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14th International Space Syntax Symposium, SSS 2024

作者： Chioni, Chiara Pezzica, Camilla Altafini, Diego Department of Civil Environmental and Mechanical Engineering University of Trento Italy Welsh School of Architecture Cardiff University United Kingdom Department of Energy Systems Territory and Construction Engineering University of Pisa Italy

ISBN: (纸本)9791256690329

The European Union's territorial expansion has brought to light the existence of regions characterised by uneven development and asymmetric connections to primary infrastructure networks. In particular, the so-called ‘Inner Areas’ cover around 60% of the Italian territory and the national cohesion strategy (SNAI) targeting them was developed to combat marginalisation and demographic decline, while enhancing resilience, adaptation to climate-change, and risk mitigation. Nevertheless, its primary focus on service and infrastructure marginality overlooks broader socio-spatial indicators, particularly resources’ accessibility and use of different mobility modes, hindering its effectiveness. Within this framework, the paper discusses the potential contributions of the configurational approach to mapping territorial accessibility levels through its application to the Italian case. Specifically, it proposes and tests the use of a configuration-informed composite index for territorial imbalances’ mapping, the ‘S3NAI’. The proposal is rooted in a nationwide Space Syntax Angular Segment Analysis performed on primary-roads elements;whence the Normalised Angular Integration, at global and local metric radii, is employed to assess municipalities’ ease of access, accounting for transport network efficiency and connectivity from a human-centred perspective. This aims to move marginality assessments beyond issues of geographical distance and travel-time to include more clearly the social and economic perspective in them. On this basis, the adoption of the configurational approach in mapping marginalities is proposed to enrich and inform the development of national- and regional-level territorial cohesion strategies. © 14th International Space Syntax Symposium, SSS 2024.

关键词： Travel time

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Application of text mining techniques to identify actual wrong-way driving (WWD) crashes in police reports

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International Journal of Transportation Science and Technology 2023年第4期12卷 1038-1051页

作者： Parisa Hosseini Seyedalireza Khoshsirat Mohammad Jalayer Subasish Das Huaguo Zhou Department of Civil and Environmental Engineering Center for Research and Education in Advanced Transportation Engineering Systems(CREATEs)Rowan University201 Mullica Hill RoadGlassboroNJ 08028USA Department of Computer&Information Sciences University of Delaware18 Amstel AveNewarkDE 19716USA Civil Engineering Program Ingram School of EngineeringTexas State UniversityRFM 5202601 University DriveSan MarcosTX 78666USA Harbert Center Department of Civil and Environmental EngineeringAuburn UniversityAuburnAL 36849USA

Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road *** being rare,crashes occurring due to WWD are more severe than other types of *** order to analyze WWD crashes,there is a need to obtain WWD incidents or crash ***,it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash *** often involves large man-hours to review hardcopy of crash narratives in the police ***,it may cause an overestimation or underestimation of WWD crash *** fill this gap,the present study,as the first-of-its-kind,aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning ***,Bidirectional Encoder Representations from Transformers(BERT)models have shown promising results in natural language *** this study,we implemented the BERT model as well as five conventional classification algorithms,including Naïve Bayes(NB),Support Vector Machine(SVM),Decision Tree(DT),Random Forest(RF),and Single Layer Perceptron(SLP)to classify crash report narratives as actual WWD and non-WWD ***-validation and different performance metrics were used to evaluate the performance of each classification *** indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%.The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.

