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Author: University of Wyoming. Department of Civil and Architectural Engineering Publisher: ISBN: Category : Highway planning Languages : en Pages : 227
Book Description
Low-volume roads are excluded from regular traffic counts except on a need to know basis. But needs for traffic volume data on low-volume roads in road infrastructure management, safety, and air quality analysis have necessitated regular traffic volume estimates. This study developed traffic volume estimation models for low-volume roads in Wyoming. A review of existing estimation models was carried out. Two main model types were identified - regression models and Travel Demand Models (TDMs). The study developed the two model types and recommended the best model for implementation. Two regression models were developed, a linear and a logistic regression model. Each of the regression models was developed using data from 13 randomly selected counties and nine counties were used in model validation. The linear regression model had an R2 of 64 percent and was verified to be a good predictor of traffic volumes across Wyoming. The logistic regression model validation indicated a prediction accuracy ranging from 78 to 89 percent. The TDM was developed using standard factors and trip rates in the NCHRP Report 365. The TDM was implemented for four south eastern counties in Wyoming. The model was then validated and calibrated by comparing actual traffic volumes to those generated by the model. The calibrated model had a Percentage Root Mean Square Error and an R 2 values of 50 and 74 percent respectively. The report compared the three models with respect to cost-effectiveness, ease of use, and accuracy and recommended the TDM for implementation. The regression models were recommended for applications requiring quick traffic volume estimations and for which lower levels of accuracy are acceptable.
Author: University of Wyoming. Department of Civil and Architectural Engineering Publisher: ISBN: Category : Highway planning Languages : en Pages : 227
Book Description
Low-volume roads are excluded from regular traffic counts except on a need to know basis. But needs for traffic volume data on low-volume roads in road infrastructure management, safety, and air quality analysis have necessitated regular traffic volume estimates. This study developed traffic volume estimation models for low-volume roads in Wyoming. A review of existing estimation models was carried out. Two main model types were identified - regression models and Travel Demand Models (TDMs). The study developed the two model types and recommended the best model for implementation. Two regression models were developed, a linear and a logistic regression model. Each of the regression models was developed using data from 13 randomly selected counties and nine counties were used in model validation. The linear regression model had an R2 of 64 percent and was verified to be a good predictor of traffic volumes across Wyoming. The logistic regression model validation indicated a prediction accuracy ranging from 78 to 89 percent. The TDM was developed using standard factors and trip rates in the NCHRP Report 365. The TDM was implemented for four south eastern counties in Wyoming. The model was then validated and calibrated by comparing actual traffic volumes to those generated by the model. The calibrated model had a Percentage Root Mean Square Error and an R 2 values of 50 and 74 percent respectively. The report compared the three models with respect to cost-effectiveness, ease of use, and accuracy and recommended the TDM for implementation. The regression models were recommended for applications requiring quick traffic volume estimations and for which lower levels of accuracy are acceptable.
Author: Dick T. Apronti Publisher: ISBN: 9781339391878 Category : Highway planning Languages : en Pages : 239
Book Description
This research project developed low-cost and effective traffic volume prediction models for rural low-volume roads in Wyoming. The models were developed to support a wide variety of design, planning, and management functions on both state and county road networks. A review of existing methods of traffic volume estimation in other states was carried out. Two main model types were identified - regression models and travel demand models. The study developed the two model types for Wyoming and recommended the best model for implementation. Two regression models were developed, a linear regression model and a logistic regression model. Each of the regression models was developed using data from 13 randomly selected counties in Wyoming and nine counties were used to verify the utility of the models. The linear regression model had an R square of 64.3% and was verified to be a good predictor of traffic volumes across Wyoming. The logistic regression model was also verified and shown to have a prediction accuracy ranging from 78% to 89%. The travel demand model (TDM) was developed using standard factors and trip rates in the NCHRP Report 365. The TDM was implemented for four south eastern counties in Wyoming. The model was then verified and calibrated by comparing actual traffic volumes to those generated by the model. The calibrated model had percentage root mean square error (%RMSE) and R square values of 50.33% and 74% respectively. The dissertation compared the three models with respect to cost-effectiveness, ease of use, and accuracy and recommended the travel demand model for implementation across Wyoming. The regression models were recommended for applications requiring quick traffic volume estimations and for which lower levels of accuracy are acceptable.
Author: Jaime Jo Hepner Publisher: ISBN: 9781339185569 Category : Highway planning Languages : en Pages : 180
Book Description
Estimating traffic on low volume roads is a cost effective alternative. Traditional traffic counts on low volume roads are relatively expensive and impractical due to the high lane mileage and the low priority placed on such roads. In this thesis, a regression model was developed to predict Annual Average Daily Traffic (AADT) on low volume roads with an AADT less than 200 in Wyoming using socio-economic, demographic and road geometric factors. Surface type, land use, highway access, tax revenue and road width were determined to be significant predictors to estimate AADT. The data used in estimating the model was obtained from four counties representing various land use types typical in Wyoming. The model was later adjusted and verified with data from Sublette County. The model was validated using the remaining counties in the state. The validation determined the prediction model was not unbiased in predicting AADT. WYDOT can use of the developed methodology to predict traffic volumes for low volume county roads in Wyoming. The methodology used to create the model can be implemented in other states to estimate traffic volumes on low volume rural roads.
