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Synthesis of Methods for Estimating Pedestrian and Bicyclist Exposure to Risk at Areawide Levels and on Specific Transportation Facilities

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Chapter 5. Risk Factors Other than Exposure

Understanding risk factors is an important step toward the improvement of pedestrian safety because a complete understanding can contribute to developing effective countermeasures. The discussion in this section focuses on significant factors that influence the risk of pedestrian and bicyclist crashes and crash-related injuries as identified in the literature.

Other Risk Factors

To perform the scalable estimate of exposure, two major levels are considered: disaggregate level (facility) and aggregate level (corridor, network/system, and regional). Risk factors other than exposure can also be divided based on these two levels. Figure 6 shows the flow chart of other risk factors.

Figure 6. This figure shows a hierarchy for other risk factors other than exposure. The first level of the hierarchy includes these two risk factor types: 1) Disaggregate level; and 2) aggregate level. Under disaggregate level, facility condition and individual risk factors are included. Under aggregate level, traffic characteristics, adjacent land use, and demographics are included.

Figure 6.  Division of Risk Factors Other Than Exposure for Pedestrian and Bicyclist Crashes

Understanding the exposure to risks is beneficial for safety improvement. For a systematic safety planning process, it is important to prioritize locations with an aim to reduce the crash risk and crash severity for pedestrians and bicyclists for a given countermeasure.

Disaggregate level

Disaggregate-level characteristics have significant influence on the occurrence of pedestrian and bicycle crashes. Many studies investigated the association between site characteristics and both pedestrian exposure and pedestrian crashes. The research synthesis on disaggregate-level risk factors focuses on two major issues: facility condition and individual level.

Facility Condition

Facility condition is subdivided into three major groups: (a) feature type, (b) segment, and (c) intersection. Table 8 lists the facility condition risk factors and related studies.

Table 8. List of Studies on Facility Condition
Category Risk Factors Studies
Feature Type Urban/Rural Harkey and Zeeger, 2004; DaSilva et al., 2003; Choueiri et al., 1993; Mueller et al., 1988; Litman and Fitzroy, 2009; Ewing and Dumbaugh, 2009; Ibrahim and Sayed, 2011; Tarko and Azam, 2011; Dixon et al., 2015; Kamyab et al., 2003; Jones and Carlson, 2003;
Divided/Undivided Obaidat et al., 2007;
Workzone Shaw et al., 2016;
Segment Posted speed Limpert, 1994; Leaf and Preusser, 1999; Litman, 2008; Peden, 2004; Renski et al., 1999; Abdel-aty and Keller, 2005;
Lighting at night Sze and Wong, 2007; Ulfarsson et al., 2010; Sullivan and  Flannagan, 2011; Aziz et al., 2013; Hunter et al., 1996; Klop and Khattak, 1999; Johnson 1997; Abdel-aty, 2003; Haleem et al., 2015; Siddiqui et al., 2006; Das and Sun, 2015;
Sidewalk Knoblauch et al., 1987; Mcmahon et al., 1999;
Bike lane Kroll and Ramey, 1977; Conway et al., 2013;
On street parking Marshall et al., 2008;
Shoulder width Dixon et al., 2015; Klop and Khattak, 1999; Mcmahon et al., 1999;
Number of lanes Aziz et al. 2013; Wang et al. 2006; Poch and Mannering 1996; Spainhour and Wootton, 2007; Das and Sun, 2015;
Bus Stop Miranda-Morenoetal., 2011; Ukkusuri et al., 2012; Wang and Kockelman, 2013; Chen and Zhou, 2016;
Intersection Signalization Abdel-aty and Keller, 2005; Koepsell, 2002; Zegeer et al., 2001; Lee and Abdel-aty 2005; Oxley et al. 1997;
Crossings Oxley et al.,1997; Coffin and Morrall, 1995; Lassarre et al., 2007;
Width Zajac and Ivan, 2003;

Feature Type

Urban or Rural Roadways

The prevalence of walking and biking is greatest in urban and urbanized areas. Most non-motorized crashes occur in urban areas, where concentrations of vehicles, pedestrians, and bicyclists are higher than in rural areas. Approximately 80 percent of injury crashes and 65 percent of fatal crashes occur in urban areas due to high non-motorist activity and traffic volumes (Harkey and Zeeger, 2004; DaSilva et al., 2003). However, the ratio of fatal crashes to injury crashes is nearly three times greater in rural areas than in urban areas (Choueiri et al., 1993), which is attributed to higher speeds on rural roadways. Other studies also concluded that pedestrians in rural areas had higher rates of injuries and severe injuries than pedestrians in urban areas (Mueller et al., 1988; Litman and Fitzroy, 2009; Ewing and Dumbaugh, 2009; Ibrahim and Sayed, 2011; Tarko and Azam, 2011). There is an abundance of studies conducted on urban non-motorized crashes. Since non-motorized trips are more likely to end in a crash in an urban location, rural non-motorized crashes have been studied less often. Dixon et al. (2015) conducted a study on the non-motorized crashes on rural two-lane and multilane roadways in Texas. The findings showed that high speeds and narrow shoulders were highly associated with fatal non-motorist crashes. These findings are in line with the findings from other studies (Kamyab et al., 2003; Jones and Carlson, 2003). Another significant variable in Jones and Carlson’s (2003) study was the flow rate of heavy vehicles in the same direction as bicyclists.

Divided or Undivided Roadways

The type of median (divided or undivided) has an effect on the safety of non-motorized trips. It is most significant for intersections. It is usually easier to cross the approach on divided roads than on undivided roads since pedestrians or bicyclists have a shorter distance to cross prior to reaching a refuge area.  Research has shown that divided roadways are safer than undivided roadways while crossing (Obaidat et al., 2007).


