Perceived Safety and Security Barriers to Walking and Bicycling: Insights from Milwaukee

Abstract

Two broad categories of barriers to improving pedestrian and bicycle transportation are concerns about traffic safety and personal security. Gathering residents’ perceptions of these barriers can help public agencies develop effective and equitable strategies to create more sustainable transportation systems. We analyzed open-ended responses to the 2020 Milwaukee Safe and Healthy Streets survey to identify common traffic safety barriers (e.g., driver behavior such as speeding and red-light-running) and personal security barriers (e.g., undesirable street behaviors such as gun violence, robbery, and assault) to walking and bicycling. Then, we developed binary logistic models to identify perceptions of neighborhood characteristics, and individual demographic characteristics related to perceiving walking or bicycling as unsafe with respect to traffic or personal security. For walking, respondents’ traffic safety concerns were most strongly associated with perceptions of fast neighborhood traffic speeds, and personal security concerns were associated with perceptions of poor neighborhood cleanliness. For bicycling, both traffic safety concerns and personal security concerns were most strongly associated with poor neighborhood opportunities for exercise. At an individual level, living in a zero-vehicle household and having self-reported poor health were associated with rating traffic safety for both walking and bicycling as unsafe; having disabilities was associated with rating walking as unsafe. In almost every aspect of our analysis, respondents living in lower-income communities reported greater barriers to pedestrian and bicycle safety and security than residents from wealthier neighborhoods. The results emphasized the importance of both the social and physical environment for improving pedestrian and bicycle transportation.

Keywords

pedestrians bicycles human factors planning and analysis pedestrian and bicycle data safety pedestrian and bicyclist safety

Globally, pedestrian and bicycle travel are being promoted to increase physical activity, mitigate climate change, improve transportation system safety, and create more equitable and socially connected communities ( 1 – 4 ). In the United States, most of the 50 largest cities and a majority of states have established goals to increase walking and bicycling ( 5 ), and incorporating pedestrian and bicyclist needs into transportation policies, plans, and projects is a cornerstone of the more than 1,500 “complete streets” policies passed over the last 15 years ( 6 ).

Although some communities have made progress in increasing walking and bicycling ( 5 ), many continue to face multiple barriers to shifting away from land use and transport systems that have been designed around automobile mobility ( 7 , 8 ). Two frequently cited barriers to improving pedestrian and bicycle transportation are traffic safety and personal security ( 9 – 12 ).

Traffic safety and personal security are sometimes grouped together in academic research and public discussions under the term “safety,” but we distinguish them in this paper. Traffic safety barriers are concerns about being involved in a collision with a motor vehicle. Personal security barriers are concerns about being a victim of crime or other unwanted interactions. It is important to examine these concepts separately because they could reveal different types of strategies to make walking and bicycling more attractive. For example, traffic safety barriers may point to the need for new pedestrian and bicycle facilities along the streets, whereas personal security barriers may suggest a need for better lighting at night or more open-street events for neighbors to meet each other.

Understanding perceived traffic safety and personal security barriers could inform roadway design, land development, and other programmatic strategies to increase walking and bicycling. However, these barriers are complex: recent research has shown that walking and bicycling safety and security may be perceived differently by people with different backgrounds ( 11 , 12 ). Therefore, we also need to investigate the perceived barriers broadly across many neighborhoods and population subgroups to advance sustainable transportation systems effectively and equitably.

For this study, we surveyed residents in the city of Milwaukee, WI (population 600,000) to investigate the following questions: (1) What do residents perceive as traffic safety and personal security barriers to pedestrian or bicycle travel? (2) What neighborhood perceptions are associated with perceiving walking or bicycling as unsafe or insecure? (3) What individual characteristics are associated with perceiving walking or bicycling as unsafe or insecure?

Literature Review

Traffic safety and personal security perceptions of walking and bicycling have been explored in previous studies. Perceptions of traffic safety affect people’s choices to walk and bicycle ( 9 , 13 ). Traffic safety perceptions have been quantified in several suitability and “level of service” studies. Pedestrians or bicyclists often feel safer from automobile traffic when there are wider sidewalks or bicycle lanes, lower motor vehicle speeds and volumes, and more prominent buffers between the designated pedestrian or bicyclist space and motor vehicle lanes ( 14 – 16 ).

Broadly, personal security concerns for pedestrians or bicyclists include crime, trash, abandoned properties, poor building maintenance, and poor lighting ( 17 , 18 ), but personal security often receives insufficient attention from planners ( 19 , 20 ). Older adult, Black residents mentioned a lack of police or security personnel and slow response times as barriers to personal security ( 17 ). Research on relationships between crime and pedestrian activity has shown mixed results ( 21 – 24 ).

More information is needed from diverse neighborhood populations about the perceived safety and security barriers to walking and bicycling. For example, Mexican-American participants tended to emphasize the social environment, whereas Non-Hispanic white participants tended to emphasize physical environment characteristics in a study of perceived walkability in Tucson, AZ ( 11 ). Specific barriers to bicycling in communities of color, including traffic collisions, fear of crime, pavement conditions, and a lack of safe bicycle storage, have been found in surveys in Portland, OR, New Jersey, and the San Francisco Bay Area ( 12 , 25 , 26 ). Lower-income neighborhood residents in San Diego, Seattle, and Baltimore rated pedestrian streetscape elements differently than higher-income neighborhood residents ( 27 ). Other studies have found significant differences in perceptions depending on walking or bicycling experience levels ( 28 , 29 ), gender ( 16 , 30 – 32 ), and age ( 33 ).

