Neighborhood Density and Travel Mode p. 1

Neighborhood Density and Travel ModeNew Survey Findings for High DensitiesSherman LewisCalifornia State University East Bay Hayward Abstract

This paper focuses exclusively on neighborhood density and travel mode. Recent data from the California Household Transportation Survey (CHTS) especially extracted at our request from the original data is used to quantify trip stages, trip distances, duration time of trips, and travel mode for five levels of neighborhood density, from rural to exurban, suburban, central city, and urban core. The urban core in this paper is denser than in most other research. Of particular interest is the relationship of density to vehicle miles traveled and walking distances. At the highest densities auto mode changes strongly to non-auto modes, suggesting a threshold for an acceleration of mode shift at higher densities. The paper also includes data from the National Household Transportation Survey, with weaker results because it does not go to a higher density. The paper generally confirms previous research, but finds a stronger role for density by looking at higher densities than other papers. These higher densities need further research by considering variables that complement density. 1 Introduction

This paper is concerned with neighborhood density and travel mode, particularly for walkable neighborhood systems, which have high densities and reduce auto dependency. Low density cannot sustain much walking, biking, or transit because the distances between destinations are so great.

Density as used here refers to a walkable neighborhood system consisting of density as such, attractive walking paths (high intersection density; small block size), local-serving businesses (mixed use, close destinations), closeness to other urban uses (employment, centrality), parking management, and high quality transit. A threshold of density is necessary to shorten travel distances in order for the other features to work. This meaning for density is grounded in urban development before high levels of auto dominance, when investment in development considered density and related factors as a whole, not as isolated features.

With high levels of auto subsidy in modern times, some high density does not have the functionality for non-auto modes of interest to this research. High density alone is no longer a guarantee of walkability; there may be barriers like large paved areas, parking, traffic, and congestion which reduce the amount of walking.

The data did not allow a distinction between the density of interest and dense places with auto dominance, but a signal still emerges despite the noise.

Neighborhood Density and Travel Mode p. 2

Shifting from auto to non-auto modes is desirable from several perspectives: general sustainability and livability, and more specifically reducing carbon emissions, less pollution, walking for health, saving open space, reducing accidents, lower living costs, less traffic congestion, and so on. We need to know more about how high density relates to shifting modes at higher densities, especially the threshold for transition to non-auto modes.

A walkable neighborhood system has enough population within walking distance to create economic demand for local business and transit ridership. Density over area refers to the walkable area of a dense residential neighborhood including neighborhood-serving land uses, and not to the density of a tract, block group, or block area. Such a neighborhood achieves economies of scale from short walking distances.

Census block groups can be grouped into walkable neighborhoods better than census tracts, which often contain internal areas with non-neighborhood uses and varying neighborhood densities. The smaller-sized block groups reduce the area that would dilute the true density. For example, a census tract could include rural, exurban, and suburban land areas and have on average an exurban density, while the block groups that compose it would report numbers closer to the densities of the smaller areas.

Even with block groups, the functional neighborhood density can be skewed by containing non-neighborhood land uses that are not on routine walking routes, like open spaces, roadways, and central business. Without these land uses, the functional density would be higher. In other cases, non-neighborhood uses are on routine walking routes, in which case they do affect the density and have to be included in the neighborhood area. Block groups in dense areas are smaller than in suburbia, reducing the size of this problem.

However, we were not able to implement this definition with the CHTS or NHTS data, and had to use aggregate data of census block groups as a proxy. Again, as with uncertainty about the kind of density, a signal emerged from the noise.

As far as we can tell, data on density and neighborhoods of interest are not available.2 Literature review

Little attention has been paid to specific high density neighborhoods and their travel modes. What is called high density in most articles turns out to be unclear as the areas meant or too low for needed economies of scale. High density neighborhoods are a very small percentage of total neighborhoods in the United States and are usually aggregated with less high densities and included in areas too big for attractive walking distances. We could find no literature on walkable neighborhood systems and density in persons per acre for walkable areas.

Neighborhood Density and Travel Mode p. 3

We examined the literature looking for walkable density for specific neighborhoods. Many studies show correlations of travel mode with density combined with many other variables but very little research has been done for the densest neighborhoods and block groups. Other studies look at a metro scale of square miles rather than walkable neighborhoods or block groups.

