Description: A Milepoint represents the location along a CDOT highway where that highway's linear reference system indicates a whole number reference point (i.e., milepoints are whole number reference points, such as 57.000). Please note, mile posts are not necessarily found at milepoints. Milepoint features are represented by point geographic shapes. Bounding coordinates and feature counts reflect a statewide extent.
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Description: Features in this dataset represent public highways that are maintained by and under the jurisdiction of the Colorado Department of Transportation. These highways consist of Interstates, US Highways, and State Highways, and are represented by polyline (linear) geographic shapes. These shapes have been generalized (some vertices removed) for simpler cartography at small map scales.
Description: LOSS is reported in this dataset only where crash patterns have been previously identified for highway segments and intersections. This product was produced in 2015 from crash data circa 2009 - 2013. LOSS is reported in levels 1 through 4 (1 = low potential for accident reduction, 2 = low to moderate potential for accident reduction, 3 = less moderate to high potential for accident reduction, 4 = high potential for accident reduction). LOSS values for total accidents may be the same as LOSS values for severity accidents, though the values can be different (e.g. LOSS total for a segment could be a 2 for Total LOSS and 3 for severity). LOSS values were determined using a cumulative probability threshold of 80th percentile and a minimum number of 5 crashes. Intersection locations were mapped using CDOT's 2013 linear referencing system. Approximately 927 out of 2457 records had no LOSS values reported. The database contains fields for LOSS Total and LOSS Severity (injury + fatal), and crash patterns by category.LOSS is calculated using DiExSys Vision Zero Suite (VZS). LOSS was generated using spreadsheets provided by David Swenka with CDOT. This dataset represents an interim product; new SPF models will soon be developed and will affect the LOSS. An additional task has already been identified to run LOSS statewide upon completion of the updated SPF models.
Copyright Text: DiExSys Visions Zero Suite (Jake Kononov & Bryan Allery), Evan Kirby, GISP with FHU, and David Swenka, Traffic Engineer with CDOT.
Description: LOSS is reported in this dataset only where crash patterns have been previously identified for highway segments and intersections. This product was produced in 2015 from crash data circa 2009 - 2013. LOSS is reported in levels 1 through 4 (1 = low potential for accident reduction, 2 = low to moderate potential for accident reduction, 3 = less moderate to high potential for accident reduction, 4 = high potential for accident reduction). LOSS values for total accidents may be the same as LOSS values for severity accidents, though the values can be different (e.g. LOSS total for a segment could be a 2 for Total LOSS and 3 for severity). LOSS values were determined using a cumulative probability threshold of 80th percentile and a minimum number of 5 crashes. Intersection locations were mapped using CDOT's 2013 linear referencing system. Approximately 927 out of 2457 records had no LOSS values reported. The database contains fields for LOSS Total and LOSS Severity (injury + fatal), and crash patterns by category.LOSS is calculated using DiExSys Vision Zero Suite (VZS). LOSS was generated using spreadsheets provided by David Swenka with CDOT. This dataset represents an interim product; new SPF models will soon be developed and will affect the LOSS. An additional task has already been identified to run LOSS statewide upon completion of the updated SPF models.
Copyright Text: DiExSys Visions Zero Suite (Jake Kononov & Bryan Allery), Evan Kirby, GISP with FHU, and David Swenka, Traffic Engineer with CDOT.
Description: LOSS is reported in this dataset only where crash patterns have been previously identified for highway segments and intersections. This product was produced in 2015 from crash data circa 2009 - 2013. LOSS is reported in levels 1 through 4 (1 = low potential for accident reduction, 2 = low to moderate potential for accident reduction, 3 = less moderate to high potential for accident reduction, 4 = high potential for accident reduction). LOSS values for total accidents may be the same as LOSS values for severity accidents, though the values can be different (e.g. LOSS total for a segment could be a 2 for Total LOSS and 3 for severity). LOSS values were determined using a cumulative probability threshold of 80th percentile and a minimum number of 5 crashes. Intersection locations were mapped using CDOT's 2013 linear referencing system. Approximately 927 out of 2457 records had no LOSS values reported. The database contains fields for LOSS Total and LOSS Severity (injury + fatal), and crash patterns by category.LOSS is calculated using DiExSys Vision Zero Suite (VZS). LOSS was generated using spreadsheets provided by David Swenka with CDOT. This dataset represents an interim product; new SPF models will soon be developed and will affect the LOSS. An additional task has already been identified to run LOSS statewide upon completion of the updated SPF models.
Copyright Text: DiExSys Visions Zero Suite (Jake Kononov & Bryan Allery), Evan Kirby, GISP with FHU, and David Swenka, Traffic Engineer with CDOT.
Description: LOSS is reported in this dataset only where crash patterns have been previously identified for highway segments and intersections. This product was produced in 2015 from crash data circa 2009 - 2013. LOSS is reported in levels 1 through 4 (1 = low potential for accident reduction, 2 = low to moderate potential for accident reduction, 3 = less moderate to high potential for accident reduction, 4 = high potential for accident reduction). LOSS values for total accidents may be the same as LOSS values for severity accidents, though the values can be different (e.g. LOSS total for a segment could be a 2 for Total LOSS and 3 for severity). LOSS values were determined using a cumulative probability threshold of 80th percentile and a minimum number of 5 crashes. Intersection locations were mapped using CDOT's 2013 linear referencing system. Approximately 927 out of 2457 records had no LOSS values reported. The database contains fields for LOSS Total and LOSS Severity (injury + fatal), and crash patterns by category.LOSS is calculated using DiExSys Vision Zero Suite (VZS). LOSS was generated using spreadsheets provided by David Swenka with CDOT. This dataset represents an interim product; new SPF models will soon be developed and will affect the LOSS. An additional task has already been identified to run LOSS statewide upon completion of the updated SPF models.
