Wyoming Low-volume Roads Traffic Volume Estimation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Wyoming Low-volume Roads Traffic Volume Estimation write by University of Wyoming. Department of Civil and Architectural Engineering. This book was released on 2015. Wyoming Low-volume Roads Traffic Volume Estimation available in PDF, EPUB and Kindle. 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.
Wyoming's Low-volume Roads Traffic Volume Estimation
Wyoming's Low-volume Roads Traffic Volume Estimation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Wyoming's Low-volume Roads Traffic Volume Estimation write by Dick T. Apronti. This book was released on 2015. Wyoming's Low-volume Roads Traffic Volume Estimation available in PDF, EPUB and Kindle. 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.
Developing a Traffic Regression Model for Low Volume Roads in Wyoming
Developing a Traffic Regression Model for Low Volume Roads in Wyoming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Developing a Traffic Regression Model for Low Volume Roads in Wyoming write by Jaime Jo Hepner. This book was released on 2015. Developing a Traffic Regression Model for Low Volume Roads in Wyoming available in PDF, EPUB and Kindle. 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.
MPC-572
MPC-572 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook MPC-572 write by Er Yue. This book was released on 2018. MPC-572 available in PDF, EPUB and Kindle. 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).
Dynamics in GIscience
Dynamics in GIscience - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dynamics in GIscience write by Igor Ivan. This book was released on 2017-08-23. Dynamics in GIscience available in PDF, EPUB and Kindle. 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.