In the United States, nearly 17 percent of work zone crashes involved a non�motorized road user in the recent years (Shaw et al., 2016). Bryden and Andrew (1999) concluded that work zone non-motorist crashes accounted for 15 percent of all serious injuries and over 40 percent of all fatalities in New York during 1993-1997. They also showed that two-thirds of the motor vehicles involved in crashes left the traffic lane and entered into the work area.


Posted Speed

Higher posted speed increases the probability of a pedestrian or bicyclist fatality. The findings of one study showed that the risk of a pedestrian crash fatality is estimated to increase from 5 percent to 45 percent when speed increases from 20 to 30 mph; the risk increases to 85 percent when speed reaches 40 mph (Limpert, 1994). Similar findings are seen in other studies (Leaf and Preusser, 1999; Litman, 2008). While pedestrians have a 90 percent chance of surviving a crash involving a vehicle traveling 20 mph or below, they have less than a 50 percent chance of surviving a crash with a vehicle traveling 30 mph or above (Peden, 2004). Many studies concluded that reduced speeds would have been effective in reducing non-motorized crashes and severities (Limpert, 1994; Leaf and Preusser, 1999; Litman, 2008; Peden, 2004; Renski et al., 1999; Abdel-Aty and Keller, 2005). The literature review on impact speed is described in a different section.


Pedestrians and bicyclists have a higher probability of being in a fatal crash under poor lighting conditions. This finding is supported by several studies (Sze and Wong, 2007; Ulfarsson et al., 2010; Sullivan and Flannagan, 2011; Aziz et al., 2013; Hunter et al., 1996; Klop and Khattak, 1999; Abdel-Aty, 2003; Haleem et al., 2015; Das and Sun 2015). The odds of a fatal injury in daylight are reduced by 75 percent at midblock locations and 83 percent at intersections compared to dark conditions with no lighting. Street lighting reduces the same odds by 42 percent at midblock locations and 54 percent at intersections (Siddiqui et al., 2006). Klop and Khattak (1999) concluded that street lighting decreased the severity of injury compared to dark conditions in rural areas. Dark lighting conditions with no streetlights are associated with slightly higher increases in pedestrian crash severity at unsignalized intersections compared to signalized intersections (Haleem et al., 2015).


Sidewalks encourage walking and improve the safety of pedestrians. Locations with no sidewalks are prone to pedestrian crashes. Knoblauch et al. (1987) determined that sites without sidewalks were more than twice as likely to be pedestrian crash prone than sites with sidewalks. The presence of a sidewalk was found to have a particularly large safety benefit in residential and mixed residential areas. However, sidewalks seem less effective on pedestrian crashes in medium or larger commercial areas. McMahon et al. (1999) found that the likelihood of a location with a paved sidewalk being a crash site was 88.2 percent lower than a location without a sidewalk.

Bike Lane

Kroll and Ramey (1977) examined the interactions between bicyclists and drivers for a bike lane by observing an affiliated cyclist riding on 10 streets with bicycle lanes and 10 streets without bicycle lanes. The results concluded that the mean separation distance between bicycles and cars was largely a function of the motorist’s available travel space (the distance between the bicyclist and the centerline) rather than the presence or absence of a bicycle lane. The results also indicated that bicycle lanes as narrow as 3 ft provided sufficient space for drivers to interact safely (Kroll and Ramey, 1977). Bicycle lane design has significant influence on bicycle crashes. Considering three types of bicycle lanes—Type 1 (a standard bicycle lane located adjacent to a travel lane and separated from the curb by vehicle parking), Type 2 (a buffered bicycle lane separated from the curb by parking and from the vehicle travel lanes by a striped buffer), and Type 3 (a curbside bicycle lane protected by parked vehicles)—Conway et al. (2013) concluded that Type 3 was safer than the other two options.

On street Parking

It is usually believed that on-street parking decreases the space for both drivers and non-motorized travelers. A study found that low-speed streets with on-street parking had the lowest fatal and severe injury non-motorized crash rates of any road category, suggesting that presence of parking had a measurable effect on vehicle speeds (Marshall et al., 2008).

Shoulder Width

One study found that an unpaved shoulder of 4 ft or more made a location 89 percent less likely to be a non-motorized crash site (McMahon et al., 1999). This finding is in line with the findings of Dixon et al. (2015). Another study concluded that shoulder width of any size was not statistically significant on crash severity compared to the absence of a shoulder (Klop and Khattak, 1999).

Number of Lanes

The number of lanes on a road is a significant factor on the severity level of non-motorized crashes. Results from one study indicated that crashes on single-lane roads had a lower probability of resulting in a fatality. Moreover, results showed that crashes on multilane roads had a higher probability of resulting in a fatality (Aziz et al., 2013). This finding is consistent with the results of previous studies (Wang et al., 2006; Poch and Mannering, 1996; Das and Sun, 2015). One study concluded that the higher the number of lanes that a pedestrian tried to cross before being hit, the more likely it was that the pedestrian was at fault (Spainhour and Wootton, 2007), partially because of the higher amount of exposure time while crossing.

Bus Stops

Several studies showed that the presence of bus stops on roadway segments is closely associated with higher non-motorist crash frequency (Miranda-Moreno et al., 2011; Ukkusuri et al., 2012; Wang and Kockelman, 2013; Chen and Zhou, 2016). Bus stops usually generate more non-motorist activities, and failure to provide clear distance for the non-motorists causes a higher number of crashes.