Rodier and Johnston (1997) stated that, “A number of studies have found that higher density cities reduce VMT per capita. Studies of higher densities near transit indicate reductions in automobile travel on the order of 4 percent over 30 years in the Seattle region, 14 percent over 20 years in Portland, Oregon, and 20 percent over 20 years based on a review of several simulation studies in the United States.” The studies they cited, however, did not have data on higher densities in specific neighborhoods.

The seminal work by Newman and Kenworthy (1999) established a strong link internationally between density and vehicles miles traveled (VMT). Brownstone and Golob (2008) claimed such “studies are flawed because they do not account for the possibility of residential self-selection…” The studies were not flawed and the issue of self-selection is irrelevant to the issue of density and mode. The Newman and Kenworthy study remains among the best studies of the simple facts of density and VMT at the metro level.

Holtzclaw’s (1994) reported on nine studies that confirmed the results of his 1991 and 1994 reports. Density was the most important variable, followed by transit accessibility, and income was not important. He presented his earlier 1991 study of five San Francisco area neighborhoods used VMT by location of auto owner, supplied by the California DMV from smog check odometer records. This uniquely valuable data, not used in other studies, produced strong correlations. At the extreme high and low densities, Nob Hill to Fisherman’s' Wharf (northeast San Francisco) had 52 people per acre compared to San Ramon, the least dense, with 2.4 people per acre. Northeast San Francisco had 3,462 VMT per capita and San Ramon had 10,591 VMT per capita (Table 6). This data indicated that a doubling of density reduced VMT by 25 to 30 percent (p. 4)

Holtzclaw (1994) also discussed densities that ranged from .1 people per acre to 52 per acre in 28 neighborhoods in San Francisco, Los Angeles, San Diego and Sacramento. Of these, only Northeast San Francisco had the high density of interest for this research. With so many lower densities, the correlations of density and VMT were not as strong as in the 1991 study. Various statistical correlations concluded that “as density doubles VMT declines by 16%...” “Testing density alone, 77% of the variance is explained by the general formulation that doubling density reduces VMT by 20%...” “And 72% of the variance is explained by the formulation that doubling density reduces VMT by 25%.”

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Doubling, unfortunately, is a problematic metric, because, at the low end of density (rural, exurban, suburban), doubling doesn’t correlate well with VMT, and at the high end doubling quickly reaches high rise densities. The high end density of 49.2 people per acre was followed by the next highest at 24.2, then 19.4, resulting an very small sample size at the high end. It is interesting that even variations of suburban densities showed reduced VMT.

Other methodological issues were using residential acres, which excluded streets and other uses and thus was not a neighborhood area. However, it was useful for estimating, especially in suburbia where whole areas had large non-neighborhood uses.

The Community data the total area, but the sizes were usually bigger than 320 acres, which is a loose guideline for an area with attractive walking distances. Of the 28 communities, 27 had over 640 acres. Holtzclaw used data on households, which is problematic because households are much smaller in dense areas than suburbia. Despite these problems, Holtzclaw’s articles were the only ones that considered specific communities with ample density on them.

Holtzclaw (2002) discussed a concept important for this paper, threshold neighborhood density, a tipping point for an acceleration in a shift to non-auto modes as density creates the supporting land use conditions. Based on the MTC survey of 1990 of over 10,000 households, he presents 5 to 10 households per residential acre, which roughly translates to 6 to 12 persons per neighborhood acre. The chart below confirms the uptick at that level. The number of auto and non-auto trips are about equal in the range of 20 to 50 households per residential acre. The middle value, 35 households per acre, is about 40 persons per neighborhood acre. Fifty households per acre is about 58 persons per neighborhood acre and achieves 70 percent non-auto mode, the goal of Walkable Neighborhood System.