Previous research has identified many perceived safety and security barriers to walking and bicycling, but these barriers range widely across studies. This may in part be a result of our limited understanding of the differences in perceptions among different population groups. This study provides additional information about perceived safety and security barriers to walking and bicycling and about who is most likely to experience these barriers.

Method

We surveyed city of Milwaukee residents to investigate perceptions of traffic safety and personal security barriers to pedestrian and bicycle travel. This section describes our data collection and analysis process. For more detail, see the full project report ( 34 ).

Survey Data

Our data came from the Milwaukee Safe and Healthy Streets survey. This survey asked about perceptions of traffic safety and personal security as well as enjoyment and use of different transportation modes, personal health, socioeconomic characteristics, and the nearest major street intersection to the respondent’s home. We worked in partnership with the City of Milwaukee Department of Public Works, Wisconsin Bike Fed, and Medical College of Wisconsin to develop and distribute this survey. After pilot testing a preliminary version of the survey instrument at community events in summer 2019, we refined the questionnaire before distributing the final version in June and July 2020.

We distributed the survey using two methods. The first method (referred to as the “mail survey” in this paper) involved mailing a paper version of the survey and prestamped return envelope to a randomly selected set of addresses. The second method (referred to as the “online survey” in this paper) involved sharing a link to an online version of the survey via email and social media.

The survey was at least partially completed by 801 respondents across all 15 City of Milwaukee alder districts, which are geographic areas of roughly equal population represented by each elected common council member (158 were mail respondents, and 643 were online respondents). The mail response rate was 14.8%. We geocoded 142 mail responses and 491 online responses to specific locations for geographic analysis.

Across all responses, people who were older than 45, White, and lived in households with more than one automobile or without children were overrepresented compared with the population of Milwaukee. The mail survey respondents were more representative of the Milwaukee population as a whole than the online respondents, probably owing to the random selection of mail survey addresses. We combined responses from both survey distribution methods in our analysis, so we used control variables to account for potential systematic differences between these two groups in our statistical models.

Two survey questions addressed perceptions of traffic safety and personal security:

“Thinking about street traffic, how safe do you think it is to travel in your neighborhood by… Walking? Bicycling? Riding the bus? Driving a car (driver)? Riding in a car (passenger)? Other?”

“Thinking about crime and personal security, how safe do you think it is to travel in your neighborhood by… Walking? Bicycling? Riding the bus? Driving a car (driver)? Riding in a car (passenger)? Other?”

Response options for each mode were “very unsafe,”“unsafe,”“neutral,”“safe,” and “very safe.” For all analyses in this paper, we group the responses of “very unsafe” and “unsafe” into a single category and refer to it as “unsafe” in relation to traffic safety and “insecure” in relation to personal security (Table 1).

Table 1.

Associations Between Individual and Neighborhood Variables and Perceiving Walking and Bicycling as Unsafe or Insecure^a

	1. Respondent traffic safety perceptions of walking (n = 574)			2. Respondent traffic safety perceptions of bicycling (n = 565)			3. Respondent personal security perceptions of walking (n = 574)			4. Respondent personal security perceptions of bicycling (n = 564)
	Unsafe (n = 115) (%)	Other (n = 459) (%)	Sig.^b	Unsafe (n = 186) (%)	Other (n = 379) (%)	Sig.^b	Insecure (n = 77) (%)	Other (n = 497) (%)	Sig.^b	Insecure (n = 67) (%)	Other (n = 497) (%)	Sig.^b
Neighborhood perception variables^c
Poor cleanliness	37	12	++	28	11	++	45	12	++	39	14	++
Poor lighting at night	30	14	++	26	14	++	42	14	++	37	15	++
Poor sidewalks	46	16	++	34	16	++	45	18	++	34	20	++
Poor street pavement	70	44	++	61	43	++	71	46	++	66	47	++
Poor traffic speeds	83	51	++	75	49	++	75	55	++	75	55	++
Poor number of places to walk to	24	8	++	19	7	++	34	8	++	36	8	++
Poor opportunities for exercise	22	5	++	18	3	++	30	5	++	36	4	++
Individual variables
Adult younger than 35 years	17	25	NS	25	23	NS	18	24	NS	10	26	−−
Older than 64 years	23	17	NS	15	18	NS	21	18	NS	24	16	NS
Female	50	55	NS	53	53	NS	62	53	NS	67	52	+
Asian	1	2	NS	1	2	NS	4	1	NS	1	2	NS
Black	14	6	++	10	5	+	21	5	++	24	4	++
Hispanic	5	3	NS	3	3	NS	6	3	NS	7	3	+
Has a disability	20	7	++	13	7	+	21	8	++	24	7	++
Self-reported health “fair” or “poor”	19	7	++	17	6	++	19	8	++	25	8	++
Zero-car household	15	4	++	9	4	+	9	5	NS	6	6	NS
Low-income neighborhood (in NRSA)	44	21	++	33	21	++	52	22	++	39	24	++
Control variable
Random-mail survey distribution	23	18	NS	21	16	NS	31	17	++	34	16	++

Note : NRSA = Neighborhood Revitalization Strategy Area; NS = no significant difference between the proportions.

