2.6 Non-Commute Vehicle Miles Traveled

While commute-related driving is the primary way cities currently evaluate their GHG transportation footprint, residents also do a significant amount of non-commute-related driving, which also has its own GHG footprint. We are developing a methodology to produce an order-of-magnitude estimation of VMTs associated with Stockton residents traveling to places of economic activity, such as grocery stores and restaurants.

The full analytical methodology is still being refined and deserves its own separate report to detail, but generally follows these steps:

We used 2018 Safegraph data to collect trip visit data to amenities in and around Stockton by Stockton residents. Safegraph reports total visits from its panel of monitored devices each month, which can be weighted to represent the full population. It indicates any census block group which is the origin of more than 5 monitored devices in a given month, which can be used to estimate the routes and VMTs for Stockton visitors, similar to how origin-destination data is used in the commute VMT analysis. We also applied revision factors to the analysis to account for: (1) converting Safegraph s reported median travel distance to mean; (2) linked trips, such as dropping by the grocery store and library on the same trip, which reduce the total VMTs; (3) carpooling; and other factors.

The map below shows the location of all points of interest in California that had recorded visitors from Stockton in 2018. Each point is colored and sized based on the VMTs we calculated associated with Stockton visitors; purple/small is low, yellow/large is high.

load("C:/Users/derek/Google Drive/City Systems/Stockton Green Economy/results.Rdata")

amenity_VMT <- location_VMT_new_join_agg
colnames(amenity_VMT)[2] <- "location name"
colnames(amenity_VMT)[3] <- "total VMT"
colnames(amenity_VMT)[4] <- "business type"
colnames(amenity_VMT)[7] <- "street address"
colnames(amenity_VMT)[10] <- "zip code"

amenity_VMT$full_address <- NULL
amenity_VMT$sub_category <- NULL
amenity_VMT$naics_code <- NULL

amenity_VMT <- amenity_VMT[,c("total VMT", "location name", "business type", "street address", "city", "state", "zip code", "geometry")]

# map <- mapview(amenity_VMT, zcol = "total VMT", legend = TRUE, cex= "total VMT", alpha = 0)
# 
# mapshot(map, url = "map-amenity-vmt-location.html")

knitr::include_url("https://city.systems/stockton-greeneconomy/map-amenity-vmt-location.html")

Figure 2.26: Location of points of interest with Stockton visitors in 2018. Source: Safegraph.

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The following plot shows the distribution of VMTs by amenity type (out of hundreds available). The largest category by far is the second tall bar, “Restaurants and Other Eating Places”, followed by “Traveler Accommodations” and “Gas Stations”.

business_listing <- unique(location_VMT_new_join_agg$top_category)
bus_list_df <- data.frame(matrix(data = 0, nrow = length(business_listing), ncol = 3))
colnames(bus_list_df) <- c("business type", "total VMT", "% VMT")

count <- 0

for (business_iter in business_listing){
  
  count <- count + 1
  bus_iter_df <- location_VMT_new_join_agg[location_VMT_new_join_agg$top_category == business_iter, ]

  bus_list_df[count, "business type"] <- business_iter
  bus_list_df[count, "total VMT"] <- sum(bus_iter_df$VMT_total, na.rm = TRUE)
  
}

bus_list_df[, "% VMT"] <- bus_list_df[, "total VMT"] / sum(bus_list_df[, "total VMT"]) * 100

bus_list_df_ordered <- bus_list_df[order(-bus_list_df$`total VMT`),]
colnames(bus_list_df_ordered) <- c("business_type", "total_VMT", "p_VMT")

bus_list_df_ordered_other_label <- "Other"
bus_list_df_ordered_other_VMT <- sum(as.numeric(bus_list_df_ordered[21:nrow(bus_list_df_ordered),'total_VMT']))
bus_list_df_ordered_other_perc <- sum(as.numeric(bus_list_df_ordered[21:nrow(bus_list_df_ordered),'p_VMT']))
bus_list_df_ordered_other <- cbind(bus_list_df_ordered_other_label, bus_list_df_ordered_other_VMT, bus_list_df_ordered_other_perc)
colnames(bus_list_df_ordered_other) <- c("business_type", "total_VMT", "p_VMT")

