EXHIBIT 1
GLENDALE VISION ZERO ACTION PLAN
July12, 2024
Project: Glendale Vision Zero Action Plan
Re: Glendale Vision Zero Action Plan Crash Analysis
Contents
Summary of Findings ..................................................................................................................................................3 Descriptive Crash Analysis Methodology ...................................................................................................................4 Crash Data Overview ..............................................................................................................................................4 Data Sources ..........................................................................................................................................................4 Study Limitations .....................................................................................................................................................4 Temporal Consistency Limitations ......................................................................................................................4 Exposure Data .....................................................................................................................................................4 Transportation Data for Future Study ..................................................................................................................5 Descriptive Crash Analysis .........................................................................................................................................5 General Crash Trends .........................................................................................................................................5 Crashes by Severity by Year ...............................................................................................................................6 Crashes Per Mode ..............................................................................................................................................6 Crash Causation .....................................................................................................................................................6 Crash Dynamics ..................................................................................................................................................6 Leading Crash Violations ....................................................................................................................................7 Contributing Behaviors ........................................................................................................................................8 Roadway Characteristics ........................................................................................................................................8 Crash Location ....................................................................................................................................................8 Functional Classification ......................................................................................................................................9 Number of Lanes .................................................................................................................................................9 Environmental Characteristics ............................................................................................................................. 10 2 Crashes by Time of Year .................................................................................................................................. 10 Crashes by Lighting Condition ......................................................................................................................... 10 COVID-19 Pandemic Impact ............................................................................................................................ 11 Crashes by Day of Week and Time of Day ...................................................................................................... 11 Crashes by Land Use ....................................................................................................................................... 12 Crashes by Park Proximity ............................................................................................................................... 13 Crashes by Healthy Places Index .................................................................................................................... 13 Party Characteristics ............................................................................................................................................ 14 Victim Age ........................................................................................................................................................ 14 Victim Gender ................................................................................................................................................... 15 High Injury Network ................................................................................................................................................. 16 Overview ........................................................................................................................................................... 16 Pedestrian Crash Density .................................................................................................................................... 17 Bicycle Crash Density .......................................................................................................................................... 18 Vehicle Crash Density .......................................................................................................................................... 19 Motorcycle Crash Density .................................................................................................................................... 20 Development of the High Injury Network ............................................................................................................. 21 High Injury Network Thresholds ........................................................................................................................... 21 Draft High Injury Network ..................................................................................................................................... 22 Revised High Injury Network ................................................................................................................................ 22 High Crash Density Intersections ......................................................................................................................... 24
3 Summary of Findings
Years of Crash Data Analyzed: 2012 - 2021
Total Crashes: 5,125
Total Fatal Crashes: 37
Total Serious Injury Crashes: 192
Total Killed and Serious Injury Crashes (KSI): 229
Crashes by Year: Crashes peaked in 2013, with a peak of KSI crashes in 2015, before falling to a low point in 2020. In 2021, both the total number of crashes and KSI crashes grew.
Injury Severity By Mode:
103 pedestrian crashes, or 44.9% of all KSI crashes in the city, resulted in a death or severe injury 20 bicycle crashes resulted in a death or severe injury 40 motorcycle crashes resulted in a death or severe injury Crashes by Mode: 71.8% of all crashes were motor vehicle-only; 17.0% of crashes were pedestrian-involved;
7.7% of crashes were bicycle-involved; 4.6% of crashes were motorcycle-involved Leading KSI Violations: Pedestrian right of way was the most common KSI crash violation type, at 17.9% of KSI crashes, followed by pedestrian violations (17.5%). Unsafe vehicle speed and failure to follow traffic signals and signs were also common violations, at 13.5% and 11.4% of KSI crashes respectively.
HPI and Equity: When normalized by miles of roadway per census tract, tracts in the lowest quartile of HPI (Healthy Places Index) scores experienced the highest rates of overall crashes and KSI crashes.
HIN Description: The proposed High Injury Network accounts for 13% of local streets in the city of Glendale, where 71% of crashes and 78% of KSI crashes occurred. Streets in the HIN are predominantly arterials in central, downtown, and West Glendale.