All variables are coded 1 if the characteristic is present and 0 if it is not. Percentages in table indicate the proportion of each group of respondents (unsafe versus other) who reported each characteristic.

Symbols indicate the outcome of a Z-test for the difference in proportions. This test compares the proportion of respondents who reported walking or bicycling to be unsafe or insecure (selected “unsafe” or “very unsafe”) with the proportion of respondents who reported walking or bicycling to be “neutral,”“safe,” or “very safe” for each specific variable. Positive signs indicate that the proportion of respondents who reported walking or bicycling to be unsafe or insecure was significantly higher than the proportion who did not report walking or bicycling to be unsafe or insecure (“++” indicates p < 0.01; “+” indicates p < 0.05 significance level). Negative signs indicate that the proportion of respondents who reported walking or bicycling to be unsafe or insecure was significantly lower than the proportion who did not report walking or bicycling to be unsafe or insecure (“−−” indicates p < 0.01; “−” indicates p < 0.05 significance level). NS indicates a non-significant relationship.

Neighborhood perception variables are based on respondent perceptions to the question, “Thinking about your neighborhood, how would you rate its… [characteristic]?” Each variable that starts with “poor” indicates that the respondent answered either “poor” or “very poor” on a five-point Likert scale.

Qualitative Analysis of Open-Ended Response Themes

To examine our first research question and identify specific barriers to pedestrian and bicycle travel in Milwaukee, we analyzed open-ended responses to the following prompts:

“Please write one or two comments about traffic safety on streets in your neighborhood.”

“Please write one or two comments about crime and security on streets in your neighborhood.”

Overall, 568 respondents provided written comments about traffic safety and 534 provided written comments about personal security. We focused our open-ended comment analysis on people who reported that traffic safety was unsafe or personal security was insecure for either walking or bicycling in the prior closed-ended questions.

Specifically, we analyzed the following four groups (some respondents were in multiple groups):

Traffic safety: walking is unsafe (144 respondents; 119 provided comments)

Traffic safety: bicycling is unsafe (228 respondents; 188 provided comments)

Personal security: walking is insecure (98 respondents; 78 provided comments)

Personal security: bicycling is insecure (93 respondents; 68 provided comments)

We began by scanning all open-ended responses to each question, identifying a set of overarching themes related to traffic safety and personal security. Then one member of the study team read the individual comments and coded them into one or more appropriate themes. Another member of the team reviewed a sample of the coded comments to confirm the selected categories.

Binary Logistic Regression

To explore our second and third research questions—to identify which neighborhood perceptions and individual characteristics were associated with walking or bicycling being perceived as unsafe or insecure—we used binary logistic regression. We developed eight separate models. Models 1a and 1b compared respondents who reported traffic safety for walking as unsafe (“unsafe” or “very unsafe”) (n = 115) with respondents who reported traffic safety for walking as “neutral,”“safe,” or “very safe” (n = 459) (Model 1a used neighborhood perception variables and Model 1b used individual characteristic variables). Models 2a and 2b compared respondents who reported traffic safety for bicycling as unsafe (n = 206) with other respondents (n = 429). Models 3a and 3b compared respondents who reported personal security for walking as insecure (n = 77) with other respondents (n = 479). Models 4a and 4b compared respondents who reported personal security for bicycling as insecure (n = 67) with other respondents (n = 497). The samples used for regression modeling were somewhat smaller than the samples used for the open-ended response analysis because some respondents did not complete all survey questions.

We included a broad set of explanatory variables in our models to capture the simultaneous relationship among multiple, theoretically important variables and respondents perceiving modes to be unsafe or insecure. Although certain variables were not statistically significant in the models (we used a 95% significance threshold), they were nonetheless important statistical controls to keep. For example, we kept the variable representing mail survey distribution versus online survey distribution in all models.

Several additional variables, including the number of children in the respondent’s household; length of time living at their current location; and ratings of neighborhood parks, street trees and landscaping, and friendliness of neighbors were collected through the survey. These were not included in the modeling process because our preliminary descriptive analysis showed little correspondence with perceived barriers to walking or bicycling among our respondents. Although we did not pursue these variables in our models, they still matter to some individuals and may be broadly important in communities beyond our study area.

Geographic Comparison Between Low-Income and Other Neighborhoods

Although our survey sample was too small for analyzing specific census tracts, we used the City’s Neighborhood Revitalization Strategy Area (NRSA) boundary to make a simple comparison between low-income and other neighborhoods. The NRSA includes Milwaukee’s lowest-income census tracts, representing approximately 61% of the population. Milwaukee’s neighborhoods are very segregated: NRSA neighborhoods on the north side of the city primarily have Black residents; NRSA neighborhoods on the south side primarily have Hispanic residents ( 35 ).

Results

The following sections present the findings from the Milwaukee Safe and Healthy Streets survey. The first covers perceived traffic safety barriers, and the second covers perceived personal security barriers.

