L&D Manual Turn Lane Storage Validation/Update

Queuing occurs at intersections mostly due to overflow or inadequacy of turn bays. The ODOT L&D Manual Volume 1 has storage requirements for both signalized and unsignalized intersections. Figures 401-9E and 401-10E of the L&D Manual provide the required turn lane storage lengths which should be compared with the real world conditions to check for adequacy of these lengths as a measure of ensuring that accesses to the left turn lanes are not blocked. In addition to the projected turn lane volume, ODOT’s methodology incorporates both deceleration (based on the speed of the roadway) and potential blockage from the adjacent through lane. Currently, however, there are no records whether these storage lengths computed by the methodology put forth in this manual are valid and accurately represent the actual conditions at intersections in Ohio. This study used real world traffic and queue storage data at some intersections and analyzed these data to validate the model ODOT is currently using. This study used the observed field data to evaluate the ODOT’s model of storage length at intersections. In addition, the queue storage lengths observed from field data were compared with the prediction results of HCS and SYNCHRO computer packages. The model evaluation task evaluated the level of precision of each of the three models (ODOT, HCS, and SYNCHRO) with respect to the field data observation. L&D Manual lead the way by accurately predicting the observed queues by about 81.6% and closely followed by HCS, which also had a 79.2% prediction accuracy. SYNCHRO was by far the lowest with a 46.0% prediction accuracy

L&D Manual Turn Lane Storage Validation/Update

Queuing occurs at intersections mostly due to overflow or inadequacy of turn bays. The ODOT L&D Manual Volume 1 has storage requirements for both signalized and unsignalized intersections. Figures 401-9E and 401-10E of the L&D Manual provide the required turn lane storage lengths which should be compared with the real world conditions to check for adequacy of these lengths as a measure of ensuring that accesses to the left turn lanes are not blocked. In addition to the projected turn lane volume, ODOT’s methodology incorporates both deceleration (based on the speed of the roadway) and potential blockage from the adjacent through lane. Currently, however, there are no records whether these storage lengths computed by the methodology put forth in this manual are valid and accurately represent the actual conditions at intersections in Ohio. This study used real world traffic and queue storage data at some intersections and analyzed these data to validate the model ODOT is currently using. This study used the observed field data to evaluate the ODOT’s model of storage length at intersections. In addition, the queue storage lengths observed from field data were compared with the prediction results of HCS and SYNCHRO computer packages. The model evaluation task evaluated the level of precision of each of the three models (ODOT, HCS, and SYNCHRO) with respect to the field data observation. L&D Manual lead the way by accurately predicting the observed queues by about 81.6% and closely followed by HCS, which also had a 79.2% prediction accuracy. SYNCHRO was by far the lowest with a 46.0% prediction accuracy

Identifying locations with high rates of alcohol related traffic crashes in Ohio

On average, every day in the United States 32 people die in motor vehicle crashes involving alcohol-impaired drivers. This is equivalent to one death every 45 minutes. The objectives of this study were three-fold: (1) to identify counties in the state of Ohio with relative high alcohol-related traffic crash rates; (2) to develop a visual presentation of results by using Geographic Information System (GIS on the Ohio map of counties); (3) to recommend areas or locations needing elevated and targeted alcohol-related driving reinforcement and educational efforts. Ohio traffic crash, number of population, number of registered vehicles, number of licensed drivers and average daily vehicle miles of travel data at the county level for 2007-2010 were used to analyze the alcohol-related traffic crash rates in counties. The results indicate that the most urbanized counties of Franklin, Cuyahoga, Hamilton, Summit, Montgomery, Lucas and their surrounding counties which are highly populated and also with high traffic volumes are the locations where most of the alcohol related traffic crashes occurred. The interesting results, however, were obtained from analysis of the risk of alcohol-related traffic crashes when the population and other exposure metrics were factored in order to determine the relative risk rates, which enable us to compare the counties fairly. Population density, daily vehicle miles of travel, the number of licensed drivers and the number of registered vehicles enabled us to capture counties with high risk rates. Generally, rural southern counties in the Appalachian areas and in the eastern parts of the state are the ones that appeared on the top of the list in almost all risk rate methods used in this study. In the northeastern area, the county of Ashtabula was the only county in that area which was ranked among the most risk counties in almost all the risk traffic crash rates methods used in this study. Counties that were highly ranked include Carroll, Harrison, Guernsey, Perry, Belmont, and Muskingum counties in the eastern part of the state and counties in the southern part highly ranked include Vinton, Ross, Pike, Adams, and Lawrence

Identifying locations with high rates of alcohol related traffic crashes in Ohio

On average, every day in the United States 32 people die in motor vehicle crashes involving alcohol-impaired drivers. This is equivalent to one death every 45 minutes. The objectives of this study were three-fold: (1) to identify counties in the state of Ohio with relative high alcohol-related traffic crash rates; (2) to develop a visual presentation of results by using Geographic Information System (GIS on the Ohio map of counties); (3) to recommend areas or locations needing elevated and targeted alcohol-related driving reinforcement and educational efforts. Ohio traffic crash, number of population, number of registered vehicles, number of licensed drivers and average daily vehicle miles of travel data at the county level for 2007-2010 were used to analyze the alcohol-related traffic crash rates in counties. The results indicate that the most urbanized counties of Franklin, Cuyahoga, Hamilton, Summit, Montgomery, Lucas and their surrounding counties which are highly populated and also with high traffic volumes are the locations where most of the alcohol related traffic crashes occurred. The interesting results, however, were obtained from analysis of the risk of alcohol-related traffic crashes when the population and other exposure metrics were factored in order to determine the relative risk rates, which enable us to compare the counties fairly. Population density, daily vehicle miles of travel, the number of licensed drivers and the number of registered vehicles enabled us to capture counties with high risk rates. Generally, rural southern counties in the Appalachian areas and in the eastern parts of the state are the ones that appeared on the top of the list in almost all risk rate methods used in this study. In the northeastern area, the county of Ashtabula was the only county in that area which was ranked among the most risk counties in almost all the risk traffic crash rates methods used in this study. Counties that were highly ranked include Carroll, Harrison, Guernsey, Perry, Belmont, and Muskingum counties in the eastern part of the state and counties in the southern part highly ranked include Vinton, Ross, Pike, Adams, and Lawrence