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Journal of Infrastructure Intelligence and Resilience 2023年第4期2卷

作者： Momtaz, Mozhgan Li, Tianshu Harris, Devin K. Lattanzi, David Sid and Reva Dewberry Department of Civil Environmental and Infrastructure Engineering George Mason University FairfaxVA United States Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA United States

Bridge condition rating is a challenging task as it largely depends on the experience-level of the manual inspection and therefore is prone to human errors. The inspection report often consists of a collection of images and sequences of sentences (text) explaining the condition of the considered bridge. In a routine manual bridge inspection, an inspector collects a set of images and textual descriptions of bridge components and assigns an overall condition rating (ranging between 0 and 9) based on the collected information. Unfortunately, this method of bridge inspection has been shown to yield inconsistent condition ratings that correlate with inspector experience. To improve the consistency among image-text inspection data and further predict the accordant condition ratings, this study first provides a collective image-text dataset, extracted from the collection of bridge inspection reports from the Virginia Department of Transportation. Using this dataset, we have developed novel deep learning-base methods for an automatic bridge condition rating prediction based on data fusion between the textual and visual data from the collected report sets. Our proposed multi modal deep fusion approach constructs visual and textual representations for images and sentences separately using appropriate encoding functions, and then fuses representations of images and text to enhance the multi-modal prediction performance of the assigned condition ratings. Moreover, we study interpretations of the deployed deep models using saliency maps to identify parts of the image-text inputs that are essential in condition rating predictions. The findings of this study point to potential improvements by leveraging consistent image-text inspection data collection as well as leveraging the proposed deep fusion model to improve the bridge condition prediction rating from both visual and textual reports. © 2023 The Authors

关键词： Inspection

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Incentive Design for Eco-driving in Urban Transportation Networks

Incentive Design for Eco-driving in Urban Transportation Net...

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European Control Conference (ECC)

作者： M. Umar B. Niazi Jung-Hoon Cho Munther A. Dahleh Roy Dong Cathy Wu Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks. Acknowledging the persuasive influence of incentives in shaping driver behavior, this paper presents the ‘eco-planner,’ a digital platform devised to promote eco-driving practices in urban transportation. At the outset of their trips, users provide the platform with their trip details and travel time preferences, enabling the eco-planner to formulate personalized eco-driving recommendations and corresponding incentives, while adhering to its budgetary constraints. Upon trip completion, incentives are transferred to users who comply with the recommendations and effectively reduce their emissions. By comparing our proposed incentive mechanism with a baseline scheme that offers uniform incentives to all users, we demonstrate that our approach achieves superior emission reductions and increased user compliance with a smaller budget.

关键词： Transportation Europe Greenhouse gases

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Analysis of Drinking Water Distribution Networks Based on Changes in Population Density and Topological Characteristics

Analysis of Drinking Water Distribution Networks Based on Ch...

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World environmental and Water Resources Congress 2022: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： González, Laura Saldarriaga, Juan G. Water Distribution and Sewerage Systems Research Center Universidad De Los Andes Bogotá Colombia Dept. of Civil and Environmental Engineering Water Distribution and Sewerage Systems Research Center Universidad De Los Andes Bogotá Colombia

ISBN: (数字)9780784484258

ISBN: (纸本)9780784484258

The water distribution systems (WDS) analysis focuses to develop optimal designs that allow implementation of water supply systems with quantity and quality needed for the human community consumption;therefore, it is necessary to characterize system topology and hydraulic variables. For identification of the most sensitive parameters involved in population density changes, three comparisons are presented related to (1) hydraulic gradient surfaces, (2) pressure surfaces, and (3) specific power maps. According with this, behavior patterns can be defined based on the networks' characterization and alterations that may occur verifying minimum design conditions of compliance and operation. The results show hydraulic gradient surface changes are proportional to demand growth, highest and lowest pressure nodes remain same for near locations, and the spend energy tends to be constant when taking a relative percent difference. © ASCE.

关键词： Water distribution systems

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Learning Realistic and Reactive Traffic Agents

Learning Realistic and Reactive Traffic Agents

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IEEE Symposium on Intelligent Vehicle

作者： Meixin Zhu Di Chen Xinyi Yuan Zhiwei Shang Chenxi Liu Systems Hub Hong Kong University of Science and Technology Guangzhou Civil and Environmental Engineering Department Sensing and Computation for Ubiquitous Internet of Things Hong Kong University of Science and Technology and Guangdong Provincial Key Lab of Integrated Communication Department of Civil and Environmental Engineering University of Washington Seattle WA USA