4 Descriptive Crash Analysis Methodology
Crash Data Overview
Crash data was obtained from the Transportation Injury Mapping System (TIMS), maintained by the UC Berkeley Safe Transportation Research and Education Center for the 10 most recent years of available data. Crashes cover the years 2012 through 2021, as available data for 2022 is currently considered provisional. TIMS provides crashes from the Statewide Integrated Traffic Records Systems (SWITRS), which is composed of data collected and maintained by the California Highway Patrol. Crashes within the study area but occurring on grade-separated federal- and state-controlled freeways, were excluded from the analysis, given the Action Plan’s focus on local roadways.
Data Sources
Dataset Source Attributes
Crash Data TIMS/SWITRS Attributes consistent with information documented in SWITRS crash reports
Road Inventory City of Glendale Number of lanes
Functional Classification
Healthy Places
Index 3.0
Healthy Places
Index
Health Places Index percentile scores
Land Use
Classification
City of Glendale Land use type by parcel
Open Space City of Glendale Parks and recreation centers
Study Limitations
Temporal Consistency Limitations
The consultant team studied crashes that occurred during a period of ten years, from 2012 through 2021. The compiled roadway data reflects current conditions according to the data made available at the time of this analysis. It can be assumed that some changes in roadway design and operations have occurred over the previous years that cannot be accounted for. For example, if a crash occurred in 2016 and number of lanes on that street were later reduced from four to two, the crash would be recorded as occurring on a two- lane street. As crash data is viewed at an aggregate level within this document, the impacts of these temporal inconsistencies are expected to be minor.
Exposure Data
The analyses reported here do not adjust for exposure rates based on volumes by modes. Therefore, results show crash density but not frequency of crashes normalized by level of traffic or pedestrian and bicycle volumes, which is also called exposure. For example, in many communities, pedestrian crashes are more common during daylight conditions than dark conditions. This does not mean that daylight conditions are more dangerous than dark conditions. Rather, it reflects the fact that people are more likely to travel, and especially more likely to travel by walking, in light conditions than in dark conditions. Some proxies for exposure are noted in this analysis, such as land use and functional classification.
5 Transportation Data for Future Study
As the City monitors its progress on Vision Zero, additional data can assist departments in understanding crash risk and identifying proactive countermeasures and strategies. Below are some recommendations for additional data components that were not available for this analysis. • Complete citywide collected bicycle and pedestrian volume data would allow for a measure of crash exposure for bicyclists and pedestrians.
• Several datasets listed below would help further identify or refine risk factors. These datasets, when combined with crash data, may grant further detail into factors potentially evident in crash patterns. At this time, this data is either not available in a geospatial format, or were available, but with limited coverage:
o Vehicle operating speeds
o Crosswalk style
o Street width
o Traffic signal phasing
o Vehicle volumes
o Intersection control type
Descriptive Crash Analysis
General Crash Trends
Figure 1 depicts the trend in all crashes and KSI crashes for all modes over the ten- year period analyzed.
Crashes in Glendale have fallen gradually from a peak of 644 in 2015, reaching the city’s lowest annual rate in the time period studied in 2020. Changes in travel patterns and behaviors associated with the COVID -19 pandemic may have influenced this drop somewhat, and are discussed further below in this memo. KSI crashes in 2019, however, had already dropped significantly from the previous year, by 33.3%, preceding the pandemic. Both overall crashes and KSI crashes later increased in 2021, with KSI crashes growing 76.9% in one year.
Figure 1. Crashes by Year, 2012 - 2021
19 35
31 24 24 25
21 14 13
23 0
5 10
15 20
25 30
35 40
0 100
200
300
400
500
600
700
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
KSI Crashes
All Crashes
Year
KSI Crashes All Crashes
6 Crashes by Severity by Year
Table 1 shows crash severity by year in the city of Glendale, across all modes . 2014 and 2017 had the most fatal crashes, though 2013 covers the highest rate of severe injury and combined KSI crashes, with 30 and 35, respectively.
Complaint of Pain Other Visible Injury Severe Fatal Total 2012 347 189 17 2 555
2013 389 170 30 5 594
2014 357 160 25 6 548
2015 423 197 22 2 644
2016 382 155 19 5 561
2017 348 165 19 6 538
2018 323 141 18 3 485
2019 317 143 12 2 474
2020 180 109 10 3 302
2021 248 153 20 3 424
Total 3314 1582 192 37 5125
Table 1. Crashes by Severity by Year
Crashes Per Mode
Table 2 shows crashes by modes involved, between 2012 and 2021, in the city of Glendale. The vast majority of collisions were motor vehicle-only, comprising 71.0% of all collisions on Glendale streets. While pedestrian- involved crashes make up just 17.0% of crashes, they are disproportionately represented in KSI crashes, making up nearly half of those. Motorcycle-involved crashes and bicycle-involved crashes, too, have higher shares of KSI crashes than overall crashes, suggesting a greater vulnerability for these modes.