Copyright Text: DiExSys Visions Zero Suite (Jake Kononov & Bryan Allery), Evan Kirby, GISP with FHU, and David Swenka, Traffic Engineer with CDOT.
Description: LOSS highway segment mapping was developed from databases exported from DiExSys Vision Zero Suite model runs by highway Safety Performance Function (SPF) type. Data was linear referenced using CDOT's current LRS.Prior to the preparation of statewide highway segnment LOSS mapping, safety performance of segments were corrected for the Regression to the Mean Bias (RTM). RTM phenomenon reflects the tendency for random events, such as vehicle crashes, to move toward the average during the course of an experiment or over time. For instance if a segment exhibits unusually high or unusually low crash frequency in a particular year, because of RTM we need to be aware that over the long run its true average is closer to the mean representing safety performance of similar facilities. The existence of the RTM bias has been long recognized and is now effectively addressed by using the Empirical Bayes (EB) method . The use of EB method is particularly effective when it takes a long time for a few accidents to occur, as is often the case on Colorado rural roads. The empirical Bayes (EB) method for the estimation of safety increases the precision of estimation and corrects for the regression to the mean bias. EB corrected values of frequency and severity of crashes were used in all SPF analysis to assess the magnitude of the safety problem.
Description: LOSS highway segment mapping was developed from databases exported from DiExSys Vision Zero Suite model runs by highway Safety Performance Function (SPF) type. Data was linear referenced using CDOT's current LRS.Prior to the preparation of statewide highway segnment LOSS mapping, safety performance of segments were corrected for the Regression to the Mean Bias (RTM). RTM phenomenon reflects the tendency for random events, such as vehicle crashes, to move toward the average during the course of an experiment or over time. For instance if a segment exhibits unusually high or unusually low crash frequency in a particular year, because of RTM we need to be aware that over the long run its true average is closer to the mean representing safety performance of similar facilities. The existence of the RTM bias has been long recognized and is now effectively addressed by using the Empirical Bayes (EB) method . The use of EB method is particularly effective when it takes a long time for a few accidents to occur, as is often the case on Colorado rural roads. The empirical Bayes (EB) method for the estimation of safety increases the precision of estimation and corrects for the regression to the mean bias. EB corrected values of frequency and severity of crashes were used in all SPF analysis to assess the magnitude of the safety problem.
Description: LOSS highway segment mapping was developed from databases exported from DiExSys Vision Zero Suite model runs by highway Safety Performance Function (SPF) type. Data was linear referenced using CDOT's current LRS.Prior to the preparation of statewide highway segnment LOSS mapping, safety performance of segments were corrected for the Regression to the Mean Bias (RTM). RTM phenomenon reflects the tendency for random events, such as vehicle crashes, to move toward the average during the course of an experiment or over time. For instance if a segment exhibits unusually high or unusually low crash frequency in a particular year, because of RTM we need to be aware that over the long run its true average is closer to the mean representing safety performance of similar facilities. The existence of the RTM bias has been long recognized and is now effectively addressed by using the Empirical Bayes (EB) method . The use of EB method is particularly effective when it takes a long time for a few accidents to occur, as is often the case on Colorado rural roads. The empirical Bayes (EB) method for the estimation of safety increases the precision of estimation and corrects for the regression to the mean bias. EB corrected values of frequency and severity of crashes were used in all SPF analysis to assess the magnitude of the safety problem.
Description: LOSS highway segment mapping was developed from databases exported from DiExSys Vision Zero Suite model runs by highway Safety Performance Function (SPF) type. Data was linear referenced using CDOT's current LRS.Prior to the preparation of statewide highway segnment LOSS mapping, safety performance of segments were corrected for the Regression to the Mean Bias (RTM). RTM phenomenon reflects the tendency for random events, such as vehicle crashes, to move toward the average during the course of an experiment or over time. For instance if a segment exhibits unusually high or unusually low crash frequency in a particular year, because of RTM we need to be aware that over the long run its true average is closer to the mean representing safety performance of similar facilities. The existence of the RTM bias has been long recognized and is now effectively addressed by using the Empirical Bayes (EB) method . The use of EB method is particularly effective when it takes a long time for a few accidents to occur, as is often the case on Colorado rural roads. The empirical Bayes (EB) method for the estimation of safety increases the precision of estimation and corrects for the regression to the mean bias. EB corrected values of frequency and severity of crashes were used in all SPF analysis to assess the magnitude of the safety problem.
Description: Features in this dataset represent public highways that are maintained by and under the jurisdiction of the Colorado Department of Transportation. These highways consist of Interstates, US Highways, and State Highways, and are represented by polyline (linear) geographic shapes. These shapes have been generalized (some vertices removed) for simpler cartography at small map scales.Represents 2017 Routes.