Statistics suggest that crossing the street at the intersection is more risky than walking or biking along the roadways. The reason is that pedestrian exposure is higher while crossing than the walking or biking along the roadway. Intersections without traffic control signals and absence of cross walks are found to be highly correlated with fatal non-motorized crashes (Sze and Wong, 2007; Moudon, 2011). Another important factor in this regard is the crossing behavior of the non-motorized traveler. One study found that fatal crashes are strongly associated with pedestrians crossing un-signalized intersections and vehicles moving straight ahead on a roadway (Ulfarsson et al., 2010; Moudon, 2011).

Signalized and Unsignalized Intersection

A study investigating the crash severities at signalized intersections found that crashes involving a pedestrian or a bicyclist and a motor vehicle turning left had a high probability of resulting in severe pedestrian or bicyclist injury (Abdel-Aty and Keller, 2005).  For pedestrians on state routes, the most dangerous action to take was to cross these routes at unsignalized intersections, where the likelihood of being involved in a fatal or severe injury crash was approximately four times that of being anywhere else on the route. This finding supported previous research and suggested that engineering approaches to road design could improve the safety of pedestrians (Koepsell, 2002; Zegeer et al., 2001). Lee and Abdel-Aty (2005) noted that drivers tended to drive more carefully when they approached traffic signals than stop or yield signs in rural areas and pedestrian crashes occurred less frequently at rural signalized intersections.


Crosswalk characteristics have been associated with severity of injury. For example, one study found that on a two-lane road, crash severity did not differ significantly for crashes occurring at marked and unmarked crosswalks, but on multilane roads, there was evidence of more fatal crashes at marked crosswalks compared to unmarked crosswalks (Zegeer et al., 2001). Additional analysis of the data revealed that the ADT level along with the presence of other traffic control devices was key to explaining the relationship. Another study showed that over 40 percent of pedestrian fatalities occurred in locations without crosswalks (Ernst, 2004). Several studies examined crossing behavior and identified behaviors with greater risk for different age groups; for example, older people’s slower gait increases the time spent crossing a road, thereby increasing exposure (Oxley et al., 1997; Coffin and Morrall, 1995; Lassarre et al., 2007).

Individual Level

Individual level of behavior is one of the most dominant factors in pedestrian and bicyclist crashes. Risk factors associated with the individual level are subdivided into four major groups: (a) vehicle related, (b) bicycle related, (c) driver related, and (d) pedestrian/bicyclist related. Table 9 lists the studies focusing on the individual-level risk factors.

Table 9. List of Studies on Individual Level Risk Factors
Category Risk Factors Studies
Vehicle related Impact speed Rosen et al., 2011; Tefft, 2011;
Vehicle age Peden, 2004; Blows, 2003;
Vehicle size Galloway and Patel, 1982; Atkins et al.,1988; Mizuno and Kajzer, 1999;
Driver Age Lee and Abdel-Aty, 2005
Distraction Dimaggio and Durkin, 2002
Gender Das and Sun, 2015
Occupants Das and Sun, 2015
Pedestrian / bicyclist Age Assailly 1997; Davies 1999; Vyrostek et al. 2001; Retting et al. 2003; Gawryszewski and Rodrigues 2006; WHO 2009; Ponnaluri and Nagar 2010;  Green et al. 2011; Hurruff et al. 1998; Fontaine and Gourlet 1997;  Rodgers 1995;
Child pedestrian Davies, 1999; USDOT, 2001; NCIPC, 2006; Connelly et al., 1998; Whitebread, 2000; Agran et al., 1994; Wills et al., 1997; Macpherson et al., 1998; Howard et al., 2005; King and Palmisano, 1992; Laflame, 2000; Lascala et al., 2000; Rivara and Barber, 1986; Carlin et al., 1995; Wazana, et al. 2000;
Gender Campbell, 2004; NCSA, 2008; Li and Baker, 1994; NHTSA, 2011;
Intoxication Lee and Abdel-Aty, 2005; Oxley et al., 2006; Spainhour et al., 2006; Clayton and Colgan, 2001; Miles-Doan, 1996; Öström and Eriksson, 2001; Holubowycz, 1995; Leaf et al., 2005;
Reflective clothing Luoma and Trauba, 1995; Owen and Sival, 1993; Tyrrell and Patton, 1998; Tyrrell et al., 2004a; Tyrrell et al., 2004b;
Cell phone Hatfield, 2007; Hyman et al., 2010; Nasar et al., 2008; Stavrinos et al., 2011;
Crossing behavior Palamarthy et al., 1994; Quddus et al., 1996; Hunter and Huang, 1995;
Temporal effect Harkey and Zeeger, 2004;  Hunter and Huang, 1995;

Vehicle related

Impact Speed

It is well established that the risk of a severe or fatal non-motorized crash is significantly associated with the impact speed (Rosén et al., 2011). Rosén et al. (2011) reviewed a substantial number of studies on the relationship between crash impact speed and pedestrian fatality risk published prior to 2010. Of the 11 studies considered for analysis, five were based on data collected prior to 1980 (including three different studies of the same data), and nine were biased due to overrepresentation of crashes. At lower speeds (like 15 mph or below), risks were low and the trend of increment was smaller with small increments in speed. At impact speeds below 15 mph, pedestrians (about 91 percent) did not endure severe injuries, and very few (about 2–5 percent) were killed. However, as speeds increased beyond this lower speed range, small changes in speed yielded a relatively larger increment in risk. At an impact speed of 25 mph, an estimated 30 percent of pedestrians sustained a severe injury, and about 12 percent were killed. Approximately half of all pedestrians (47 percent) struck at 30 mph sustained severe injury, and one in five (20 percent) died. Risks for a pedestrian struck at any given speed by a light truck were higher than if struck at the same speed by a car. Risks were higher for an older pedestrian struck at any given speed than for a younger pedestrian struck at the same speed (Tefft, 2011).

