Project: #198 Proactive management of mobility impact of interdependent subsurface utility and roadway construction through incentives Progress Report - Reporting Period Ending: March 30, 2020 Principal Investigator: Burcu Akinci Status: Active Start Date: Jan. 1, 2018 End Date: June 30, 2020 Research Type: Applied Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2017 Mobility21 UTC Progress Report (Last Updated: March 31, 2020, 7:36 a.m.) % Project Completed to Date: 70 % Grant Award Expended: 70 % Match Expended & Document: 70 USDOT Requirements Accomplishments In the past six months, we have tested the proposed random-effect censored model to predict bridge maintenance cost, using data from bridges in Pennsylvania. We used Rooted Mean Square Error (RMSE) to evaluate the predicted bridge maintenance cost. We temporally split the training and test data sets to simulate the process maintenance agencies obtaining data and predicting the future. Through cross-validation on training-test partitions, we validated that the average RMSE of the proposed censored model is 1.02% smaller than the one obtained using Ordinary Least Squares (OLS) model. The proposed random-effect censored model also performs better in cases where there are less training data available. Hence, the propose model predicts the bridge maintenance cost more accurately and in a more robust manner while using smaller data sets. We also analyzed the safety impact of work zones. An analyses of databases including PennDOT crash data, PennDOT roadway data and INRIX weather information showed that the odds of crashes occurs in roadways with work zones is 1.36 times higher than the ones without work zones. In addition, the roadways located at the upstream of (before) work zones could experience 2.07 times higher crash risk than that without work zones. Morevover, we also found that road characteristics, such as Annual Average Daily Traffic (AADT) and the number of intersections near the work zones, have positive effects on crash risk on work zones. Finally, in relation to the characteristics of the work zone deployment, we found that longer the geographic length of a work zone is, higher the rate of crash occurrence. We also tested our prediction model for traffic flow changes due to maintenance disruptions. We used MAC-POSTS, developed by the Co-PI Qian under another UTC project ‘Mobility Data Analytics’, that encapsulates impacts of right-of-way construction projects (i.e., constructions on bridges or pipelines) on two classes of vehicles; cars, and trucks. We performed several case studies on bridges in Pittsburgh. For instance, performing maintenance work on one of the selected bridges (The bridge carrying State Road 28 over Bridge Street) would increase about 10k hours of the travel time for trucks and about 40k hours for cars in Great Pittsburgh Area. In the remainder of the project period, we will develop a holistic framework incorporating the bridge maintenance cost prediction model, traffic safety impact model, and traffic mobility impact model. This framework aims to provide a comprehensive benefit and cost analysis for maintenance agencies’ maintenance decisions. We will discuss whether this model can be utilized in general maintenance projects in other infrastructure systems, such as pipelines. This project has supported one PhD research assistant. We have discussed the initial outcomes with PennDOT. Impacts 1. This project, if successful, would be able to provide a fundamental knowledge to understand infrastructure failures and their associated costs (both maintenance cost and social costs) from analyzing real-world data 2. This project, if successful, would be able to provide a holistic framework for maintenance agencies to analyze benefits (avoid infrastructure maintenance cost) and cost (traffic mobility and safety impact) analysis for infrastructure deterioration. 3. Overall, this project, if successful, would help maintenance agencies to optimally design maintenance schedules and strategies. 4. Part of the research was presented to students at Carnegie Mellon University, which has great potential to educate the next generation of engineers, economists, and scientists. 5. The initial outcomes from this research have been shared with PennDOT through emails and phone discussions, with Cranberry Township through in-person meetings. Other 1. A database contains Pennsylvania bridge issues repaired by maintenance, with related weather, traffic flow, and bridge inspection information. 2. A collection of statistical models predicts bridge maintenance costs, safety impacts, and mobility impacts of infrastructure maintenance projects. 3. A software package to perform a holistic benefit and cost analysis for bridge and roadway maintenance work. Outcomes New Partners n/a Issues n/a