UA cloudflare authentication

 

Research and Publications - Alabama Transportation Institute

Permanent URI for this collection

https://ir.ua.edu/handle/123456789/8238

Browse

Recent Submissions

Now showing 1 - 20 of 107
  • Thumbnail Image
    Item
    Understanding the Factors That Are Associated with MotorcycleCrash Severity in Rural and Urban Areas of Ghana
    (Wiley, 2021-12-22) Agyemang, William; Adanu, Emmanuel Kofi; Jones, Steven
    Like many countries in sub-Saharan Africa, Ghana has witnessed an increase in the use of motorcycles for both commercial transport and private transport of people and goods. The rapid rise in commercial motorcycle activities has been attributed to the problem of urban traffic congestion and the general lack of reliable and affordable public transport in rural areas. This study investigates and compares factors that are associated with motorcycle crash injury outcomes in rural and urban areas of Ghana. This comparison is particularly important because the commercial use of motorcycles and their rapid growth in urban areas are a new phenomenon, in contrast to rural areas where people have long relied on motorcycles for their transportation needs. Preliminary analysis of the crash data revealed that more of the rural area crashes occurred under dark and unlit roadway conditions, while urban areas recorded more intersection-related crashes. Additionally, it was found that more pedestrian collisions happened in urban areas, while head-on collisions happened more in rural areas. The model estimation results show that collisions with a pedestrian, run-off-road, and collisions that occur under dark and unlit roadway conditions were more likely to result in fatal injury. Findings from this study are expected to help in crafting and targeting appropriate countermeasures to effectively reduce the occurrence and severity of motorcycle crashes throughout the country and, indeed, sub-Saharan Africa.
  • Thumbnail Image
    Item
    A low-cost approach to identify hazard curvature for local road networks using open-source data
    (Elsevier, 2021-06) Hu, Qinglin; Li, Xiaobing; Liu, Jun; Adanu, Emmanuel Kofi
    Vehicle crashes are a leading cause of death in the United States. Curvature in local roadways has been identified as one of the most significant factors that lead to fatal crashes. Given the large number of local roads and their relatively low traffic volume ‐ compared with interstates or freeways ‐ most local roads may not receive priorities in the first phase of highway upgrades, and critical locations, e.g., sharp curves (vertical and/or horizontal), in the network may be a deadly threat for both advanced autonomous vehicles and conventional vehicles. Furthermore, identifying local roadway curvatures presents various obstacles, such as high budgets and lack of survey data. To fill this gap, this study offers a low‐cost approach to constructing three‐dimensional geometric profiles for local roads in a relatively large study area using open‐source data. Given these profiles, critical road segments, including extreme horizontal and vertical curves and their combinations, can be identified. This study re‐classifies the local road segments into 20 sub‐categories based on the calculated vertical grades and curve radii and incorporates those segments into a zero‐inflated native binomial model for crash occurrence. Model results showed that grades or curves were associated with decreased crash frequency compared with straight and flat roads. However, segments with larger horizontal curve radii and low grades were found to be associated with increased crash frequency. Further implications are discussed in the paper.
  • Thumbnail Image
    Item
    Effects of the autonomous vehicle crashes on public perception of the technology
    (Elsevier, 2021-06-01) Penmetsa, Praveena; Sheinidashtegol, Pezhman; Musaev, Aibek; Adanu, Emmanuel Kofi; Hudnall, Matthew
    In March 2018, an Uber-pedestrian crash and a Tesla's Model X crash attracted a lot of media attention because the vehicles were operating under self-driving and autopilot mode respectively at the time of the crash. This study aims to conduct before-and-after sentiment analysis to examine how these two fatal crashes have affected people's perceptions of self-driving and autonomous vehicle technology using Twitter data. Five different and relevant keywords were used to extract tweets. Over 1.7 million tweets were found within 15 days before and after the incidents with the specific keywords, which were eventually analyzed in this study. The results indicate that after the two incidents, the negative tweets on “self-driving/autonomous” technology increased by 32 percentage points (from14% to 46%). The compound scores of “pedestrian crash”, “Uber”, and “Tesla” keywords saw a 6% decrease while “self-driving/autonomous” recorded the highest change with an 11% decrease. Before the Uber incident, 19% of the tweets on Uber were negative and 27% were positive. With the Uber-pedestrian crash, these percentages have changed to 30% negative and 23% positive. Overall, the negativity in the tweets and the percentage of negative tweets on self-driving/autonomous technology have increased after their involvement in fatal crashes. Providing opportunities to interact with this developing technology has shown to positively influence peoples' perception.
  • Thumbnail Image
    Item
    An Analysis of the Effects of Crash Factors and Precrash Actions on Side Impact Crashes at Unsignalized Intersections
    (Wiley, 2021) Adanu, Emmanuel Kofi; Li, Xiaobing; Liu, Jun; Jones, Steven