Traffic Safety Barriers

The closed-ended traffic safety question showed that 20% of respondents (144 of 724) felt that walking was unsafe and 32% (228 of 706) felt that bicycling was unsafe.

Commonly Cited Traffic Safety Barriers

Our analysis of the open-ended follow-up question identified four broad themes associated with traffic safety barriers: undesirable driver behavior; street design, infrastructure, or maintenance; lack of law enforcement; and traffic congestion (Table 2). Overall, the most commonly mentioned traffic safety barriers were similar among the respondents who perceived walking as unsafe and perceived bicycling as unsafe. This is not surprising, given the overlap of respondents reporting both walking and bicycling as unsafe. Of the 144 respondents who said walking was unsafe, 117 (84%) also said that bicycling was unsafe (22 said bicycling was safe or neutral, and 5 did not answer).

Table 2.

Traffic Safety Barriers Cited by Respondents

Barrier theme	Respondents reporting traffic safety as unsafe for walking (n = 144)		Respondents reporting traffic safety as unsafe for bicycling (n = 228)
Barrier theme	Respondents mentioning theme	Percentage of all respondents mentioning theme^a	Respondents mentioning theme	Percentage of all respondents mentioning theme^a
Driver behavior^b	104	72.2	163	71.5
Speeding	62	43.1	98	43.0
Red-light running	33	22.9	44	19.3
Reckless driving^c	20	13.9	32	14.0
Lack of yielding to pedestrians	18	12.5	28	12.3
Passing on the right^d	12	8.3	18	7.9
Street design, infrastructure, or maintenance problems	28	19.4	54	23.7
Lack of enforcement	13	9.0	16	7.0
Traffic congestion	3	2.1	5	2.2

Some comments were coded as covering multiple themes, so the percentages sum to greater than 100%.

The overarching theme of “driver behavior” includes other general comments related to driver behavior that did not fit into the specific subcategories.

The “reckless driving” theme included any comment that used the word “reckless” to describe a driver behavior.

The “passing on the right” theme included any comment that referred to drivers illegally passing other drivers on the right, including in bike lanes or parking lanes. This is currently a frequent behavior in Milwaukee.

The greatest proportions of respondents emphasized that driver behaviors created an unsafe street environment (72% of people who reported walking as unsafe and 71% of those who reported bicycling as unsafe). Among driver behavior barriers, speeding was cited most often (mentioned by 43% of both respondents who reported walking and who reported bicycling as unsafe), followed by red-light running, reckless driving, lack of yielding to pedestrians, and passing on the right in the bike lane or parking lane. Mentions of unsafe driver behaviors were pervasive throughout the open-ended responses. More than half the people who said walking was neutral or safe (56%) or that bicycling was neutral or safe (53%) still raised these types of concerns. The following example quotes highlight these driver behavior themes:

“You can’t get across the street quick enough—for fear of cars” (female, age 80 to 89, Alder District 14).

“People run the red lights all of the time. Walking is out of the question—Milwaukee is known for hit & runs on pedestrians” (female, age 60 to 69, Alder District 1).

“Drivers DON’T look before doing right turns or blowing through lights. As a walking pedestrian, I’ve had drivers within 3 ft of hitting me on a weekly basis over the past 3 months” (male, age 30 to 39, Alder District 15).

“My husband who does not drive had to give up riding his bike to work because too many people almost hit him” (female, age 40 to 49, Alder District 11).

“Too many people speeding. Too many people disobeying traffic lights … Also too many drivers are passing from the right side using the bike lane” (unreported gender and age, Alder District 7).

Street design, infrastructure, or maintenance barriers were mentioned by approximately one-fifth of respondents who perceived walking to be unsafe, and slightly more respondents who perceived bicycling to be unsafe. This category included concerns about rough street surface conditions, not enough features to stop or slow drivers (especially stop signs and speed humps), and a lack of pedestrian crossing facilities and separated bicycle lanes. The following quotes underscore these themes:

“In my neighborhood we really need speed bumps … the cars flys down the street like it’s a drag way” (female, age 60 to 69, Alder District 1).

“I think the more dead end and reroutes for cars would cause lower speeds and safer ways for pedestrians and bikers—those most vulnerable to the very real reckless driving thing happening in Milwaukee” (male, age 20 to 29, Alder District 6).

“Streets are built for speed and need traffic calming measures. We need truly protected bike lanes badly and to build streets for pedestrians not cars” (male, age 30 to 39, Alder District 13).

“Traffic safety is compromised by cars passing on the right, so we need jutting our corner curbs as well as a curb to protect the bike lane” (female, age 20 to 29, unreported alder district).

Just under 10% of respondents who perceived walking or bicycling to be unsafe emphasized a lack of traffic enforcement in Milwaukee. Many of these responses expressed frustration,

“Basic traffic laws are not obeyed in my neighborhood or enforced … speeding through neighborhood streets, stopping in middle of road, parking all over. It is not safe to be on these streets, period” (female, age 30 to 39, Alder District 6).

“Car keys turn people in MKE into killers. ZERO traffic law enforcement … ZERO!” (male, age 50 to 59, Alder District 14).

