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IMTRegression.bib
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@article{zisner_datafits_2023,
title = {{DataFITS}: {A} {Heterogeneous} {Data} {Fusion} {Framework} for {Traffic} and {Incident} {Prediction}},
volume = {24},
issn = {1558-0016},
shorttitle = {{DataFITS}},
url = {https://ieeexplore.ieee.org/document/10149132},
doi = {10.1109/TITS.2023.3281752},
abstract = {This paper introduces DataFITS (Data Fusion on Intelligent Transportation System), an open-source framework that collects and fuses traffic-related data from various sources, creating a comprehensive dataset. We hypothesize that a heterogeneous data fusion framework can enhance information coverage and quality for traffic models, increasing the efficiency and reliability of Intelligent Transportation System (ITS) applications. Our hypothesis was verified through two applications that utilized traffic estimation and incident classification models. DataFITS collected four data types from seven sources over nine months and fused them in a spatiotemporal domain. Traffic estimation models used descriptive statistics and polynomial regression, while incident classification employed the k-nearest neighbors (k-NN) algorithm with Dynamic Time Warping (DTW) and Wasserstein metric as distance measures. Results indicate that DataFITS significantly increased road coverage by 137\% and improved information quality for up to 40\% of all roads through data fusion. Traffic estimation achieved an {\textbackslash}text R{\textasciicircum}2 score of 0.91 using a polynomial regression model, while incident classification achieved 90\% accuracy on binary tasks (incident or non-incident) and around 80\% on classifying three different types of incidents (accident, congestion, and non-incident).},
number = {10},
urldate = {2024-03-29},
journal = {IEEE Transactions on Intelligent Transportation Systems},
author = {Zißner, Philipp and Rettore, Paulo H. L. and Santos, Bruno P. and Loevenich, Johannes F. and Lopes, Roberto Rigolin F.},
month = oct,
year = {2023},
note = {Conference Name: IEEE Transactions on Intelligent Transportation Systems},
keywords = {Correlation, Data integration, Data models, heterogeneous data fusion, incident classification, Intelligent transportation systems, Sensors, Social networking (online), Spatiotemporal phenomena, traffic estimation, Transportation},
pages = {11466--11478},
}
@article{mumtarin_traffic_2023,
title = {Traffic {Incident} {Management} {Performance} {Measures}: {Ranking} {Agencies} on {Roadway} {Clearance} {Time}},
volume = {13},
copyright = {http://creativecommons.org/licenses/by/4.0/},
shorttitle = {Traffic {Incident} {Management} {Performance} {Measures}},
url = {https://www.scirp.org/journal/paperinformation.aspx?paperid=125607},
doi = {10.4236/jtts.2023.133017},
abstract = {This study develops a procedure to rank agencies based on their incident responses using roadway clearance times for crashes. This analysis is not intended to grade agencies but to assist in identifying agencies requiring more training or resources for incident management. Previous NCHRP reports discussed usage of different factors including incident severity, roadway characteristics, number of lanes involved and time of incident separately for estimating the performance. However, it does not tell us how to incorporate all the factors at the same time. Thus, this study aims to account for multiple factors to ensure fair comparisons. This study used 149,174 crashes from Iowa that occurred from 2018 to 2021. A Tobit regression model was used to find the effect of different variables on roadway clearance time. Variables that cannot be controlled directly by agencies such as crash severity, roadway type, weather conditions, lighting conditions, etc., were included in the analysis as it helps to reduce bias in the ranking procedure. Then clearance time of each crash is normalized into a base condition using the regression coefficients. The normalization makes the process more efficient as the effect of uncontrollable factors has already been mitigated. Finally, the agencies were ranked by their average normalized roadway clearance time. This ranking process allows agencies to track their performance of previous crashes, can be used in identifying low performing agencies that could use additional resources and training, and can be used to identify high performing agencies to recognize for their efforts and performance.},
language = {en},
number = {3},
urldate = {2024-03-29},
journal = {Journal of Transportation Technologies},
author = {Mumtarin, Maroa and Knickerbocker, Skylar and Litteral, Theresa and Wood, Jonathan S.},
month = jun,
year = {2023},
note = {Number: 3
Publisher: Scientific Research Publishing},
pages = {353--368},
}
@article{wali_heterogeneity_2022,
title = {Heterogeneity assessment in incident duration modelling: {Implications} for development of practical strategies for small \& large scale incidents},
volume = {26},