Neighborhood Density and Travel Mode p. 5

< 2 2 - 5 5 - 10 10 - 20 20 - 50 > 500

1

2

3

4

5

6

7

Density and Mode, 1990 MTC Survey

Daily Veh Trips/Hh

Non-auto modes

Households/Residential Acre

Trip

s per

day

, veh

icle

Trip

s per

day

, non

-aut

o

The Brownstone and Golob (2008) studied VMT at the block group level

(2008). They found that VMT and fuel use decline with increasing density, but the highest density block group range started at 5,000 units per square mile (7.81 units per acre, about 20 persons per acre), on the border between suburban and central city densities and well below the 40-60 person per acre inflection point addressed in this paper. They concluded “…the impacts of increased residential density are too small to make increasing density a relevant policy tool for trying to reduce VMT…” However, it seems likely that the small urban core was swamped by the much larger central city data. Even so, they found a 43% reduction in VMT going from a low density of 1,000 to 3,000 units per square mile up to their high density of over 5,000 units per square mile. VMT dropped from 29,800 miles per unit to 16,900 miles per unit at the higher density.

Siembab and Rhoads (2009) studied neighborhoods in eight cities near the Los Angeles Airport. They surveyed residents about their travel patterns to seven routine destinations and related them to density among other factors. The study is unusual in recognizing the importance of density over area, approximated in this study by using block groups: “An important dimension of density is its scale.” The study also was sensitive to the need to measure density more accurately by using only areas with housing, which they called net density as opposed to gross density of a larger area with

Neighborhood Density and Travel Mode p. 6

other land uses. Going from gross to net increases the density and gets closer to what is experienced by residents, although without necessarily having the contiguity required for density over area. In particular, while they recognized the problem of discontinuity, they did not apply it extensively. Inglewood, for example, had the highest density but in scattered pieces, losing the concentration needed for a walkable system, and Inglewood had a weak reduction in VMT. They found that moderate densities showed signs of mode shift, but the densities of the neighborhoods were too low for significant change. They ranged from 34.1 to 61.9 persons per acre but it is unclear if streets were included in the housing acreage to know how close their numbers might come to block group densities. Their study is among the few to measure specific neighborhoods and their VMT.

Leck (2006) identified 40 published studies of the influence of the built environment on travel mode. “The findings reaffirm the role of residential density as the most important built environment element influencing travel choice.” (Leck 2006, abstract) He does not mention any specific density or neighborhood. He cites a number of studies supporting this conclusion and a few contrary, but none of these studies report any specific densities for walkable areas, census areas, or the urban core where mode change seems to be the strongest.

Ewing and Cervero (2010) summarized over 200 studies without mentioning neighborhood population density per acre. They focus on density and VMT correlations without discussion of how non-linear the relationship might be at the high density end of the spectrum. It is not clear if any of the studies are dealing with high density walkable neighborhoods.

Ewing and Cervero estimated that a doubling of neighborhood density would reduce both per capita VT (vehicle trips) and VMT by approximately 5% if all other variables were kept the same. After reviewing 40 published studies of the built environment and travel, 17 were selected that met minimum methodological and statistical criteria. They found that residential density, employment density, and land use mix are inversely related to VMT at the p < .001 significance level. However, they did not identify the density range over which the doubling would happen, a problem similar to Holtzclaw’s paper.

These studies generally find that increased density in neighborhoods correlates with increased walkability and decreased VMT, but none report neighborhood densities and mode splits along a range of densities. They do not discuss walkable systems, which are different from walkability. They do not report the higher ends of the density range. The role of density is mixed in with other variables. Most of the area in the U.S. is too dispersed to support walking, so research that does not include those few areas of highest density may overlook their mode shift. A number of articles report how a percentage shift in density is correlated with a percentage shift in VMT: “A 10 percent increase in local density and local design, for example, was associated with a 0.5 percent decline in vehicle trips...” (Leck,

Neighborhood Density and Travel Mode p. 7

reporting on Ewing and Cervero, 2001). Since autos are needed across most of the density spectrum, VMT does not change much until the urban core. The research for this paper indicates 40 to 60 persons per acre in a walkable area as necessary for a significant transition to non-automotive modes.3 Data analysis

The California Household Transportation Survey (CHTS) data do not include such design, but do reveal the significant influence of density alone on vehicle miles traveled and amount of walking. The general influence of density on travel mode is well known, but the CHTS data improves the quantification of the relationship, especially for urban core block groups, those with over 50 persons per acre, which is not otherwise available.

