An exploratory investigation of public perceptions towards safety and security from the future use of flying cars in the United States

Eker, Ugur; Ahmed, Sheikh Shahriar; Fountas, Grigorios; Anastasopoulos, Panagiotis Ch.

doi:10.1016/j.amar.2019.100103

Cited by 45 publications

(31 citation statements)

References 60 publications

(78 reference statements)

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“…Including random effects for the connected environment also allows us to analyse the variation in change of driving behaviour compared to the baseline condition. This model setting is also known as group random parameters (Oviedo-Trespalacios et al, 2020, Eker et al, 2019, Fountas et al, 2018, Heydari et al, 2018, implying that the parameters follow a certain probability distribution across the observation with each group (or repeated drive).…”

Section: Hazard-based Duration Modelmentioning

confidence: 99%

Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

Ali

Bliemer

Zheng

et al. 2020

Transportation Research Part C: Emerging Technologies

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Lane-changing is one of the complex driving tasks that depends on the number of vehicles, objectives, and lanes. A driver often needs to respond to a lane-changing request of a lane-changer, which is a function of their personality traits and the current driving conditions. A connected environment is expected to assist during the lane-changing decision-making process by increasing situational awareness of surrounding traffic through vehicle-to-vehicle communication and vehicle-to-infrastructure communication. Although lane changing decision making process in a traditional environment (an environment without driving aids) has been frequently investigated, our understanding of drivers' interactions during the lane-changing decisionmaking process in a connected environment remains elusive due to the novelty of the connected environment and the scarcity of relevant data. As such, this study examines drivers' responses to lane-changing requests in a connected environment using the CARRS-Q Advanced Driving Simulator. Seventy-eight participants responded to the lane-changing request of a lanechanger under two randomised driving conditions: baseline (traditional environment without driving aids) and connected environment (with driving aids). A segmentation-based approach is employed to extract drivers' responses to the lanechanging request and subsequently estimate their response time from trajectory data. Additionally, drivers' response times are modelled using a random parameter accelerated failure time (AFT) hazard-based duration model. Results reveal that drivers tend to be more cooperative in response to a lane-changing request in the connected environment compared with the baseline condition whereby they tend to accelerate to avoid the lane-changing request. The AFT model suggests that on average drivers' response times are shorter in the connected environment, implying that drivers respond to the lane-changing request faster in the presence of driving aids. However, at the individual level, connected environment's impact on drivers' response times is mixed as drivers' response times may increase or decrease in the connected environment compared to the baseline condition, for instance, we find that female drivers have lower response times in the connected environment than that of male drivers. Overall, this study finds that drivers in connected environment, on average, take less time to respond and appear to be more cooperative, and thus, are less likely to be engaged in safety-critical events.

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Section: Hazard-based Duration Modelmentioning

confidence: 99%

Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

Ali

Bliemer

Zheng

et al. 2020

Transportation Research Part C: Emerging Technologies

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“…It was conducted in an online platform called SurveyMonkey, and a total of 692 respondents from 19 different countries participated in the survey. A number of exploratory studies have been conducted so far, based on the data collected in the aforementioned survey (Ahmed et al, 2019;Eker et al, 2019Eker et al, , 2020a. Here, we briefly summarize and illustrate the key issues investigated in the aforementioned works, as they will directly influence future policies and regulations associated with emergent technological advances.…”

Section: Exploratory Human Factors To Inform Future Flying Cars Policymentioning

confidence: 99%

“…A well-balanced regulatory framework for flying cars is ideally the first step toward ensuring safety for all stakeholders (from passengers, to operators, to public or private infrastructure owners). With an objective to form a baseline for regulations and security measures, Eker et al (2019) evaluated the feasibility of four security measures in terms of public acceptability and trust on the measures. These measures are: (a) use of existing FAA regulations for flying car air traffic control; (b) establishing air-road police force with flying police cars; (c) detailed profiling and background checking for flying car owners and operators; and (d) establishing no-fly zones for flying cars near sensitive locations, such as military bases, power/energy plants, government facilities, and major transportation hubs, to name a few.…”