Note: the total number of crashes listed in the first column exceeds the total number analyzed, as there are a small number of pedestrian-bicycle collisions and pedestrian-motorcycle collisions.
# of Crashes % of Crashes # of KSI Crashes % of KSI Crashes Pedestrian-
Involved Crash
870 17.0% 103 44.9%
Bicycle-Involved
Crash
393 7.7% 20 8.7%
Motorcycle-
Involved Crash
237 4.6% 40 17.5%
Motor Vehicle-
Only Crash
3637 71.0% 66 28.8%
Table 2. Crashes by Mode
Crash Causation
Crash Dynamics
The crash dynamics listed in Table 3 describe the general type of crash. Broadside crashes were the most common among all crashes in Glendale (1,987 of 5,125 crashes), but they made up only 22.3 % of KSI crashes.
Vehicle/pedestrian crashes were the most common KSI crash type, at 39.3% of KSI crashes. Bicycle-involved 7 crashes, though another vulnerable road user category as a road user not in a motor vehicle, are not categorized separately as pedestrian-involved crashes are. Rear end crashes were also a common overall dynamic, and this type was less likely to result in KSI crashes (21.0% of all crashes, 7.0% of KSI crashes).
Crash Dynamic # of
Crashes
% of
Crashes
# of KSI
Crashes
% of KSI
Crashes
Broadside 1987 38.8% 51 22.3%
Head-On 417 8.1% 28 12.2%
Hit Object 145 2.8% 13 5.7%
Overturned 59 1.2% 4 1.7%
Rear End 1078 21.0% 16 7.0%
Sideswipe 472 9.2% 12 5.2%
Vehicle/Pedestrian 754 14.7% 90 39.3%
Other 80 1.6% 9 3.9%
Not provided 133 2.6% 6 2.6%
Grand Total 5125 100.0% 229 100.0%
Table 3. Crash Dynamics
Leading Crash Violations
Table 4 shows the most common crash violations recorded for all crashes and KSI crashes. Automobile Right of Way, which includes violations where a driver did not cede proper right-of-way to other drivers while making a turn or proceeding at an intersection, was the most common overall with 23.6% of crashes. Unsafe speed is also commonly cited as a violation, with 20.4% of all crashes.
The most common violation cited in KSI crashes was Pedestrian Right of Way, in which a driver has failed to properly yield to a pedestrian. Pedestrian Violations, cited when a pedestrian does not yield to a vehicle outside of crosswalks and intersections, was the second-highest violation for crashes resulting in KSIs.
Violation Types # of Crashes % of Crashes # of KSI
Crashes
% of KSI
Crashes
Automobile Right of
Way
1208 23.6% 23 10.0%
Unsafe Speed 1043 20.4% 31 13.5%
Improper Turning 577 11.3% 22 9.6%
Traffic Signals and
Signs
720 14.1% 26 11.4%
Pedestrian Right of
Way
462 9.0% 41 17.9%
Pedestrian Violation 187 3.7% 40 17.5%
Driving or Bicycling
Under the Influence of
Alcohol or Drug
146 2.9% 17 7.4%
Unsafe Starting or
Backing
119 2.3% 4 1.8%
Wrong Side of the
Road
144 2.8% 2 0.9%
8 Other Hazardous
Violation
72 1.4% 2 0.9%
Unsafe Lane Change 79 1.5% 2 0.9%
Other Violations 440 8.6% 10 9.2%
Total 100.0% 229 100.0%
Table 4. Top Crash Violations
Contributing Behaviors
Table 4 and Table 5 depict crash behaviors contributing to crashes and severity. The former shows crashes cited where one of the parties was found to have been drinking. This occurred at higher rates for KSI crashes, making up 14.0% of crashes resulting in fatalities and severe injuries.
Table 5 shows the share of crashes that involved a hit and run, where the at-fault party left the scene of the crash.
