Civil & Environmental Engineering

Permanent URI for this communityhttp://hdl.handle.net/1903/2221

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Author: search.filters.author.Kabiri, Aliakbar

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    A DATA-DRIVEN FRAMEWORK FOR THE PREDICTION OF NON-RECURRENT TRAFFIC CONGESTION RECOVERY TIME ON FREEWAYS
    (2024) Kabiri, Aliakbar; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This study introduces a comprehensive approach aimed at improving the management of incident durations. It delves into enhancing traffic incident management by integrating diverse incident datasets, including Maryland State Police incident data and Coordinated Highways Action Response Team (CHART) incident data, to improve the assessment of traffic incident durations. The dissertation employs spatial and temporal thresholds to explore matching different incident datasets and identifies discrepancies between various incident reports. The dissertation also explores methodologies for estimating traffic recovery times of each incident, utilizing historical data and pre-incident conditions as baselines to establish normal traffic conditions. A novel framework is introduced to estimate non-recurrent traffic congestion recovery time, revealing that many incidents recover faster than their reported clearance times. In these cases, traffic flow returns to normal conditions quickly.Further, the study examines predictive modeling for traffic recovery time, highlighting the Random Forest model's effectiveness among various machine learning algorithms. This model's superiority, based on precision, recall, and F1-scores, underlines its potential in accurately predicting traffic incident recovery time categorized as short-duration, medium-duration, and long-duration incidents. In particular, the random forest model results in a precision of 0.7 for short-duration incidents, 0.3 for medium-duration incidents, and 0.5 for long-duration incidents. For instance, the precision of 0.5 for long-duration incidents indicates that half of the cases predicted as long-duration incidents are indeed long-duration incidents. Key predictors such as link-level vehicle volume, clearance time, response time, and number of lanes closed are identified, providing valuable insights for traffic management strategies. This dissertation underscores the importance of data-driven approaches in traffic incident management, aiming to enhance the efficiency of transportation systems through accurate prediction and estimation of incident recovery times.
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    COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages
    (Wiley, 2022-11-02) Sun, Qianqian; Zhou, Weiyi; Kabiri, Aliakbar; Darzi, Aref; Hu, Shonghua; Younes, Hannah; Zhang, Lei
    Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: “best performance,” “effort,” and “consistency.” We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.
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    DEVELOPING A TOUR-BASED TRIP IDENTIFICATION ALGORITHM USING MOBILE DEVICE LOCATION DATA
    (2022) Kabiri, Aliakbar; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis presents a novel trip identification algorithm that supports travel behavior analysis based on mobile device location data. The proposed trip identification algorithm is applied to a large-scale Location-based Service (LBS) dataset consisting of the location points of a large representative sample of United States residents with over 40 million users in January 2020. Firstly, the proposed framework divides sightings into long-distance and short-distance home-based tours and then identifies the trips on each type of tour using different methods. Furthermore, the Maryland Statewide Household Travel Survey 2018/2019 and the National Household Travel Survey (NHTS) 2017 validate the derived trips. The results showed that several metrics of the trips from mobile device location data and travel surveys follow similar trends. In addition, the impact of coronavirus disease 2019 (COVID-19) on the travel behavior of the population is studied as a real-world application of the proposed algorithm.