    Annually, side impact crashes contribute to a significant proportion of road fatalities. These crashes typically occur as a result of traffic violations at intersections. This study contributes to efforts in addressing side impact crashes at unsignalized intersections by performing a path analysis to unravel some behavioral trajectories through which these crashes occur. The study further investigated how these behavioral pathways influence the severity of the crashes. Crashes that occurred at unsignalized four-way intersections and T-junctions in Alabama were used for model estimations. Three precrash actions, failed to yield right-of-way at the stop sign, failed to yield right-of-way at a turn, and running stop sign, were considered. +e model estimation results reveal that some of the crash factors were more associated with certain precrash factors but not others at either four-way intersections or T-junctions or both. It was observed that side impact crashes that occurred under daylight conditions at four-way intersections, for instance, were less likely to involve running a stop sign but more likely to involve failure to yield at the stop sign and failure to yield right-of-way at a turn, but under dark and unlit roadway conditions, the at-fault drivers were more likely to run a stop sign or fail to yield at a stop sign but less likely to be involved in failure to yield right-of-way at a turn. +is approach to injury severity analysis uncovers complex underlying relationships between precrash actions, other contributing factors, and crash outcomes.
  • Thumbnail Image
    Item
    Empowering Compartmental Modeling With Mobility and Shelter-in-Place Analysis
    (Frontiers, 2021-04-29) Ramezani, Somayeh Bakhtiari; Rahimi, Shahram; Amirlatifi, Amin; Hudnall, Matthew; Pate, Jeremy; Penmetsa, Praveena; Qian, Xinwu
    A model that is capable of handling the non-linear trend of COVID-19 throughout the US and evaluate different effects of interstate/intrastate mobility measures can help decision-makers adjust guidelines and state-wide mandates to contain the pandemic's spread. The abundance of cellular-based data has made it possible to study many aspects of users' mobility, including their travel, contact, and dwell patterns. This study uses a compartmental metapopulation model to present a correlation between the contact and mobility indices and the likelihood of being susceptible to infection. We studied the effect of travel from other states on overall infections in a destination state and observed a strong inverse correlation of 0.98 between the contact index and social awareness compartment, i.e., individuals who are no longer susceptible to infection. The shelter-in-place what-if analysis for travelers from other states on the course of infection in the destination state showed a possible reduction of over 22% in the total number of infections and death if travelers sheltered in place for 5–7 days.
  • Thumbnail Image
    Item
    Electronic crash reporting: Implementation of the Model Minimum Uniform crash Criteria (MMUCC) and crash record life cycle comparison
    (Elsevier, 2021-03) Nie, Qifan; Crawford, P. Shane; Bill, Andrea; Parker, Steven T.; Graettinger, Andrew J.; Smith, Randy K.; Elliot, Terry B.; Paschal, E. Neal
    Electronic crash records have become the standard crash reporting method in most states, but crash report elements vary between states and there is little publicly available information detailing the data provenance and life cycles for these records. As crash report granularity increases, proper data management is critical to ensure report quality with minimal errors. States are encouraged to meet federal standards for minimum crash report elements detailed in the Model Minimum Uniform Crash Criteria. MMUCC standardization helps states manage long-term crash data, enables multi-state analysis, and promotes sound traffic safety policies. This paper details how the states of Alabama and Wisconsin manage crash reports by evaluating their compliance with MMUCC standards as well as tracking the life cycle of crash reports in the two states from provenance, through approval and validation processes, to preservation as official records. A web-based MMUCC Compliance Tool was developed using elements from Alabama and Wisconsin crash reports, and aids other states in mapping existing crash report elements to MMUCC standards. The tool uses an automated check to map crash report elements and flags missing elements. The output of the tool includes a report detailing changes needed for the crash report to more closely align with the MMUCC standards. Common crash report lifecycle elements in Alabama and Wisconsin were identified, including initial web service validations, supervisor review, transactional database backups, linkage to driver record systems, and processes in place to manage and fix void requests. To aid crash recording procedures nationwide, a set of best practices for crash report lifecycle management is synthesized from the analysis.
  • Thumbnail Image
    Item
    Automated Unity: Evaluating the Uniform Law Commission's Autonomous Vehicle Act
    (Washburn University School of Law, 2021-01) Hockstad, Trayce; Fisher, Justin
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Senior Driver Lighting Issues
    (2020-11-30) Brown, David B.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    COVID Fatal Crash Special Study Summary
    (2020-10) Brown, David B.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Urban Traffic Travel Time Short-Term Prediction Model Based onSpatio-Temporal Feature Extraction
    (2020) Kang, Leilei; Hu, Guojing; Huang, Hao; Lu, Weike; Liu, Lan
    In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. *e dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. *rough the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. *e results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.
  • Thumbnail Image
    Item
    CARE IMPACT Study COVID vs Normal Times
    (2020-07-28) Brown, David B.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item