Felony hit and run charges represent crashes resulting in more serious injuries and outcomes, while misdemeanor charges are typically charged when a hit and run did not result in an injury to other parties. Hit and runs were slightly more common for KSI crashes, making up 12.7%, than for crashes overall, where they occurred in 7.7%.
Alcohol
Involvement
# of Crashes % of Crashes # of KSI
Crashes
% of KSI
Crashes
Yes 293 5.7% 32 14.0%
No 4832 94.3% 197 86.0%
Grand Total 5125 100.0% 229 100.0%
Table 5. Crashes by Alcohol Involvement
Hit and Run
Type
# of Crashes % of Crashes # of KSI
Crashes
% of KSI
Crashes
Felony 233 4.5% 29 12.7%
Misdemeanor 164 3.2% 0 0.0%
Not Hit and Run 4728 92.3% 200 87.3%
Grand Total 5125 100.0% 229 100.0%
Table 6. Crashes by Hit and Run Type
Roadway Characteristics
Crash Location
Nearly half of all crashes in Glendale took place at an intersection, as reported by responding officers via collected SWITRS data. That rate was lower for KSI crashes, which were more likely to take place along a street segment or away from an intersection. KSI crashes occurred at an intersection at slightly lower rates, about
39.7% of the time.
9 All Crashes % of All
Crashes
# of KSI Crashes % of KSI Crashes
Intersection 2366 46.2% 91 39.7%
Street Segment 2735 53.4% 138 60.3%
Table 7. Crash Location
Functional Classification
Across the approximately 355 miles of streets and alleys in Glendale, both overall crashes and KSI crashes predominantly on streets classified as major and minor arterials. Approximately 41.0% of crashes occurred on major arterial roadways, and 40.2% of KSI crashes. Normalized by mile, the classification saw 65 crashes per mile and 2.84 KSI crashes per mile. Minor arterials and urban collector roadways also saw higher rates of crashes, and represented 1.83 and 1.62 KSI crashes per mile, respectively.
Though minor/local streets make up 58% of streets in the city by length, only about 14.6% of all crashes took place on one.
# of All
Crashes
% of All
Crashes
# of KSI
Crashes
% of KSI
Crashes
Miles
of
Road
Crashes
per Mile
KSI
Crashes
per Mile
Major Arterial 2085 41.0% 91 40.2% 32.1 65.0 2.84
Minor Arterial 1083 20.6% 44 18.8% 24.1 45.0 1.83
Urban Collector 654 12.7% 32 14.3% 19.8 33.0 1.62
Community Collector 345 6.7% 20 8.6% 35.9 9.6 0.56
Neighborhood Collector 87 1.7% 6 2.5% 16.6 5.2 0.36
Minor/Local 763 14.6% 34 14.7% 226.2 3.4 0.15
Alley 10 0.2% 0 0.0% 29.6 0.3 0
Not Captured 98 2.2% 2 0.8% N/A N/A N/A
Total 5125 100.00% 229 100.00% 354.7
Table 8. Crashes by Street Classification
Number of Lanes
A plurality of overall crashes and KSI crashes (47.4% and 48.0%) occurred on streets with four through lanes.
Normalized by mileage, however, streets with five through lanes saw the most crashes of both categories. Streets with five through lanes are less common in Glendale, making up just about 2.7 miles of roadway in the city. They include portions of streets such as Mountain Street, Brand Boulevard, Glendale Avenue and Central Avenue.
Number of Lanes # of Crashes % of
Crashes
# of KSI
Crashes
% of KSI
Crashes
Crashes
by Mile
KSI Crashes
by Mile
0 (Alleys) 10 0.2% 0 0.0% 0.3 0.0
2 2066 40.3% 100 43.7% 6.8 0.3
3 64 1.2% 1 0.4% 54.4 0.8
4 2427 47.4% 110 48.0% 55.0 2.5
5 216 4.2% 8 3.5% 80.7 3.0
10 6 238 4.6% 8 3.5% 53.3 1.8
7 6 0.1% 0 0.0% 46.3 0.0
No data 98 1.9% 2 0.9%
Grand Total 5125 100.0% 229 100.0%
Table 9. Crashes and Number of Lanes
Environmental Characteristics
Crashes by Time of Year
Table 10 shows crashes grouped by the month. The distribution of crashes over the year is largely even, with slightly more crashes occurring overall in December and November. KSI crashes occurred most frequently in October (13.1%) and were also slightly more frequent in November and December.
