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Comparative analysis of pedestrian volume models: Agent-based models, machine learning methods and multiple regression analysis

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2025年 117卷

作者： Wolpert, Lior Omer, Itzhak Tel Aviv Univ Porter Sch Environm & Earth Sci Dept Geog & Human Environm 10 Zelig St Tel Aviv Israel

Pedestrian flow distributions can inform planning for walkability and improve understanding of factors that influence pedestrian activity. However, detailed data is rarely available so pedestrian volume models, commonly relying on the Space Syntax framework, are often utilized to predict pedestrian volumes. This study compares the performance and dominant variables of three modelling families - multiple regression analyses, machine learning models, and agent-based models - in Tel Aviv-Yafo, Israel. Using 247 flow observations, optimal models from each family were fitted and validated for 3 separate areas that differ in their urban growth and morphological characteristics, as well for the whole city. Results showed that ensemble-based machine learning models were best for city-wide predictions while agent-based models had an advantage at the local scale of neighborhoods - especially in neighborhoods that did not develop in a self-organized process. regression analyses fell short for all areas, even when using principal component analysis to reduce multicollinearity and overfitting. These differences are attributed to the relative influence of cognitive-behavioral and structural factors on pedestrian flows: agent-based models outperform statistical models in individual areas, where behavior is captured more accurately using a small set of cognitive-behavioral parameters. Statistical models are dominant in the city-wide context, where structural variables can predict aggregate patterns. This is crucially important when evaluating the distribution of pedestrians in a planned urban environment. Overall, our results indicate that stepwise regression are not sufficient for pedestrian volume modelling, that agent-based models better capture complex interactions between independent variables, and that machine learning models have a strong potential for city-wide pedestrian volume modelling.

关键词： Pedestrian volume modelling Agent-based model Machine learning Principal component analysis multiple regression analysis Space syntax

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Institutional investors’ distraction and the quality of accounting information disclosure: A data-driven approach using multiple regression analysis

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Journal of Data, Information and Management 2025年第1期7卷 21-36页

作者： Fang, Si Cao, Chongyan School of Management University of Science and Technology of China Anhui Province Hefei 230000 China

In today’s data-intensive environment, model-based data analysis plays a critical role in providing enterprises with quantitative insights to optimize decision-making and manage risks. This study examines the relationship between institutional investor distraction and the quality of accounting information disclosure, focusing on Chinese A-share listed companies from 2010 to 2020. By leveraging Kempf’s formula and methodology, the paper quantifies “distraction” through analyzing extreme industry returns, institutional holdings, and market capitalization. Findings indicate that institutional investor distraction can reduce the quality of disclosed accounting information, a trend more pronounced among firms with high pressure resistance. Further analysis reveals that increased attention from securities analysts and investor research activities can mitigate this negative impact. This research contributes to understanding the role of institutional investors in corporate governance from the perspective of exogenous shocks and demonstrates the application of data-driven analytic in assessing information disclosure quality. Insights from this study offer policy recommendations for enhancing regulatory frameworks on information management and corporate governance. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Accounting information quality Analyst attention Data-driven corporate governance Institutional investigation Institutional investor distraction multiple regression analysis

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multiple regression analysis of postdevelop unbiased line width roughness and etch resistance for high-accuracy estimations of postetch pattern roughness

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JOURNAL OF MICRO-NANOPATTERNING MATERIALS AND METROLOGY-JM3 2022年第2期21卷

作者： Lee, Mihyun Miyake, Masayuki Otsuka, Noboru Kawakami, Takanori Kim, Hyun-Woo Hong, Suk-Koo Samsung Elect Mat Dev Team Hwaseong Si South Korea JSR Corp Tokyo Japan

