Common use of Analysis Results Clause in Contracts

Analysis Results. Table 23 shows travel distances by travel modes for the baseline and ambitious scenarios. At baseline, the population mean of active transport duration in California was 40.5 minutes per person per week. Active transport and transit comprised approximately 4.9% of all distance traveled in the state in 2010. The distances traveled by different modes varied across MPOs in California due to the patterns of land development. The San Francisco Bay Area has the highest walking and cycling distances owing to its moderate to high development densities. The San ▇▇▇▇▇▇▇ Valley is partially rural while Southern California and San Diego are known for sprawling land developments. Table 24 shows the percent increase of active transportation and transit usage according to the MPO’s preferred SCSs, as compared to the 2010 baseline condition. Table 25 shows the reduction (i.e., compared to the 2010 baseline scenario) in death and ▇▇▇▇▇, predicted by ▇▇▇▇▇, of the preferred SCSs of the MPOs. Table 27 shows the reduction in death and ▇▇▇▇▇ by specific health conditions of the preferred SCSs, while Table 27 for the four ambitious scenarios. Source: Maizlish, Linesch, and ▇▇▇▇▇▇▇▇ (2017) Source: Maizlish, Linesch, and ▇▇▇▇▇▇▇▇ (2017) Source: Maizlish, Linesch, and ▇▇▇▇▇▇▇▇ (2017) Table 25 shows that health benefits of California MPO’s SCSs follow respective increases in active transportation and transit. The SCSs of San Diego and Southern California had the largest increases in active travel (see Table 24) and the largest decrease in ▇▇▇▇ rates. These two regions had net positive ▇▇▇▇ rates for road traffic injuries. The preferred SCS of Sacramento consists of significantly ambitious transit expansion but modest increase in active transport (see Table 24), resulting in large decreases in ▇▇▇▇ rates from fatalities and injuries from traffic crashes. Table 26 shows that all six MPO SCSs combined can decrease the annual number of deaths by 909 and DALYs by 16,089. The population attributable fraction (PAF) is the proportional reduction in death or DALYs that would occur if active transportation (i.e., physical activity) were increased to the levels assumed by the SCSs. Cardiovascular diseases accounted for the largest share of the reduced disease burden as measured by death and DALYs. Table 27 shows that, of all ambitious scenarios, Cycle had the largest reduction in deaths and DALYs. Cardiovascular disease, diabetes, and dementia were important contributors to the reduction. The health benefits of the Walk scenario are similar to those of Cycle. However, increased pedestrians on the roads increase death and injuries from traffic accidents, but increasing bicyclists are expected to decrease traffic deaths and injuries. This reflects the difference in the average travel speeds between pedestrians and bicycles. Traffic injuries were estimated by multiplying the baseline crash rates of victim modes with a composite of the changes in miles traveled by both the victim modes (i.e., pedestrians or bicyclists) and striking modes (i.e., automobiles). To engage in the same 283 minutes of active transportation, bicyclists can travel much longer distances than pedestrians, resulting in significantly more automobile VMT being replaced by bicycles than pedestrians. Thus, in the Cycle scenario, the probability for increased bicyclists to increase traffic death and injuries is offset by decreased automobile VMT on the roads (i.e., replaced by bicycles). The Transit scenario does not produce as much chronic disease reduction as the Walk and Cycle scenarios. It is noted that, unlike the Walk and Cycle scenarios, in which half of the study population are assumed to engage in physical activity meeting the physical activity guidelines, the assumption of Transit scenario does not result in sufficient level of physical activity (i.e., by walking or cycling) involvement. Thus, the health benefits from cardiovascular disease and diabetes are relatively small when compared to rest of the scenarios. However, like the Cycle scenario, increasing transit ridership can also reduce traffic accidents with less automobiles on the road. The Blend scenario is indeed a combination of the other three scenarios, thus showing reduction in all health benefits that are less than Cycle or Walk scenario, but higher than Transit. Nevertheless, the Blend scenario with equal amount engagement in walking, cycling, and transit use represents a more likely scenario for the future than the Walk or Cycle scenario. Table 28 shows that, with the projected 2040 population, the preferred SCSs of all five MPOs combined cannot keep carbon emissions below the 2010 baseline condition. It was noted that, despite meeting per capita targets (CARB, 2010), the increase in MPO-wise walking and cycling specified in the preferred SCSs was not sufficient to meet the goals of the California Department of Transportation (Caltrans), which seeks to double walking miles and triple cycling from 2010 numbers by 2020 (Caltrans, 2015). Southern California with its vast sprawling land developments generates significantly higher carbon emissions (i.e., estimated by projected VMT) than the other four MPOs. However, the Cycle and Transit scenarios are the most effective in reducing carbon emissions among all scenarios. The San Francisco Bay Area was the only MPO with consistent reductions in carbon emissions in the Cycle, Transit, and Blend scenarios owing to higher baseline transit ridership and active transportation participation as well as a lower VMT per vehicle when compared to other MPOs.