Months # of Crashes % of
Crashes
# of KSI
Crashes
% of KSI
Crashes
Jan 367 7.2% 16 7.0%
Feb 405 7.9% 16 7.0%
Mar 445 8.7% 17 7.4%
Apr 442 8.6% 19 8.3%
May 396 7.7% 15 6.6%
Jun 403 7.9% 12 5.2%
Jul 417 8.1% 15 6.6%
Aug 405 7.9% 18 7.9%
Sep 410 8.0% 20 8.7%
Oct 446 8.7% 30 13.1%
Nov 470 9.2% 28 12.2%
Dec 519 10.1% 23 10.0%
Total 5125 100.0% 229 100.0%
Table 10. Crashes by Month
Crashes by Lighting Condition
The vast majority of crashes in Glendale took place during daylight conditions. Fewer than a quarter of crashes took place under dark conditions, with most of those occurring where there are already street lights. Pedestrian crashes in the dark took place at a slightly higher rate than all crashes overall.
Lighting
Condition
# of
Crashes
% of
Crashes
# of KSI
Crashes
% of KSI
Crashes
# of
Bicycle
Crashes
% of
Bicycle
Crashes
# of Ped
Crashes
% of Ped
Crashes
Dark - No
Street Lights 42 0.8% 5 2.2% 2 0.5% 14 1.6%
Dark - Street
Lights 1221 23.8% 92 40.2% 69 17.6% 223 25.6%
Dark - Street
Lights Not
Functioning
8 0.2% 3 1.3% 0 0.0% 5 0.6%
11 Daylight 3678 71.8% 123 53.7% 308 78.4% 601 69.1%
Dusk -
Dawn 151 2.9% 6 2.6% 11 2.8% 24 2.8%
Not
Provided 25 0.5% 0 0.0% 3 0.8% 3 0.3%
Total 5125 100.0% 229 100.0% 393 100.0% 870 100.0%
Table 11. Crashes by Lighting Condition
COVID-19 Pandemic Impact
The COVID-19 pandemic significantly reshaped travel patterns due to lockdown periods, increased rates of telework, and broader socioeconomic changes. Overall, the rates of collisions slowed during the COVID-19 pandemic, which continued across other vulnerable modes such as bicyclists and pedestrians. However, KSI crashes increased slightly during the first two years of the pandemic, comprising a greater percent (4.9%) of all crashes in that period. Immediately prior to the pandemic, KSI crashes made up 3.6% of all c rashes.
All
Crashes
KSI
Crashes
Bicycle
Crashes
Pedestrian
Crashes
Motorcycle
Crashes
Pre-Covid Total 959 35 58 164 44
2018 485 21 28 77 25
2019 474 14 30 87 19
COVID-19 Total 726 36 42 141 35
2020 302 13 23 64 15
2021 424 23 19 77 20
Table 12. Crashes by COVID-19 Impact
Crashes by Day of Week and Time of Day
Crashes in Glendale most commonly occurred in the afternoon, between the hours of 3:00 pm and 6:00pm on most days. On Fridays and weekends, slightly more crashes occur later at night and in the early morning. Most crashes, 15.8%, occurred on Fridays.
Table 14 shows KSI crashes by time of day and day of the week. KSI crashes were more likely to take place later in the evening, between 6:00 pm and 9:00 pm, and on Fridays and Saturdays. Mondays, however, saw the most KSI crashes, with 39 crashes or 17.0%.