Section: Recommendations and Directions Toward Future Researchmentioning

confidence: 99%

“…Evaluation and statistical analysis of public perception toward a forthcoming transportation technology pose significant methodological challenges in terms of unobserved heterogeneity and temporal instability (Mannering and Bhat, 2014;Mannering et al, 2016;Fountas et al, 2018;Mannering, 2018). A number of recent studies have demonstrated that people's perception toward potential benefits and concerns from the future use of flying cars, as well as the associated safety and security issues are multifaceted, and influenced by a broad range of socio-demographic factors (Eker et al, 2019(Eker et al, , 2020a. In addition, whether general population is willing to embrace and pay for flying cars as personal vehicles and/or as a shared mobility service are major research questions that have been investigated as well (Ahmed et al, 2019;Eker et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

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The Flying Car—Challenges and Strategies Toward Future Adoption

Ahmed

Hulme²,

Fountas

et al. 2020

Front. Built Environ.

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In recent years, our surface transportation infrastructure is suffering from overuse, extreme traffic congestion, and roadway disrepair. Instead of following the traditional infrastructure expansion policy, current transportation research focuses on developing innovative and novel solutions to the aforementioned issues. Current pathways to overcoming these issues include the gradual transition toward a number of emerging transportation technologies, such as, autonomous motor vehicles for human transport, as well as unmanned aerial vehicles (UAV's) and "drone" technologies for surveillance, and package deliveries. However, as a long-term solution, transportation scientists are also investigating the once-seemingly futuristic notion of flying car technology-a convergent form of ground/air vehicle transportation, and assessing associated regulations. In this paper, an extensive review of current literature is conducted to explore the technological capabilities of flying cars-each requiring appropriate regulations and governance-to become fully sustainable. Specifically, issues pertinent to training, safety, environment, navigation, infrastructure, logistics/sustainability, and cybersecurity and human factors are explored. This paper concludes with a preliminary quantitative analysis exploring the public perceptions associated with flying cars-including anticipated benefits, concerns, and willingness to both hire and acquire the technology once available to consumers. Insights offered by this data will help inform next-generation policies and standards associated with the gradual advancement of flying cars.

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“…Recent studies have shown that ignoring the underlying interactions may result in biased estimations and restrictive inferences (8,13). Over the last few years, the interactions of unobserved characteristics have been increasingly studied by introducing correlations between random parameters into the model (14)(15)(16)(17)(18)(19). In addition, a few studies have investigated the unobserved heterogeneity resulting from the interactions of observed factors by allowing the means/ variances of random parameters to be an estimable function of explanatory variables (20)(21)(22)(23)(24)(25).…”

mentioning

confidence: 99%

A Correlated Random Parameters Model with Heterogeneity in Means to Account for Unobserved Heterogeneity in Crash Frequency Analysis

Huo

Leng

Hou

et al. 2020

Transportation Research Record

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Unobserved heterogeneity induced by omitted variables is a major challenge in developing reliable road safety models. In recent years, the random parameters negative binomial (RPNB) model has been used frequently in crash frequency analysis to account for unobserved heterogeneity. However, the majority of past studies of the RPNB model assumed that there was no correlation between different sources of unobserved heterogeneity, which is not always true given the complex interactions of safety factors. Compared with the RPNB model, a more flexible random parameters model that is the correlated random parameters negative binomial with heterogeneity in means (CRPNBHM) model was proposed in this study. Results indicate that the CRPNBHM model could not only capture the otherwise unobserved heterogeneity, but also track the underlying correlation among different sources of unobserved heterogeneity, thus outperforming the RPNB model. In addition, new insights into the interactions of safety factors (e.g., the joint safety effects of heavy trucks and pavement rutting depth) were obtained from the CRPNBHM model and these are expected to be beneficial in developing effective safety countermeasures. Results from this study demonstrated the CRPNBHM model to be a good alternative for crash frequency analysis, particularly when unobserved heterogeneity was detected.

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An exploratory investigation of public perceptions towards safety and security from the future use of flying cars in the United States

Cited by 45 publications

References 60 publications

Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

Cooperate or not? Exploring drivers’ interactions and response times to a lane-changing request in a connected environment

The Flying Car—Challenges and Strategies Toward Future Adoption

A Correlated Random Parameters Model with Heterogeneity in Means to Account for Unobserved Heterogeneity in Crash Frequency Analysis

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