bus_list_df_ordered_new <- rbind(bus_list_df_ordered[1:20,], bus_list_df_ordered_other)
colnames(bus_list_df_ordered_new) <- c("business_type", "total_VMT", "p_VMT")
bus_list_df_ordered_new$total_VMT <- as.numeric(bus_list_df_ordered_new$total_VMT)

ggplot(
  bus_list_df_ordered_new,
  aes(
    x = business_type,
    y = total_VMT
  )
) + 
  geom_bar(stat = 'identity', aes(fill = total_VMT), position = "dodge") +
  labs(x = "Amenity Type", y = "Total VMT", title = "Amenity Type VMT Count") +
  theme(axis.text.x = element_text(angle = 35, hjust = 1)) +
  theme(legend.position = "none")
Non-commute VMT by type of amenity. Source: Safegraph 2018.

Figure 2.27: Non-commute VMT by type of amenity. Source: Safegraph 2018.

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The following table shows the top 50 individual points of interest from the same dataset.

location_VMT_CA_table <-
  location_VMT_new_join_agg %>%
  transmute(
    'Location Name' = location_name,
    'Total Stockton VMT' = VMT_total %>% as.numeric(),
    'Amenity Type' = top_category,
    'Street Address' = street_address,
    'City' = city
  ) %>% 
  arrange(desc(`Total Stockton VMT`))

location_VMT_CA_table$geometry <- NULL
location_VMT_CA_table$`Total Stockton VMT` <- prettyNum(round(location_VMT_CA_table$`Total Stockton VMT`,-4),big.mark=",")