Only recently has high quality household travel information become available with details on respondent home density. This research uses block group data from the California Household Transportation Survey (CHTS).1 This data is of higher quality than previous household surveys and has details not reported in the literature.

The Bay Area is a subset of the CHTS and is partially available online. The Metropolitan Transportation Commission (MTC) of the San Francisco Bay Area was a major participant in the CHTS research. Caltrans oversampled the Bay Area to update the MTC’s Bay Area Transportation Survey (BATS) survey of 2000 to gain the statistical validity MTC needed. The SF Bay Area Survey allows estimates of vehicle miles travelled (VMT) and other outcomes by neighborhood density. The survey used GPS reporting from devices worn by participants, as well as the usual diary and interview methods. The California Department of Transportation website at http://www.dot.ca.gov/hq/tpp/offices/omsp/statewide_travel_analysis/chts.html has more details.

Data on neighborhoods defined by aggregation by density of adjacent block populations for specific neighborhoods is not available, so we treated all the block groups in a given range as approximating a probable density of specific neighborhoods. For this to make any sense, the densest block 1 The public data on the web for the CHTS does not have trip data by neighborhood density or trips measured using block groups. The National Renewable Energy Laboratory (NREL), Transportation Secure Data Center, manages the database. The large spreadsheets were difficult to use. After conferring with the NREL, they agreed to extract what was needed from the larger data. They were supplied with the FIPS (geographic) codes for all the Bay Area’s 4,744 block groups and they extracted the data for this research. Block groups were aggregated by the density ranges used in Table 2 above. The NREL used the ranges for person densities for block groups as shown in Table 2. An email from Evan Burton of Dec. 4, 2014, attached Caltrans_trip_stages_out_full.xlsx, which is available in the Dropbox for this paper. The NREL websites are www.nrel.gov/tsdc and http://www.nrel.gov/vehiclesandfuels/secure_transportation_data.html.

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groups generally had to cluster close to each other into neighborhood areas, with a few isolated areas not affecting the overall result very much. We studied choropleth maps which confirmed the clustering pattern. The premise is not that dense block groups individually have certain mode characteristics, but that a dense neighborhood of block groups does. Neighborhood Density

In the San Francisco Bay Area, 54 percent of the population is in suburbia, which is too dispersed to support walking and transit efficiently.

Table 1 and 2 provide background for the finding. The block group density data for the Bay Area was stratified into density levels shown in Table 1 for 2000 and Table 2 for 2010. These tables show two different ways of defining density levels along spectrum and are meant to give a sense of proportions, not definitive definitions. Using both tables gives better sense of the approximate role of the five densities than using just one table.

Table 1: Density of Land Use in the San Francisco Bay Area 2000Density of Land Use in the San Francisco Bay Area 2000

Neighborhood Density

persons per square mile

square miles

percent of region

populationpercent of

region

average persons per acre low limit

Rural <500 5,569.46 80.5% 369,523 5.4% 0.1 0.00Exurban 500 - 1,000 305.21 4.4% 222,584 3.3% 1.1 0.78Suburbia 1,000 - 10,000 896.96 13.0% 3,647,552 53.8% 6.4 1.56Central City 10,000 - 20,000 119.36 1.7% 1,596,294 23.5% 20.9 15.63Urban Core >20,000 31.58 0.5% 947,807 14.0% 46.9 31.25Region 6,922.57 100.0% 6,783,760 100.0% 1.52000 Census, 4,422 block groups, 9 county Bay AreaBay Area Alliance for Sustainable Communities, Bay Area Indicators: Measuring Progress Toward Sustainability , January 2003, p. 27, based on Metropolitan Transportation Commission, ftp://ftp.abag.ca.gov/pub/mtc/census2000/PL94171: BlockGroup-shp -pldata.zip and related population files for 4,422 census block groups. Research by Sherman Lewis