关键词： Wrong-Way Driving Crashes Crash Report Narratives Text Mining Text Classification

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Road Network Accessibility Analysis Based on Dynamic Diversity Entropy 24

Road Network Accessibility Analysis Based on Dynamic Diversi...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Hu, Dingkang Zhao, Tingting Zhang, Yu Ma, Wei School of Systems Science Beijing Jiaotong Univ. Beijing China Dept. of Civil and Environmental Engineering Univ. of South Florida TampaFL United States Dept. of Civil and Environmental Engineering Hong Kong Polytechnic Univ. Kowloon Hong Kong

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

In this work, we propose a novel accessibility measure based on Dynamic Diversity Entropy to evaluate the difficulty of citizens accessing various nodes with resources based on network structure. Furthermore, the self-avoiding random walk is adopted for improving the computational efficiency of evaluating accessibility of nodes in a scaled urban street network. Dynamic Diversity Entropy is introduced to measure the outcome of self-avoiding random walks. Then, we introduce link travel times representing the time-varying traffic status, and spatial heterogeneous Point of Interest’s density representing nodes’ resource weights in the accessibility assessment is used. A case study on a partial road network inside Beijing’s third ring road is conducted. The spatial accessibility distribution and the relationship between accessibility to restaurants and time limit are visualized to investigate the performance of the proposed accessibility assessment indicators. © ASCE.

关键词： Roads and streets

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Artificial intelligence is transforming the research paradigm of environmental science and engineering

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environmental Science and Ecotechnology 2024年第3期19卷 1-3页

作者： Xu Wang Guangtao Fu Nan-Qi Ren State Key Laboratory of Urban Water Resource and Environment School of Civil and Environmental EngineeringHarbin Institute of TechnologyShenzhenShenzhen518055China Centre for Water Systems College of EngineeringMathematics and Physical SciencesUniversity of ExeterExeterEX44QFUnited Kingdom

Artificial intelligence(AI),particularly through its datacrunching powerhouse,machine learning(ML),is revolutionizing the way we approach high-throughput data to obtain underlying mechanisms,characterizing,forecasting,and making evidencebased decisions in many fundamental science ***,a pressing question has emerged in the scientific community in recent years:How can we effectively interpret AI algorithms and gauge their contributions to fundamental research?

关键词： has gauge forecasting

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Fairness-Driven Multi-Objective Optimization for Evacuation Planning in Natural Disasters

Fairness-Driven Multi-Objective Optimization for Evacuation ...

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International Conference on Transportation and Development 2024: Transportation Planning, Operations, and Transit, ICTD 2024

作者： Gupta, Himadri Sen Gonzalez, Andres D. Jnad, Rasha Kameshwar, Sabarethinam School of Industrial and Systems Engineering Univ. of Oklahoma NormanOK United States Dept. of Civil and Environmental Engineering Louisiana State Univ. Baton RougeLA United States

ISBN: (数字)9780784485521

ISBN: (纸本)9780784485521

In the context of natural disasters such as earthquakes and tsunamis, efficient and equitable evacuation strategies are critical for safeguarding human lives. This paper introduces a multi-objective optimization model that seeks to minimize both the overall transportation distance for affected individuals and the average unutilized space in designated evacuation centers. The model explicitly incorporates fairness, ensuring that all residents are equally prioritized during the evacuation process. We tested the model’s applicability using a real-world scenario in Seaside, Oregon, which is a vulnerable town to frequent natural hazards. The case study aims to address the specific logistical challenges associated with evacuating a population of approximately 6,000 residents under tsunami hazard. Our analysis indicates that the transportation mode and capacity are pivotal variables for achieving an effective and equitable evacuation procedure. This research extends the existing literature on disaster preparedness by emphasizing the crucial role of transportation logistics in ensuring successful and fair evacuations. © ASCE.

关键词： Disasters

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ZIF-derived N-doped carbon nanorods supporting bimetallic CoFe single-atoms/nanoclusters as bifunctional oxygen electrocatalysts for stable Zn-air batteries

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Rare Metals 2024年第11期43卷 5769-5780页

作者： Hong-Shuang Fan Fei-Xiang Ma Zi-Hao Liu Wen-Hui Wang Zheng-Qi Liu Xiong-Yi Liang Yue Du Yang-Yang Li Liang Zhen Cheng-Yan Xu Sauvage Laboratory for Smart Materials School of Materials Science and EngineeringHarbin Institute of Technology(Shenzhen)Shenzhen 518055China School of Civil and Environmental Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China Department of Materials Science and Engineering City University of Hong KongHong Kong 999077China Peng Cheng Laboratory Shenzhen 518055China MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing Harbin Institute of TechnologyHarbin 150080China

