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
Kang, Mingyu; Moudon, Anne Vernez; Kim, Haena; Boyle, Linda Ng. (2019). Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS. International Journal Of Environmental Research And Public Health, 16(19).
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Abstract
Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modelling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.
Keywords
Traffic Crash; Walking; Collisions; Accidents; Models; Pedestrian Safety; Spatial Autocorrelation; Algorithm
Kang, Bumjoon; Scully, Jason Y.; Stewart, Orion; Hurvitz, Philip M.; Moudon, Anne V. (2015). Split-Match-Aggregate (SMA) Algorithm: Integrating Sidewalk Data with Transportation Network Data in GIS. International Journal Of Geographical Information Science, 29(3), 440 – 453.
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Abstract
Sidewalk geodata are essential to understand walking behavior. However, such geodata are scarce, only available at the local jurisdiction and not at the regional level. If they exist, the data are stored in geometric representational formats without network characteristics such as sidewalk connectivity and completeness. This article presents the Split-Match-Aggregate (SMA) algorithm, which automatically conflates sidewalk information from secondary geometric sidewalk data to existing street network data. The algorithm uses three parameters to determine geometric relationships between sidewalk and street segments: the distance between streets and sidewalk segments; the angle between sidewalk and street segments; and the difference between the lengths of matched sidewalk and street segments. The SMA algorithm was applied in urban King County, WA, to 13 jurisdictions' secondary sidewalk geodata. Parameter values were determined based on agreement rates between results obtained from 72 pre-specified parameter combinations and those of a trained geographic information systems (GIS) analyst using a randomly selected 5% of the 79,928 street segments as a parameter-development sample. The algorithm performed best when the distances between sidewalk and street segments were 12m or less, their angles were 25 degrees or less, and the tolerance was set to 18m, showing an excellent agreement rate of 96.5%. The SMA algorithm was applied to classify sidewalks in the entire study area and it successfully updated sidewalk coverage information on the existing regional-level street network data. The algorithm can be applied for conflating attributes between associated, but geometrically misaligned line data sets in GIS.
Keywords
Geodatabases; Sidewalks; Algorithms; Pedestrians; Digital Mapping; Algorithm; Gis; Pedestrian Network Data; Polyline Conflation; Sidewalk; Built Environment; Physical-activity; Mode Choice; Urban Form; Land-use; Travel; Generation; Walking
Lin, Xiongbin; Maclachlan, Ian; Ren, Ting; Sun, Feiyang. (2019). Quantifying Economic Effects of Transportation Investment Considering Spatiotemporal Heterogeneity in China: A Spatial Panel Data Model Perspective. The Annals Of Regional Science, 63(3), 437 – 459.
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Abstract
Transportation investment plays a significant role in promoting economic development. However, in what scenario and to what extent transportation investment can stimulate economic growth still remains debatable. For developing countries undergoing rapid urbanization, answering these questions is necessary for evaluating proposals and determining investment plans, especially considering the heterogeneity of spatiotemporal conditions. Current literature lacks systematical research to consider the impacts of panel data and spatial correlation issue in examining the economic effects of transportation investment. To fill this gap, this study collects provincial panel data in China from 1997 to 2015 to evaluate multi-level temporal and spatial effects of transportation investment on economic growth by using spatial panel data analysis. Results show that transportation investment leads to significant and positive effects on growth and spatial concentration of economic activities, but these results vary significantly depending on the temporal and spatial characteristics of each province. The economic impacts of transportation investment are quite positive even considering the time lag effects. This study suggests that both central and local governments should carefully evaluate the multifaceted economic effects of transportation investment, such as a balanced transportation investment and economic development between growing and lagging regions, and considering the spatiotemporal heterogeneity of the economic environment.
Keywords
High-speed Rail; Infrastructure Investment; Causal Relationship; Empirical-analysis; Growth; Impact; Productivity; Efficiency; Spillover; Agglomeration; C33; R40; R58; Spatial Analysis; Time Lag; Urbanization; Transportation; Heterogeneity; Economic Growth; Economic Models; Economic Impact; Data Analysis; Spatial Data; Panel Data; Economic Development; Developing Countries--ldcs; Investments; Economic Analysis; Investment; Local Government; China