率分别为14%、21%与40%，对应结果文件中分别标记为ODFD_1、ODFC_2与ODFD_3。本动态按需送餐问题的求解结果均通过Python语言生成。
所有数据集文件均采用CSV格式，可通过任意表格软件或文本编辑器打开查看。