Hong, Jinhyun; Shen, Qing; Zhang, Lei. (2014). How Do Built-Environment Factors Affect Travel Behavior? A Spatial Analysis at Different Geographic Scales. Transportation, 41(3), 419 – 440.
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Abstract
Much of the literature shows that a compact city with well-mixed land use tends to produce lower vehicle miles traveled (VMT), and consequently lower energy consumption and less emissions. However, a significant portion of the literature indicates that the built environment only generates some minor-if any-influence on travel behavior. Through the literature review, we identify four major methodological problems that may have resulted in these conflicting conclusions: self-selection, spatial autocorrelation, inter-trip dependency, and geographic scale. Various approaches have been developed to resolve each of these issues separately, but few efforts have been made to reexamine the built environment-travel behavior relationship by considering these methodological issues simultaneously. The objective of this paper is twofold: (1) to better understand the existing methodological gaps, and (2) to reexamine the effects of built-environment factors on transportation by employing a framework that incorporates recently developed methodological approaches. Using the Seattle metropolitan region as our study area, the 2006 Household Activity Survey and the 2005 parcel and building data are used in our analysis. The research employs Bayesian hierarchical models with built-environment factors measured at different geographic scales. Spatial random effects based on a conditional autoregressive specification are incorporated in the hierarchical model framework to account for spatial contiguity among Traffic Analysis Zones. Our findings indicate that land use factors have highly significant effects on VMT even after controlling for travel attitude and spatial autocorrelation. In addition, our analyses suggest that some of these effects may translate into different empirical results depending on geographic scales and tour types.
Keywords
Land-use; Urban Form; Multilevel Models; Physical-activity; Neighborhood; Choice; Impact; Specification; Accessibility; Causation; Built Environment; Travel Behavior; Self-selection; Spatial Autocorrelation; Bayesian Hierarchical Model
Liang, Huakang; Lin, Ken-yu; Zhang, Shoujian; Su, Yikun. (2018). The Impact Of Coworkers’ Safety Violations On An Individual Worker: A Social Contagion Effect Within The Construction Crew. International Journal Of Environmental Research And Public Health, 15(4).
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Abstract
This research developed and tested a model of the social contagion effect of coworkers' safety violations on individual workers within construction crews. Both situational and routine safety violations were considered in this model. Empirical data were collected from 345 construction workers in China using a detailed questionnaire. The results showed that both types of safety violations made by coworkers were significantly related to individuals' perceived social support and production pressure. Individuals' attitudinal ambivalence toward safety compliance mediated the relationships between perceived social support and production pressure and both types of individuals' safety violations. However, safety motivation only mediated the effects of perceived social support and production pressure on individuals' situational safety violations. Further, this research supported the differences between situational and routine safety violations. Specifically, we found that individuals were more likely to imitate coworkers' routine safety violations than their situational safety violations. Coworkers' situational safety violations had an indirect effect on individuals' situational safety violations mainly through perceived social support and safety motivation. By contrast, coworkers' routine safety violations had an indirect effect on individuals' routine safety violations mainly through perceived production pressure and attitudinal ambivalence. Finally, the theoretical and practical implications, research limitations, and future directions were discussed.
Keywords
Health-care Settings; Job Demands; Attitudinal Ambivalence; Industry Development; Workplace Safety; Behavior; Climate; Model; Risk; Employee; Social Contagion; Situational Safety Violations; Routine Safety Violations; Social Learning; Social Information Processing
Kim, Taehoon; Kim, Yong-woo; Lee, Dongmin; Kim, Minju. (2022). Reinforcement Learning Approach To Scheduling Of Precast Concrete Production. Journal Of Cleaner Production, 336.
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Abstract
The production scheduling of precast concrete (PC) is essential for successfully completing PC construction projects. The dispatching rules, widely used in practice, have the limitation that the best rule differs according to the shop conditions. In addition, mathematical programming and the metaheuristic approach, which would improve performance, entail more computational time with increasing problem size, let alone its models being revised as the problem size changes. This study proposes a PC production scheduling model based on a reinforcement learning approach, which has the advantages of a general capacity to solve various problem conditions with fast computation time and good performance in real-time. The experimental study shows that the proposed model outperformed other methods by 4-12% of the total tardiness and showed an average winning rate of 77.0%. The proposed model could contribute to the successful completion of off-site construction projects by supporting the stable progress of PC construction.
