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arXiv 2023年

作者： Yang, Hongtai Lei, Boyi Han, Ke Liu, Luna School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Institute of System Science and Engineering Southwest Jiaotong University Chengdu611756 China

Construction waste hauling trucks (CWHTs), as one of the most commonly seen heavy-duty vehicles in major cities around the globe, are usually subject to a series of regulations and spatial-temporal access restrictions because they not only produce significant NOx and PM emissions but also causes on-road fugitive dust. The timely and accurate prediction of CWHTs’ destinations and dwell times play a key role in effective environmental management. To address this challenge, we propose a prediction method based on an interpretable activity-based model, input-output hidden Markov model (IOHMM), and validate it on 300 CWHTs in Chengdu, China. Contextual factors are considered in the model to improve its prediction power. Results show that the IOHMM outperforms several baseline models, including Markov chains, linear regression, and long short-term memory. Factors influencing the predictability of CWHTs’ transportation activities are also explored using linear regression models. Results suggest the proposed model holds promise in assisting authorities by predicting the upcoming transportation activities of CWHTs and administering intervention in a timely and effective manner. Copyright © 2023, The Authors. All rights reserved.

关键词： Trucks

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A Cooperative Truck Platooning Approach on Ramp Merging Area of Highway in Mixed Traffic

A Cooperative Truck Platooning Approach on Ramp Merging Area...

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International Conference on transportation Information and Safety (ICTIS)

作者： Xiao Sang Linhan Bai Tao Zhou Fangfang Zheng School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu China Jiangsu Railway Group Nanjing China

Truck platooning has emerged as a promising solution for improving traffic efficiency on highways. This paper proposes an optimization model for connected automated trucks (CATs) that addresses the trajectory optimization problem for CATs as human-driven trucks (HDTs) merge onto highways from on-ramps in a mixed traffic scenario. To maximize the speed of the mixed traffic system, we propose a mixed integer programming (MIP) model based on a rolling horizon algorithm. The algorithm optimizes CATs’ trajectories iteratively, taking into account real-time updates on their driving state, and guides HDTs to follow the trajectories of CATs. The numerical experiments demonstrate that the increasing number of HDTs on the on-ramp negatively impacts the overall system performance, leading to higher traffic delay and reduced system stability. Furthermore, accounting for the stochastic behavior of HDTs can lead to increased instability in the system. Finally, we show that the objective function that comprehensively considers the system speed and platooning time effectively minimizes the delay of all vehicles.

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Urban Water Sprinkler Routing: A Multi-Depot Mixed Capacitated Arc Routing Problem Incorporating Real-Time Demands

arXiv

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arXiv 2024年

作者： Yang, Hongtai Liu, Luna Han, Ke Lei, Boyi School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Institute of System Science and Engineering Southwest Jiaotong University Chengdu611756 China School of Economics and Management Southwest Jiaotong University Chengdu611756 China

Fugitive road dust (FRD), as one of the major pollutants in the city, poses great harm to the environment and the physical health of citizens. A common countermeasure adopted by government agencies is employing on-road water trucks (sprinklers) to spray water (sprinkle) on urban streets to reduce the FRD. Currently, the traveling routes of sprinklers are usually planned based on drivers’ experience, which may lead low operation efficiency and could not respond to the real-time sprinkling demands. To address these issues, this study formulates the routes planning of sprinklers as a multi-depot mixed capacitated arc routing problem with real-time demands with the aim of minimizing the sprinklers’ travel distance. We develop an improved adaptive large neighborhood search (ALNS) algorithm that incorporates a tabu-list and a perturbation mechanism to solve this problem. Furthermore, a problem-specific acceleration mechanism is designed to reduce unnecessary search domains to improve the efficiency of the algorithm. Empirical experiments are conducted based on various scenarios and the results demonstrate that the proposed algorithm generates solutions that are superior or at least comparable to the solutions generated by the traditional ALNS algorithm but with significantly lower computation time. Sensitivity analysis is conducted to explore the effects of relevant parameters on the results. This study is the first to incorporate real-time FRD pollution information, gathered through multiple data sources via IoT technology, into urban sprinkling operations, extending the traditional CARP from a tactical planning to a real-time operational environment. A real-world implementation case is also presented. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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The Vcg Pricing Policy with Unit Reserve Prices for Ride-Sourcing is 3/4-Individual Compatibility

SSRN

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SSRN 2024年

作者： Li, Ruijie Chen, Haiyuan Liu, Xiaobo Zhang, Kenan School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Chengdu611756 China Switzerland

