The total number of 2015 jobs located in planned 2012 HQTAs. High Quality Transit Areas (HQTAs) are defined as being within 1/2-mile of fixed guideway transit and/ or bus rapid transit stations with service every 15 minutes or less during peak commute times. SCAG published two shapefiles of (HQTAs): one for actual HQTAs in 2012, and one for planned HQTAs in 2040. HQTAs were mapped for the county, along with population density.

The total number of 2015 jobs located in planned 2040 HQTAs. High Quality Transit Areas (HQTAs) are defined as being within 1/2-mile of fixed guideway transit and/ or bus rapid transit stations with service every 15 minutes or less during peak commute times. SCAG published two shapefiles of (HQTAs): one for actual HQTAs in 2012, and one for planned HQTAs in 2040. HQTAs were mapped for the county, along with population density.

Emissions from passenger vehicles and trucks are estimated based on VMT by vehicle type. VMT for each jurisdiction is estimated using trip-based travel forecasting models developed by Southern California Association of Governments (SCAG). SCAG’s regional travel demand model analyzes transportation network and socioeconomic data such as population, household, and employment, to forecast daily vehicle trips for each traffic
analysis zone (TAZ). Model outputs include:
• Vehicle trips by type: including cars, light trucks, medium duty trucks, heavy duty trucks, and transit vehicles
• Vehicle trip lengths by trip purpose
• Vehicle trip origins and destinations

Based on vehicle trips analysis, VMT calculations are performed for all cities within LA County (except for the City of Avalon), including unincorporated areas that are under direct County control. Using CARB Emissions Factors (EMFAC) model, CO2 and N2O emissions are estimated by multiplying emissions rates with vehicle activity data in all cities and unincorporated areas within the South Coast (SC) sub-area and the Mojave Desert (MD) sub-area. Sub-area emissions are then disaggregated based on speed bin by time of day and vehicle profile to estimate GHG emissions from into passenger vehicle and heavy duty trucks.

Emissions from passenger vehicles and trucks are estimated based on VMT by vehicle type. VMT for each jurisdiction is estimated using trip-based travel forecasting models developed by Southern California Association of Governments (SCAG). SCAG’s regional travel demand model analyzes transportation network and socioeconomic data such as population, household, and employment, to forecast daily vehicle trips for each traffic
analysis zone (TAZ). Model outputs include:
• Vehicle trips by type: including cars, light trucks, medium duty trucks, heavy duty trucks, and transit vehicles
• Vehicle trip lengths by trip purpose
• Vehicle trip origins and destinations

Based on vehicle trips analysis, VMT calculations are performed for all cities within LA County (except for the City of Avalon), including unincorporated areas that are under direct County control. Using CARB Emissions Factors (EMFAC) model, CO2 and N2O emissions are estimated by multiplying emissions rates with vehicle activity data in all cities and unincorporated areas within the South Coast (SC) sub-area and the Mojave Desert (MD) sub-area. Sub-area emissions are then disaggregated based on speed bin by time of day and vehicle profile to estimate GHG emissions from into passenger vehicle and heavy duty trucks.

See https://data.lacounty.gov/Transportation/County-Employee-Average-Vehicle-Ridership-AVR-2012/b92g-vuub for more detail.

• Average Vehicle Ridership (AVR) is the figure derived by dividing the employee population at a given worksite that reports to work weekdays between 6:00 a.m. and 10:00 a.m. by the number of vehicles driven by these employees commuting from home to the worksite during these hours. The AVR is calculated using a weekly averaging period. The applicable employee population is multiplied by the number of weekdays in the selected averaging period, then divided by the total number of vehicles driven by these employees to the worksite during the same period.
• The survey is administered to each county department by location. The calculated AVR for each responding location was included in the data from the CSO (who received the data from the Department of Human Resources) for years 2012-2018, except 2017. An AVR of “1” means that every employee drove alone; therefore a greater AVR is a better outcome. The target AVR is set for each county department, and targets range mostly from 1.5-1.75.
• For each year, a weighted average for the total AVR across county departments was calculated to account for the varied number of employees at each location and graphically presented the data.