kable(location_VMT_CA_table[1:50,], booktabs = TRUE, caption = "Top 50 points of interest for Stockton residents in 2018. Source: Safegraph.") %>% kable_styling() %>% scroll_box(width = "100%", height = "500px")
Table 2.19: Top 50 points of interest for Stockton residents in 2018. Source: Safegraph.
Location Name Total Stockton VMT Amenity Type Street Address City
Pilot Travel Centers 26,730,000 Gasoline Stations 345 roth road lathrop
Love’s Travel Stop 23,200,000 Gasoline Stations 15250 thornton road lodi
Red Chillies 20,560,000 Restaurants and Other Eating Places 167 south main street milpitas
Park West Place 16,330,000 Lessors of Real Estate 10318 10940 trinity parkway stockton
Pilot Travel Centers 14,370,000 NA NA NA
san francisco international airport 14,260,000 Support Activities for Air Transportation sfo san francisco
The Outlet Thrift Store 12,700,000 Used Merchandise Stores 1212 east harding way stockton
La Quinta Inns & Suites 12,290,000 Traveler Accommodation 2710 west march lane stockton
Hilton International 11,190,000 Traveler Accommodation 2323 grand canal boulevard stockton
metropolitan oakland intl airport 10,690,000 Support Activities for Air Transportation metropolitan oakland intl airport oakland
sacramento intl airport 8,320,000 Support Activities for Air Transportation sacramento intl airport sacramento
Weberstown Mall Vicinity 7,350,000 Lessors of Real Estate 4950 pacific avenue stockton
Amtrak 6,980,000 NA NA NA
Four Copies 6,660,000 Office Supplies, Stationery, and Gift Stores 2538 west lane stockton
Pilot Travel Centers 6,520,000 Gasoline Stations 15100 north thornton road lodi
san joaquin general hospital 6,480,000 General Medical and Surgical Hospitals 500 west hospital road french camp
Herbert S Miles Safety Roadside Rest 6,410,000 NA NA NA
Sherwood Mall Vicinity 5,730,000 Lessors of Real Estate 5308 pacific avenue stockton
Cinnabon 5,610,000 Restaurants and Other Eating Places 15327 thornton road lodi
San Francisco International Airport Terminal 3 E 5,580,000 Traveler Accommodation 806 south airport boulevard san francisco
Courtyard by Marriott 5,490,000 Traveler Accommodation 3252 west march lane stockton
SUBWAY 5,230,000 NA NA NA
Fairfield Inn & Suites by Marriott 5,200,000 Traveler Accommodation 227 west san marcos boulevard san marcos
Jackson Rancheria Casino Resort 5,190,000 Gambling Industries 12222 new york ranch road jackson
san joaquin delta college 5,160,000 Colleges, Universities, and Professional Schools san joaquin delta college stockton
Boron Rest Area 5,130,000 NA NA NA
Walmart 5,010,000 General Merchandise Stores, including Warehouse Clubs and Supercenters 3223 east hammer lane stockton
SUBWAY 5,000,000 Restaurants and Other Eating Places 15237 thornton road lodi
alex g spanos center 4,960,000 Promoters of Performing Arts, Sports, and Similar Events 1040 dave brubeck way university of the pacific stockton
The Center 4,860,000 Lessors of Real Estate the center stockton
Walmart 4,690,000 General Merchandise Stores, including Warehouse Clubs and Supercenters 10355 trinity parkway stockton
Candy Cane Inn 4,680,000 Traveler Accommodation 1747 south harbor boulevard anaheim
College Square 4,670,000 Lessors of Real Estate 909 west march lane stockton
Pilot Travel Centers 4,580,000 NA NA NA
Depot Deli 4,520,000 Beer, Wine, and Liquor Stores 200 west florida avenue hemet
Certified Grocers of California Salvage 4,140,000 General Merchandise Stores, including Warehouse Clubs and Supercenters 1990 piccoli road stockton
st josephs medical center of stockton 4,140,000 General Medical and Surgical Hospitals 1800 north california street stockton
University Plaza Waterfront Hotel 4,030,000 Traveler Accommodation 110 west fremont street stockton
7D Experience 3,890,000 Amusement Parks and Arcades pier 39 building m 1 san francisco
Margaret’s Cafe & Bakery 3,710,000 Restaurants and Other Eating Places 12222 new york ranch road jackson
DeRosa University Center 3,600,000 Restaurants and Other Eating Places 901 presidents drive stockton
University of the Pacific 3,580,000 Colleges, Universities, and Professional Schools 3601 pacific avenue stockton
Holiday Inn Express 3,550,000 Traveler Accommodation 5045 kingsley road stockton
Eagle Cafe 3,540,000 NA NA NA
Best Western 3,520,000 Traveler Accommodation 111 east march lane stockton
Zuniga Ramiro MD San Joaquin General Hospital 3,440,000 Offices of Physicians 500 west hospital road french camp
Anello Family Crab & Seafood 3,410,000 NA NA NA
Coastal Pacific Food 3,400,000 Grocery Stores 1015 performance drive stockton
disney california adventure park 3,330,000 NA NA NA
Stockton Metropolitan Airport 3,320,000 Traveler Accommodation 5000 south airport way stockton

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The following map shows the same data aggregated back to Stockton blockgroups, and shown as VMT per capita. For most of Stockton there is no clear pattern, but some there are notable block groups at the periphery that have high VMT per capita. Transit and ride-sharing strategies, or land use policy and development that brings needed amenities closer to where people live, could target VMT reduction where it is needed the most.

# map <- mapview(bg_VMT, zcol = c("per_capita"), legend = TRUE, layer.name = "Non-commute<br>VMTper capita")
# 
# mapshot(map, url = "map-amenity-vmt-bg.html")

knitr::include_url("https://city.systems/stockton-greeneconomy/map-amenity-vmt-bg.html")

Figure 2.28: Non-commute VMT per capita by block group. Source: Safegraph 2018.

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Overall, the VMTs associated with non-commute travel in 2018 sum up to about 1.2 billion, compared to an estimated 2.2 billion for commute travel, a roughly 1:2 ratio. This preliminary work suggests that strategies to reduce non-commute travel, which may be much more actionable for local cities, may have impacts up to half the size of commuter-oriented strategies, and should not be ignored.