We consider a customized pricing and matching strategy for ride-sourcing services in which drivers and riders disclose their willingness to pay (WTP) per unit distance and willingness to accept (WTA) per unit distance, respectively. The platform then matches drivers and riders based on their reports to maximize the total social welfare. While this strategy is motivated to promote transparency, fair pricing, and participant satisfaction, it faces a particular challenge to ensure riders and drivers truthfully report their valuations, which are considered private information. To encourage truthful reports, we implement the Vickrey-Clarke-Groves pricing policy augmented with unit reserve prices, referred to as VCG-URP. For the riders, the reserve price is set as the minimum WTP per unit distance, while for the drivers, it is the maximum WTA per unit distance. We demonstrate that VCG-URP ensures that riders provide truthful valuations. However, it does not guarantee the same level of honesty from drivers in reporting their valuations. While drivers cannot enhance their utility by overstating their unit WTA, they may benefit from understating it. These theoretical results are confirmed by numerical experiments with several additional findings: i) although the underreporting strategy of the drivers theoretically has an opportunity to achieve a higher utility, it always leads to a decrease in expected utility over randomly generated matching scenarios;ii) the introduction of reserve prices has larger impacts on riders than drivers and benefits the platform in excluding less profitable trips;and iii) the performance of VCG-URP is reasonably robust towards the market density, while the VCG price is less applied when the market becomes sparse, mainly due to the loss of substitutability among participants. All these findings bring valuable insights into ride-sourcing services in practice. © 2024, The Authors. All rights reserved.

关键词： Costs

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Integrated Optimization of Autonomous Intersection Management for Connected and Automated Vehicle

Integrated Optimization of Autonomous Intersection Managemen...

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2022世界交通运输大会(WTC2022)

作者： Hui Hua Zhanbo Sun School of Transportation and Logistics Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology

With the development of Connected and Automated Vehicles(CAVs),wireless communication,smart sensor and other technologies,Autonomous Intersection Management(AIM) is more appropriate than the conventional signal intersection *** this work,a bi-level dynamic programming is proposed into signal-free intersection management,where the optimal control problem for controlling vehicle acceleration and deceleration is embedded in the scheduling problem as a *** a centralized control framework,information of CAVs can be *** control center predicts status and potential conflicts of all vehicles,finely analyzes and puts forward acceleration/deceleration strategies to vehicles,to avoid the potential collision and guide the trajectories of subsequent *** proposed method is tested by simulation and numerical experiment,compared with First-come-first-service(FCFS) *** show that the proposed optimization framework perform is better than FCFS in different measurements,and the average travel time can be reduced by up to 35 %.

关键词： CAV and road transport Bi-level programming Dynamic programming(DP) Signal-free intersection management

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Stealthy False data Injection Attacks in Multi-Channel Vehicular Communication: White-Box and Gray-Box Strategies

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IEEE Transactions on Vehicular technology 2025年

作者： Xu, Chongcong Zheng, Fangfang Guo, Ge Southwest Jiaotong University School of Transportation and Logistics Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Chengdu611756 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

Understanding the vulnerabilities of vehicular networks from an attacker's perspective is crucial for identifying stealthy false data injection (FDI) attacks and developing robust defense methods. This paper investigates stealthy FDI attacks in multi-channel vehicular communication systems. We construct a multi-source state estimation system using a sequential Kalman filter to integrate and estimate redundant state information transmitted by preceding vehicles across multiple channels. To enhance the system's resilience against FDI attacks, we incorporate a Sage-Husa adaptive filter and a chi-squared detector, which dynamically estimate and detect channel noise based on estimation errors. Within this framework, we utilize real-time state information and channel transmission residual to determine the probability of an attack window occurring. The attack process is divided into three phases, during which critical noise disturbances are introduced to create attack windows, allowing the attack to progressively align with the target state. Based on this analysis, we develop an optimization model for attack generation with complete channel information (AG-with-CCI) to facilitate a white-box attack. Additionally, acknowledging that attackers may not have access to real-time information from all channels, we analyze the iterative equations of state estimation errors and residuals to establish the conditions under which stealthy FDI attacks can be successfully executed, enabling the attack generation with attacked channel information (AG-with-ACI) to achieve a gray-box attack. Numerical simulations demonstrate that both attack methods can evade current detection mechanisms and achieve their intended objectives. © 2025 IEEE.

关键词： Adaptive filtering

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Research on optimization of emergency rescue plan based on risk measurement 23

Research on optimization of emergency rescue plan based on r...

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Proceedings of the 2023 3rd International Conference on big data, Artificial Intelligence and Risk Management

作者： Fanbiao Bu Xuhao Chen Ying Lv Hengrui Xu Overall Demonstration Center Qian Xuesen Institute of Military Systems Engineering China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Institute of Transportation System Science and Engineering School of Traffic and Transportation China and Shijiazhuang Land and Space Planning and Design Institute Co. Ltd. China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Institute of Transportation System Science and Engineering School of Traffic and Transportation China Data Center Agricultural Bank of China Limited China

ISBN: (纸本)9798400716478

In order to reduce the impact of emergencies, this paper takes emergencies in complex geological environment as the background to study the optimization problem of emergency rescue routes. We measure the casualty consequences caused by the delay of rescue time based on conditional value at risk, and adopt multiple modes of transportation to coordinate rescue. A bi-objective optimization model is established with the total risk and total cost as the objectives, then the constraint method is applied to transform the model into a single objective optimization model for solution. Finally, an example is designed to analyze the relationship between the total risk and the total cost, and the rescue path scheme under the condition that the demand information is determined. The results indicate that the established model can provide a reference basis for emergency rescue of emergencies in the process of major transportation engineering construction.