See https://data.lacounty.gov/Transportation/County-Employee-Average-Vehicle-Ridership-AVR-2012/b92g-vuub for more detail.

• Average Vehicle Ridership (AVR) is the figure derived by dividing the employee population at a given worksite that reports to work weekdays between 6:00 a.m. and 10:00 a.m. by the number of vehicles driven by these employees commuting from home to the worksite during these hours. The AVR is calculated using a weekly averaging period. The applicable employee population is multiplied by the number of weekdays in the selected averaging period, then divided by the total number of vehicles driven by these employees to the worksite during the same period.
• The survey is administered to each county department by location. The calculated AVR for each responding location was included in the data from the CSO (who received the data from the Department of Human Resources) for years 2012-2018, except 2017. An AVR of “1” means that every employee drove alone; therefore a greater AVR is a better outcome. The target AVR is set for each county department, and targets range mostly from 1.5-1.75.
• For each year, a weighted average for the total AVR across county departments was calculated to account for the varied number of employees at each location and graphically presented the data.

Average Vehicle Ridership (AVR) is the figure derived by dividing the employee population at a given worksite that reports to work weekdays between 6:00 a.m. and 10:00 a.m. by the number of vehicles driven by these employees commuting from home to the worksite during these hours. The AVR is calculated using a weekly averaging period. The applicable employee population is multiplied by the number of weekdays in the selected averaging period, then divided by the total number of vehicles driven by these employees to the worksite during the same period.
The survey is administered to each county department by location. The calculated AVR for each responding location was included in the data from the CSO (who received the data from the Department of Human Resources) for years 2012-2018, except 2017. An AVR of “1” means that every employee drove alone; therefore a greater AVR is a better outcome. The target AVR is set for each county department, and targets range mostly from 1.5-1.75.
For each year, a weighted average for the total AVR across county departments was calculated to account for the varied number of employees at each location and graphically presented the data.

Point data from the Transportation Injury Mapping System (TIMS) for all collisions in L.A. County from 2006-2015
• Geocoded point data from the Transportation Injury Mapping System (TIMS) for all collisions in LA County from 2006-2015 (the latest year available) was downloaded. The data set was narrowed to collisions in which bicyclists or pedestrians were the victims. The data set was further narrowed to fatalities and severe injuries for two reasons. First, this best addresses the goals of the County’s Vision Zero Plan. Vision Zero Plans, in the County and elsewhere, aim to prevent fatalities by reducing vehicle speed to minimize the risk of severe injury from collisions. Second, TIMS data is less reliable for minor injuries and does not represent frequently unreported collisions.
• A pivot table was used to calculate the number of bicyclist and pedestrian fatalities and severe injuries from 2006-2015 and presented this data graphically.
• The TIMS data was loaded into GIS and overlaid it on a map of LA County. Transparent points were used to convey what areas have had the highest number of collisions between 2006-2015.

• Commute Mode Share: The U.S. Census American Community Survey (ACS) 1yr Table B08134 for the years 2005 to 2017 was used to calculate the mode share for commute trips for these years. The ACS gives total number for the following categories: drove alone, carpooled, public transit, walk, and other. These numbers were divided by the total number of survey respondents to calculate the percent of commuters utilizing each of these modes of transportation and presented the information graphically and in tabular form.
• Average Commute Time: Data from the U.S. Census American Community Survey 1yr Table S0802 for the years 2005 to 2017 was used for average commute time and presented this data for those years in a table with the commute mode share.

Commute Mode Share: The U.S. Census American Community Survey (ACS) 1yr Table B08134 for the years 2005 to 2017 was used to calculate the mode share for commute trips for these years. The ACS gives total number for the following categories: drove alone, carpooled, public transit, walk, and other. These numbers were divided by the total number of survey respondents to calculate the percent of commuters utilizing each of these modes of transportation and presented the information graphically and in tabular form.
Average Commute Time: Data from the U.S. Census American Community Survey 1yr Table S0802 for the years 2005 to 2017 was used for average commute time and presented this data for those years in a table with the commute mode share.