| 1. |
- Cui, Shaohua, 1995, et al.
(författare)
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Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications
- 2024
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Ingår i: Transportation Research Part A: General. - 0965-8564. ; 183
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Tidskriftsartikel (refereegranskat)abstract
- The maturation and scalability of unmanned aerial vehicle (UAV) technology offer transformative opportunities to revolutionize prompt delivery. This study explores integrating UAVs with public transportation vehicles (PTVs) to establish a novel delivery paradigm that enhances revenue for public transit operators and improves transport system efficiency without compromising passenger convenience or operational efficiency. Employing hexagonal planning technology, this study identifies and quantifies the available spatio-temporal resources of PTVs for UAV integration. This involves aligning the spatio-temporal dynamics of prompt delivery orders with PTV ridership, based on field data from Beijing's Haidian District. Utilizing these outputs, we quantitatively analyze the benefits of integrating UAVs with PTVs on increasing public transit revenue, and potentials of reducing carbon emissions and mitigating congestion. Furthermore, we quantify the long-term benefits of UAV-PTV integration by predicting future increases in delivery demand. Based on obtained quantitative results, this study discusses practical and policy implications to support the sustainable integration of UAVs with PTVs.
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| 2. |
- Yao, Baozhen, et al.
(författare)
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Dynamic Pricing for Mobile Charging Service Considering Electric Vehicles Spatiotemporal Distribution
- 2023
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Ingår i: Smart Innovation, Systems and Technologies. - 2190-3026 .- 2190-3018. ; 356, s. 23-33
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Konferensbidrag (refereegranskat)abstract
- As mobile charging service has the advantages of flexible charging and simple operation, it is selected by more and more users of electric vehicles. However, due to the differences in road network density and traffic flow distribution, the uneven distribution of charging demand occurs in different regions. It reduces the service efficiency of mobile charging vehicles during the peak charging demand period, thus affecting the final revenue of operators. In order to solve this problem, this paper proposes a dynamic pricing strategy considering the spatiotemporal distribution of charging demand to induce users to transfer between different regions, which can alleviate the phenomenon that users wait too long during peak demand. In order to realize the city-level operation of mobile charging service, we divide the region into hexagons and make statistics on the charging demand in each region. The established demand updating model can reflect the impact of charging price on users’ charging behavior. Finally, we simulate the generation of charging demand in Haidian District, Beijing. According to the demand of each area, a thermodynamic diagram of charging demand is generated.
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| 3. |
- Yao, Baozhen, et al.
(författare)
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LSTM-Based Vehicle Trajectory Prediction Using UAV Aerial Data
- 2023
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Ingår i: Smart Innovation, Systems and Technologies. - 2190-3026 .- 2190-3018. ; 356, s. 13-21
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Konferensbidrag (refereegranskat)abstract
- Accurately predicting the trajectory of a vehicle is a critical capability for autonomous vehicles (AVs). While human drivers can infer the future trajectory of other vehicles in the next few seconds based on information such as experience and traffic rules, most of the widely used Advance Driving Assistance Systems (ADAS) need to provide better trajectory prediction. They are usually only of limited use in emergencies such as sudden braking. In this paper, we propose a trajectory prediction network structure based on LSTM neural networks, which can accurately predict the future trajectory of a vehicle based on its historical trajectory. Unlike previous studies focusing only on trajectory prediction for highways without intersections, our network uses vehicle trajectory data from aerial photographs of intersections taken by Unmanned Aerial Vehicle (UAV). The speed of vehicles at this location fluctuates more frequently, so predicting the trajectory of vehicles at intersections is of great importance for autonomous driving.
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