Day of the
Week
12-3
AM
3-6
AM
6-9
AM
9 AM-
12 PM
12-3
PM
3-6 PM 6-9 PM 9 PM-
12 AM
Left
Blank
Total
Monday 15 10 90 138 158 186 138 47
782
Tuesday 15 9 103 141 153 189 117 55
782
Wednesday 7 8 90 130 165 208 127 54 1 790
Thursday 14 6 86 111 170 172 149 51
759
Friday 9 9 79 140 168 191 149 68
813
Saturday 34 12 35 128 173 127 95 54
658
Sunday 37 10 30 89 129 112 88 46
541
12 Total 131 64 513 877 1116 1185 863 375 1 5125
Table 13. Crashes by Day of Week and Time of Day
Days of the
Week
12-3
AM
3-6
AM
6-9
AM
9 AM-
12 PM
12-3
PM
3-6 PM 6-9 PM 9 PM-
12 AM
Total
Monday 4 3 5 3 13 8 3 39
Tuesday 1 1 3 1 2 4 14 4 30
Wednesday 1 1 7 3 4 12 4 32
Thursday 2 1 4 4 3 6 3 23
Friday 1 1 5 7 12 9 3 38
Saturday 3 1 3 3 5 4 7 3 29
Sunday 3 2 4 5 10 5 3 6 38
Total 14 6 19 26 34 45 59 26 229
Table 14. KSI Crashes by Day of Week and Time of Day
Crashes by Land Use
Crash data was matched to the closest parcel to determine adjacent land use types. Crashes in Glendale (44.1% of all crashes, 41.5% of KSI crashes) in this period largely occurred near commercial land uses such as retail and offices, many of which are served by wide, arterial roadways.
The government use type refers to a range of city-owned uses, such as educational campuses and city facilities.
Normalized miles of roadway by land use were not included due to a data limitation in the available data.
Land Use
Type
# of Crashes % of Crashes # of KSI
Crashes
% of KSI
Crashes
Commercial 2262 44.1% 95 41.5%
Government 619 12.1% 37 16.2%
Industrial 161 3.1% 4 1.7%
Institutional 96 1.9% 1 0.4%
Irrigated Farm 1 0.0% 0 0.0%
Recreational 45 0.9% 1 0.4%
Residential 1809 35.3% 83 36.2%
Miscellaneous 76 1.5% 6 2.6%
No data 56 1.1% 2 0.9%
Grand Total 5125 100.0% 229 100.0%
Table 15. Crashes by Land Use
13 Crashes by Park Proximity
Crashes were identified as being adjacent to a Glendale park if they were within 250 feet from parcels identified as City recreation centers or city parks. This distance was selected in order to eliminate the inadvertent selection of crashes on the opposite side of blocks not facing parks, due to the short block length (about 330 feet) in some parts of the city.
About 5.6% of crashes occurred at or adjacent to parks in Glendale. Slightly more, 7.0%, KSI crashes and pedestrian-involved crashes occurred near parks.
Relation to
Park
# of Crashes % of Crashes # of KSI
Crashes
% of KSI
Crashes
# of Pedestrian
Crashes
% of Pedestrian
Crashes
Not at park 4836 94.4% 213 93.0% 809 93.0%
At Park 289 5.6% 16 7.0% 61 7.0%
Total 5125 100.0% 229 100.0% 870 100.0%
Table 16. Crashes and Parks
Crashes by Healthy Places Index
The Healthy Places Index tracks social and economic indicators associated with public health and equitable outcomes. Census tracts receive a score under the index, which are then compared across the state on a percentile basis. A high score and high percentile are associated with indicators of healthier outcomes, such as higher per capita income and lower rates of air pollution. HPI quartiles represent the lowest-scoring 25% to highest-scoring 25% of census tracts.
In Glendale, as shown in Figure 2, the lower quartile census tracts are located in central, southwestern, and western parts of the city, while the higher-scoring tracts are in eastern and northern neighborhoods. Table 11 shows that nearly 40% of crashes and 38% of KSI crashes occurred in the census tracts that fell in the second- lowest quartile, the most of any quartile. Normalized by mile of roadway in those census tracts, tracts in the lowest quartile saw the highest rates of crashes and KSI crashes, indicating that communities under the greatest health, environmental, and socioeconomic burdens experience the highest rates and severity of crashes.
HPI Quartile # of Crashes % of
Crashes
# of KSI
Crashes
% of KSI
Crashes
Crashes per
Mile of
KSI Crashes
per Mile
1, Lowest 1713 33.4% 74 32.3% 27.7 1.2
2 2043 39.9% 86 37.6% 24.8 1.0
3 1138 22.2% 62 27.1% 7.0 0.4
4, Highest 150 2.9% 7 3.1% 1.7 0.1
No data 81 1.6% 0 0.0% 479.9 0.0
Totals 5125 100% 229 1.728503
Table 17. Crashes by Healthy Places Index
14 Figure 2. HPI and KSI Crashes
Party Characteristics
Victim Age
More than one-quarter of crash victims in Glendale were younger than 25 years old. Younger people make up a smaller portion of KSI crash victims, as 20.6% of KSI crash victims are younger than 25 years old.