Neighborhood Density and Travel Mode p. 9

Table 2: Density of land use in the San Francisco Bay Area 2010

Density

Density

range, persons per acre

Acrespercent

of region

numberpercent

of region

average persons per

acrenumber

percent of

region

Rural <1 3,585,675 81.1% 408,225 5.7% 0.11 315 6.6%

Exurban 1 - <5 410,858 9.3% 993,250 13.9% 2.42 595 12.5%

Suburb 5 - <20 362,458 8.2% 3,791,393 53.1% 10.46 2,578 54.3%

Central City 20 - <50 56,503 1.3% 1,595,033 22.3% 28.23 1,045 22.0%

Urban Core > 50 5,022 0.1% 355,885 5.0% 70.86 211 4.4%

all BGs 4,420,516 100.0% 7,143,786 100.0% 1.62 4744 100.0%

Urban Core detail50 to 60 2,697 0.061% 147,677 2.07% 54.75 92 1.9%

60 to 70 732 0.017% 47,647 0.67% 65.11 30 0.6%

70 to 80 559 0.013% 41,070 0.57% 73.42 28 0.6%

80 to 100 533 0.012% 47,789 0.67% 89.72 25 0.5%

100 to 150 348 0.008% 40,205 0.56% 115.57 21 0.4%

150 to 200 101 0.002% 17,928 0.25% 178.36 8 0.2%

over 200 53 0.001% 13,569 0.19% 257.89 7 0.1%

total 5,022 0.114% 355,885 4.98% 70.86 211 4.4%

based on 2010 census block groups

Acres Population Block Groups

Neighborhood Density in the San Francisco Bay Area, 2010

Table 2 for 2010 uses ranges with higher densities than those for 2000, and also uses persons per acre instead of persons per square mile. Persons per square mile is frequently used in the literature, but walkable system neighborhoods are almost always smaller than that, and density per acre provides more transparency in that it allows the reader to visualize the kind of density involved. The pattern is similar, with 81 percent of the area being rural, containing 6 percent of the population. Exurbia makes up more of the region in terms of area and population, probably because of the higher density range pulling in more territory and people. For the same reason, suburbia shrinks in area while keeping the same number of people due to the density range increase. Table 1 for 2000 used 1.56 persons per acre for the low end of suburbia, while Table 2 for 2010 puts it at 5 persons per acre. Likewise, the urban core density increases from over 31 per acre to over 50 per acre and almost disappears in area while still retaining 5 percent of the population.

The threshold for urban core of 50 persons per acre was established by studying block groups in Hayward selected by using the Federal Information Processing Standard (FIPS) codes and maps. There were the six block groups with 30 to 50 persons per acre which were examined in Google Street View and on foot. The block groups despite their density had

Neighborhood Density and Travel Mode p. 10

extensive area in paving, low-rise buildings, limited walking, and infrequent transit. This density level also was consistent with finding from research I did on old Toronto.

In 2010, the suburban areas of the Bay Area, defined as five to 20 persons per acre, covered about 8 percent of the Bay Area and had about 53 percent of the population.

Central city areas with 20 to 50 persons per acre covered a much smaller area, under two percent, yet still had over one fifth of the population. Their density gives them a greater ability to support non-auto modes, but they are still largely auto dependent. The urban core has densities of over 50 persons per acre, possibly high enough to support a walking system if the walking area is large enough. The urban core covers only a tiny part of the land area, a half a percent, yet holds five percent of the regional population. This land area is not significant in size, but has enough people to warrant special attention for livable sustainability.4 Results4.1 CHTS

Table 3 shows the major variables of the CHTS database for the Bay Area, which was further broken down for additional tables.

Table 3: CHTS Data Base for S.F. Bay AreaCHTS Data Base for S.F. Bay AreaVariables Numbertravel modes 27density 5 levels by persons per block grouphouseholds 3,403persons 6,841travel days 17,151trips 109,519trip stages 144,815block count 2,239total minutes 1,476,149avg trip stage minutes 10.19total trip stage miles 649,904avg trip stage miles 4.49std trip stage minutes 18.85std trip stage miles 29.67

The number of persons from the CHTS in Table 3 above can be compared to the full census, as shown in Table 4. The CHTS is fairly close to the census distribution for each density level.

