| 1. |
- Cui, Shaohua, 1995, et al.
(author)
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Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications
- 2024
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In: Transportation Research Part A: General. - 0965-8564. ; 183
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Journal article (peer-reviewed)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.
(author)
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Dynamic Pricing for Mobile Charging Service Considering Electric Vehicles Spatiotemporal Distribution
- 2023
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In: Smart Innovation, Systems and Technologies. - 2190-3026 .- 2190-3018. ; 356, s. 23-33
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Conference paper (peer-reviewed)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.
(author)
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LSTM-Based Vehicle Trajectory Prediction Using UAV Aerial Data
- 2023
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In: Smart Innovation, Systems and Technologies. - 2190-3026 .- 2190-3018. ; 356, s. 13-21
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Conference paper (peer-reviewed)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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| 4. |
- Cui, Shaohua, et al.
(author)
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Adaptive Collision-Free Trajectory Tracking Control for String Stable Bidirectional Platoons
- 2023
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In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 24:11, s. 12141-12153
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Journal article (peer-reviewed)abstract
- Autonomous vehicle (AV) platoons, especially those with the bidirectional communication topology, have significant practical value, as they not only increase link capacity and reduce vehicle energy consumption, but also reduce the consumption of communication resources. Small gaps between AVs in a platoon easily lead to emergency braking or even collisions between consecutive AVs. This paper applies barrier Lyapunov functions to collision avoidance between AVs in a bidirectional platoon during trajectory tracking. Based on backstepping technique, an adaptive collision-free platoon trajectory tracking control algorithm is developed to distributedly design control laws for each AV in the platoon. The control algorithm does not need to introduce additional car-following models to simulate AV driving, and only needs to integrate the position trajectories of consecutive AVs to avoid inter-vehicle collisions. Two sign functions are introduced into the control laws of each AV to ensure strong string stability for bidirectional AV platoons. Moreover, uncertainties and external disturbances in vehicle motion are effectively compensated by introducing adaptation laws. Strong string stability is rigorously proved. CarSIM-based comparison simulations verify the effectiveness of the proposed control algorithm in avoiding inter-vehicle collisions, compensating for uncertainties in vehicle motion, and suppressing the amplification of spacing errors along the platoon.
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| 5. |
- Cui, Shaohua, 1995, et al.
(author)
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Delay-throughput tradeoffs for signalized networks with finite queue capacity
- 2024
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In: Transportation Research Part B: Methodological. - 0191-2615. ; 180
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Journal article (peer-reviewed)abstract
- Network-level adaptive signal control is an effective way to reduce delay and increase network throughput. However, in the face of asymmetric exogenous demand, the increase of network performance via adaptive signal control alone is at the expense of service fairness (i.e., phase actuation fairness and network resource utilization fairness). In addition, for oversaturated networks, arbitrary adaptive signal control seems to have little effect on improving network performance. Therefore, under the assumption that the mean routing proportions/turn ratios of vehicles at intersections are fixed, this study investigates the problem of optimally allocating input rates to entry links and simultaneously finding a stabilizing signal control policy with phase fairness. We model the stochastic optimization problem of maximizing network throughput subject to network stability (i.e., all queue lengths have finite means) and average phase actuation constraints to bridge the gap between stochastic network stability control and convex optimization. Moreover, we further propose a micro-level joint admission and bounded signal control algorithm to achieve network stability and throughput optimization simultaneously. Joint control is implemented in a fully decomposed and distributed manner. For any arrival rate, joint control provably achieves network throughput within O(1/V) of optimality while trading off average delay with O(V), where V is an adjusted control parameter. Through a comparative simulation of a real network with 256 O-D pairs, the proposed joint control keeps network throughput at maximum, guarantees service fairness, and fully utilizes network capacity (i.e., increases network throughput by 17.54%).
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| 6. |
- Cui, Shaohua, 1995, et al.
(author)
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Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers
- 2023
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In: Transportation Research Part E. - : Elsevier BV. - 1366-5545 .- 1878-5794. ; 180
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Journal article (peer-reviewed)abstract
- Owing to the high acquisition costs, maintenance expenses, and inadequate charging infrastructure associated with electric buses, achieving a complete replacement of diesel buses with electric counterparts in the short term proves challenging. A substantial number of bus operators currently find themselves in a situation where they must integrate electric buses with their existing diesel fleets. Confronted with the constraints of limited electric bus range and charging infrastructure, the primary concern for bus operators is how to effectively utilize their mixed bus fleets to adhere to pre-established bus timetables while maximizing the deployment of electric buses, known for their zero pollution and cost-effective travel. Consequently, this paper introduces the concept of the joint optimization problem for vehicle and recharging scheduling within mixed bus fleets operating under constrained charging conditions. To tackle this issue, a mixed integer linear model is formulated to optimize the coordination of bus schedules and recharging activities within the context of limited charging infrastructure. By establishing a set of feasible charging activities, the problem of electric buses queuing for charging at constrained charging stations is transformed into a linear optimization model constraint. Numerical simulations are conducted within the real transit network of the Dalian Economic Development Zone in China. The results indicate that the judicious joint optimization of vehicle and charging scheduling significantly enhances the service frequency of electric buses while reducing operational costs for bus lines. Notably, the proportion of total trips performed by electric buses rises to 80.4%.
