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The goal of this multi-phase project is to optimize traffic signals in the City of Tucson to improve mobility, safety, and efficiency. Multi-source data was automatically collected or estimated city wide, and a website (Tucson.UA-Star.org) was developed to implement and analyze the big traffic data. The data is used to provide support for signal re-timing and safety studies.

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Red-light running (RLR) behavior is one of the riskiest behaviors at signalized intersections and is becoming a prominent cause of intersection-related crashes (P. Chen et al., 2017). According to the report published by AAA Foundation for Traffic Safety, during 2019 more than two people were killed every day due to noncompliance with red signal indications (AAA Foundation for Traffic Safety, 2020). The City of Phoenix has teamed up with the University of Arizona (UArizona) to draft a comprehensive literature review on the factors influencing RLR violation (such as yellow light interval, clearance time), and identify potential solutions and countermeasures (such as photo enforcement cameras, modifying yellow/clearance time).

To enhance the operations of traffic signal timing and improve the corridor progression and coordination, the City of Phoenix is implementing emerging technologies field pilots. As one of the emerging technologies, Rhythm Engineering is implementing its Timing Optimization System for a pilot study on one corridor in the City of Phoenix. The City of Phoenix has teamed up with the University of Arizona (UArizona) to evaluate the operational effectiveness of the Timing Optimization System by Rhythm Engineering in terms of intersection and corridor performance evaluation. Below is the detailed scope of this project.

The goal of the UArizona Team is to help the Town of Gilbert understand a variety of Adaptive Signal Control (ASC) and other signal timing improvement solutions in the market and assess the feasibility of implementing the selected solution. The study will be conducted on 26 traffic signals around the area of San Tan Mall and will help give the Town of Gilbert more knowledge about the different types of ASC systems.

The goal of the UArizona Team is to provide a statistical comparison of multiple traffic data sources for network performance evaluation for conducting traffic signal retiming. The study will include the investigation of more innovative signal re-timing strategies that will help improve the efficiency of traffic signal operations and optimal progression along key corridors. This will ultimately save money and resources within the City of Phoenix.

The objective of this project is to develop data-driven decision-making models and computational methods for shared mobility system design and operation using a shared e-scooter system as a representative, with the ultimate goal of facilitating an electric shared-mobility revolution that promises a more sustainable future. We aim at solving the urgent questions that arise at the e-scooter sharing company and policymaker level (e.g., planning, operations). Specifically, we will (i) develop a data-driven robust optimization model to provide the decision-maker with a robust solution enabling low cost and high service quality by explicitly capturing endogenous uncertainty in demand in case of limited demand information; (ii) design computationally efficient methods with solution quality guarantees for solving the e-scooter sharing system design and operation problems.

The goal of this project is for the UA project team to maintain the ramp metering evaluation tool that was developed in the “Data-Driven Evaluation for ADOT Ramp Metering: Developing Ramp Metering Evaluation Tool” project, and to expand the functionality of the tool to handle more traffic operations issues. This tool has now come to be known as the Statewide Mobility Analytics in Real Time (SMART) tool. Modules have been developed to perform a variety of analysis such as analytics of speed, flow, delay, cost-benefit, level of service, on top of real time monitoring capabilities for freeways in and around the Phoenix metro area.

The goal of this project is to develop a comparative analysis approach to integrate region-wide traffic data and provide guidelines to manage and maintain regional traffic data to improve PAG’s traffic count program and to calibrate and validate our ongoing regional modeling efforts. This project will use existing/available and collected traffic data from the various sources of region-wide traffic data using the existing sensors in the Tucson Metropolitan Area along with additional sources of data, i.e., traffic count data and crowd-sourced data, to answer the following questions: 1)What kind of regional traffic data is available and where are the sources of data located? 2) How do the various data sources interrelate? 3) How to integrate different sources of data and maintain the quality of regional traffic data?