Author: Er Yue Publisher: ISBN: Category : Tourism Languages : en Pages : 7
Book Description
Tourism trips occupy a major part of traffic volume, especially in frequently visited areas. Historically, transportation planning has been led by urban and metropolitan planning. Much of this effort has been directed towards reducing traffic congestion and providing adequate capacity. More recently, there has been increased emphasis on estimating traffic on rural lowvolume roads driven at least in part by safety and air quality concerns. So far, tourism traffic is barely recognized in travel demand model due to available data in this field (Hofer et al. 2016).
Author: Marwan Hafez Publisher: ISBN: 9781339441450 Category : Low-volume roads Languages : en Pages : 188
Book Description
In 2014, Wyoming Technology Transfer Center/Local Technical Assistance Program (WYT2/LTAP) initiated a pavement management system (PMS) program for county roads in the State of Wyoming. Building a PMS for county roads provides assistant and defensible tools for legislatures to allocate appropriate funds to maintain county roads. In Wyoming, there are total 2,444 miles of county paved roads managed and maintained under the supervision of local governments and municipalities. More than 50% of county paved roads have an average daily traffic (ADT) less than 400 vehicles per day. These roads are considered as low-volume roads. There is no legal requirement to implement a typical pavement management system on county and local roads. However, the funding constraints for maintaining county roads increase the importance of implementing a pavement management system on the lower systems. The most important issue in managing county paved roads as low-volume roads is to define practices and polices applied within the available resources. This study investigates appropriate tools to better manage low-volume paved roads. The tools provide effective guidelines and statistical techniques to reduce the costs of collecting pavement condition data. Online surveys were disseminated for all experts and pavement managers who are involved in preserving low-volume paved roads in Colorado and nationwide. This study developed four surveys. The summaries of only two surveys were included in this thesis since the two other surveys are in progress. A feedback from TRB standing committee members and specialist engineers in Colorado Department of Transportation (CDOT) was analyzed. The most appropriate practices and recommended tools were developed for managing low-volume paved roads using effective strategies. These strategies help local governments in Wyoming manage their county paved roads in a cost-effective manners. The automated techniques used to collect pavement condition data are relatively expensive for local agencies. In addition, there are questions about the needs to collect pavement condition data annually since county roads have relatively low traffic volumes. In order to optimize the cost of data collection, this study evaluates the possibility of reducing the amount of pavement condition data collected in each survey. Reducing the amount of collected data provides missing values. This study applies multiple imputation analyses as an assistant tool to predict the uncollected data at the network level. Another objective of this study is to determine the most cost-effective pavement condition data collection frequencies. The study uses a historical PMS data of the State secondary highways in Wyoming as a case study. A comparison between different frequencies was developed. It was concluded that uncollected pavement condition indices can be predicted using initial/historical values. The imputation models, developed in this study, provided a good estimation of the uncollected pavement condition indices. Therefore, pavement condition data of low-volume paved roads is not recommended to be collected for the whole network annually. Instead, a less expensive sequence can be adopted where the data which is not collected can be predicted using multiple imputation models developed in this study.
Author: Igor Ivan Publisher: Springer ISBN: 3319612972 Category : Science Languages : en Pages : 420
Book Description
This book is intended for researchers, practitioners and students who are interested in the current trends and want to make their GI applications and research dynamic. Time is the key element of contemporary GIS: mobile and wearable electronics, sensor networks, UAVs and other mobile snoopers, the IoT and many other resources produce a massive amount of data every minute, which is naturally located in space as well as in time. Time series data is transformed into almost (from the human perspective) continuous data streams, which require changes to the concept of spatial data recording, storage and manipulation. This book collects the latest innovative research presented at the GIS Ostrava 2017 conference held in 2017 in Ostrava, Czech Republic, under the auspices of EuroSDR and EuroGEO. The accepted papers cover various aspects of dynamics in GIscience, including spatiotemporal data analysis and modelling; spatial mobility data and trajectories; real-time geodata and real-time applications; dynamics in land use, land cover and urban development; visualisation of dynamics; open spatiotemporal data; crowdsourcing for spatiotemporal data and big spatiotemporal data.
Author: University of Wyoming. Department of Civil and Architectural Engineering
Author: University of Wyoming. Department of Civil and Architectural Engineering
Author: Dick T. Apronti
Author: Jaime Jo Hepner
Author: Er Yue
Author: United States. Bureau of Public Roads
Author: 
Author: Marwan Hafez
Author: United States. Federal Highway Administration
Author: Igor Ivan
Author: Fred Cammack