Keywords
Precast Concrete; Reinforcement Learning; Deep Q -network; Production Scheduling; Minimize; Model
Migliaccio, G. C.; Bogus, Susan M.; Cordova-Alvidrez, A. A. (2014). Continuous Quality Improvement Techniques for Data Collection in Asset Management Systems. Journal Of Construction Engineering And Management, 140(4).
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Abstract
Transportation infrastructure assets are among the largest investments made by governmental agencies. These agencies use data on asset conditions to make decisions regarding the timing of maintenance activities, the type of treatment, and the resources to employ. To collect and record these data, agencies often utilize trained evaluators who assess the asset either on site or by analyzing photos and/or videos. These visual assessments are widely used to evaluate conditions of various assets, including pavement surface distresses. This paper describes a Data Quality Assessment & Improvement Framework (DQAIF) to measure and improve the performance of multiple evaluators of pavement distresses by controlling for subjective judgment by the individual evaluators. The DQAIF is based on a continuous quality improvement cyclic process that is based on the following main components: (1)assessment of the consistency over timeperformed using linear regression analysis; (2)assessment of the agreement between evaluatorsperformed using inter-rater agreement analysis; and (3)implementation of management practices to improve the results shown by the assessments. A large and comprehensive case study was employed to describe, refine, and validate the framework. When the DQAIF is applied to pavement distress data collected on site by different evaluators, the results show that it is an effective method for quickly identifying and solving data collection issues. The benefit of this framework is that the analyses employed produce performance measures during the data collection process, thus minimizing the risk of subjectivity and suggesting timely corrective actions. The DQAIF can be used as part of an asset management program, or in any engineering program in which the data collected are subjected to the judgment of the individuals performing the evaluation. The process could also be adapted for assessing performance of automated distress data acquisition systems.
Keywords
Asset Management; Civil Engineering Computing; Data Acquisition; Decision Making; Inspection; Maintenance Engineering; Quality Control; Regression Analysis; Roads; Transportation; Continuous Quality Improvement Techniques; Asset Management System; Governmental Agencies; Transportation Infrastructure Assets; Maintenance Activities; Visual Assessment; Pavement Surface Distresses; Data Quality Assessment & Improvement Framework; Dqaif; Linear Regression Analysis; Interrater Agreement Analysis; Data Collection Process; Automated Distress Data Acquisition System; Manual Pavement Distress; Pavement Management; Quantitative Analysis; Data Collection; Assets; Reliability; Case Studies
Stewart, Orion T.; Moudon, Anne Vernez; Littman, Alyson J.; Seto, Edmund; Saelens, Brian E. (2018). The Association between Park Facilities and Duration of Physical Activity During Active Park Visits. Journal Of Urban Health, 95(6), 869 – 880.
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Abstract
Public parks provide places for urban residents to obtain physical activity (PA), which is associated with numerous health benefits. Adding facilities to existing parks could be a cost-effective approach to increase the duration of PA that occurs during park visits. Using objectively measured PA and comprehensively measured park visit data among an urban community-dwelling sample of adults, we tested the association between the variety of park facilities that directly support PA and the duration of PA during park visits where any PA occurred. Cross-classified multilevel models were used to account for the clustering of park visits (n=1553) within individuals (n=372) and parks (n=233). Each additional different PA facility at a park was independently associated with a 6.8% longer duration of PA bouts that included light-intensity activity, and an 8.7% longer duration of moderate to vigorous PA time. Findings from this study are consistent with the hypothesis that more PA facilities increase the amount of PA that visitors obtain while already active at a park.