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Mitigating Traffic Oscillations in Mixed Traffic Environments Using an Asynchronous Control Framework with Bayesian Optimization

SSRN

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SSRN 2024年

作者： Liu, Can Zheng, Fangfang Liu, Henry X. Liu, Xiaobo School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China University of Michigan Transportation Research Institute University of Michigan Ann ArborMI48109 United States

This study presents a comprehensive longitudinal control framework for Connected and Automated Vehicles (CAVs) aimed at smoothing mixed traffic flow. The framework features a hierarchical and asynchronous control architecture that seamlessly integrates Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) methods, enabling real-time motion control for CAVs. In the MPC model, the desired velocity of downstream traffic is explicitly considered to promote smoother traffic flow. Additionally, a Bayesian optimization-based parameter tuning algorithm is developed for the proposed CAV control architecture, allowing it to dynamically adapt to changing oscillations and improve control performance. To validate the effectiveness of the proposed framework, two simulation experiments are conducted: one for mixed platoons and another for large-scale mixed traffic scenarios. The results demonstrate that the proposed control framework effectively mitigates oscillations and improves traffic flow smoothness while ensuring real-time applicability. Further investigation into large-scale mixed traffic reveals that even at lower CAV penetration rates (e.g., 10%, 20%), the proposed method effectively mitigates oscillations and enhances traffic smoothness. Notably, the performance in scenarios with front and random positioning (FP and EP) significantly outperforms middle and trail positioning (MP and TP), highlighting the influence of CAV distribution on traffic stability and oscillation mitigation. © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

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Fundamental diagram and stability analysis of mixed traffic considering heterogeneous car-following behaviors and platoon factors

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Intelligent transportation Infrastructure 2024年第3期3卷

作者： Sun, Zhanbo Yan, Qiruo Liu, Yafei Fu, Zhijian Yang, Lei School of Transportation and Logistics Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory for Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China

With the advancement of connected automated vehicles (CAVs), it is anticipated that mixed traffic environments, where human-driven vehicles (HVs) coexist with CAVs, will become prevalent in the future. The study aims to investigate the impact of heterogeneous car-following behaviors of HVs (e.g. aggressive, normal, and mild driving styles) and platoon factors of CAVs (i.e. platoon intensity and maximum platoon size) on the fundamental diagram and stability of mixed traffic. Firstly, a Markov chain approach is employed to describe the probability distributions of different leader-follower combinations, enabling us to construct a comprehensive mixed traffic model. Subsequently, a general modeling framework based on the mixed traffic model is established to examine the effects of heterogeneous car-following behaviors and platoon factors on the fundamental diagram and stability of mixed traffic. The results from numerical experiments reveal several findings: 1. an increase in the proportion of aggressive driving style enhances both the capacity and stability of mixed traffic;2. larger platoon intensity and maximum platoon size contribute to improved capacity, particularly in scenarios where a large fraction of HVs exhibit aggressive driving behavior;3. platoon intensity has a positive impact on traffic flow stability, while larger maximum platoon size leads to reduced stability;and 4. increasing CAV penetration without considering platoon intensity may lead to reduced stability compared to scenarios with a substantial proportion of aggressive drivers. © 2024 The Author(s).

关键词： connected automated vehicles fundamental diagram heterogeneous car-following behavior Markov chain mixed traffic traffic stability

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Flood Resilience Assessment of Riverside Urban Communities - Taking Ciqikou Street as an example 9

Flood Resilience Assessment of Riverside Urban Communities -...

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9th Academic Conference of Geology Resource Management and Sustainable Development

作者： Xu, Hui Zhang, Shuang School of Economics and Management Chongqing University of Posts and Telecommunications Chongqing400707 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. National Engineering Laboratory for Industrial Big-data Application Technology Chongqing400707 China

ISBN: (纸本)9781921712784

Riverside urban communities are the most vulnerable urban unit to flood disasters due to their characteristics of severe disaster exposure, weak resistance to risks and high requirements for emergency management. This article uses "community flood resilience" to describe its stability, recovery and adaptability in the face of flood disasters. A flood resilience indicator system of riverside communities using 18 indicators separated into 5 domains is constructed through field research and literature review. Combining expert opinions and using network analysis, the weight of each indicator is determined. Indicator data is obtained by processing remote sensing image, geographic information data and other means. The results show that disaster relief, public health management, medical facilities, etc. contribute more to flood resilience in study area;ability to reducing runoff velocity and storing water, road profiles and transportation methods are the key points to improve the resilience of the study area. The article recommends measures such as strengthening technological early warning and command, building rainwater mitigation facilities, and enhancing residents' ability and awareness of flood avoidance to minimize the loss of future flood disasters in this street. © 2022 by Aussino Academic Publishing House.

关键词： Floods

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