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In recent years, remarkable strides have been made in the field of autonomous driving, with a particular focus on enhancing perception and prediction capabilities through the integration of big data and advanced deep learning algorithms. Despite these advancements, the persistent challenge of effectively validating the performance of autonomous vehicles (AVs) remains a critical issue. In the realm of microscopic traffic simulation, a noteworthy challenge persists – that of bridging the behavior gap between simulated scenarios and real-world driving situations. Efforts to define agent behavior in simulations manually or replay observed behaviors have proven to be inefficient and prone to inaccuracies, mainly because simulated agents often fail to authentically react to actions initiated by AVs. While rule-based traffic simulation models offer plausible behaviors, they exhibit limitations in adapting to diverse and data-driven behavior patterns within complex driving interactions. Addressing these challenges, we propose a learning-based method for traffic agent simulation, emphasizing realism and reactivity. This involves learning data-driven agent models from real-world driving data with detailed interaction information and high-definition (HD) maps. Utilizing a convolutional neural network (CNN), we extract features and predict future trajectories, achieving realism and reactivity through closed-loop simulation at inference. The proposed model is evaluated using real-world data, demonstrating its effectiveness in simulating diverse and realistic traffic behaviors, like stopping at red traffic lights, yielding to other vehicles during right-turn-on-red, and car following.

关键词： Meters Adaptation models Traffic control Predictive models Feature extraction Data models Trajectory

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URBAN CO-MODALITY WITH ELASTIC PASSENGER AND FREIGHT DEMANDS 26

URBAN CO-MODALITY WITH ELASTIC PASSENGER AND FREIGHT DEMANDS

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26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022

作者： Ma, Mingyou Zhang, Fangni Liu, Wei Dixit, Vinayak Research Centre for Integrated Transport Innovation School of Civil and Environmental Engineering University of New South Wales SydneyNSW2052 Australia Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Hong Kong Department of Aeronautical and Aviation Engineering Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9789881581402

This paper analytically investigates urban co-modality, i.e., using public transit systems for transporting both passengers and freight, considering endogenous freight and passenger demands and levels of services. We model and examine the characteristics of a co-modal system, where one freight operator serves freight customers, one transit operator serves passengers, and the transit operator also serves the freight operator by carrying part of the freight. The systemwide impact of the co-modality operation is examined. The analytical conditions under which there exists a co-modality strategy that improves consumer surpluses of transit and freight users and two operators' profits against the status quo without co-modality are derived. We also analytically and numerically compare the optimal operation decisions, operators' profits and users' welfare under different games between operators. Our results show introducing the comodality holds the potential to improve levels of services for both passengers and freight customers and can yield a Pareto- improving outcome. © 2022 Proceedings of the 26th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2022. All Rights reserved.

关键词： Profitability

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Tracking Perimeter Control for Two-Region Macroscopic Traffic Dynamics: An Adaptive Dynamic Programming Approach

Tracking Perimeter Control for Two-Region Macroscopic Traffi...

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International Conference on Intelligent Transportation

作者： Can Chen Yunping Huang Hongwei Zhang Shu-Chien Hsu Renxin Zhong Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Leveraging the concept of the macroscopic fundamental diagram (MFD) concept, perimeter control can be implemented in some identified critical intersections to alleviate network-level congestion effectively. Considering the time-varying nature of the travel demand pattern and the equilibrium of the accumulation state, we reformulate the conventional set-point perimeter control (SPC) problem for the two-region MFD system into an optimal tracking perimeter control problem (OTPCP). Unlike the SPC schemes that stabilize the traffic dynamics to the desired equilibrium point, the proposed tracking perimeter control (TPC) scheme will regulate the traffic dynamics to a desired trajectory in a differential framework. Due to the inherent network uncertainties, such as uncertain dynamics of heterogeneity and demand disturbance, the system dynamics could be uncertain or even unknown. To address these issues, we propose an adaptive dynamic programming (ADP) approach to solving the OTPCP without utilizing knowledge of the system dynamics. Finally, numerical experiments demonstrate the effectiveness of the proposed ADP-based TPC. Compared with the SPC scheme, the proposed TPC scheme achieves a 20.01% reduction in total travel time and a 3.15% improvement in cumulative trip completion in our case study.

关键词： Economics Uncertainty Adaptive systems System dynamics Dynamic programming Trajectory Time-varying systems Numerical stability Intelligent transportation systems

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Fractal-Based Analysis of the Optimal Hydraulic Gradient Surface in the Optimized Design of Water Distribution Networks

Fractal-Based Analysis of the Optimal Hydraulic Gradient Sur...