Keywords
Park Facilities; Physical Activity; Park Use; Recreation; Built Environment; Global Positioning System; Accelerometer; Gis; Gps; Accelerometer Data; United-states; Adults; Proximity; Features; Walking; Size; Attractiveness; Improvements; Environment; Parks & Recreation Areas; Parks; Luminous Intensity; Clustering; Urban Areas
Migliaccio, G. C.; Bogus, Susan M.; Cordova-Alvidrez, A. A. (2014). Techniques for Continuous Improvement of Quality of Data Collection in Systems of Capital Infrastructure Management. Journal Of Construction Engineering And Management, 140(4).
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Abstract
oLa infraestructura del transporte es una de las mas grandes inversiones que realizan los gobiernos. Las agencias gubernamentales de transporte administran este capital y utilizan la informacion de las condiciones de este para decidir la programacion y tipo de mantenimiento y recursos a ejercer. Para recolectar la informacion pertinente, las agencias emplean evaluadores adiestrados para evaluar la infraestructura, ya sea en sitio o analizando fotografias y/o videos. Las evaluaciones visuales son empleadas para inspeccionar las condiciones de la infraestructura, incluyendo el desgaste de la superficie de los caminos y carreteras. Este articulo describe un Data Quality Assessment & Improvement Framework (DQAIF) (Sistema de Evaluacion y Mejora de la Calidad de la Informacion) para medir y controlar los datos de los evaluadores del deterioro de carreteras, al controlar el criterio de estos. El DQAIF es en un proceso ciclico de Mejora Continua de Calidad compuesto por: a)la evaluacion del nivel de acuerdo entre evaluadores -por medio del analisis estadistico (inter-rater agreement analysis), b)la evaluacion de la consistencia a traves del tiempo -mediante analisis de regresion lineal, y c)la implementacion de practicas gerenciales para mejorar los resultados mostrados en las evaluaciones anteriores. Se llevo a cabo un estudio de caso para validar el sistema propuesto. Los resultados mostraron que el DQAIF es efectivo para identificar y resolver problemas de la calidad de los datos obtenidos en las inspecciones de infraestructura. Con este sistema se garantiza la reduccion del riesgo de la subjetividad y asi aplicar acciones de mantenimiento mas oportunas. El DQAIF puede ser empleado en un programa de gerencia de infraestructura o en cualquier programa de ingenieria en donde la informacion esta sujeta al juicio o criterio personal de los individuos que realizan la evaluacion. Este proceso puede ser adaptado, incluso, para evaluar el desempeno de sistemas automatizados de evaluacion de pavimentos.
Keywords
Manual Pavement Distress; Quality Control; Pavement Management; Inspection; Quantitative Analysis; Data Collection; Assets; Reliability; Construction Materials And Methods
Tenneson, Karis; Patterson, Matthew S.; Mellin, Thomas; Nigrelli, Mark; Joria, Peter; Mitchell, Brent. (2018). Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA. Remote Sensing, 10(3).
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Abstract
Historical forest management practices in the southwestern US have left forests prone to high-severity, stand-replacement fires. Reducing the cost of forest-fire management and reintroducing fire to the landscape without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to increase ability of managers to monitor AGB and other forest metrics at reduced cost. We developed a regional lidar-based statistical model to estimate AGB for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. Model selection was performed using Bayesian model averaging (BMA) to reduce researcher bias, fully explore the model space, and avoid overfitting. The selected model includes measures of canopy height, canopy density, and height distribution. The model selected with BMA explains 71% of the variability in field-estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model is structured in accordance with previously described local models, and performs equivalently to these smaller scale models. We have demonstrated the effectiveness of lidar for developing cost-effective, robust regional AGB models for monitoring and planning adaptively at the landscape scale.
Keywords
Laser Scanner Data; Landscape Restoration Program; Canopy Fuel Parameters; Discrete-return Lidar; Western United-states; Wave-form Lidar; Airborne Laser; Tropical Forest; Climate-change; Adaptive Management; Forest Biomass; Aboveground Biomass; Airborne Lidar; Monitoring; Regional Forest Inventory; Variable Selection; Bayesian Model Averaging; Multiple Linear Regression
Chen, Peng. (2015). Built Environment Factors in Explaining the Automobile-Involved Bicycle Crash Frequencies: A Spatial Statistic Approach. Safety Science, 79, 336 – 343.
