An Overview of Drone Energy Consumption Factors and Models

Beigi, Pedram; Rajabi, Mohammad Sadra; Aghakhani, Sina

doi:10.48550/arxiv.2206.10775

Cited by 10 publications

(8 citation statements)

References 64 publications

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“…Technological advances have been addressing engineering and construction challenges in recent years, particularly in construction safety and facility management [5][6][7][8][9][10][11]. Prior studies used different technologies and tools, including social media platforms [12], Immersive Virtual Reality (IVR), and Serious Games (SG) to educate people regarding safety…”

Section: Introductionmentioning

confidence: 99%

Improving Emergency Training for Earthquakes through Immersive Virtual Environments and Anxiety Tests: A Case Study

2022

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Because of the occurrence of severe and large magnitude earthquakes each year, earthquake-prone countries suffer considerable financial damages and loss of life. Teaching essential safety measures will lead to a generation that can perform basic procedures during an earthquake, which is an essential and effective solution in preventing the loss of life in this natural disaster. In recent years, Virtual Reality (VR) technology has been a tool used to educate people on safety matters. This paper evaluates the effect of education and premonition on the incorrect decision-making of residents under the stressful conditions of an earthquake. For this purpose, a virtual model has been designed and modeled based on a proposed classroom in a school in the city of Tehran to simulate a virtual learning experience. In contrast, the classroom represents a realistic method of learning. Accordingly, each educational scenario, presented in reality and the virtual model, respectively, was conducted on a statistical sample of 20 students within the range of 20 to 25 years of age. Among the mentioned sample, the first group of 10 students was taught safety measures in a physical classroom. The second group of 10 students participated in a virtual classroom. Evaluation tests on safety measures against earthquakes were distributed after two weeks. Two self-reporting tests of Depression, Anxiety, Stress Scale (DASS) and Beck Anxiety Inventory (BAI) tests were assigned to the second group to evaluate the effect of foresight under two different scenarios. The results indicate that teaching through VR technology yields a higher performance level than the in-person education approach. Additionally, the ability to detect earthquakes ahead is an influential factor in controlling anxiety and determining the right decisions should the event occur.

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Section: Introductionmentioning

confidence: 99%

Improving Emergency Training for Earthquakes through Immersive Virtual Environments and Anxiety Tests: A Case Study

2022

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“…For light-weighted parcels weighing less than five pounds, the energy required to power drones is 94% less than the energy consumed by delivery trucks [71]. On the other hand, drone activities are more energy-sensitive than conventional vehicle operations [72]. The drone's autonomy can be affected by environmental factors and by the weight of the package that needs to be transported.…”

Section: Security Issues Related To Cargo Bikes and Dronesmentioning

confidence: 99%

Integrating Cargo Bikes and Drones into Last-Mile Deliveries: Insights from Pilot Deliveries in Five Greek Cities

Athanasopoulos,

Chatziioannou,

Boutsi

et al. 2024

Sustainability

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Currently, there is a growing consensus that the use of more sustainable urban freight transportation has the potential to offer major energy and efficiency benefits which can be achieved through the appropriate combination of cargo bikes and the integration of drones into the urban logistics system. This study presents the results of a stepwise regression analysis that examines the role, benefits, and impact of electric vehicles such as e-bikes, cargo bikes, and drones for intermodal freight transportation in five Greek cities to support the green logistics paradigm. The results show that between routes of almost equal length, the most bicycle-friendly routes, such as routes along pedestrian areas or residential streets, should be avoided, as they reduce delivery speed and increase delivery costs in terms of energy and time expenditure. In addition, priority roads, which usually have higher traffic speeds and more trucks, are preferred by cyclists due to the quality of the road surface, even though the feeling of safety is lower on such roads. Finally, regarding drones, energy consumption is relatively low in the 0–5 mph and 6–10 mph wind speed ranges, indicating efficient energy use. In the 11–15 mph and 16–20 mph wind speed ranges, energy efficiency is significantly lower than the battery capacity, indicating that the cargo drone exhibits excessive energy consumption under these conditions.

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“…Recently, engineering and transportation challenges are being addressed with technological advances, especially in system management, scheduling, and safety concerns Omer et al, 2022;. Particularly, in recent years transportation researchers tried to utilize emerging Artificial Intelligence (AI) and Machine Learning (ML) tools to predict and simulate traffic behavior, especially in megacities Beigi et al, 2022;Lotfi et al, 2022;moeinifard et al, 2021;Mudiyanselage et al, 2021;Rajabi, Beigi, et al, 2022;Rajabi, Taghaddos, et al, 2022;Shakerian et al, 2022). In terms of the scope of this paper, when traffic conditions are relatively stable, a reasonably simple estimation can be used, but a prediction model is required when conditions are rapidly changing.…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

The development of BPR models in smart cities using loop detectors and license plate recognition technologies: A case study

Rajabi¹,

Habibpour²,

Bakhtiari³

et al. 2023

10.5267/j.jfs

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The trend toward sustainable city development is associated with intelligent transportation systems (ITS). Automation, efficiency, safety, security, and cost-effectiveness are critical factors in establishing each aspect of a smart city. Real-time data obtained from ITS play an essential role in improving the level of service of road segments, enhancing road safety, and supporting road users with road circumstances information. Travel time information is applicable in travel time maps, decision makings for traffic congestion, dynamic pricing of the network, emergency relief services, traffic flow monitoring, traffic jams management, and air quality analysis. Travel time on a road segment highly depends on geometrical specifications, environmental and weather conditions, traffic flow, and driving behavior. Due to specific driving behavior and road conditions, the above parameters are not essentially applicable in another region. The present research uses the data collected from loop detectors and License Plate Recognition (LPR) systems to develop a Bureau of Public Roads (BPR) model for Iran’s freeway network (Tehran-Qom Freeway). Because of the large amount of data, the SQL server program was used for creating and organizing the database and the BPR model was calibrated using SPSS statistical software. The results of the BPR model were evaluated with an ANOVA test, indicating that the derived model can estimate the travel time at freeway sections with a %5.2 error for the volume-to-capacity ratio (V/C) of less than 0.8.

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An Overview of Drone Energy Consumption Factors and Models

Cited by 10 publications

References 64 publications

Improving Emergency Training for Earthquakes through Immersive Virtual Environments and Anxiety Tests: A Case Study

Improving Emergency Training for Earthquakes through Immersive Virtual Environments and Anxiety Tests: A Case Study

Integrating Cargo Bikes and Drones into Last-Mile Deliveries: Insights from Pilot Deliveries in Five Greek Cities

The development of BPR models in smart cities using loop detectors and license plate recognition technologies: A case study

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