issn = {1547-2450, 1547-2442},
shorttitle = {Heterogeneity assessment in incident duration modelling},
url = {https://www.tandfonline.com/doi/full/10.1080/15472450.2021.1944135},
doi = {10.1080/15472450.2021.1944135},
language = {en},
number = {5},
urldate = {2024-03-29},
journal = {Journal of Intelligent Transportation Systems},
author = {Wali, Behram and Khattak, Asad J. and Liu, Jun},
month = sep,
year = {2022},
pages = {586--601},
}
@book{shah_development_2022,
address = {Washington, D.C.},
title = {Development of {Guidelines} for {Quantifying} {Benefits} of {Traffic} {Incident} {Management} {Strategies}},
isbn = {978-0-309-28705-0},
url = {https://www.nap.edu/catalog/26488},
urldate = {2024-03-30},
publisher = {Transportation Research Board},
author = {Shah, Vaishali and Hatcher, Greg and Greer, Elizabeth and Fraser, Janet and Franz, Mark and Sadabadi, Kaveh and {National Cooperative Highway Research Program} and {Transportation Research Board} and {National Academies of Sciences, Engineering, and Medicine}},
month = feb,
year = {2022},
doi = {10.17226/26488},
note = {Pages: 26488},
}
@techreport{schultz_analysis_2023,
title = {Analysis of {Benefits} of {UDOT}’s {Expanded} {Incident} {Management} {Team} {Program}},
url = {https://rosap.ntl.bts.gov/view/dot/72257},
abstract = {In 2019, the Utah Department of Transportation (UDOT) funded a research study evaluating the performance measures of UDOT’s expanded Incident Management Team (IMT) program. The number of IMTs patrolling Utah roadways increased from 13 to 25 between 2018 and 2020. Crash data were collected from the Utah Highway Patrol’s Computer Aided Dispatch database and from the UDOT TransSuite database to compare IMT performance measures between the two years and to evaluate the benefits of the expanded IMT program. However, these data were compromised due to the effects of the COVID-19 pandemic. This study collected data for 2022 using the same methodology as the Phase II study to compare IMT performance measures in 2022 with those of 2018 after traffic volumes had returned to a similar level as those of pre-pandemic levels. There were 283 and 307 incidents for the years of 2018 and 2022, respectively, that were analyzed for IMT performance measures which include response time, roadway clearance time, and incident clearance time. There were 172 and 236 incidents for the years of 2018 and 2022, respectively, that were analyzed for the user impact categories of affected volume, excess travel time, and excess user costs. Results of the statistical analyses conducted on the 2018 and 2022 datasets show that IMTs can respond more quickly to incidents in a larger coverage area with significantly reduced user impacts. The expanded IMT program is also able to respond to more incidents, including those of high severity, while significantly decreasing congestion.},
language = {English},
number = {UT-23.05},
urldate = {2024-04-01},
author = {Schultz, Grant G. and Hyer, Joel and Holdsworth, W Harrison and Eggett, Dennis L. and Macfarlane, Gregory S and {Brigham Young University. Department of Civil and Construction Engineering}},
month = sep,
year = {2023},
doi = {10.21949/1528563},
keywords = {Emergency response time, Incident Clearance Time, Incident management, Performance measurement, Roadway Clearance Time, Traffic incidents, Variable costs},
}
@misc{ellis_value_2017,
title = {Value of {Delay} {Time} of {Use} in {Mobility} {Monitoring} {Efforts}},
url = {https://static.tti.tamu.edu/tti.tamu.edu/documents/TTI-2017-10.pdf},
abstract = {The 2016 value of delay time estimate for passenger vehicle motorists and truck drivers incorporates several changes from previous estimates. The value of delay time for passenger vehicle motorists now uses the median hourly wage rate for all occupations as produced by the Bureau of Labor Statistics (BLS) as a base. Researchers estimate the 2016 value of delay time for personal travel at \$17.81 per person. The commercial value of travel time is now based on the American Transportation Research Institute (ATRI) annual survey modified by speed, type of vehicle, and vehicle occupancy and is estimated to be \$53.69 per vehicle per hour for 2016. Neither the value of delay time for personal nor commercial vehicles include the cost of fuel.},
urldate = {2024-04-01},
author = {Ellis, David R.},
month = jul,
year = {2017},
}
@misc{schultz_ut1503_2015,
title = {{UT15}.03 {I15} {Express} {Lanes} {Study} {Phs} {II}.pdf},
url = {https://drive.google.com/file/d/12aFWIZui8HGvf7c4JQcIRSaXe5vPHQGw/view?usp=sharing&usp=embed_facebook},
abstract = {The 2016 value of delay time estimate for passenger vehicle motorists and truck drivers incorporates several changes from previous estimates. The value of delay time for passenger vehicle motorists now uses the median hourly wage rate for all occupations as produced by the Bureau of Labor Statistics (BLS) as a base. Researchers estimate the 2016 value of delay time for personal travel at \$17.81 per person. The commercial value of travel time is now based on the American Transportation Research Institute (ATRI) annual survey modified by speed, type of vehicle, and vehicle occupancy and is estimated to be \$53.69 per vehicle per hour for 2016. Neither the value of delay time for personal nor commercial vehicles include the cost of fuel.},
