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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.

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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?

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This project outlines a data-driven approach to achieve three primary objectives. The first, evaluating and quantifying the best approach for speed management tactics for conventional arterials. The second, evaluating the transferability of using conventional mobility management strategies on connected roadways. The third, scaling speed management tactics for mixed traffic flow and evaluating the impact of the speed management tactics on pedestrians and cyclists' safety.

This project is a partnership with PCDOT to perform various acts of technical support for their department. This involves researching the best practices, software tools, and approaches to resolve PCDOT business problems, as well as researching and comparing available datasets and recommending the best data sets for analysis. UArizona is also working closely with Pima County IT and PCDOT to provide input to the design and implementation of complex spatial-analytical models, products, and services, including web map, web app, and reporting. There is also the intention to perform independent and cooperative complex analysis as well as evaluate management problems and recommend decisions regarding the proper course of action

Intelligent transportation systems (ITS) significantly change our communities by improving the safety and convenience of people's daily mobility. The system relies on multimodal traffic monitoring, that needs to be reliable, efficient and detailed traffic information for traffic safety and planning. Signalized traffic intersections are critical spots for collecting such mix-traffic data because the most conflicts and crash occurrences involve multiple transportation modes, such as pedestrians, bicyclists, motorcyclists, and cars. How to reliably and intelligently monitor intersection traffic with multimodal information is one of the most critical topics in intelligent transportation research.

This project will Investigate a low-cost, low-weight, compact size, and reliable monitoring platform. This platform will incorporate mmWave radar and machine learning techniques to collect multimodal traffic data at intersections is robust to light and adverse weather conditions. The products of this project consist of 1) a prototype of the proposed multimodal traffic, and 3) a demo platform at a road intersection to illustrate the performance in terms of measuring multimodal traffic counts, speeds, and directions.

With this project, we aim to contribute to addressing the problem of lack of a comprehensive framework and appropriate tools for understanding the long-term effects of AV. To tackle this research problem, our first objective is to develop a parsimonious conceptual framework for examining AV’s long-term effects implement it as a modeling tool, and test and validate the tool with a hypothetical model city to check for reasonable sensitivities. The second objective is to transfer the insights from our conceptual framework and modeling tool to improve the capacity of operational modeling systems at MPOs through the Portland, OR and Tucson, AZ case studies and assessing the impacts of AV and various scenarios in these two regions with different travel and land use patterns and regulatory environments.

With this project, we aim to contribute to addressing the problem of lack of a comprehensive framework and appropriate tools for understanding the long-term effects of AV. To tackle this research problem, our first objective is to develop a parsimonious conceptual framework for examining AV’s long-term effects implement it as a modeling tool, and test and validate the tool with a hypothetical model city to check for reasonable sensitivities. The second objective is to transfer the insights from our conceptual framework and modeling tool to improve the capacity of operational modeling systems at MPOs through the Portland, OR and Tucson, AZ case studies and assessing the impacts of AV and various scenarios in these two regions with different travel and land use patterns and regulatory environments.

The Advanced Traffic Management System (ATMS) plays a critical role in traffic management because the ATMS communicates with all traffic signals and sensors. The ATMS is considered the “brain” of each traffic management center (TMC). Therefore, the ATMS helps traffic engineers manage all traffic signals and sensors to improve traffic operations in a city. However, various jurisdictions use different ATMS products and no consensus has been made on how to decide which system most cost efficient. There is no standard procedure to compare those ATMS. In the Phoenix Metropolitan area, four products are typically used. The primary goal of this project is to compare all four systems through a multi-criteria decision analysis process. The proposed process will be able to compare all four products depending on the criteria determined by each participating jurisdiction.