Table 4: CHTS Sample Size Compared to CensusTable 4: CHTS Sample Size

Compared to Censusdensity perso percent of percent

Neighborhood Density and Travel Mode p. 11

levels n count region from census1 Rural 510 7.5% 5.7%2

Exurban 1,115 16.3% 13.9%3

Suburb 3,896 57.0% 53.1%4 Dense

Suburb 1,073 15.7% 22.3%5 Urban

Core 247 3.6% 5.0%total

reported 6,841 100.0% 100.0%

The CHTS used 27 modes, but 11 modes were excluded because of small sample size and lack of relevance for typical personal travel. The 16 selected modes added up to 99.8 percent of trips. The mode data was consolidated for the two auto modes (driver, passenger) and for the 12 transit modes.

The survey reports trips and trip stages. A trip consists of stages for each mode of travel used. A trip is classified by the longest mode of a multi-mode trip. It is important to consider trip stages. There are 32 percent more trip stages than trips. Mode of travel is best understood by stages rather than trips that combine modes and report only the longest mode for the whole trip.

The trip stage data covered over two travel days so the data was divided to estimate one day of travel. The number of respondents was divided by total travel days to adjust trip stages to trip stages per day. For example, there are 510 persons and 1,239 travel days in the rural sample, producing an adjustment of .4116. The total trip stages of 10,239 were reduced to 4,214 trip stage days. When divided by 510, the number of trip stages per day per person in rural neighborhoods was 6.22.

Chart 1 shows the results for the five density levels for auto and walk miles. The walk miles axis was calibrated to be about as high as the vehicle miles axis, providing a rough way of equilibrating driving and walking. Thirty-seven vehicle miles corresponds to about a walk mile. The data suggests that planners of suburbia for cars need to dramatically change to much shorter distances for pedestrians and walkable systems.

Neighborhood Density and Travel Mode p. 12

Chart 1: Auto and Walk Miles by Neighborhood Density

Rural Exurban Suburb Central City Urban Core15

20

25

30

35

40

45

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.2044

35

31

23

15

0.460.49

0.58

0.76

1.19

Auto and Walk Miles by Neighborhood Density

Vehi

cle m

iles p

er p

erso

n pe

r day

Wal

k m

iles p

er p

erso

n pe

r day

Walk miles

Vehicle miles

The chart indicates the location of the inflection point for the take-off of non-auto modes. The data quantifies the commonly understood relationship between VMT and density, and adds new data on walk miles and how they relate to VMT, especially for high density. With increasing density, VMT drops sharply, from 44 to 15 miles, while walk miles increase from .46 to 1.19 miles. The chart shows a high-density category, urban core neighborhoods with block groups over 50 persons per acre, which is denser and more revealing than other studies. Walk miles in the urban core go up faster than VMT goes down.

The data show how behavioral choices respond to neighborhood density independent of other variables such as design. These other variables are important and tend to support the density, but often can be improved to increase non-auto modes further. Further research would show how design can affect performance by density levels in walkable areas.

Chart 2 shows non-auto miles traveled per day per person. It does not show the number of trips person, which for transit is low. Thus, in rural areas, with few using transit, the distance per person for the whole population is low and does not reveal the fact that those few who do ride go fairly long distances. At the other end of the spectrum, people in the urban

Neighborhood Density and Travel Mode p. 13

core use transit frequently, so the average trip distance is closer to the distance for those who use transit.

Chart 2: Miles by density and non-auto mode

1 Rural 2 Exurban 3 Suburb 4 Central City 5 Urban Core0.000.250.500.751.001.251.501.752.002.252.502.753.003.253.503.754.004.254.504.755.005.25

1.59

1.94 1.87

2.90

5.00

0.420.25 0.33 0.44 0.45

0.46 0.49 0.580.76

1.19

Non-auto miles by density

transit bike

walk

Mile

s per

per

son

per d

ay

All modes combined, density is correlated with significant decreases in time spent traveling and in miles per person per day, as shown in Chart 3. Slower travel speeds are more than offset by the increase in the density of destinations shortening trip distances. In other words, a slower travel speed may take less time if a destination is so much closer that it more than compensates for the speed. The CHTS findings (not all reported here) show that urban core respondents spend less time traveling while making a similar number of trips for similar purposes.