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Abstract
The objective of this study is to understand the relationship between built environment factors and bicycle crashes with motor vehicles involved in Seattle. The research method employed is a Poisson lognormal random effects model using hierarchal Bayesian estimation. The Traffic Analysis Zone (TAZ) is selected as the unit of analysis to quantify the built environment factors. The assembled dataset provides a rich source of variables, including road network, street elements, traffic controls, travel demand, land use, and socio-demographics. The research questions are twofold: how are the built environment factors associated with the bicycle crashes, and are the TAZ-based bicycle crashes spatially correlated? The findings of this study are: (1) safety improvements should focus on places with more mixed land use; (2) off-arterial bicycle routes are safer than on-arterial bicycle routes; (3) TAZ-based bicycle crashes are spatially correlated; (4) TAZs with more road signals and street parking signs are likely to have more bicycle crashes; and (5) TAZs with more automobile trips have more bicycle crashes. For policy implications, the results suggest that the local authorities should lower the driving speed limits, regulate cycling and driving behaviors in areas with mixed land use, and separate bike lanes from road traffic. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords
Injury Crashes; Risk Analysis; Models; Infrastructure; Dependence; Counts; Level; Bicycle Crash Frequency; Hierarchal Bayesian Estimation; Poisson Lognormal Random Effects Model; Built Environment; Traffic Analysis Zone
Chen, Peng; Hu, Songhua; Shen, Qing; Lin, Hangfei; Xie, Chi. (2019). Estimating Traffic Volume for Local Streets with Imbalanced Data. Transportation Research Record, 2673(3), 598 – 610.
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Abstract
Annual average daily traffic (AADT) is an important measurement used in traffic engineering. Local streets are major components of a road network. However, automatic traffic recorders (ATRs) used to collect AADT are often limited to arterial roads, and such information is, therefore, often unavailable for local streets. Estimating AADT on local streets becomes a necessity as local street traffic continues to grow and the capacity of arterial roads becomes insufficient. A challenge is that an under-represented sample of local street AADT may result in biased estimation. A synthetic minority oversampling technique (SMOTE) is applied to oversample local streets to correct the imbalanced sampling among different road types. A generalized linear mixed model (GLMM) is employed to estimate AADT incorporating various independent variables, including factors of roadway design, socio-demographics, and land use. The model is examined with an AADT dataset from Seattle, WA. Results show that: (1) SMOTE helps to correct imbalanced sampling proportions and improve model performance significantly; (2) the number of lanes and the number of crosswalks are both positively associated with AADT; (3) road segments located in areas with a higher population density or more mixed land use have a higher AADT; (4) distance to the nearest arterial road is negatively correlated with AADT; and (5) AADT creates spatial spillover effects on neighboring road segments. The combination of SMOTE and GLMM improves the estimation accuracy on AADT, which contributes to better data for transportation planning and traffic monitoring, and to cost saving on data collection.
Keywords
Average; Prediction; Network; County
Estiri, Hossein; Krause, Andy; Heris, Mehdi P. (2015). Phasic Metropolitan Settlers: A Phase-Based Model for the Distribution of Households in US Metropolitan Regions. Urban Geography, 36(5), 777 – 794.
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Abstract
In this article, we develop a model for explaining spatial patterns in the distribution of households across metropolitan regions in the United States. First, we use housing consumption and residential mobility theories to construct a hypothetical probability distribution function for the consumption of housing services across three phases of household life span. We then hypothesize a second probability distribution function for the offering of housing services based on the distance from city center(s) at the metropolitan scale. Intersecting the two hypothetical probability functions, we develop a phase-based model for the distribution of households in US metropolitan regions. We argue that phase one households (young adults) are more likely to reside in central city locations, whereas phase two and three households are more likely to select suburban locations, due to their respective housing consumption behaviors. We provide empirical validation of our theoretical model with the data from the 2010 US Census for 35 large metropolitan regions.
Keywords
Residential-mobility; Life-course; Housing Consumption; Family; Satisfaction; Migration; Geography; Context; Age; Distribution Patterns; Us Metropolitan Regions; Household