Older people make up a larger portion of fatal and severe injury crash victims: 23 .0% of KSI victims were 70 years old or older, compared to 9.8% of all crash victims.
15 Figure 3. Crashes by Victim Age
Victim Gender
Crash victims overall are more likely to be women than men (50.6% to 43.1%). Men, however, are more likely to be victims of KSI crashes, making up 59.7% of KSI crashes.
Figure 4. Crashes by Victim Gender
0%
2%
4%
6%
8%
10%
12%
14%
16%Percent of Crashes
Age of Victim
All Crashes KSI Crashes
43%
51%
6%
60%
36%
4%
0% 10% 20% 30% 40% 50% 60% 70%
Male
Female
No data
KSI Victims All Victims
16 High Injury Network
Overview
Using the methodologies outlined in the Crash Analysis Methodology, the project team produced the crash density map results based on the Sliding Window analysis. Outcomes in this section set the foundation for High Injury Network development.
As described in the Crash Analysis Methodology, the Sliding Windows Analysis identifies segments with the highest crash density, weighted by crash severity. The analysis was conducted by determining the number and severity of crashes in a half-mile window on a roadway and shifting that virtual “window” along the roadway 1/10 of a mile at a time, counting the number of crashes by severity and mode that occurred within each successive half-mile segment. The sliding window scores weight the most severe crashes more heavily than lower severity crashes. The total number of crashes were then aggregated along a corridor while incorporating the crash severity weighting, resulting in scored results applied to roadway segments.
Sliding Windows outputs in the following pages are the result of scoring applied by crash severity and crash frequency, and maps are shown with the lowest-scoring segments hidden for legibility.
17 Pedestrian Crash Density
Figure 5. Pedestrian Sliding Windows Analysis
Pedestrian crashes largely occurred in the central, downtown, and eastern areas of Glendale, with a significant grouping of crashes in West Glendale near Glenoaks Boulevard and Sonora Avenue. Many of these areas have a high density of destinations, such as local shops, schools, and parks, where residents and visitors are more likely to be walking and in greater numbers. The highest scoring streets in the outputs include major arterials that connect to regional destinations, such as Brand Boulevard, Glendale Avenue, Verdugo Road south of the CA-134 freeway, and Colorado Boulevard. In addition, a number of smaller streets near downtown Glendale appear in the results, such as Louise Street.
18 Bicycle Crash Density
Figure 6. Bicycle Sliding Windows Analysis
Bicycle crashes were similarly focused on arterial streets throughout Glendale, but especially those that connect to central Glendale, the downtown core, and other central neighborhoods. Additionally, some smaller streets that may be used as common bike routes showed higher crash densities, likely due to a more frequent presence of people biking on those streets. These include Columbus Avenue and Maple Avenue in southwestern Glendale.
19 Vehicle Crash Density
Figure 7. Vehicle Sliding Windows Analysis
Vehicle crashes, as shown in Figure 7, are spread further throughout the city. As with bicycle and pedestrian crashes, highest scoring segments appear on major arterials that connect to downtown Glendale, such as Brand Boulevard and Glendale Avenue. Other significant densities appear in major streets in western and northern Glendale, such as Verdugo Road in Montrose. However, a number of high-scoring segments also appear at access routes to the freeways that serve Glendale: these include Honolulu Avenue/Lowell Avenue at the I-210 on- ramps and Goode Avenue and Sanchez Drive near CA-134.
20 Motorcycle Crash Density
Figure 8. Motorcycle Sliding Windows Analysis
Motorcycle crashes, shown in Figure 8, are focused primarily on arterial streets that serve central Glendale.
However, in addition to central streets such as Colorado Boulevard, higher scoring segments appear in arterials outside of the center of the city, such as Verdugo Road and Glenoaks Boulevard.
21 Development of the High Injury Network
The draft High Injury Network is developed based on the results of the crash density maps from the sliding window analysis. The HIN aims to help identify corridors that may warrant special attention. Identification of these streets will help the City of Glendale prioritize investment in the areas where crash history demonstrates the most serious problems and easily communicate those priorities to the community.