Analysis Results. Table 23 shows travel distances by travel modes for the baseline and ambitious scenarios. At baseline, the population mean of active transport duration in California was 40.5 minutes per person per week. Active transport and transit comprised approximately 4.9% of all distance traveled in the state in 2010. The distances traveled by different modes varied across MPOs in California due to the patterns of land development. The San Francisco Bay Area has the highest walking and cycling distances owing to its moderate to high development densities. The San ▇▇▇▇▇▇▇ Valley is partially rural while Southern California and San Diego are known for sprawling land developments. Table 24 shows the percent increase of active transportation and transit usage according to the MPO’s preferred SCSs, as compared to the 2010 baseline condition. Table 25 shows the reduction (i.e., compared to the 2010 baseline scenario) in death and ▇▇▇▇▇, predicted by ▇▇▇▇▇, of the preferred SCSs of the MPOs. Table 27 shows the reduction in death and ▇▇▇▇▇ by specific health conditions of the preferred SCSs, while Table 27 for the four ambitious scenarios. Table 23 Per Capita Travel Distance and Active Travel Times by Modes and Scenarios‌ Source: Maizlish, Linesch, and ▇▇▇▇▇▇▇▇ (2017) Source: Maizlish, Linesch, and ▇▇▇▇▇▇▇▇ (2017) Table 25 Reduction in Number and Rate of Deaths and DALYs of the Preferred SCSs‌ Source: Maizlish, Linesch, and ▇▇▇▇▇▇▇▇ (2017); Note: Rate is for every 100,000 people. Source: Maizlish, Linesch, and ▇▇▇▇▇▇▇▇ (2017) Table 27 Change in the Burden of Disease and Injury by Scenarios of Walking, Cycling, and Transit‌ Source: Maizlish, Linesch, and ▇▇▇▇▇▇▇▇ (2017) Table 25 shows that health benefits of California MPO’s SCSs follow respective increases in active transportation and transit. The SCSs of San Diego and Southern California had the largest increases in active travel (see Table 24) and the largest decrease in ▇▇▇▇ rates. These two regions had net positive ▇▇▇▇ rates for road traffic injuries. The preferred SCS of Sacramento consists of significantly ambitious transit expansion but modest increase in active transport (see Table 24), resulting in large decreases in ▇▇▇▇ rates from fatalities and injuries from traffic crashes. Table 26 shows that all six MPO SCSs combined can decrease the annual number of deaths by 909 and DALYs by 16,089. The population attributable fraction (PAF) is the proportional reduction in death or DALYs that would occur if active transportation (i.e., physical activity) were increased to the levels assumed by the SCSs. Cardiovascular diseases accounted for the largest share of the reduced disease burden as measured by death and DALYs. Table 27 shows that, of all ambitious scenarios, Cycle had the largest reduction in deaths and DALYs. Cardiovascular disease, diabetes, and dementia were important contributors to the reduction. The health benefits of the Walk scenario are similar to those of Cycle. However, increased pedestrians on the roads increase death and injuries from traffic accidents, but increasing bicyclists are expected to decrease traffic deaths and injuries. This reflects the difference in the average travel speeds between pedestrians and bicycles. Traffic injuries were estimated by multiplying the baseline crash rates of victim modes with a composite of the changes in miles traveled by both the victim modes (i.e., pedestrians or bicyclists) and striking modes (i.e., automobiles). To engage in the same 283 minutes of active transportation, bicyclists can travel much longer distances than pedestrians, resulting in significantly more automobile VMT being replaced by bicycles than pedestrians. Thus, in the Cycle scenario, the probability for increased bicyclists to increase traffic death and injuries is offset by decreased automobile VMT on the roads (i.e., replaced by bicycles). The Transit scenario does not produce as much chronic disease reduction as the Walk and Cycle scenarios. It is noted that, unlike the Walk and Cycle scenarios, in which half of the study population are assumed to engage in physical activity meeting the physical activity guidelines, the assumption of Transit scenario does not result in sufficient level of physical activity (i.e., by walking or cycling) involvement. Thus, the health benefits from cardiovascular disease and diabetes are relatively small when compared to rest of the scenarios. However, like the Cycle scenario, increasing transit ridership can also reduce traffic accidents with less automobiles on the road. The Blend scenario is indeed a combination of the other three scenarios, thus showing reduction in all health benefits that are less than Cycle or Walk scenario, but higher than Transit. Nevertheless, the Blend scenario with equal amount engagement in walking, cycling, and transit use represents a more likely scenario for the future than the Walk or Cycle scenario. Table 28 shows that, with the projected 2040 population, the preferred SCSs of all five MPOs combined cannot keep carbon emissions below the 2010 baseline condition. It was noted that, despite meeting per capita targets (CARB, 2010), the increase in MPO-wise walking and cycling specified in the preferred SCSs was not sufficient to meet the goals of the California Department of Transportation (Caltrans), which seeks to double walking miles and triple cycling from 2010 numbers by 2020 (Caltrans, 2015). Southern California with its vast sprawling land developments generates significantly higher carbon emissions (i.e., estimated by projected VMT) than the other four MPOs. However, the Cycle and Transit scenarios are the most effective in reducing carbon emissions among all scenarios. The San Francisco Bay Area was the only MPO with consistent reductions in carbon emissions in the Cycle, Transit, and Blend scenarios owing to higher baseline transit ridership and active transportation participation as well as a lower VMT per vehicle when compared to other MPOs.