Vehicle Age

The association between vehicle age and risk of non-motorized crashes has been investigated in a few studies (e.g., Peden, 2004; Blows, 2003). These studies quantify the increased risk of car crash injury associated with older vehicle year.

Vehicle Size

Some studies observed that larger cars resulted in more serious pedestrian injuries (Galloway and Patel, 1982; Atkins et al., 1988) and higher pedestrian fatality rates (Mizuno and Kajzer, 1999). The larger vehicles were related with more traumatic brain, thoracic, and abdominal injuries. At higher speeds, there was no association with size of vehicles. The study suggested that the occurrence of these injuries is independent of vehicle type for certain threshold speeds. Compared to conventional cars, pedestrians hit by sport utility vehicles and pick-up trucks were more likely (with odds of 1.48) to have higher injury severity or be killed, with odds of 1.72 (Mizuno and Kajzer, 1999).

Driver related

Driver Age

One study showed that middle-age (25–64) and male drivers are more prone to be involved in non-motorist crashes (Lee and Abdel-Aty, 2005).

Driver Distraction

One study concluded that driver distraction and associated situational factors increase the probability of non-motorized crashes (Dimaggio and Durkin, 2002).

Driver Gender

Das and Sun (2015) found that male drivers have a greater association with severe and moderate injury pedestrian crashes. The study also showed that female drivers are associated with a higher number of pedestrian crashes during inclement weather.

Number of Occupants

Das and Sun (2015) found that drivers with multiple passengers are associated with a higher number of pedestrian crashes.

Pedestrian/Bicyclist Related


Age is a significant personal-level factor in non-motorized crashes. Children are particularly at risk in road traffic crashes (Assailly, 1997; Davies, 1999; Vyrostek et al., 2001; Retting et al., 2003; Gawryszewski and Rodrigues, 2006; WHO, 2009; Ponnaluri and Nagar, 2010). Many researchers focused on child pedestrians in their investigations (e.g., Green et al., 2011). Harruff et al. (1998) performed a retrospective analysis of 217 pedestrian traffic fatalities in Seattle, Washington. The study concluded that elderly pedestrians were most vulnerable because they are more likely to be injured as a pedestrian and are more likely to die because of their vulnerabilities. Fontaine and Gourlet (1997) concluded that younger and older pedestrians showed more exposure risk than other age groups. Rodgers (1995) determined that bicyclists over 65 years old were significantly more likely to be in fatal crashes than bicyclists from other age groups.

Child Pedestrian

A range of demographic factors is associated with child pedestrian risk, such as the age of the child. Epidemiological influences of age are predominant among these factors. Studies showed that middle childhood is a time of increased risk for child pedestrian injury (Assailly, 1997; USDOT, 2001; NCIPC, 2006). As children grow older, between the ages of 5 and 9, their pedestrian skills gradually increase (Connelly et al., 1998; Whitebread and Neilson, 2000). Some studies showed that children wandering farther from home while unsupervised accounts for the increase in the injury rate (Agran et al., 1994; Wills et al., 1997; Macpherson et al., 1998). Children from an ethnic minority background are at higher risk for pedestrian injury (Howard et al., 2005; King and Palmisano, 1992; Laflamme, 2000; LaScala et al., 2000). This finding may be due to their homes typically being located in urban areas with greater traffic density and higher rates of unemployment. Pedestrian injury rates are higher in low socioeconomic status urban areas with higher traffic density, denser housing units, and fewer safe areas for children to play (Laflamme, 2000; Rivara and Barber, 1986). Exposure time studies showed increased injury rate odds of 2.2 for children riding bicycles for more than 3 hours per week compared to children riding less than 1 hour. Riding more than 5 km on the sidewalk was also associated with increased injury risk, with odds of 3.1 (Carlin et al., 1995). One study concluded that child pedestrian injury rates are 2.5 times higher on one-way than on two-way streets (Wazana et al., 2000).


The behavior and actions of pedestrians significantly affect a crash outcome. Research suggests that males have a higher probability than females to be killed in a crash. Males showing higher risk behaviors than females (Campbell, 2004) explains why more males than females die as the outcome of a non-motorized crash. In 2000, the fatality rate of male pedestrians was twice that of female pedestrians (National Center for Statistics and Analysis [NCSA], 2008) in the United States. Based on daily trips, men were found to be at a slightly lower injury risk than women (Li and Baker, 1994). Recent National Highway Traffic Safety Administration (NHTSA, 2011) data show that in 2009, 549 male bicyclists were killed and another 41,000 were injured. This was compared to 81 female bicyclist fatalities and 10,000 injuries. For child pedestrians, gender is also associated with risk for injury (Assailly, 1997; Howard et al., 2005), with boys experiencing injury at a rate roughly double that of girls (NCIPC, 2006).


Research suggests that intoxicated pedestrians are at significantly higher risk of injury (Clayton and Colgan, 2001). Moreover, as a non-motorist’s blood alcohol concentration (BAC) increases, the probability of that non-motorist being involved in a fatal crash increases. Non-motorists under the influence of alcohol have also been shown to exhibit risky road-crossing behaviors (Lee and Abdel-Aty, 2005; Oxley et al., 2006; Spainhour et al., 2006). One study found that 43 percent of male pedestrians and 21 percent of female pedestrians involved in fatal crashes had BACs at or above .08 g/dL (Leaf et al., 2005). Miles-Doan (1996) showed that impaired pedestrians were more involved in crashes and their odds of dying compared to surviving were higher. Ostrom and Eriksson (2001) found that impaired pedestrians were more severely injured and suffered more head injuries. Some studies considered the mixed effect of several factors like pedestrian age, gender, and alcohol use on the risk outcomes. For example, Holubowycz (1995) reported that young and middle-age intoxicated males were high-risk non-motorist groups.