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| 7. |
- Cui, Shaohua, et al.
(author)
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Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers
- 2024
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In: Sammanställning av referat från Transportforum 2024. - Linköping : Statens väg- och transportforskningsinstitut. ; , s. 169-170
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Conference paper (other academic/artistic)abstract
- The transportation industry is a major energy consumer and carbon emitter. It is reported that the carbon emissions of transportation industry in developed countries in Europe and the United States account for about 1/3 of total carbon emissions. Diesel buses make up only a small fraction of urban road traffic, but have a huge environmental impact. The main reason is that compared with private cars, buses have long operating hours, slow running speed, frequent acceleration and deceleration, and long mileage. Various countries are promoting the replacement of diesel buses with electric buses. Due to the high purchase cost, maintenance costs and insufficient charging infrastructure of electric buses, it is difficult to realize the full replacement of diesel buses by electric buses in the short term. A large number of bus operators are currently in a state of mixing diesel buses with electric buses. Typically, an electric bus will have a third of the range of a diesel bus, and in cold months the ratio may be lower. Even with some super-fast chargers, it takes 30 to 50 minutes for an electric bus to be fully charged, while a diesel bus takes just 3 to 5 minutes to replenish its diesel.Faced with the limited range of electric buses, low charging rates, and limited chargers, how bus operators use the existing mixed bus fleets to implement the pre-designed bus timetables while making full use of electric buses with zero pollution and low travel costs becomes the key. How to determine the chargers being used, and the waiting time for using each charger urgently need to be solved in an urban-scale public transit network under limited chargers. Therefore, this paper proposes the joint optimization problem of vehicle and recharging scheduling for mixed bus fleets under limited chargers to promote the trip execution frequency of electric buses in the case of insufficient electric buses and chargers.
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| 8. |
- Cui, Shaohua, et al.
(author)
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Temporal Finite-Time Adaptation in Controlling Quantized Nonlinear Systems Amidst Time-Varying Output Constraints
- 2024
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In: IEEE Transactions on Automation Science and Engineering. - 1558-3783 .- 1545-5955. ; In Press
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Journal article (peer-reviewed)abstract
- Using the backstepping technique, this paper formulates innovative adaptive finite-time stabilizing controllers for uncertain nonlinear systems featuring nonuniform input quantization and asymmetric, time-varying output constraints. These novel controllers leverage the consistent characteristics of both hysteresis quantizers and logarithmic quantizers. Quantization errors, when consistent, become unbounded and contingent on control input, rendering them incompatible with the growth conditions of nonlinear systems. Consequently, the developed adaptive controllers eliminate the reliance on growth conditions, effectively addressing the impact of unbounded quantization errors on finite-time stability. This adaptability allows the controllers to function effectively with systems employing either hysteresis quantizers or logarithmic quantizers. The paper establishes the convergence of these controllers through the finite-time Lyapunov stability theorem. It also provides a comprehensive guideline for tuning settling time, enabling fine-grained control over finite-time convergence and adjustable tracking error performance. Additionally, the controllers rigorously maintain system output within predefined limits. Their effectiveness and low computational burden are demonstrated through three comparative numerical simulations and a practical simulation in collision-free trajectory tracking control of an autonomous vehicle platoon using the vehicle motion software CarSim. These simulations confirm the advanced performance of the adaptive controllers. Note to Practitioners—This paper introduces an innovative approach to control uncertain nonlinear systems encountering intricate input quantization and output constraints. Employing the sophisticated backstepping technique, the authors present adaptive finite-time-stabilizing controllers engineered to address nonuniform input quantization and asymmetric, time-varying output restrictions. What distinguishes these controllers is their reliance on the consistent behavior exhibited by hysteresis and logarithmic quantizers. This unique feature equips them to effectively counteract unbounded quantization errors influenced by control input. Most notably, these controllers eliminate the conventional growth conditions typically demanded by nonlinear systems. As a result, they extend their applicability to a broad spectrum of systems employing either hysteresis or logarithmic quantizers. The research also provides practitioners with a valuable guideline for precisely adjusting settling time. This enables the attainment of desired convergence rates while permitting adaptable tracking error performance. Additionally, these controllers guarantee that the system’s output adheres to predefined limits. The practical significance of this study is highlighted through three comparative numerical simulations and a real-world application simulation. This real-world simulation involves collision-free trajectory tracking control of an autonomous vehicle platoon, executed using the vehicle motion software CarSim. These simulations unequivocally demonstrate the effectiveness and low computational burden of the developed controllers, thereby establishing them as a valuable resource for practitioners facing complex control challenges in various domains.
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| 9. |
- Yang, Ying, et al.
(author)
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Data-driven rolling eco-speed optimization for autonomous vehicles
- 2024
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In: Frontiers of Engineering Management. - 2096-0255 .- 2095-7513. ; In Press
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Journal article (peer-reviewed)abstract
- In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.
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