Potential Impact of Autonomous Vehicles on Movement Behavior: A Scoping Review

Spence, John C.; Kim, Yeong-Bae; Lamboglia, Carminda Maria Goersch Fontenele; Lindeman, Cliff; Mangan, Amie; McCurdy, Ashley; Wohlers, Brendan; Sivak, Allison; Clark, Marianne

doi:10.1016/j.amepre.2020.01.010

Cited by 31 publications

(11 citation statements)

References 51 publications

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“…With the advent of artificial intelligence, many pilot and commercial studies have been conducted for autonomous vehicles and intelligent vehicles [ 56 , 57 ]. We could embed the prediction models to the central processor of the vehicles.…”

Section: Results and Comparisonmentioning

confidence: 99%

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Chui¹,

Gupta

Liu

et al. 2021

Sensors

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Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

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Section: Results and Comparisonmentioning

confidence: 99%

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Chui¹,

Gupta

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Additionally, widespread use of such vehicles could increase both urban sprawl and congestion, decreasing walkability and making investment in transit less appealing [70]. Several reviews of the potential health impacts of autonomous vehicles have been published [71][72][73], with some emphasizing the role of local governments in determining the place of autonomous vehicles in US transportation networks [74]. Leaders of the Innovative Practices and Building Strategic Institutional Relationships themes are developing materials to further disseminate ideas and perspectives developed at the CHAT.…”

Section: Innovative Practices Building Strategic Institutional Relationships and Cross-cutting Needsmentioning

confidence: 99%

The 2019 Conference on Health and Active Transportation: Research Needs and Opportunities

Berrigan

Dannenberg

Lee³

et al. 2021

IJERPH

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Active transportation (AT) is widely viewed as an important target for increasing participation in aerobic physical activity and improving health, while simultaneously addressing pollution and climate change through reductions in motor vehicular emissions. In recent years, progress in increasing AT has stalled in some countries and, furthermore, the coronavirus (COVID-19) pandemic has created new AT opportunities while also exposing the barriers and health inequities related to AT for some populations. This paper describes the results of the December 2019 Conference on Health and Active Transportation (CHAT) which brought together leaders from the transportation and health disciplines. Attendees charted a course for the future around three themes: Reflecting on Innovative Practices, Building Strategic Institutional Relationships, and Identifying Research Needs and Opportunities. This paper focuses on conclusions of the Research Needs and Opportunities theme. We present a conceptual model derived from the conference sessions that considers how economic and systems analysis, evaluation of emerging technologies and policies, efforts to address inclusivity, disparities and equity along with renewed attention to messaging and communication could contribute to overcoming barriers to development and use of AT infrastructure. Specific research gaps concerning these themes are presented. We further discuss the relevance of these themes considering the pandemic. Renewed efforts at research, dissemination and implementation are needed to achieve the potential health and environmental benefits of AT and to preserve positive changes associated with the pandemic while mitigating negative ones.

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“…• People who historically have difficulties with driving, such as disabled people, older citizens, as well as the very young, would be able to experience the freedom of car travel. A study from (Spence et al, 2020) generalized this question of mobility for all people. The AVs will change the way people are moving and their conclusion is that AVs might be the most used travel means for local travel in 2050s,…”

Section: Societal Impact Of Cadmentioning

confidence: 99%

Are Connected and Automated Vehicles the Silver Bullet for Future Transportation Challenges? Benefits and Weaknesses on Safety, Consumption, and Traffic Congestion

Gruyer

Orfila

Glaser

et al. 2021

Front. Sustain. Cities

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With the growing development of Cooperative, Connected, and Automated Mobility (CCAM), questions arise about the real impact of this innovative mobility on our daily life. CCAM originally promised to improve road safety. It is now a holistic solution for future mobility: the CCAM is there to optimize traffic, which can translate into strategies for reducing energy consumption or polluting emissions, without compromising road safety. The capability of CCAM is dependent on the reliability and robustness of its components, as it will be making life-impacting decisions. It is therefore necessary to be able to guarantee a high-level quality of sensors, communication, software, and hardware architecture. In this mobility ecosystem, the infrastructure and data that it will be able to produce is at the heart of current research issues. This paper addresses the following question: Are the Connected and automated Vehicles (CAVs) the silver bullet solution with which to answer the issues of the current mobility systems? This question is discussed by investigating the technologies used, the digital infrastructures, its robustness to cyber-attack, and their relationship with the claimed benefits on safety, energy and pollution management, traffic optimization, deployment strategy, and a link with the new generation of road infrastructures.

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Potential Impact of Autonomous Vehicles on Movement Behavior: A Scoping Review

Cited by 31 publications

References 51 publications

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

The 2019 Conference on Health and Active Transportation: Research Needs and Opportunities

Are Connected and Automated Vehicles the Silver Bullet for Future Transportation Challenges? Benefits and Weaknesses on Safety, Consumption, and Traffic Congestion

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