Project: #338 Joint optimization of school bus routes and last mile services Progress Report - Reporting Period Ending: Sept. 30, 2020 Principal Investigator: Peter Zhang Status: Active Start Date: July 1, 2020 End Date: June 30, 2021 Research Type: Applied Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2020 Mobility21 UTC Progress Report (Last Updated: Oct. 5, 2020, 6:24 a.m.) % Project Completed to Date: 60 % Grant Award Expended: 60 % Match Expended & Document: 60 USDOT Requirements Accomplishments School bus routing across multiple districts requires the coordination of many resources. In this project, we plan to understand the challenges in current school bus routing through a partnership with Allies for Children, and improve the mobility of students via decision analytical models and deployment of information technology tools. Our main focus is to find efficient, safe, and implementable multi-modal transportation routes to help children move to and from schools. To this end, we have performed theoretical analysis to identify conditions for which collaborations between schools would be beneficial, and numerical simulation to validate the results. Through this process, three graduate research assistants were trained in transportation literature search, analytical derivations in transportation, Python programming, data visualization, coding of optimization algorithms and machine learning methods. We have finished a working paper to document the last-mile component of the transportation problem (to be submitted to Transportation Science or an equal caliber journal), and another work-in-progress to document the findings on the benefic of school bus collaboration between districts. Two presentations have been given on this topic: one poster presentation during Civil and Environment Engineering Summer Research Symposium at CMU, and another at a summer paper oral presentation at the Tepper School of Business at CMU. During the remaining phase of the project, we plan to package the findings in a more robust format for demonstration purpose: including a website prototype that allows users (e.g., schools) to interact with the optimization software for route selection and collaboration; finish and submit papers; prepare a policy report that translate the academic findings to implementable policies; and engage the county to plan pilot studies based on the initial theoretical and numerical results. Impacts To the extent of our knowledge, this work is the first one to rigorously examine the feasibility, benefits, and costs of collaboration between schools for school bus transportation. In doing so, we include important factors of the school bus routing problem (SBRP): stop selection, route optimization, and last-mile and first-mile optimization. Under such consideration, we give analytical (closed form) estimation of the relationship between the benefit of collaboration and problem features such as distance between schools, student and stop distribution, geographical boundary of different school districts, and cross-district school attendance. We believe this is an important work that adds to the academic literature in a novel way, and also more importantly, has the potential to improve Pareto efficiency of school bus transportation in terms of both school operational costs and student welfare. Other Website (under development) for the management of publicly available data and visualization of routes with RStudio interactive maps. Tabu-search meta-heuristic algorithm to jointly optimization first/last-mile and route planning for multiple schools. Analytical and numerical analysis of the benefit function for collaboration in SBRP. Teaching materials prepared for one lecture of content in CMU Heinz course 94-867 Decision Analytics for Business and Policy. Outcomes New Partners N/A Issues N/A