As device scaling continues, development of photoresists with low pattern roughness and high sensitivity has become challenging. One obstacle that delays the material development process is the discrepancy between postdevelop and postetch pattern roughness, where a photoresist with high postdevelop patterning performance shows poor postetch pattern roughness. Herein, we demonstrate that pattern roughness after a nontrim etch process can be accurately estimated by a multiple regression analysis of a power spectral density (PSD) variable of postdevelop roughness and an etch resistance parameter of resins. The nontrim etch process here refers to an etch condition which leads to increased pattern roughness. Unbiased line width roughness (LWR) shows the highest correlation with postetch LWR among postdevelop roughness PSD variables. An etch resistance parameter also correlates well with postetch roughness. A multiple regression analysis reveals that the contributions of postdevelop unbiased LWR and etch resistance to the postetch LWR are 59% and 41%, respectively. Based on the calculated contributions, postetch LWR is estimated with a high accuracy (R-2 > 0.93). This estimation method allows for an efficient material screening at a lithography level without assessing postetch patterning performance, thus the process of material development could be accelerated. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： photolithography power spectral density post-etch roughness photoresists unbiased line width roughness multiple regression analysis

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multiple regression analysis of hydrogen sulphide poisoning in molten carbonate fuel cells used for waste-to-energy conversions

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2011年第13期36卷 8119-8125页

作者： Zaza, Fabio Paoletti, Claudia LoPresti, Roberto Simonetti, Elisabetta Pasquali, Mauro ENEA RC Casaccia Renewable Sources Dept Hydrogen Fuel Cells & Energy Storage Syst Unit I-00123 Rome Italy Univ Roma La Sapienza Chem Mat & Environm Engn Dept I-00161 Rome Italy

Bioenergy addresses three important social concerns: security of energy supply, mitigation of greenhouse gas emissions and development of agriculture. Bioenergy is the energy generated by either direct or indirect combustion of biomass, which is non-fossil organic material of both vegetable and animal origins. Different technologies are currently available for bioenergy production, but fuel cells are one of the most interesting devices because of their environmental benefits and their high efficiency. In particular, molten carbonate fuel cells are suitable devices for waste-to-energy conversion because of their ability to be fed with biogas, which is biomass-derived gas rich in methane and carbon dioxide. Indeed, methane can be internally reformed to hydrogen, carbon dioxide is a safe diluent preventing electrolyte loss, and carbon monoxide acts both as a hydrogen supplier and as an actual fuel. Unfortunately, biogas impurities, such as sulfur, halogen and nitrogen compounds, cause adverse effects on cell performances. The most dangerous impurities are sulfur compounds, among whom hydrogen sulphide is the predominant and the most harmful chemicals. It reacts with nickel-based anode to form nickel sulphides, that block catalytic sites, slowing hydrogen oxidation down, and change the anode wettability in molten carbonates, compromising the optimal electrolyte distribution within cell components. Poisoning mechanism of hydrogen sulphide depends on operating conditions such as current density, anodic gas composition, temperature and pressure. The aim of this work is to study hydrogen sulphide effects on MCFCs by means of multiple regression analysis. The mathematical approach gives us tools to define the main sulfur poisoning mechanism under MCFC operating conditions, quantify the effects of the main parameters affecting poison actions, such as current density, hydrogen and hydrogen sulphide, and identify their mutual interactions. Also it is possible to formulate a mul

关键词： MCFC Biogas Waste-to-Energy Hydrogen sulphide multiple regression analysis

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multiple regression analysis as a tool for the identification of relations between semi-quantitative LC-MS data and cytotoxicity of extracts of the fungus Fusarium avenaceum (syn. F-arthrosporioides)

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TOXICON 2006年第5期48卷 567-579页

作者： Uhlig, Silvio Jestoi, Marika Knutsen, Ann Kristin Heier, Berit T. Natl Vet Inst N-0033 Oslo Norway Finnish Food Safety Author Evira Res Unit Chem & Toxicol FI-00790 Helsinki Finland