Reflective Clothing

Several studies established that wearing retroreflective materials increased recognition distance (Luoma et al., 1995; Owen and Sivak, 1993). Research shows that pedestrians usually overestimate their own visibility to drivers and underestimate the benefits of retro-reflective materials in dark conditions (Tyrell and Patton, 1998; Tyrell et al., 2004a; Tyrell et al., 2004b).

Cell Phone

Research has found that improper situational awareness and distracted attention levels are significant factors among pedestrians using mobile phones (Hatfield and Murphy, 2007; Hyman et al., 2010; Nasar et al., 2008; Stavrinos, 2011). Field studies (Hatfield and Murphy, 2007; Nasar et al., 2008) observed that pedestrians made more unsafe street crossings when conversing on a cell phone than when undistracted.

Crossing Behavior

Palamarthy et al. (1994) conducted a detailed study on the crossing behavior of pedestrians. Findings showed that 18 percent crossed during a no-walking signal and only 9 percent waited for the next steady walking signal. This type of behavior is associated with a higher number of pedestrian and bicycle crashes (Abdulsattar, 1996). A study by Hunter and Huang (1995) concluded that the most common bicyclist crash contributing factors were failure to yield (represented 21 percent of crashes), stop sign violations (represented 7.8 percent of crashes), and safe movement violations (represented 6.1 percent of crashes). The condition of the bicycle was found to be without defects in 91 percent of cases.

Temporal Effect

A study by Campbell (2004) showed that the highest proportion of pedestrian crashes happened between 3 p.m. and 6 p.m. Most pedestrian fatalities tend to occur at night (Campbell, 2004; Harkey and Zeeger, 2004). Significant numbers of older pedestrian crashes occurred in fall and winter months, whereas younger pedestrian crashes occurred significantly during the spring and summer months (Campbell, 2004). Harkey and Zeeger (2004) determined higher clustering of pedestrian fatalities on weekend days. Hunter and Huang (1995) showed that more crashes involving a bicyclist occur during the fair-weather months of April to October and on weekdays.

Aggregate Level

Many studies used larger spatial units (like U.S. census county, tract, block group, and block) to determine the key association factors in pedestrian and bicycle crashes. This level is divided into three groups:  (a) traffic characteristics, (b) land use characteristics, and (c) demographics.

Traffic Characteristics

Traffic characteristics like traffic volume and non-motorist volume are closely associated with non-motorist crash risks. Roadways with higher traffic and non-motorist traffic increase risk exposures significantly. Table 10 lists the studies on traffic characteristics.

Table 10. List of Studies on Traffic Characteristics
Category Risk Factors Studies
Traffic volume AADT Garder, 2004; Lee and Abdel-Aty, 2005; Loukaitou-Sideris et al., 2007; Wier et al., 2009; Cottrill and Thakuriah, 2010; Siddiqui and Abdel-Aty, 2012; Abdel-Aty et al., 2013;
Walk and bike trips Dixon et al., 2015;
Percentage of trucks Retting, 1993;

Traffic Volume Related


Research suggests that crash risk from a pedestrian’s perspective is more influenced by pedestrian volume than vehicle volume (Garder, 2004). In many places, it is problematic to determine pedestrian or bicyclist volume of different age groups at intersections or roadway segments. Thus, it is difficult to determine what proportion of the pedestrians were actually involved in crashes. Frequency of pedestrian crashes usually increases with traffic volume up to a certain threshold. This indicates that pedestrian or bicycle crashes are more likely to occur at intersections or segments with higher traffic volume since higher volume increases the potential conflict points between non-motorists and vehicles. However, it appears that the rate of increase gradually decreases as traffic volume increases after certain thresholds for different roadway classes (Lee and Abdel-Aty, 2005; Loukaitou-Sideris et al., 2007; Wier et al., 2009; Cottrill and Thakuriah, 2010; Siddiqui and Abdel-Aty, 2012; Abdel-Aty et al., 2013).

Walk and Bike Trips

Dixon et al. (2015) conducted research to evaluate the relationship between non-motorized trips and risk outcomes on rural two-lane and multi-lane roadways. The relationship between higher non-motorized trips and crash severity outcomes was not significant at 95 percent confidence interval.

Percentage of Trucks

One study found that pedestrian fatalities involving large trucks were more likely to occur at intersections. Results also showed that large truck fatal crashes involved pedestrians aged 60 or above, and other vehicle fatal crashes involved pedestrians aged 40 and above (Retting, 1993). The two most common crash scenarios, representing 47 percent of all crashes, involved large trucks proceeding forward either at intersections or segment locations. Trucks turning and striking pedestrians with the front of the trucks, the rear wheels, or the trailers accounted for another 24 percent, and trucks backing up fatally injured pedestrians in 10 percent of cases (Retting, 1993).

