Author
Emmanuel Kofi Adanu
Other affiliations: University of Alabama System
Bio: Emmanuel Kofi Adanu is an academic researcher from University of Alabama. The author has contributed to research in topics: Crash & Poison control. The author has an hindex of 6, co-authored 29 publications receiving 210 citations. Previous affiliations of Emmanuel Kofi Adanu include University of Alabama System.
Papers
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TL;DR: In this paper, the authors explore opinions regarding the perceived benefits and challenges of AVs among vulnerable road users, in particular pedestrians and bicyclists, and evaluate whether interaction experiences with AVs influence perceptions among vulnerable pedestrians and cyclists.
153 citations
TL;DR: The results reveal disparities in serious injury crash rate as well as significant proportions of serious injury crashes involving no seatbelt usage, driving under influence (DUI), unemployed drivers, young drivers, distracted driving, and African American drivers among some regions.
60 citations
TL;DR: The model estimation results indicate a significant association of severe injury crashes to risk factors such as driver unemployment, driving with invalid license, no seatbelt use, fatigue, driving under influence, old age, and driving on county roads for both weekdays and weekends.
59 citations
TL;DR: In this article, the authors 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.
Abstract: 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 (from 14% 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.
29 citations
TL;DR: In this article, the authors investigated how the COVID-19 pandemic affected road crashes and crash outcomes in Alabama and found that although traffic volumes and vehicle miles traveled had significantly dropped during lockdown, there was an increase in the total number of crashes and major injury crashes compared to the period prior to the lockdown order, with speeding, DUI, and weekends accounting for a significant proportion of these crashes.
27 citations
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TL;DR: In this article, a review of the existing studies on AV acceptance is presented, and the authors find that people in Europe and Asia have substantial differences in attitudes toward AVs and that safety is one of the most concerned factors of AVs.
Abstract: Excessive dependence on autonomous vehicles (AVs) may exacerbate traffic congestion and increase exhaust emissions in the future. The diffusion of AVs may be significantly affected by the public’s acceptance. A few factors that may affect people’s acceptance of AVs have been researched in the existing studies, one-third of which cited behavioral theories, while the rest did not. A total of seven factors with behavior theories are screened out that significantly affect the acceptance intention, including perceived ease of use, attitude, social norm, trust, perceived usefulness, perceived risk, and compatibility. Six factors without behavior theories are summed up that affect AV acceptance, namely safety, performance-to-price value, mobility, value of travel time, symbolic value, and environmentally friendly. We found that people in Europe and Asia have substantial differences in attitudes toward AVs and that safety is one of the most concerned factors of AVs by scholars and respondents. Public acceptance of the different types of AVs and consumers’ dynamic preferences for AVs are highlighted in the review too. The quality of literature is systematically assessed based on previously established instruments and tailored for the current review. The results of the assessment show potential opportunities for future research, such as the citation of behavior theories and access to longitudinal data. Additionally, the experimental methods and the utilization of mathematical and theoretical methods could be optimized.
124 citations
TL;DR: In this article, the authors developed a theoretical model to identify the latent factors influencing public acceptance of autonomous vehicles and examined their interrelationships by applying three diverse research paradigms anchoring on innovation diffusion, customer utility and social psychology.
115 citations
Journal Article•
TL;DR: In this article, the major lane changing models in the literature are categorized into two groups: models that capture the lane changing decision-making process, and models that aim to quantify the impact of lane changing behavior on surrounding vehicles.
Abstract: This paper comprehensively reviews recent developments in modeling lane-changing behavior. The major lane changing models in the literature are categorized into two groups: models that aim to capture the lane changing decision-making process, and models that aim to quantify the impact of lane changing behavior on surrounding vehicles. The methodologies and important features (including their limitations) of representative models in each category are outlined and discussed. Future research needs are determined.
108 citations
01 Jan 1994
TL;DR: By analyzing fatal MVC crash characteristics in regions with different population densities, many crash variables were found to be related to population density.
Abstract: Motor vehicle crash (MVC) fatality rates have been shown to be inversely related to population density. The purpose of this study is to describe and compare crash variables of fatal MVCs between urban and rural regions, as well as among rural regions with different population densities. Data from four midwestern states over the five year period from 1986 to 1990 were retrospectively analysed. The study population included 10.932 people in 6.318 vehicles involved in 4.926 crashes resulting in 4.970 fatalities. Several variables that were related to population density were found, which may help planners to develop interventions for rural populations. For the covering abstract of the conference see IRRD 873507.
97 citations
TL;DR: How social influence, system characteristics, and individual factors determine individual acceptance of autonomous driving is revealed, with implications for practitioners.
95 citations