“Drivers ignore speed limits and run red lights. Please start enforcing traffic laws with cameras & mailing tickets to drivers” (unreported gender, age 60 to 69, Alder District 5).

Neighborhood Perceptions Associated With Traffic Safety Barriers

Our binary logit regression models identified several statistically significant variables associated with perceived traffic safety barriers. The first set of models assessed how people who reported traffic safety barriers to walking and bicycling perceived certain neighborhood characteristics (Table 3, Model 1a and Model 2a). Respondents who perceived either walking or bicycling to be unsafe were significantly more likely than others to perceive neighborhood traffic speeds, opportunities for exercise, cleanliness, and sidewalks as poor. For walking, fast traffic speeds were prominent: respondents perceiving traffic safety barriers to walking were more than three times likelier than others to rate traffic speeds as poor. This is consistent with the open-ended themes discussed above.

Table 3.

Model Results: Neighborhood Perception Variables Associated With Perceiving Traffic Safety or Personal Security Barriers

	Model 1a. Respondents reporting traffic safety as unsafe for walking (n = 115) versus others (n = 459)		Model 2a. Respondents reporting traffic safety as unsafe for bicycling (n = 186) versus others (n = 379)		Model 3a. Respondents reporting personal security as insecure for walking (n = 77) versus others (n = 497)		Model 4a. Respondents reporting personal security as insecure for bicycling (n = 67) versus others (n = 497)
Neighborhood perception variables^a	Exp(B)^b	Sig.	Exp(B)^b	Sig.	Exp(B)^b	Sig.	Exp(B)^b	Sig.
Poor cleanliness	2.46	***	1.82	**	2.85	***	2.06	**
Poor lighting at night	0.89	NS	0.94	NS	1.72	NS	1.35	NS
Poor sidewalks	2.74	***	1.73	**	1.77	NS	0.86	NS
Poor street pavement	1.42	NS	1.21	NS	1.51	NS	1.22	NS
Poor traffic speeds	3.46	***	2.70	***	1.65	NS	2.05	**
Poor number of places to walk to	1.38	NS	1.07	NS	2.02	NS	1.81	NS
Poor opportunities for exercise	2.46	**	4.14	***	2.46	**	5.24	***
Other variables	Exp(B)^b	Sig.	Exp(B)^b	Sig.	Exp(B)^b	Sig.	Exp(B)^b	Sig.
Random-mail survey distribution	1.30	NS	1.39	NS	1.83	NS	2.31	**
Constant	0.04	***	0.16	***	0.03	***	0.03	***
Overall model
−2 log likelihood	471		633		363		336
Cox and Snell R²	0.17		0.14		0.14		0.13
Nagelkerke R²	0.26		0.19		0.26		0.24

Values of Exp(B) above 1 indicate that the variable is associated with a higher likelihood of a respondent perceiving that walking or bicycling is unsafe or insecure. Values below 1 indicate that the variable is associated with a lower likelihood of a respondent perceiving that walking or bicycling is unsafe or insecure.

p < 0.05; ***p < 0.01; NS indicates a non-significant relationship.

For bicycling, opportunities for exercise were emphasized: respondents perceiving traffic safety barriers to bicycling were more than four times likelier than others to rate opportunities for exercise as poor. This finding is a good example of how our models do not indicate the direction of potential causal relationships between these variables. Perceiving bicycling to be unsafe could make the perception of bicycling for exercise seem unpleasant. Alternatively, perceiving that fewer people bicycle for exercise could make people believe that bicycling is less safe.

Individual Characteristics Associated With Traffic Safety Barriers

Our second set of binary logit regression models revealed the types of people who were more likely to report traffic safety barriers to walking and bicycling (Table 4, Model 1b and Model 2b). Respondents in zero-car households were the most likely to perceive traffic safety barriers to walking. People with disabilities and people living in low-income neighborhoods were also significantly more likely than others to perceive traffic safety barriers to walking. These findings are troubling because people with these characteristics are more likely to depend on walking and other modes besides cars for transportation ( 36 ). Adults younger than 35 were less likely than others to perceive walking as unsafe. Respondents with self-reported poor health were likely to perceive traffic safety barriers to both walking and bicycling.

Table 4.

Model Results: Individual Variables Associated With Perceiving Traffic Safety or Personal Security Barriers

Individual variables	Model 1b. Respondents reporting traffic safety as unsafe for walking (n = 115) versus others (n = 459)		Model 2b. Respondents reporting traffic safety as unsafe for bicycling (n = 186) versus others (n = 379)		Model 3b. Respondents reporting personal security as insecure for walking (n = 77) versus others (n = 497)		Model 4b. Respondents reporting personal security as insecure for bicycling (n = 67) versus others (n = 497)
Individual variables	Exp(B)^a	Sig.	Exp(B)^a	Sig.	Exp(B)^a	Sig.	Exp(B)^a	Sig.
Adult younger than 35 years	0.53	**	1.02	NS	0.64	NS	0.38	**
Older than 64 years	1.23	NS	0.73	NS	0.99	NS	1.25	NS
Female	0.71	NS	0.90	NS	1.27	NS	1.51	NS
Asian	0.36	NS	0.14	NS	3.17	NS	1.04	NS
Black	1.14	NS	1.01	NS	1.74	NS	3.30	***
Hispanic	2.18	NS	0.71	NS	2.65	NS	3.99	**
Has a disability	2.19	**	1.39	NS	1.77	NS	2.15	NS
Zero-car household	5.26	***	2.23	**	1.55	NS	1.24	NS
Self-reported health “fair” or “poor”	2.35	**	3.18	***	1.55	NS	1.98	NS
Low-income neighborhood (in NRSA)	2.84	***	1.84	***	3.45	***	1.53	NS
Other variables	Exp(B)^a	Sig.	Exp(B)^a	Sig.	Exp(B)^a	Sig.	Exp(B)^a	Sig.
Random-mail survey distribution	1.11	NS	1.33	NS	1.72	NS	1.71	NS
Constant	0.16	***	0.37	***	0.06	***	0.06	***
Overall model
−2 log likelihood	508		678		399		355
Cox and Snell R²	0.11		0.07		0.09		0.10
Nagelkerke R²	0.17		0.09		0.16		0.18