Land Use Characteristics

A wide selection of spatial units has been examined in macro-level crash modeling for non-motorized trips in the safety literature. This includes block group (Levine et al., 1995), TAZ (Abdel-Aty et al., 2011; Guevara et al., 2004; Hadayeghi et al., 2003; Hadayeghi et al., 2006; Hadayeghi et al., 2010; Ng et al., 2002; Washington et al., 2010; Naderan and Shahi, 2010), census wards (Noland and Quddus, 2004a; Quddus, 2008), standard statistical regions (Noland and Quddus, 2004b), census tract (Lascala et al., 2000; Quddus, 2008; Loukaitou-Sideris et al., 2007; Wier et al., 2009; Cottrill and Thakuriah, 2010; Ukkusuri et al., 2011), county (Aguero-Valverde and Jovanis, 2006; Amonros et al., 2003; Huang et al., 2010; Karlaftis and Tarko, 1998; Noland and Oh, 2004), state (Noland, 2003), local health areas (Macnab, 2002), and grid-based structure (Kim et al., 2006).  Table 11 lists the studies associated with land use characteristics.

Table 11. List of Studies on Adjacent Land Use
Category Risk Factors Studies
Land use Socio-economic Lascala, 2000; Wier et al., 2009; Qin and Ivan, 2001;
Crime Bagley, 1992; Cottrill and Thakuriah, 2010; Green et al., 2011;
Ethnicity Rivara and Barber, 1986; Agran et al., 1998; Hilton, 2006; Sciortino et al., 2005;
Income Mueller et al., 1988; LaScala, 2000; Noland and Quddus, 2004; Wier et al., 2009; Agran et al., 1998; Chakravarthy et al., 2010; Roberts et al., 1995; Delmelle et al., 2012; Graham and Glaister, 2003; Green et al., 2011; Cottrill and Thakuriah, 2010; Siddiqui and Abdel-Aty, 2012; Abdel-Aty et al., 2013
Neighborhood Campbell, 2004; Noland and Quddus, 2004; Clifton and Kreamer-Fults, 2007;
Households Mcmahon et al., 1999; Zajac and Ivan, 2003;
Area Qin and Ivan, 2001;
Public schools Clifton and Kreamer-Fults, 2007;

Socioeconomic Structure

Two studies using data from San Francisco found a relationship between socioeconomic structure and pedestrian crash severities of all ages (LaScala, 2000; Wier et al., 2009). Qin and Ivan (2001) concluded that a large number of studies recognized the indirect relationship between pedestrian crashes and socioeconomic factors.


Bagley (1992) investigated the probability of sites being hazardous given socioeconomic and crime data. Pedestrian injuries are more dominant in areas with high measures of social disadvantage, such as crime and domestic violence (Cottrill and Thakuriah, 2010; Green et al., 2011).


Past studies noted a relationship between economic and ethnic differences with pedestrian crash rates.  Low income and household crowding are factors associated with greater pedestrian injuries (Rivara and Barbar, 1986). Other studies have linked poverty and the lack of English language fluency with pedestrian injuries (Agran et al., 1998). The 2006 national data showed that minorities of almost all age groups were more likely to be involved in a fatal non-motorist crash than the non- Hispanic white population (Hilton et al., 2006). Underreporting of pedestrian injuries among minorities is also cited in the literature and would likely increase the amount of vulnerabilities in certain ethnicities (Sciortino et al., 2005; Abdel-Aty et al., 2013).


In the U.S., children from families with low income are seven times more likely to be injured than children from families with high income (Mueller et al. 1988). Chakravarthy et al. (2010) found that the percentage of the population living in low-income households was the strongest predictor of pedestrian injuries, with pedestrian crashes four times more likely in poor neighborhoods. This finding is repeated in a study that found that the risk of injury for children in the lowest socioeconomic stratum is more than twice that of children in higher socioeconomic categories (Roberts et al., 1995). These results are consistent with previous research that looked at pedestrian injury crashes on a smaller geographic scale (LaScala et al., 2000; Noland and Quddus, 2004a; Wier et al., 2009; Agran et al., 1998; Cottrill and Thakuriah, 2010; Green et al., 2011; Cottrill and Thakuriah, 2010; Siddiqui and Abdel-Aty, 2012; Abdel-Aty et al., 2013). Minority populations were found to have a higher incidence of non-motorist fatalities. It relates to geometric factors associated with lower-income areas, such as high-speed roads. Graham and Glaister (2003) found evidence to support this among patterns of childhood pedestrian fatalities, which are strongly associated with more poor income areas. However, a study conducted by Delmelle et al. (2012) in Buffalo, New York, found minimal effect of income on pedestrian crashes. This study showed that bicycle and pedestrian crashes were related to factors like ethnicity, educational status, and land use.


A study by Epperson (1995) recognized that the economic status of a neighborhood was significantly related to the number of pedestrian crashes. The characteristics of the area or the neighborhood had significant contributions as well. Severity of pedestrian crashes was found to be higher outside of urban zones in another study (Campbell, 2004). In standard statistical regions of the U.K., lower-income areas and increased per capita expenditure on alcohol were associated with severe or fatal pedestrian crashes (Noland and Quddus, 2004).


McMahon et al. (1999) studied land use variables such as the percentage of single parents with children, percentage of housing stock built after 1980, percentage of households composed of families, and percentage of the unemployment rate. The study showed that the percentage of single parents with children and percentage of housing stock built after 1980 were significantly related to segment-related pedestrian crashes. Some specific areas (for example, downtown, compact residential, low-density commercial, and medium-density commercial areas) are more likely to experience lower pedestrian crash severity than other areas, like villages or low-density residential areas (Zijac and Ivan, 2003).

Area Type

Research shows that area type is significantly related to non-motorized crashes. Because campus areas as well as tourist zones have the greatest positive effect on pedestrian exposure, Qin and Ivan (2001) argued that these zones deserve additional consideration for improvements in pedestrian facilities, such as warning devices, speed limits, stop signs, and marked crosswalks.