The cytotoxicity of methanolic extracts from rice cultures of 53 Fusarium avenaceum strains, which had been isolated from different host organisms in Northern Europe, Canada and Australia/New Zealand, was investigated in a rat hepatoma (H4IIE-W), porcine epithelial kidney (PK-15), foetal feline lung fibroblast, dog lymphoblast (D3447), and a human hepatocarcinorna (Hep G2) cell line using the Alamar Blue (TM) assay. All extracts were screened for known fungal metabolites using high-performance liquid chromatography with photodiode array and mass spectrometric detection, and both known and unknown metabolites were semi-quantified. Known metabolites that were determined in the cultures include acuminatopyrone, 2-amino-14,16-dimethyloctadecan-3-ol (2-AOD-3-ol), antibiotic Y, aurofusarin, chlamydosorol, chlamydospordiol, enniatins, fusarin A and C, and moniliformin. multiple regression analysis was used in order to relate fungal metabolites to the cytotoxicity of the extracts. Separate linear regression models were constructed for each cell line. Eleven different fungal metabolites were related to the cytotoxicity (P < 0.05). Out of these, nine metabolites were siginificantly related to the cytotoxicity in only one of the five models, while two, namely enniatins and 2-AOD-3-ol, were significant contributors in three or four regression models, respectively. This paper describes how multiple regression analysis may be applied for the assignment of bioactivity/toxicity to the constituents of a multi-component mixture. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： Alamar blue assay cytotoxicity fungal metabolites Fusarium multiple regression analysis mycotoxin

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multiple regression analysis of risk factors related to radiation pneumonitis

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World Journal of Clinical Cases 2023年第5期11卷 1040-1048页

作者： Ling-Ling Shi Jiang-Hua Yang Hong-Fa Yao Department of Radiology Huzhou Central HospitalAffiliated Central Hospital of Huzhou UniversityHuzhou 313000Zhejiang ProvinceChina

BACKGROUND Radiation pneumonitis(RP)is a severe complication of thoracic radiotherapy that may lead to dyspnea and lung fibrosis,and negatively affects patients’quality of *** To carry out multiple regression analysis on the influencing factors of radiation *** Records of 234 patients receiving chest radiotherapy in Huzhou Central Hospital(Huzhou,Zhejiang Province,China)from January 2018 to February 2021,and the patients were divided into either a study group or a control group based on the presence of radiation pneumonitis or *** them,93 patients with radiation pneumonitis were included in the study group and 141 without radiation pneumonitis were included in the control *** characteristics,and radiation and imaging examination data of the two groups were collected and *** to the statistical significance observed,multiple regression analysis was performed on age,tumor type,chemotherapy history,forced vital capacity(FVC),forced expiratory volume in the first second(FEV1),carbon monoxide diffusion volume(DLCO),FEV1/FVC ratio,planned target area(PTV),mean lung dose(MLD),total number of radiation fields,percentage of lung tissue in total lung volume(vdose),probability of normal tissue complications(NTCP),and other *** The proportions of patients aged≥60 years and those with the diagnosis of lung cancer and a history of chemotherapy in the study group were higher than those in the control group(P<0.05);FEV1,DLCO,and FEV1/FVC ratio in the study group were lower than those in the control group(P<0.05),while PTV,MLD,total field number,vdose,and NTCP were higher than in the control group(P<0.05).Logistic regression analysis showed that age,lung cancer diagnosis,chemotherapy history,FEV1,FEV1/FVC ratio,PTV,MLD,total number of radiation fields,vdose,and NTCP were risk factors for radiation *** We have identified patient age,type of lung cancer,history of chemotherapy,lung function,and radiotherapy pa

关键词： Radiation pneumonitis Influencing factors Radiotherapy multiple regression analysis

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multiple regression analysis of repetitive finger tapping parameters 22

Multiple regression analysis of repetitive finger tapping pa...

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22nd Telecommunications Forum Telfor (TELFOR)

作者： Miler-Jerkovic, Vera Djuric-Jovicic, Milica Perovic-Belic, Minja Jecmenica-Lukic, Milica Petrovic, Igor N. Radovanovic, Sasa M. Kostic, Vladimir S. Popovic, Mirjana B. Univ Belgrade Sch Elect Engn Belgrade Serbia Univ Belgrade Sch Elect Engn Innovat Ctr Belgrade Serbia Univ Belgrade Fac Med Clin Ctr Serbia Neurol Clin Belgrade Serbia Univ Belgrade Inst Med Res Belgrade Serbia

ISBN: (纸本)9781479961917

In this paper, we present methodology for investigating relations among kinematic parameters of finger tapping, typically used for motor assessment of upper limb by implementing multiple linear regression analysis. Duration, speed and angle were selected as the most frequently used to describe taping performance. The goal of this study was to determine if these basic finger tapping parameters can be estimated through other parameters and to quantify their relations and contributions to the obtained estimation. The study included 12 patients with Parkinson's disease (PD) and 12, age and gender matched healthy subjects (HS). Results showed that selected three kinematic parameters can be estimated with error less than 7% for healthy subjects and less than 10% error for patients with PD.