urldate = {2024-04-01},
journal = {Google Docs},
author = {Schultz, Grant G. and Mineer, Samuel T. and Hamblin, Cody A. and Halliday, David B. and Groberg, Christopher C. and Burris, Mark W.},
month = feb,
year = {2015},
}
@techreport{macfarlane_simulating_2024,
title = {Simulating {Incident} {Management} {Team} {Response} and {Performance}},
url = {https://rosap.ntl.bts.gov/view/dot/74034},
abstract = {The effectiveness of Incident Management Teams (IMT) in reducing the duration and impact of traffic incidents is well documented. The capacity of large-scale simulation models to illustrate the negative effects of these incidents on vehicle delays and excess user costs (EUC) is also widely recognized. However, there is a gap in research integrating large-scale simulation modeling with IMT performance analysis. This study uses the Multi-Agent Transport Simulation (MATSim) framework to simulate the impact of incidents and evaluate the performance of IMT across the regional network of Utah’s Wasatch Front, analyzing their operation in various hypothetical situations. Our findings validate the role of IMT in decreasing delays and EUC. The simulation also investigates the potential effects of increased incident frequency and IMT expansion, revealing that more incidents increased delays, whereas additional IMT units can mitigate these effects and improve response times. The MATSim model we developed demonstrates the potential of dynamic large-scale modeling to evaluate incident management strategies in ways that previous studies did not. This model could serve as a valuable tool for further evaluating the performance of Utah’s IMT program, with the potential to offer new perspectives on optimizing team deployment and scheduling efficiency.},
language = {English},
number = {UT-23.22},
urldate = {2024-06-12},
author = {Macfarlane, Gregory S and Jarvis, Daniel and Woolley, Brynn and Schultz, Grant G. and {Brigham Young University. Department of Civil and Environmental Engineering}},
month = mar,
year = {2024},
keywords = {Incident management, Incident Management Teams, Incident Simulation, Multi-agent systems, Performance, Real time control, Simulation, Traffic, Transportation Modeling},
}
@misc{conkliin_data_2013,
title = {Data {Capture} for {Performance} and {Mobility} {Measures} {Reference} {Manual}},
url = {chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.its.dot.gov/research_archives/data_capture/pdf/data_capture_reference_manual.pdf},
abstract = {The Guide to Transportation Management Center (TMC) Data Capture for Performance and Mobility Measures is a two-volume document consisting of a summary Guidebook and this Reference Manual. These documents provide technical guidance and recommended practices regarding concepts, methods, techniques, and procedures for collecting, analyzing, and archiving TMC operations data to develop measures of roadway and TMC performance, as well as documenting the benefits of TMC activities for a variety of stakeholders. This guide is designed to be used by TMC technical and management staff involved in developing, implementing, and/or refining a TMC performance monitoring program. Effective performance monitoring efforts can assist the user in a variety of tasks including traffic performance monitoring, asset management, evaluation of TMC activities and strategies, and planning and decision-making. They can also provide persuasive data in support of continued or enhanced TMC programs; conversely, a lack of available data regarding the value of TMC programs can make agencies more vulnerable to budget reductions when resources are constrained and the remaining budgets are being allocated. The contents of this guide are based on a literature survey, a survey of TMC Pooled-Fund Study (PFS) members, follow-up interviews, and the project study team’s experience and judgment. The study team began with a literature survey of publications regarding TMC data, performance data, performance measures, performance analysis, and reporting. Next, a survey of the PFS members was performed to gain an understanding of the current state of the practice and to determine PFS member needs. The team conducted follow-up discussions with members as needed and then selected a core set of performance measures that would form the basis for this guide.},
urldate = {2024-06-18},
publisher = {FHWA},
author = {Conkliin, Clifford A.},
month = mar,
year = {2013},
}
@inproceedings{deublein_user_2013,
title = {User cost estimation on road networks by means of bayesian probabilistic networks},
isbn = {978-91-637-2973-7},
url = {https://trid.trb.org/View/1358790},
urldate = {2024-07-10},
author = {Deublein, Markus and Adey, Bryan T.},
year = {2013},
}