Public Schools

Clifton and Kreamer-Fults (2007) showed that the presence of a driveway or turning bay at a school entrance decreased non-motorist crashes and injury severity. On the other hand, the presence of recreational facilities near public schools was positively associated with a higher number of non-motorist crashes.


Demographics influence risk intensity of non-motorists. Since land use, socio-economic status, and demographics are closely associated, this study used three broad groups as risk factors associated with demographics. These factors are: (a) population density, (b) number of licensed drivers, and (c) vehicle ownership. Table 12 lists the studies associated with demographics.

Table 12. List of Studies on Demographics
Category Risk Factors Studies
Demographics Population density LaScala, 2000; Loukaitou-Sideris et al., 2007; Chakravarthy et al., 2010; Siddiqui and Abdel-Aty, 2012; Abdel-aty et al., 2013; Graham and Glaister, 2003; Greene-Roesel et al., 2007; Miranda-Moreno, 2011;
Number of licensed drivers DaSilva et al., 2003;
Vehicle ownership Qin and Ivan, 2001;

Population Density

Many studies considered population density as a contributing factor (LaScala, 2000; Loukaitou-Sideris et al., 2007; Chakravarthy et al., 2010; Siddiqui and Abdel-Aty, 2012; Abdel-Aty et al., 2013). However, some studies showed that pedestrian safety analyses based on population density might distort the true risk values. The population variable captures the inherent likelihood of non-motorized crashes due to greater risk from pedestrian-vehicle interactions. At intersections, risk exposure has been found to be a function of pedestrian activity and traffic volume (Greene-Roesel et al., 2007; Miranda-Moreno et al., 2011). In many cases, data on pedestrian volume were not achievable and total population was used as proxy of exposure. LaScala et al. (2000) conducted a spatial regression analysis of pedestrian injuries associated with motor vehicles in San Francisco. The results showed that pedestrian injuries were associated with increased traffic flow and population density (as measured per kilometer of road length). Areas with higher unemployment were associated with higher injury rates, whereas areas with more high school graduates had lower injury rates. This finding is similar to the results of Graham and Glaister (2003), who used an areawide deprivation score in their study. This research found that larger numbers of children (ages 0 to 15) in an area were associated with fewer pedestrian injuries, contrary to the findings of other studies.

Number of Licensed Drivers

A study by DaSilva (2003) reported that there were about 0.38 drivers involved in pedestrian crashes per 1,000 licensed drivers. Younger drivers (20 years or less) held the highest probability of being involved in a crash with pedestrians, at over 0.8 drivers per 1,000 licensed drivers. The second-highest involvement rate based on the licensed driver population is associated with older drivers (85 years or more), showing nearly six drivers involved in crashes per 10,000 licensed drivers.

Vehicle Ownership

It is evident that the vehicle-owner ratio is higher in rural areas. These areas have different demographic factors, such as neighborhood environment, household median income, and unemployment, compared to urban environments. Thus, Qin and Ivan’s (2001) study suggested the necessity of considering an urban setting and rural setting separately.

Proxy variables, Exposure, and Pedestrian/Bicyclist Crashes

This chapter provides a summarized view on the studies focusing on pedestrian and bicyclist risk factors other than exposure. Interested reader can consult other studies providing systematic synthesis on pedestrian and bicyclist risk factors (Karsch et al., 2012; PEDSAFE, 2013; BIKESAFE, 2014;). It is important to note that NCHRP Report 803 described most of these risk factors (for example, population density, employment density, transit stop density, presence of sidewalk, socioeconomic characteristics) as demand proxy variables (Lagerwey et al., 2015). Moreover, findings from most of these studies indicate association, not causation, between these risk factors and crash outcomes. In many of these studies described in this chapter, risk factors were associated with pedestrian/bicyclist crashes without considering exposure into account. There is a small body of research literature that examined the link between pedestrian/bicyclist injury counts and both vehicle and pedestrian/bicycle flows at intersection locations. Zegeer et al. (1985) conducted study on pedestrian crashes at 1,297 signalized intersections in 15 cities. The findings showed that the volume of pedestrians crossing at an intersection was the most influential variable in explaining the variation in pedestrian crashes. This study also showed that vehicle volume was the second most important factor in explaining pedestrian crashes. Similar findings were found in other studies (Brude and Larsson, 1993; Lyon and Persaud, 2002;  Zegeer et al., 2005;). Leden (2002) compared pedestrian crashes associated with left-turning traffic with pedestrian crashes with right-turning traffic. The results showed that left-turn volume was highly associated with a larger increase in pedestrian crashes compared to right-turn volume. Schneider et al. (2011) used vehicle and pedestrian volume data from 81 intersections. The results from negative binomial regression model showed that significantly more pedestrian crashes occurred at intersections with more right-turn-only lanes, more nonresidential driveways within 50 ft, more commercial properties within 0.1 mi, and a greater percentage of younger (< 18 years old) residents within 0.25 mi. Miranda-Moreno et al. (2011) used disaggregate vehicle and cyclist flows to develop cyclist injury frequency models. The findings showed that a 10% increase in bicycle flow was associated with a 4.4% increase in the frequency of cyclist injuries and a 10% increase in vehicle flow would result in a 3.4% increase in cyclist injury occurrence.