Note: NRSA = Neighborhood Revitalization Strategy Area.

Values of Exp(B) above 1 indicate that the variable is associated with a higher likelihood of a respondent perceiving that walking or bicycling is unsafe. Values below 1 indicate that the variable is associated with a lower likelihood of a respondent perceiving that walking or bicycling is unsafe.

p < 0.05; ***p < 0.01; NS indicates a non-significant relationship.

Traffic safety perceptions of people living in low-income neighborhoods warrant special attention. Living in a low-income neighborhood was significantly associated with perceived traffic safety barriers to both walking and bicycling (Table 4, Model 1b and Model 2b). Geographically, alder districts with the highest percentages of respondents who reported walking as unsafe corresponded closely with the lowest-income neighborhoods (Figure 1). The map of respondents perceiving bicycling as unsafe was similar. To avoid potential bias from the online survey’s convenience sample (participants were more likely to be wealthier and live outside of the NRSAs), we used the subset of randomly distributed mail survey responses to compare those living in low-income neighborhoods versus other parts of the city. Again, low-income neighborhood respondents were significantly more likely than other respondents to report traffic safety barriers to walking and bicycling (Figure 2).

Figure 1.

Low-income neighborhoods and traffic safety: (a) Neighborhood Revitalization Strategy Areas and (b) proportion perceiving walking as unsafe.

Figure 2.

Traffic safety concerns: low-income- versus other neighborhoods.

Personal Security Barriers

The closed-ended personal security question showed that 14% of respondents (98 of 721) felt that walking was insecure and 13% (93 of 701) felt that bicycling was insecure.

Commonly Cited Personal Security Barriers

We identified three broad themes associated with personal security barriers: undesirable street behavior, lack of law enforcement, and lack of lighting (Table 5). The personal security barriers were similar among the respondents who perceived walking as insecure and perceived bicycling as insecure. Of the 93 respondents who said bicycling was insecure, 67 (72%) also said that walking was insecure (26 said walking was safe or neutral).

Table 5.

Personal Security Barriers Cited by Respondents

	Respondents reporting personal security as insecure for walking (n = 98)		Respondents reporting personal security as insecure for bicycling (n = 93)
Barrier theme	Respondents mentioning theme	Percentage of all respondents mentioning theme^a	Respondents mentioning theme	Percentage of all respondents mentioning theme^a
Undesirable street behavior^b	56	57.1	49	52.7
Violent crime^c	11	11.2	10	10.8
Theft and break-ins	9	9.2	13	14.0
Drug dealing	7	7.1	10	10.8
Harassment	7	7.1	4	4.3
Homelessness/panhandling	4	4.1	3	3.2
Prostitution	3	3.1	2	2.2
Loitering/drinking/drug use	3	3.1	5	5.4
Lack of enforcement	10	10.2	7	7.5
Lack of lighting	1	1.0	3	3.2

Some comments were coded as covering multiple themes, so the percentages sum to greater than 100%.

The overarching theme of “undesirable street behavior” includes other general comments related to street user behavior that did not fit into the specific subcategories.

Violent crimes mentioned by respondents included gun violence, robbery, assault, car-jacking, and homicide.

Undesirable street behaviors were the most commonly mentioned personal security barriers. More than half the respondents who said that walking or bicycling was insecure responded to open-ended questions with comments related to this type of barrier. Street behaviors mentioned multiple times included violent crimes (e.g., gun violence, robbery, assault), theft and break-ins, drug dealing, harassment, homelessness/panhandling, prostitution, and loitering/drinking/drug use. Some respondents noted that they were more concerned about these at night or during darkness than in the daytime. The following example quotes illustrate how these concerns impede walking and bicycling:

“There is a lot of crime. I do not walk in my neighborhood for fear of my life. I drove to a safe area to walk” (female, age 60 to 69, unreported alder district).

“Several people have been robbed and attacked in our neighborhood, [it’s] not safe to walk too far by yourself anymore” (female, age 40 to 49, Alder District 11).

“My wife will not walk anywhere alone, even during the daytime, due to the high rate of muggings. If I were to call for help on the street, I don’t think anyone would hear me and come to my aid” (male, age 20 to 29, Alder District 6).

