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

The relationship between the environment and physical activity-related motivational trajectories

The study explores motivational profiles for physical activity, using self-determination theory's full continuum of motivational regulations, and examines their stability over three months. Furthermore, it investigates whether physical environment and community characteristics are associated with transitioning between profiles, as well as the sociodemographic differences in these motivational transition pathways. Data were collected from 305 U.S. residents at three time points. The three profiles-'low in motivation' (23.5 % of the sample in wave 1), 'self-determined motivation' (41.4 %), and 'ambivalent motivation' (35.0 %)-were relatively stable. Staying in the low-in-motivation profile was negatively associated with being active in social settings, community support, perceived environmental restorativeness, and availability of physical activity opportunities. Having a higher education and income, being male, employed, married or in a partnership, and identifying as White were associated with being in a motivationally positive profile in the last wave of the study. These profiles reported higher activity and life satisfaction.

Mobility Infrastructures and Health: Scoping Review of studies in Europe

Objectives

Movement-friendly environments with infrastructure favouring active mobility are important for promoting physical activity. This scoping literature review aims at identifying the current evidence for links between mobility infrastructures and (a) behaviour regarding active mobility, (b) health outcomes and (c) co-benefits.

Method

This review was conducted in accordance with the PRISMA scoping review guidelines using PubMed and EMBASE databases. Studies included in this review were conducted in Europe, and published between 2000 and March 2023.

Results

146 scientific articles and grey literature reports were identified. Connectivity of sidewalks, walkability, and accessibility of shops, services and work are associated with walking. Cycling is positively associated with cycle-paths, separation of cycling from traffic and proximity to greenspaces, and negatively associated with traffic danger. Increased active transportation has a protective effect on cardiovascular and respiratory health, obesity, fitness, and quality of life. Co-benefits result from the reduction of individual motorized transportation including reduced environmental pollution and projected healthcare expenditure.

Conclusion

Mobility infrastructure combined with social and educational incentives are effective in promoting active travel and reducing future healthcare expenses. A shift to active transportation would increase both individual and community health and decrease greenhouse gas emissions.

Study on the Extraction Method of Sub-Network for Optimal Operation of Connected and Automated Vehicle-Based Mobility Service and Its Implication

There have been enormous efforts to implement automated vehicle-based mobility (AVM) by considering smart infrastructure such as cooperative intelligent transportation system. However, there is lack of consideration on economical approach for an optimal deployment strategy of the AVM service and smart infrastructure. Furthermore, the influence of travel demand in service area has been ignored. We develop a new framework for maximizing the profit of connected and automated vehicle-based mobility (CAV-M) service using cost modeling and metaheuristic optimization algorithm. The proposed framework extracts an optimal sub-network, which is selected by a set of optimal links in the service area, and identifies an optimal construction strategy for the smart infrastructure depending on given operational design domain and travel demand. Based on service network analyses with varying demand patterns and volumes, we observe that the optimal sub-network varies with the combination of trip demand patterns and volumes. It is also found that the benefit of deploying the smart infrastructure is obtainable only when there are sufficient travel demands. Furthermore, the optimal sub-network is always superior to raw network in terms of economical profit, which suggests the proposed framework has great potential to prioritize road links in the target area for the CAV-M service.

Exploring the implications of autonomous vehicles: a comprehensive review

Over the last few years, a large emphasis has been devoted to autonomous vehicles (AVs), as vehicle automation promises a large number of benefits such as: improving mobility and minimization of energy and emissions. Additionally, AVs represent a major tool in the fight against pandemics as autonomous vehicles can be used to transport people while maintaining isolation and sterilization. Thus, manufacturers are racing to introduce AVs as fast as possible. However, laws and regulations are not yet ready for this change and the legal sector is following the development of autonomous vehicles instead of taking the lead. This paper provides a comprehensive review of the previous studies in the transportation field that involve AVs with the aim of exploring the implications of AVs on the safety, public behaviour, land use, economy, society and environment, public health, and benefits of autonomous vehicles in fighting pandemics.

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

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.

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

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.

The potential effects of autonomous vehicles on walking

Vehicle automation is progressing rapidly and autonomous vehicles (AVs) are forecast to become a central feature of transportation systems globally. This development has the potential to result in profound changes in walking behaviors. The present study examined this issue from the perspective of relevant experts for the purpose of informing health policy. Interviews were conducted with 44 key stakeholders in Australia (n = 34), the European Union (n = 5), the UK (n = 4), and the US (n = 1). The stakeholders represented a wide range of sectors including government, AV manufacturing/servicing companies, transport policy consortiums, technology firms, insurers (public and private), trade unions, consumer representation organizations, and academia. Two potential scenarios were evident in interviewees' discussions of the ways AVs are likely to be introduced and the implications for walking behaviors. The most beneficial scenario, but the least likely to eventuate, was considered to be the situation where people relinquish private vehicle ownership and rely on a combination of walking, public transport, and on-demand transport. The alternative scenario involved greater private AV ownership, traffic congestion, and urban sprawl, resulting in less walking activity. The convergence of the stakeholders' views around the opposing identified scenarios highlights the need for proactive policy development to ensure the emerging transport transformation does not result in substantial increases in sedentarism.