Neighborhood Density and Travel Mode p. 14

Chart 3: Minutes and miles of travel by density

1 Rural 2 Exurban 3 Suburb Central City 5 Urban Core0

10

20

30

40

50

60

70

80

90

10099

86 85 85

80

49.1

43.1

37.232.6

25.8

Minutes and miles of travel by density

min

utes

and

mile

s per

day

minutes per person per day

miles per person per day

Non-auto modes-- walk, transit, and bike—increase with density and auto modes drop significantly. For number of trips at higher density, walking is by far the highest mode followed by auto, transit, and biking. For longer distances, however, transit is most important, trailed by walking and then biking.

Interestingly, biking is a leading mode of transportation in many European cities, which indicates a lack of biking density, infrastructure, and design in cities in the United States. The usual exception is campus towns, but a few non-campus cities are making progress.

Chart 4 shows that trip stages are correlated with density. Besides miles of travel per day, trip stages also indicate the impact of density on mode.

Neighborhood Density and Travel Mode p. 15

Chart 4: Mode stages by density

1 Rural 2 Exurban 3 Suburb 4 Dense Suburb 5 Urban Core0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.505.11 5.13

4.88

4.02

2.11

0.09 0.14 0.170.46

0.91

0.07 0.10 0.12 0.21 0.24

2.94 3.043.30

3.78

4.10

Chart 4: Mode stages by density

auto

transit

bike

walk

Num

ber o

f trip

stag

es p

er p

reso

n pe

r day

While auto stages and auto miles go down dramatically, auto in the

urban core still exceed non-auto modes by a long shot, as shown in Table 5.Table 5: Miles by density and modeTable 5: Miles by

density and modenumber of

miles/person/day

auto

transit

bike

walk

1 Rural4

4.21

.590.

420.

462

Exurban3

5.31

.940.

250.

49

3 Suburb3

1.11

.870.

330.

584 Dense 2 2 0. 0.

Neighborhood Density and Travel Mode p. 16

Suburb 3.4 .90 44 765 Urban

Core1

5.25

.000.

451.

19While speed, miles, and trips are all important, trips are the most

important. Density brings destinations closer together so that a trip can be made at a slower speed over a shorter distance. Such trips can achieve the travel objective in less time than going faster over a longer distance. 4.1 NHTS

The data in the National Household Transportation Survey (NHTS) has a lower density for its high density strata,

My unpublished research using the used three way tables on household income, density, and vehicle miles travelled.

Evidence from the NHTS (2016 table from 2009 survey emailed by Tim Reuscher <reuschertr@ornl.gov> to Sherman Lewis, June 1 and 3, 2016) for eight density ranges shows a non-linear decrease from the most rural (up to 16 persons per 100 acres) to the five middle densities. These middle densities have, then, a linear decline. Then it increases going to the next-to-densest range (15.6 to 39.1 persons per acre), drops even more going from there to densities over 39.1 persons per acre, creating a mild sine curve. Excel shows that a power 3 polynomial curve fits the data very well.

5 ConclusionResearch on neighborhood density and mode shows a correlation

between density and non-auto modes, especially walking, and an inverse relationship between density and vehicle miles travelled. VMT can also be influenced by a multitude of other factors such as increased access to non-auto modes, auto costs, congestion, mixed land use, parking policies, cultural preference, and income, but density seems to be the most important factor, certainly for making non-auto modes attractive, after which related factors of attractive walking distances, mixed use, transit, parking management and centrality become important..

Density is important for creating more sustainable communities that rely less on personal vehicle usage. High-density neighborhoods, though uncommon, show high levels of non-auto use. With sufficient density over area and support from design, the acceleration of a shift to non-auto modes seems to take place somewhere between 40 and 60 persons per acre.

More research would lead to a more comprehensive understanding of the point of inflection where walking, biking and transit surpass auto-modes. We need data on many dense neighborhoods ranked in density

Neighborhood Density and Travel Mode p. 17

ranges of ten persons per acre for the strata from 40 to 100 persons per acre. Finer grained data would allow a better understanding of the take-off of non-auto modes and the role of supporting factors once sufficient density is reached. 6 ReferencesFiles used in this paper are in a Dropbox with a link available from the

authors. Brownstone, D., and G. Thomas F. 2008. The Impact of Residential Density

on Vehicle Usage and Energy Consumption. Journal of Urban Economics.

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