“Cyclists are subject to harassment, mostly from motorists or vehicle passengers” (male, age 40 to 49, Alder District 3).

Approximately 10% of respondents who perceived walking as insecure and slightly fewer who perceived bicycling as insecure mentioned lack of law enforcement as a personal security barrier,

“Crime and insecurity are on the rise due to lack of police” (female, age 30 to 39, Alder District 10)

“Not enough police. We need bike and beat cops. Please!!!” (male, age 70 to 79, Alder District 3)

“I have alley – it not safe sometime light go out, lack of police traveling in my neighborhood; would like to see more” (unreported gender and age, Alder District 2)

Neighborhood Perceptions Associated With Personal Security Barriers

Our final two statistical models in Tables 3 and 4 examine the characteristics associated with perceived personal security barriers to walking and bicycling. Considering neighborhood perceptions, poor neighborhood cleanliness and -opportunities for exercise had significant associations with personal security barriers for both modes (Table 3, Model 3a and Model 4a). For bicycling, respondents perceiving poor opportunities for exercise were more than five times likelier than others to report personal security to be insecure. Fast traffic speeds were also significantly associated with perceiving bicycling as insecure.

Individual Characteristics Associated With Personal Security Barriers

Black and Hispanic respondents were more likely than others to perceive bicycling to be insecure (Table 4, Model 4b), but these relationships were not significant for personal security while walking (Table 4, Model 3b). These findings deserve further exploration since Model 4b is the only model in which any of the race or ethnicity variables were statistically significant. Other research on bicycling in Black and Hispanic communities has highlighted concerns about both traffic safety and personal security ( 12 , 26 ). Our finding may also reflect a lack of distinction between traffic safety and personal security that has been found in other research ( 32 ).

Respondents living in these neighborhoods were more than three times likelier than others to perceive personal security barriers to walking (Table 4, Model 3b). Examining only the mail survey responses showed that lower-income neighborhood respondents were significantly more likely to perceive personal security barriers while walking and bicycling (Figure 3).

Figure 3.

Personal security concerns: low-income- versus other neighborhoods.

Other Findings

Responses to the question about enjoyment of different modes highlighted that many Milwaukeeans like walking and bicycling. Yet, a Z-test of proportions on random-mail responses showed that significantly fewer respondents from lower-income neighborhoods rated walking as “enjoyable” or “very enjoyable” than other respondents (48% from low-income versus 75% from other neighborhoods, p < 0.01). This gap was even wider for the enjoyment of bicycling (11% from low-income versus 54% from other neighborhoods, p < 0.01). There were also significant gaps between the responses of lower-income- and other neighborhood respondents for perceived neighborhood friendliness (35% from low-income versus 63% from other neighborhoods rated friendliness of neighbors as “good” or “very good,”p < 0.01).

Open-ended respondents from low-income areas provided insights into these disparities. Street behaviors, especially driver behaviors, reduced their satisfaction with walking, bicycling, and their neighborhoods as a whole,

“Good neighborhood, bad traffic” (female, age 60 to 69, Alder District 1).

“Strong, safe, pleasant, beautiful, accessible … yet, crime and high speed automobiles could destroy that very fabric” (male, age 40 to 49, Alder District 6).

“great neighborhood, except for the crazy cars and one or two drug houses” (female, age 40 to 49, Alder District 15).

“People here are nice and basically good, but the littering and fast/careless driving are bad” (unreported gender, age 60 to 69, Alder District 6).

Another prominent concern among respondents were characteristics related to neighborhood disinvestment, including a lack of cleanliness, having few places to walk to, and having few opportunities for exercise,

“the neighbors have been nice … and I enjoy going for walks. I wish there were more nice businesses in walking distance” (unreported gender, age 40 to 49, Alder District 9).

“Largely abandoned and not maintained. Nothing notable on my street. No businesses to patronize except liquor stores and a gas station” (female, age 20 to 29, Alder District 6).

“Businesses close and trash builds up and no one cleans it up” (female, age 60 to 69, Alder District 2).

We also asked, “If street/sidewalk/trail conditions were better, what activities would you like to do more in your neighborhood?” The two most popular response categories were that they would bicycle more (237 respondents) and walk more (184 respondents). Only 64 respondents indicated that their current situation was fine or that their activities would not change. The following example responses to this question indicated repressed interest in walking and bicycling for transportation, and as well as socializing, that could be realized if these modes were safer:

“I would love to bike and walk to do many more things other than just recreation. If it were safer, protected from speedy cars … I would much more likely bike to work and to visit friends or get groceries” (female, age 30 to 39, Alder District 3).

“Bike, jog, play in the street, socialize in the street … It would be nice to be able to cross the streets without the fear of being killed, or being disrespected by motorists” (male, age 20 to 29, Alder District 6).

“A protected bike lane … in neighborhood would be awesome …We would bike more together as a family” (female, age 30 to 39, Alder District 3).

Discussion

Broadly, the Milwaukee Safe and Healthy Streets survey revealed that perceptions of traffic safety and personal security for walking and bicycling related to respondents’ surrounding social and physical environments. This emphasis on both social and physical factors corroborated recent research across diverse populations ( 11 , 12 ) and has practical implications.