High-performance bifunctional oxygen electrocatalysts that simultaneously boost the sluggish oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)need to be developed for advanced rechargeable Znair battery *** this work,a zeolitic imidazolate framework(ZIF)-phase conversion associated with a subsequent thermal fixing strategy was developed to fabricate bimetallic CoFe single atoms/clusters embedded in N-doped carbon(denoted as CoFe-N-C)nanorods,which can serve as efficient bifunctional ORR/OER *** observation and X-ray absorption spectroscopy analysis confirm the co-existence of highly active Co/Fe-N_(x) dual sites and CoFe alloy nanoclusters.X-ray photoelectron spectroscopy(XPS)results prove that implanting secondary Fe atoms into Co-N-C matrix nanorods can induce electronic redistribution of atomic Co/Fe active sites and generate synergistic effects,which would optimize the adsorption energy of the reaction intermediates and thus enhance the bifunctional ORR/OER *** bimetallic CoFe-N-C nanorods exhibit significantly enhanced bifunctional ORR/OER activity and stability than the monometallic Co/Fe-N-C nanorods in alkaline electrolytes in terms of a very positive half-wave potential of 0.90 V(*** hydrogen electrode(RHE))for ORR,and an overpotential of 440 mV to reach current density of 10 mA·cm^(-2)for OER,yielding a small overpotential gap of 0.77 ***,the rechargeable Zn-air batteries using bimetallic CoFe-N-C nanorods as air-cathode catalyst demonstrates peak power density of200.7 mW·cm^(-2)and robust cycling stability of up to200 h,corresponding to 1200 discharge-charge cycles.

关键词： Bimetallic electrocatalyst Single-atom catalysts Oxygen reduction reaction Oxygen evolution reaction Zn-air batteries

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INTEGRATED DESIGN FOR WAVE ENERGY CONVERTER FARMS: ASSESSING PLANT, CONTROL, LAYOUT, AND SITE SELECTION COUPLING IN THE PRESENCE OF IRREGULAR WAVES

INTEGRATED DESIGN FOR WAVE ENERGY CONVERTER FARMS: ASSESSING...

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ASME 2024 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2024

作者： Azad, Saeed Khanal, Suraj Herber, Daniel R. Jia, Gaofeng Department of Systems Engineering Colorado State University Fort CollinsCO80523 United States Department of Civil and Environmental Engineering Colorado State University Fort CollinsCO80523 United States

ISBN: (纸本)9780791888360

A promising direction towards reducing the levelized cost of energy for wave energy converter (WEC) farms is to improve their performance. WEC design studies generally focus on a single design domain (e.g., geometry, control, or layout) to improve the farm’s performance under simplifying assumptions, such as regular waves. This strategy, however, has resulted in design recommendations that are impractical or limited in scope because WEC farms are complex systems that exhibit strong coupling among geometry, control, and layout domains. In addition, the location of the candidate site, which has a large impact on the performance of the farm, is often overlooked. Motivated by some of the limitations observed in WEC literature, this study uses an integrated design framework, based on simultaneous control co-design (CCD) principles, to discuss the impact of site selection and wave type on WEC farm design. Interactions among plant, control, and layout are also investigated and discussed using a wide range of simulations and optimization studies. All of the studies were conducted using frequency-domain heaving cylinder WEC devices within a farm with a linear reactive controller in the presence of irregular probabilistic waves. The results provide high-level guidelines to help the WEC design community move toward an integrated design perspective. Copyright © 2024 by ASME.

关键词： Wave energy conversion

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Building-Integrated Photovoltaic Performance Modelling: Evaluation of the System Advisor Model (SAM) Based on Experimental Data from a Grid-Connected Rainscreen System 5th

Building-Integrated Photovoltaic Performance Modelling: Eval...