关键词： repetitive finger tapping multiple regression analysis Parkinson's disease

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multiple regression analysis Model on Power Dispatch

Multiple Regression Analysis Model on Power Dispatch

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1st International Conference on Energy and Environmental Protection (ICEEP 2012)

作者： Zhou, Huaren Qian, Yuehong Liu, Xiqiang Wu, Ou PLA Univ Sci & Tch Inst Sci Nanjing 211101 Jiangsu Peoples R China PLA Univ Sci & Tch Engn Inst Engn Corps Nanjing 211101 Jiangsu Peoples R China

ISBN: (纸本)9783037854143

The multiple linear regression method is used, the method of calculating the active power flow and the unit output is discussed, a simple approximate expression is designed, and the corresponding error value is given, a simple calculation rules of congestion cost is given, calculation rules for the actual cost minus the theoretical costs and requirements of the actual costs is as low as possible to avoid blocking;Block can not be avoided, then try to avoid the wind up.

关键词： Power dispatch multiple regression analysis mathematic model

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multiple regression analysis of IEEE 802.16j Relay Network Throughput

Multiple Regression Analysis of IEEE 802.16j Relay Network T...

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16th International Conference on Advanced Communication Technology (ICACT)

作者： Higo, Kohei Hasegawa, Go Taniguchi, Yoshiaki Nakano, Hirotaka Matsuoka, Morito Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 565 Japan Osaka Univ Cybermedia Ctr Suita Osaka 565 Japan

ISBN: (纸本)9788996865032

In IEEE 802.16j relay networks, time division multiple access (TDMA) is adopted for sharing wireless network resources while avoiding radio wave interference. Time slot assignment determines the degree of spatial reuse of wireless network resources and radio interference strength, which affects network performance. The relay network performance is also influenced by various factors, such as background noise level, path loss exponent, density of relay nodes, and transmission signal strength. In this paper, we present the results of multiple regression analysis of IEEE 802.16j relay network throughput. We first summarize the network environment parameters that affect the throughput of relay networks and conduct extensive simulation experiments with various sets of parameter values. From the simulation results, we investigate the relative effect of parameters on throughput performance by multiple regression analysis. The analysis results show that background noise level and path loss exponent are the key parameters for determining relay network throughput. We also obtain a regression equation to estimate the throughput with enough accuracy.

关键词： IEEE 802.16j time slot assignment SINR throughput multiple regression analysis

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multiple regression analysis as a Comprehensive Tool to Model Flood Hazard in Sewersheds 11th

Multiple Regression Analysis as a Comprehensive Tool to Mode...

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11th International Conference on Urban Drainage Modelling (UDM)

作者： Jato-Espino, Daniel Sillanpaa, Nora Andres-Domenech, Ignacio Rodriguez-Hernandez, Jorge Univ Cantabria GITECO Res Grp Ave Castros 44 E-39005 Santander Spain Aalto Univ Sch Engn Dept Built Environm POB 15200 Aalto 00076 Finland Univ Politecn Valencia Inst Univ Invest Ingn Agua & Medio Ambiente IIAMA Cno Vera S-N E-46022 Valencia Spain

ISBN: (纸本)9783319998671;9783319998664

Flood modelling in urban areas is usually undertaken using stormwater tools, which are complex and time-consuming in terms of parametrization. To replace them, this research developed a methodology for predicting flooding probability in urban watersheds (sewersheds) through the modelling of peak flow rates from a set of watershed and sewer network-related factors relevant for the occurrence of floods. This was addressed through the stepped integration of multiple Linear regression (MLR), multiple Nonlinear regression (MNR) and multiple Binary Logistic regression (MBLR). A case study of a sewershed in Espoo (Finland) was used to validate the proposed approach and test it for future estimates, enabling the prediction of flooding probabilities under different Climate Change scenarios.

关键词： Flood hazard modelling multiple regression analysis Sewersheds

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