Physical environment changes, such as redesigning streets with shorter crossing distances, protected bike lanes, and lower speed limits are likely to improve perceptions of traffic safety for walking and bicycling. Yet, the most commonly mentioned traffic safety and personal security barriers to walking and bicycling had to do with the social environment—undesirable driver behaviors and street behaviors, respectively. Practitioners who mainly seek to increase walking and bicycling by addressing physical environment barriers through design and infrastructure strategies should take note of this emphasis on the social environment. Behaviors such as drivers speeding and running red lights or people shooting guns, stealing cars, and robbing others are highly relevant to many community members; they should be addressed for the public to view walking and bicycling as attractive transportation options. Researchers should build on studies that have explored pedestrian and bicyclist perceptions of the social environment ( 11 – 13 , 17 – 20 ).

Planners, designers, and engineers may strengthen their case for physical environment changes if they can connect how infrastructure might address the most immediate social environment concerns of local residents. For example, speeding can be mitigated by narrowing roadways and applying other traffic calming measures ( 37 ). Bicycle and pedestrian infrastructure may also support local businesses and neighborhood reinvestment ( 38 ). Adequate street lighting improves perceptions of personal security among pedestrians and may also deter criminal activity ( 39 ). At a minimum, practitioners should acknowledge the importance of social barriers when engaging community members in pedestrian and bicycle public meetings and planning processes.

Importantly, some groups perceived greater walking and bicycling barriers than others. Relatively few studies have examined perceptions by income level ( 27 ), but residents of low-income neighborhoods indicated significantly more concern about pedestrian and bicycle traffic safety and personal security than residents in other parts of Milwaukee. This is a critical equity issue. Transportation agencies should prioritize pedestrian and bicycle projects and programs to address barriers in areas with the greatest need. They should also devote sufficient resources to engage groups such as people of color, people with disabilities, people without automobiles, and people in poor health about how to reduce the barriers they experience to make walking and bicycling accessible and attractive to the entire community.

Some respondents emphasized the need for more law enforcement to address both driver behavior and street crimes. This issue should be a part of public discussions around improving pedestrian and bicycle conditions. What constitutes equitable, community-supported law enforcement? To what degree should police officers be involved, and to what degree can other agencies, community members, or programs provide social deterrents to dangerous behavior or mitigate underlying social conditions that may correspond with higher rates of reckless driving and crime in lower-income neighborhoods?

Considerations for Future Research

We distributed the survey using two different methods to try to reach a broad cross-section of Milwaukee residents. Nonetheless, compared with the overall population of Milwaukee, our sample underrepresented young adults, households with zero vehicles, and Black, Hispanic, and Asian residents. Using other distribution methods, such as surveying people in person on neighborhood streets ( 20 ), at community-led neighborhood meetings and events, or in other convenient places may help reach more members of these underrepresented groups.

Our survey was conducted during the COVID-19 pandemic, which affected many people’s lives, including their travel patterns, experiences within their neighborhoods, and interactions in public space. Responses were provided within this specific context, so it would be interesting to compare perceptions of safety and security in 2020 with future, postpandemic perceptions.

Similar surveys should be conducted to gather neighborhood resident perceptions of walking and bicycling in other communities. Whereas Milwaukee may share some physical, social, and economic characteristics with other mid-sized U.S. cities, it is unique. In particular, certain driver behaviors (e.g., passing on the right in bike lanes, red-light-running) often witnessed by survey respondents in Milwaukee may seem exceptional to people from other places. Collecting local-specific perception data from residents could help agencies better understand how to make pedestrian and bicycle transportation safer and more secure in diverse neighborhoods throughout their communities.

Additional modeling approaches could also be explored. For example, since we found several of the same variables to be significantly associated with both perceived safety and perceived security barriers (e.g., poor neighborhood cleanliness, poor neighborhood opportunities for exercise), future studies could try to model these perceptions jointly.

Milwaukee respondents communicated clear themes about dangerous driver behaviors and undesirable street behaviors that make walking and bicycling feel unsafe and insecure. Our models showed that their perceptions of unsafe and insecure walking and bicycling conditions have statistically significant associations with perceptions of fast traffic speeds, poor cleanliness, and poor opportunities for exercise in neighborhoods. We also found that some of the strongest perceptions of pedestrian and bicycle safety and security barriers were in lower-income communities. Finally, our results strongly suggest that future interventions aiming to increase walking and bicycling should address both social and physical environmental barriers to be effective. We hope that our analysis helps build the foundation for future perception-based research and provides a deeper understanding of safety and security concerns as communities seek to advance sustainable transportation systems.

Footnotes

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: R. J. Schneider; data collection: A. Schmitz, R. J. Schneider; analysis and interpretation of results: R. J. Schneider, H. Wiers, A. Schmitz; draft manuscript preparation: R. J. Schneider, H. Wiers. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by “Safe & Healthy Streets: Enhancing Systems to Increase Walking & Biking Infrastructure in Milwaukee,” an Advancing a Healthier Wisconsin grant administered through the Medical College of Wisconsin.

ORCID iDs

Robert James Schneider

Hayley Wiers

Andrew Schmitz

Data Accessibility Statement

The data used for this study is not available publicly because of human subjects protections given to participants under the Medical College of Wisconsin Institutional Research Board.

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