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5th International Conference on Building Energy and Environment, COBEE 2022

作者： Jazouli, Soukaïna Wilson, Helen Rose Eisenlohr, Johannes Kapsis, Konstantinos Department of Civil and Environmental Engineering University of Waterloo WaterlooON Canada Fraunhofer Institute for Solar Energy Systems Freiburg Germany

ISBN: (纸本)9789811998218

As global efforts on the electrification of the built environment intensify, buildings are expected to become energy-flexible and provide different services to the grid, including onsite electricity generation. Distributed energy sources such as building-integrated photovoltaics (BIPV) have a key role to play in this transition. Thus, it is essential for building professionals to use accurate and reliable tools to predict the energy performance of these installations. BIPV refers to photovoltaic systems that can replace conventional building components. The electrical performance of BIPV can differ from that of conventional PV due to their tilt angles, poorer rear ventilation, and partial shading by the surroundings. The objective of this study is to review existing performance models and evaluate their applicability to BIPV. In this study, the performance of a 5.3 kWp south-east facing BIPV rainscreen system of an institutional building was evaluated for one year on the basis of monitored data. High-resolution meteorological, electrical, and surface temperature data were collected and analysed. A model was created based on the BIPV system specifications using the NREL System Advisor Model (SAM). The analysis revealed that SAM can generate accurate and reliable data when it comes to predicting the electricity-generating and temperature behaviour of a BIPV façade, with an overall coefficient of determination (R2) ranging from 48 to 95%, depending on the simulation timestep used. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Specifications

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FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection 38

FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalou...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Coursey, Austin Ji, Junyi Quinones-Grueiro, Marcos Barbour, William Zhang, Yuhang Derr, Tyler Biswas, Gautam Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems United States Department of Computer Science United States

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and mistakes from manual crash reporting records make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Tennessee Department of Transportation Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://***/ft-aed/to facilitate future research. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep Temporal Neural Networks for Water Level Predictions of Watershed systems

Deep Temporal Neural Networks for Water Level Predictions of...

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2023 systems and Information engineering Design Symposium, SIEDS 2023

作者： Huff, Jordan Watts, Jeremy Khojandi, Anahita Hathaway, Jon University of Tennessee Department of Industrial and Systems Engineering KnoxvilleTN United States University of Tennessee Department of Civil and Environmental Engineering KnoxvilleTN United States

ISBN: (纸本)9798350300642

Rainfall-runoff systems are complex hydrological environments that play a critical role in flood prevention. Currently, physics-based, process-driven computational models are often used to forecast future flooding events. However, these physics-based models are computationally expensive and require intensive physical measurements of hydrological environments beyond remote data collection. There is a growing body of literature that applies deep neural networks to time-series data for computationally efficient, real-time flooding predictions without the need for the complete virtual modeling of the hydrological system. However, these deep-learning networks' robustness at forecasting far into the future remains an open question. In this study, we examine the capabilities of Long Short-Term Memory (LSTM) networks and Temporal Convolutional Networks (TCN), state-of-the-art temporal deep neural networks, to forecast rainfall-runoff system depths. Specifically, this study leverages primary, multi-modal, time-series data collected by remote sensors in the watershed system of Conner Creek, a tributary of the Clinch River in eastern Tennessee. These data were collected in 5-minute intervals over a course of 5 months. Notably, the Conner Creek watershed system consists of four interconnected reservoir basins. We forecast the water level of each reservoir basin independently for times ranging from five minutes to two hours into the future. Our results show that both the LSTM and TCN can effectively model and forecast future reservoir basin water levels. Specifically, when averaged across the four reservoir basins, the LSTM has an mean absolute error (MAE), with a 95% confidence interval, of 0.158 ± 0.049 ft and 0.490 ± 0.260 ft at 5 minutes and 120 minutes into the future, respectively. In comparison, the TCN has an MAE of 0.258 ± 0.160 ft and 0.375 ± 0.245 ft at 5 minutes and 120 minutes into the future, respectively. Our results show that the LSTM model outperforms the TCN fo

关键词： Watersheds

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