Description: This dataset is based on the outputs from SCAG’s 2012 Regional Model and shows the arrival locations and densities of PEVs during peak morning hours. Using surveys of household travel behavior, SCAG’s travel demand model estimates the number of trips from home to work, school, and other destinations by time of day. The morning peak period represents weekday trips that occur between 6 and 9 a.m. (i.e., commutes to work) and the mid-day period represents weekday trips that occur between 9 a.m. and 3 p.m. (i.e., trips to run errands). The model does not distinguish commuting patterns by vehicle type, so it is assumed that the commuting patterns of PEVs are the same as those of conventional vehicles. The data on PEV registrations comes from automotive data vendor IHS Automotive, which provided the number of PEVs registered as new within each 2010 Census tract from December 2010 through September 2016. It is important to note that these morning peak and mid-day destination TAZs receive vehicles from outside the COG.
Description: This dataset is based on the outputs from SCAG’s 2012 Regional Model and shows the arrival locations and densities of PEVs during peak morning hours. Using surveys of household travel behavior, SCAG’s travel demand model estimates the number of trips from home to work, school, and other destinations by time of day. The morning peak period represents weekday trips that occur between 6 and 9 a.m. (i.e., commutes to work) and the mid-day period represents weekday trips that occur between 9 a.m. and 3 p.m. (i.e., trips to run errands). The model does not distinguish commuting patterns by vehicle type, so it is assumed that the commuting patterns of PEVs are the same as those of conventional vehicles. The data on PEV registrations comes from automotive data vendor IHS Automotive, which provided the number of PEVs registered as new within each 2010 Census tract from December 2010 through September 2016. It is important to note that these morning peak and mid-day destination TAZs receive vehicles from outside the COG.
Description: This dataset is based on the outputs from SCAG’s 2012 Regional Model and shows the arrival locations and densities of PEVs during peak morning hours. Using surveys of household travel behavior, SCAG’s travel demand model estimates the number of trips from home to work, school, and other destinations by time of day. The morning peak period represents weekday trips that occur between 6 and 9 a.m. (i.e., commutes to work) and the mid-day period represents weekday trips that occur between 9 a.m. and 3 p.m. (i.e., trips to run errands). The model does not distinguish commuting patterns by vehicle type, so it is assumed that the commuting patterns of PEVs are the same as those of conventional vehicles. The data on PEV registrations comes from automotive data vendor IHS Automotive, which provided the number of PEVs registered as new within each 2010 Census tract from December 2010 through September 2016. It is important to note that these morning peak and mid-day destination TAZs receive vehicles from outside the COG.
Description: In 2018, the Mobile Source Air Pollution Reduction Review Committee (MSRC) commissioned the UCLA Luskin Center for Innovation (LCI) to identify locations within Southern California where investments in charging infrastructure could displace cVMT with eVMT. LCI focused on two types of locations have higher than usual hurdles for installing charging equipment locations: workplaces and MUDs, such as apartments and condos. This spatial layer focuses on workplaces and provides an estimate of the additional eVMT can be supported daily in TAZs when PHEVs used for commutes are able to receive a supplemental charge while parked at workplaces. The eVMT estimates were derived from the following inputs: (a) SCAG’s regional travel demand model of daily workplace commutes; (b) residential locations of PHEVs according to IHS Markit vehicle registration data; (c) electric ranges by PHEV model according to the fuel economy databasehosted by the U.S. Department of Energy and Environmental Protection Agency; (d) point-to-point commute distances based on road network centerlines from OpenStreetMap; and (e) existing charging network from the Alternative Fuels Data Center (AFDC). The AFDC data is used to adjust the additional eVMT score so that it accounts for the eVMT that may already be supported by existing charging opportunities (assuming that each public charger is already supporting a PHEV driver’s commute home).