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World environmental and Water Resources Congress 2022: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Jaramillo, Andres Saldarriaga, Juan Water Distribution and Sewerage Systems Research Center Universidad De Los Andes Bogotá Colombia Dept. of Civil and Environmental Engineering Water Distribution and Sewerage Systems Research Center Universidad De Los Andes Bogotá Colombia

ISBN: (数字)9780784484258

ISBN: (纸本)9780784484258

Hydraulic-based optimization methodologies are reliable and computationally effective techniques to determine a least capital cost design in water distribution networks. Some of these approaches rely on the calculation of the optimal hydraulic gradient surface (OHGS), a geometrical body that establishes the way in which the available hydraulic head should be spent within a network to ensure the calculation of a minimum capital cost diameter configuration. Given its amorphous morphology, the fractal dimension can be used as a parameter to understand the mathematical and physical properties of the OHGS. In the present study, the OHGS of a variety of theoretical and real water distribution systems is calculated by employing the optimal power use surface optimization methodology. The fractal analysis is then carried out by an image fractal dimension estimator algorithm, including the examination of random non-optimal designs. Moreover, the fractal dimension of the topology, flow, and energy distribution of the optimized networks was also calculated by a box-covering algorithm and compared to the fractal dimension of the OHGSs. The results show that the energy dissipation pattern in an optimal design exhibits certain fractal properties that can be used to distinguish it from more expensive diameter configurations. In addition, it was found that the fractal properties of the OHGS and of the topology, flow, and energy distribution inside the studied networks are somehow related. These findings may be applied in prospective optimization methodologies, operational improvement, or renewal procedures. © ASCE.

关键词： Topology

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Coordination for Connected Automated Vehicles at Merging Roadways in Mixed Traffic Environment

Coordination for Connected Automated Vehicles at Merging Roa...

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IEEE Conference on Decision and Control

作者： Viet-Anh Le Hao M. Wang Gábor Orosz Andreas A. Malikopoulos Department of Mechanical Engineering University of Delaware Newark DE USA Systems Engineering Cornell University Ithaca NY USA Department of Mechanical Engineering University of Michigan Ann Arbor MI USA Department of Civil and Environmental Engineering University of Michigan Ann Arbor MI USA School of Civil and Environmental Engineering Cornell University Ithaca NY USA

In this paper, we present an optimal control framework to address motion coordination of connected automated vehicles (CAVs) in the presence of human-driven vehicles (HDVs) in merging scenarios. Our framework combines an unconstrained trajectory solution of a low-level energy-optimal control problem with an upper-level optimization problem that yields the minimum travel time for CAVs. We predict the future trajectories of the HDVs using Newell's car-following model. To handle potential deviations of HDVs' actual behavior from the predicted one, we design a safety filter for CAVs based on control barrier functions. The effectiveness of the proposed control framework is demonstrated via simulations with heterogeneous human driving behaviors.

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Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable Artificial Intelligence

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Cancer Communications 2024年第5期44卷 584-588页

作者： Debaditya Chakraborty Elizabeth Gutierrez-Chakraborty Cristian Rodriguez-Aguayo Hakan Başağaoğlu Gabriel Lopez-Berestein Paola Amero School of Civil and Environmental Engineering and Construction Management The University of Texas at San AntonioSan AntonioTexasUSA Department of Cell Systems and Anatomy University of Texas Health Science CenterSan AntonioTexasUSA Department of Experimental Therapeutics The University of Texas MD Anderson Cancer CenterHoustonTexasUSA Edwards Aquifer Authority San AntonioTexasUSA

High-Grade Serous Ovarian Cancer(HGSC)is the most prevalent and lethal form of gynecologic malignancies[1],accounting for 70%-80%of ovarian cancer *** decades of research,the overall survival rate for HGSC has remained largely unchanged[2],and patients with advanced stages of the disease have only a 41%chance of surviving beyond five years[3].

关键词： cancer Cancer targeted

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Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging

Connected and Automated Vehicles in Mixed-Traffic: Learning ...

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IEEE Conference on Decision and Control

作者： Nishanth Venkatesh Viet-Anh Le Aditya Dave Andreas A. Malikopoulos Systems Engineering Cornell University Ithaca NY USA Department of Mechanical Engineering University of Delaware Newark DE USA School of Civil and Environmental Engineering Cornell University Ithaca NY USA

Highway merging scenarios featuring mixed traffic conditions pose significant modeling and control challenges for connected and automated vehicles (CAVs) interacting with incoming on-ramp human-driven vehicles (HDVs). In this paper, we present an approach to learn an approximate information state (AIS) model of CAV-HDV interactions. Thus, the CAV learns the behavior of an incoming HDV using the AIS model and uses it to generate a control strategy for merging. First, we validate the efficacy of this framework on real-world data by using it to predict the behavior of an HDV in situations with other HDVs extracted from the Next-Generation Simulation repository. Then, we generate simulation data for HDV-CAV interactions in a highway merging scenario using a standard inverse reinforcement learning approach. Without assuming a prior knowledge of the generating model, we show that our AIS model learns to predict the future trajectory of the HDV using only observations. Subsequently, we generate safe merging control policies for a CAV when merging with HDVs that demonstrate a spectrum of driving behaviors, from aggressive to conservative. We establish the effectiveness of the proposed approach by performing numerical simulations.

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