Summary and Conclusions

This chapter summarizes the state-of-the-art research synthesis on pedestrian and bicyclist risk factors other than exposure. The risk factors are explained based on two large categories: a) disaggregate level risk factors, and b) aggregate level risk factors. Table 13 and Table 14 show influence of the risk factors of non-motorist crashes by using selected number of major studies. An upper arrow (⇑) sign indicates that the corresponding factor is associated with higher number of non-motorized crashes and a down arrow (⇓) sign indicates that the factor is negatively associated with non-motorized crash frequencies.

Disaggregate Level

Disaggregate-level risk factors mainly associate facility condition and individual level. Facility condition considers risk factors associated with feature type (e.g., urban/rural, divided/undivided), segment, and intersection. Non-motorized trips are more likely to end in a crash in an urban location, but the ratio of fatal crash to injury crash is higher in rural locations. Many studies showed that posted higher speeds on roadway segments are closely associated with higher pedestrian and bicyclist crashes. Moreover, no lighting at dark, absence of sidewalks or bike lanes, and presence of bus stops are significant risk factors for roadway segments. On intersections, the key risk factors are wider crossings and unsignalized conditions. The individual level indicates risk factors involved with an individual unit or person. The significant risk factors are characteristics of drivers (e.g., age, distraction), characteristics of pedestrians/bicyclists (e.g., age, impairment, cell phone use), and properties of vehicles/bicycles (e.g., speed, lighting). These factors can contribute to determining direct measures of crash involvement and crash severity. For example, an intersection with a wider crosswalk is a crash-prone location for older pedestrians. This finding may be attributed to both exposure time and direct measurement of crashes. For safety improvement, the reduction of the length of crosswalks is helpful because it decreases the exposure time that an older pedestrian needs to cross the road.

Aggregate Level

 Many studies examined a wide selection of spatial units in aggregate-level analyses. This includes census block, block group, tract, county, state, TAZ standard statistical regions, and grid-based structure. In this research synthesis, aggregate-level risk factors are divided into three properties: traffic condition, land use characteristics, and demographics. Traffic condition involves risk factors like motorized and non-motorized traffic volume for different spatial units. Risk factors associated with land use characteristics involve income level, household size, percentage of minorities, etc. Many studies showed that specific socioeconomic structures (e.g., poor neighborhood, higher density of minority households) are closely associated with higher crash risks for non-motorists. Demographics include broader spatial characteristics like population density, number of licensed drivers, etc. Analyzing risk factors at the aggregate level usually directs the focus toward a systematic safety investigation. For example, a higher number of public schools in a census block group is likely to increase crashes or crash severities. To improve safety for a larger spatial unit, the authority needs to consider a wider variety of countermeasures in determining a systematic approach to safety.

Table 13. Impact of the Risk Factors at Disaggregate Level
Variables Abdel-Aty and Keller, 2005 Aziz et al., 2013 Conway et al., 2013 Das and Sun, 2015; Dixon et al., 2015 Howard et al. 2005 Knoblauch et al., 1987 LaScala et al., 2000 Leaf et al., 2005 Lee and Abdel-Aty, 2005 Mcmahon et al., 1999; Nasar et al., 2008 Peden, 2004 Ponnaluri and Nagar, 2010 Retting et al., 2003 Shaw et al., 2016 Spainhour and Wootton, 2007 Tefft, 2011 Tyrrell et al., 2004 Zegeer et al., 2001
Facility Condition
Urban roadways - - - - - - - - - - - - - - - - -
Work zone - - - - - - - - - - - - - - - - - - -
Roadway Segment
Higher speed - - - - - - - - - - - - - - - - - -
No lighting at night - - - - - - - - - - - - - - - - - -
No sidewalks - - - - - - - - - - - - - - - - -
No Bike Lanes - - - - - - - - - - - - - - - - - - -
Narrow shoulders - - - - - - - - - - - - - - - - - - -
Number of lanes - - - - - - - - - - - - - - - - -
Unsignalized - - - - - - - - - - - - - - - - -
Wider crosswalks - - - - - - - - - - - - - - - - -
Impact speed - - - - - - - - - - - - - - - - - - -
Older vehicles - - - - - - - - - - - - - - - - - - -
Age - - - - - - - - - - - - - - - - - - -
Number of occupants - - - - - - - - - - - - - - - - - - -
Age - - - - - - - - - - - - - - - - - -
Child pedestrian - - - - - - - - - - - - - - - - - -
Intoxication - - - - - - - - - - - - - - - - - -
No retro-reflective wearing - - - - - - - - - - - - - - - - - - -
Cell phone - - - - - - - - - - - - - - - - - - -

Notes: ⇑ : Increase in risk, ⇓: Decrease in risk, - : Not considered

Table 14. Impact of the Risk Factors at Aggregate Level
Variables Abdel-Aty et al., 2013 Chakravarthy 2011 Cottrill and Thakuriah, 2010 DaSilva et al., 2003 Dixon et al., 2015 Green et al. (2011) LaScala et al., 2000 Loukaitou-Sideris et al., 2007 Noland and Quddus, 2004 Retting, 1993 Siddiqui and Abdel-Aty, 2012 Ukkusuri et al., 2011 Wier et al., 2009
Traffic and Transportation
Traffic Volume - - - - - - -
Walk and bike trips - - - - - - - - - - - -
Percentage of trucks - - - - - - - - - - - -
Land use
Crime - - - - - - - - - - -
Low income - - - - - - - -
Minority - - - - - - - -
Non-English speaker - - - - - - - - - - - -
Alcohol Availability - - - - - - - - - - -
Housing density - - - - - - - - - - -
Public Schools - - - - - - - - - - -
Population density - - - - -
Number of licensed drivers - - - - - - - - - - - -

Notes:⇑ : Increase in risk, ⇓ : Decrease in risk, - : Not considered

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