Alabama Transportation Institute
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Leading the Way for Emerging Transportation Solutions
Our interdisciplinary institute enables Alabama to lead the way on emerging issues like developing creative solutions for financing the construction and maintenance of roads and bridges, advancing transportation safety research, and evaluating the impact that a quality transportation system will have on Alabama’s economic future. ATI serves as an independent resource that develops unbiased information for local, state, and national leaders in developing transportation policy. The result is more and better-informed decision-making that leads to innovative, data-driven, cost-effective solutions that advance Alabama’s economy, safety, and quality of life through transportation.
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Item 2018 Transportation Midterm Election Results(2019-09) Mozayen, BouranPublication 2021 Annual Report(Alabama Transportation Institute, 2021)Publication 2022 Annual Report - Executive Summary(Alabama Transportation Institute, 2022)Item A Predictive 0-D HCCI Combustion Model for Natural Gas, Ethanol, Gasoline, and Primary Reference Fuel Blends(2018-10-13) Zhou, Yingcong; Hariharan, Deivanayagam; Yang, Ruinan; Mamalis, Sotirios; Lawler, BenjaminHomogeneous Charge Compression Ignition (HCCI) is a promising advanced combustion concept with high thermal efficiency and low exhaust emissions. This work purposes a computationally-efficient, zero-dimensional (0-D) HCCI combustion model that does not need to solve any differential equations. To develop the burn rate correlations, experimental HCCI data of ethanol, natural gas, E10-gasoline, and Primary Reference Fuel (PRF) blends was collected on a CFR engine. The burn rate model is built based on the individual cycle mass fraction burned (MFB) curves calculated from the experimental data. CA0 can be predicted from an ignition delay correlation. Once CA0 is known, CA50 and CA90 can be predicted based on CA0 and the charge-mass equivalence ratio . Then, with CA0, 50 and 90 known, a Wiebe function can be constructed to represent the MFB curve and burn rate. For PRF blends, low-temperature and high-temperature heat releases (LTHR and HTHR) are modeled by two Wiebe functions with and PRF number dependence. The fitted model has high accuracy compared to the experimental data with R2 values generally greater than 0.97. Finally, various ignition delay correlations from the literature are also tested against the experimentally collected data and some modifications to the correlations are suggested.Item Addressing Alabama’s Transportation Infrastructure: Roads and Bridges(Alabama Transportation Institute, 2019) Nambisan, Shashi; Smyth, Justice; Polunsky, Steven; Adanu, Kofi; Hainen, Alex; Hudnall, Matthew; Ijaz, Ahmad; Lidbe, Abhay; Liu, Jun; McNamara, Maggie; Penmetsa, Praveena; Tedla, Elsa; Turner, Dan; Wang, Teng; Ellis, David; Lomax, Tim; Glover, Brianne; Boroweic, Jeff; Huntsman, Brett; Koeneman, Pete; Kuzio, Jacqueline; Schrank, David; Steadman, Max; Wang, TengxiIn the year 2040, Alabamians will take stock of their transportation network and how it provides for economic growth and quality of life. They will look back on decisions made in 2019 by Alabama’s citizens, business community, and elected leadership. At the time of writing this report (2018), exotic and disruptive innovations such as self-driving vehicles, networked ridesharing, cars and trucks communicating electronically with each other and the roadside, and unmanned aerial, marine, and terrestrial vehicles are moving through research and development phases into real-world testing and eventual deployment. Regardless of their future promise or impact, infrastructure demand for the next 20 years is expected to be overwhelmingly focused on well-constructed and maintained roads and bridges with sufficient capacity and consideration for safety to enable efficient freight and passenger movement across the state. This report examines the extent, condition, and use of the Alabama road network. The authors received input from a wide range of stakeholders, including legislators, residents, the business community, shippers, truck and auto drivers, passengers, and others involved in the development and use of the network.Item Alabama Road Bid Bundling Authority(2019-10) Fisher, JustinItem An Assessment of Alabama’s Electric Vehicle Charging Infrastructure and Policies: Identiyfing Gaps and Needs(2020-01) Bredikhina, Olga A.; Fisher, Justin; Hockstad, Trayce; Mozayen, Bouran; Rafique, Sanaa; Wheeler, MelissaItem Analysis of Distracted Driving in Alabama 2012-2016 Data(2017-06) Brown, David B.; Stricklin, RhondaItem Analysis of Fatal Crashes in CY2016 as Compared to CY2014(2017-08) Brown, David B.; Stricklin, Rhonda; Norris, JesseItem Analysis of Motorcycle Caused and Motorcycle Victim Crashes CY2010-2014 Data(2015-10-23) Brown, DaveThis report has the objective of presenting a problem identification that was done on motorcycle (MC) crashes with the goal of establishing and improving countermeasures for reducing these crash frequencies and severities in the future.Item Analysis of Speed-Related Crashes in CY2012-2016(2017-09) Brown, David B.; Stricklin, Rhonda; Norris, JesseItem Analysis of the Most Critical Factors in Young (16-20 Year Old) Driver-Caused Vehicle Crashes(2017-10-08) Brown, David B.Item Analysis of Vehicle Defective Brakes and Tires(2020-02-15) Brown, David B.Item Autonomous Vehicle Definitions in SEC States(2019-06) Transportation Policy Research CenterItem Autonomous Vehicles: Overview of Federal and Alabama Statute and Regulation(2019-11) Bredikhina, Olga A.Item CARE IMPACT Study COVID vs Normal Times(2020-07-28) Brown, David B.Item CARE IMPACT Study Failure to Yield and Ran (FtY)(2020) Brown, David B.Item CARE IMPACT Study of Age 0-15 Year Old Occupants(2019-07-24) Brown, David B.Item CARE IMPACT Study of Railroad Involved Crashes(2020-06-29) Brown, David B.Item CARE IMPACT Study of Senior Driver Caused Traffic Crashes: 2013-2017 Data(2018-05-15) Brown, David B.The comparisons in this document are between those crashes that were caused by senior drivers (age 65 or older) compared to all other crashes. This enabled the characteristics for these crashes to surface so that traffic safety professionals can determine their magnitude and optimize senior driver safety programs to place emphasis on the most important factors. In many cases the comparison led to conclusions that were expected, being well established over the years. A very important general finding that confirms studies done by CAPS personnel from well over a decade ago is that senior drivers are relatively risk averse compared to younger drivers. This will be noticed in virtually all of the IMPACT comparisons below: the older driver red bars will be higher in those categories that generally involve lower risk. Examples include lower speeds, avoidance of late night driving and bad weather, and many other categories that will be noticed as risk-avoidance. Since these results should be well understood, they will not be discussed unless there some aspects of them that bear mentioning.