Developing an HIN is an iterative process. It is expected that the City of Glendale will review the HIN produced through this analysis. The process of generating the HIN is as follows:
1. Map the Sliding Windows analysis results for each mode (pedestrian, bicycle, motorcycle, and motor vehicle) individually. As detailed in the Crash Analysis Methodology Memo, analysis outputs reflect the weighting applied by severity, that places a greater focus on fatal and severe injury (KSI) crashes: KSI crashes received a score of 3, and other injury crashes received a score of 1. See the above Crash Density maps for sliding window analysis results.
2. For each mode, determine the threshold of the Sliding Window score required to be included in the HIN.
This step eliminates streets that have a lower crash density thereby prioritizing streets that have higher crash severities and frequencies.
3. Review and manually adjust for false-positive segments that have a high crash score due to a single intersection crash but do not have any other crashes along the corridor.
Segments manually excluded due factors such as false-positive intersection crashes are:
• Pioneer Avenue (Pacific Avenue to Central Avenue): excluded from the Bicycle HIN cutoff due to crashes being accounted for on Pacific and Central Avenue segments • Palmer Avenue (Brand Boulevard to Glendale Avenue): excluded from the Bicycle HIN cutoff due to crashes already being accounted for in Brand Boulevard and Glendale Avenue segments • Glenoaks Boulevard (service road north of the Glenoaks Boulevard arterial, Pacific Avenue to Central Avenue): excluded from the Bicycle HIN cutoff due to crash already being accounted for at the Pacific Avenue intersection
• El Cino Place (Verdugo Road to Montrose Community Park): excluded from the Motorcycle HIN cutoff due to crash being accounted for on Verdugo Road
• School Street (Glenwood Road to Olmsted Drive): excluded from the Motorcycle HIN cutoff due to short segment length (940 feet)
High Injury Network Thresholds
The goal of the minimum HIN threshold setting process is to settle on a minimum sliding window score for each mode independently that will create a network that covers a selective set of the city streets but a relatively large share of crashes with an emphasis on KSI crashes, achieved via severity weighting.
Thresholds (inclusive and above the values listed) for each mode included in the HIN are listed below. A segment that meets or exceeds the weighted crash score threshold noted below for each mode were included in each mode-specific HIN and the overall HIN. The weighted crash score thresholds for areas included in the HIN do not exactly follow the weighted crash score ranges from the sliding windows, as the HIN is a compilation of the highest weighted crash scores.
» Pedestrian: 16
» Bicycle: 6
» Motorcycle: 5
» Motor Vehicle: 42
22 Draft High Injury Network
The Draft High Injury Network shown in Figure 9 integrates the above threshold scores for crashes of all modes. It accounts for 68% of all crashes, and 75% of KSI crashes, on 12% of the local streets within the City of Glendale.
Figure 9. Draft High Injury Network
Revised High Injury Network
Following the presentation to the Project Development Team, the Draft High Injury Network was revised based on stakeholder input regarding existing safety conditions and need. Additions included:
Inclusion of the full extent of Foothill Boulevard in northern Glendale Inclusion of the segments of Verdugo Road previously excluded (Fern Lane to Mountain Street, Bagdad Place to La Crescenta Avenue) to cover the roadway’s full extent Extension of Louise Avenue north from Harvard Street to Lexington Drive Figure 10 on the following page depicts the High Injury Network with these revisions. This HIN accounts for 71% of all crashes,78% of KSI crashes, and covers 13% of local streets within Glendale.
23 Figure 10. Revised High Injury Network
24 High Crash Density Intersections
The intersections detailed in Table 18 were determined by identifying clusters of intersection crashes that took place within 50 feet of one another. The below list represents a set of intersections with the most crashes of any modes between 2012 and 2021. It should be noted that while these represent locations with frequent crashes, other intersections along each HIN segment similarly experience high rates of crashes and will also merit study for safety improvements.
Primary Road Secondary Road Number of Crashes
(all)
San Fernando Road Los Feliz Road 21
Central Avenue Chevy Chase Drive 18
Brand Boulevard Colorado Street 18
Central Avenue Broadway 21
Verdugo Road Honolulu Avenue 21
San Fernando Road Western Avenue 27
Brand Boulevard Goode Avenue 21
Brand Boulevard Sanchez Avenue 29
Honolulu Avenue Lowell Avenue 35
Table 18. High Crash Density Intersections (all crashes)