DDOT Bicyclist Advisory Committee Presentation

A presentation by DDOT staff on the Strategic Bikeways Plan.

1. 11
DISTRICT DEPARTMENT OF
TRANSPORTATION
StrategicBikewaysPlan
Existing Conditions
DC Bicycle Advisory Committee Meeting
July 10, 2024
Chris Berg, Bicycle/Pedestrian Program Specialist, DDOT
JimSebastian, Senior Planner,TooleDesign

2. 22
Strategic Bikeways Plan Overview and Agenda
•Strategic Bikeways Plan Overview
•Existing Conditions
•Bikeways and Bike Volumes
•Crash Analysis
•Level of Traffic Stress
•Network Analysis
•Case Studies

3. 3
Strategic Bikeways Plan

4. 4
Bikeways and Bike
Volumes
OvertheYears

5. 5
Bike Lane Growth Over the Years
0
20
40
60
80
100
120
20012002200320042005200620072008200920102011201220132014201520162017201820192020202120222023
Bike Lane Centerline Miles
Regular Bike Lanes Protected Bike Lanes

6. 6
Bikeways By Ward
0
5
10
15
20
25
30
35
Ward 1Ward 2Ward 3Ward 4Ward 5Ward 6Ward 7Ward 8
Bike Lane Centerline Miles
Regular Bike Lanes Protected Bike Lanes

7. 7
Bike Volumes Over the Years
0
5
10
15
20
25
30
201520162017201820192020202120222023
Ridership Millions
Replica Ride Report (SMM) CaBi (DC Only)
Replicais a ‘Big Data’
service that uses
smartphone location-
based services and
other data sources to
model transportation
activity. Replica has
medium confidence
on bike trips. Year-to-
year comparison is
more valuable.
Shared Micromobility
(SMM) is data from
dockless bike and
scooter companies.
CapitalBikeshare
tripsshow trips
traveling in D.C.

8. 8
Crash Analysis
Preliminary Findings

9. 9
Crash Analysis
•Crashes involving bicyclists only
•2017-2023 (inclusive)
•Crashes in DC and Crash Details Table in Open Data DC
•4,002crash points(average = 570 per year)
•8,744persons involved

10. 10
Crash Severity
Fatal (K), 12, 0%
Major Injury (A),
295, 7%
Minor Injury (B),
2,657, 67%
Suspected/No
Injury (C/O),
1,038, 26%
Major injuries include
concussions, apparent
broken bones, severe cuts,
unconsciousness, etc.
Minor injuries include
bleeding, pain, swelling,
etc.

11. 11
Crashes by Year vs. Annual Bicycle Ridership
818
671
645
359
404
352
446
64
57
57
35
26
34
34
0
5
10
15
20
25
0
100
200
300
400
500
600
700
800
900
1000
2017201820192020202120222023
Annual Bicycle Ridership (in millions)
Number of Crashes
Year
Non-KSI KSI 2019-23 Annual Bike Ridership (Replica)
Crashes have remained
relatively flat since the
pandemic despite an
increase in bike volume
based on Replica data

12. 12
Crashes by Day of Week and Time of Day*
Total
9 PM-
12 AM
6-9
PM
3-6
PM
12-3
PM
9 AM-
12 PM
6-9
AM
3-6
AM
12-3
AM
Day of
Week
99262012176567Monday
1253414222074717Tuesday
1393336151854820Wednesday
1563834183173718Thursday
13526221715981325Friday
1142318118551430Saturday
98202812432821Sunday
866200172107113423163138Total
*Time indicated is usually shortly after the time of the crash.

13. 13
Crashes by Ward vs. 2023 Average Daily Ridership
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
Ward 1Ward 2Ward 3Ward 4Ward 5Ward 6Ward 7Ward 8
Average Daily Ridership
Number of Crashes
Non-KSI KSI 2023 Average Daily Ridership (Replica)
Replicadatahelps
distinguish
between the
number of
crashes and the
crash rate.

14. 1414
Crashes by Contributing Factor
Speeding
•20 persons speeding involved in crashes
(out of 8,744 total persons)
•19 drivers
•1 bicyclist
Impairment
•44 impaired persons involved in crashes
•31 bicyclists
•12 drivers
•1 passenger
•Both figures likely low due to crash reporting practices

15. 15
Crashes by Location
Intersection,
2,586, 65%
Midblock,
1,416, 35%

16. 16
Major Arterial
29%
Minor Arterial
37%
Collector
16%
Local
17%
Service
1%
Off Road
0%
Restricted Access
Freeway
0%
Crashes by DDOT Functional Classification
Major Arterial
9%
Minor Arterial
13%
Collector
12%
Local
56%
Service
0%
Off
Road
4%
Freeway
6%
Bicycle Crashes
Network Mileage

17. 17
Bike Crashes Mapped
(2017-2023)

18. 18
Bike Crash Hotspots +
Bike Ridership
Hotspots = higher
concentrations of all crashes
Darker blue lines = higher
bike volumes (from Replica)
Gives us an idea of exposure:
bike crashes relative to bike
volumes

19. 19
KSI Bike Crash Hotspots
+ Bike Ridership

20. 20
KSI Bike Crashes with RidershipBike Crashes with Ridership
9
th
St NW
14
th
St NW
M St NW
15
th
St NW
R St NW

21. 21
Crash Analysis Takeaways
•Most reported bike crashes involve minor injuries
or no injuries.
•Crashes have remained relatively flat over the last
four years despite an increase in bike volumes
(based on Replica data).
•Most (66%) of bike crashes take place on major and
minor arterial streets.
•Most crashes take place at or near intersections.
•Some streets with high overall crashes have
relatively low KSI crashes.

22. 22
Level of Traffic Stress
Analysis
Preliminary Findings

23. 23
LTS Definitions
Level of Traffic Stress (LTS) is an industry-
standard rating system given to a road
segment indicating the traffic stress it imposes
on bicyclists. It use factors such atype of
bicycle facility,traffic speed, volume, width of
bike lanes, and presence of parking.
Source: Furth, Peter G. (2014). Level of Traffic Stress Criteria.
https://peterfurth.sites.northeastern.edu/level-of-traffic-stress/

24. 24
Level of TrafficStress
District-wide
LTS 1
67%
LTS 2
9%
LTS 3
9%
LTS 4
15%
Percentage of Network by LTS
(District-wide)

25. 25
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
City-WideWard 1Ward 2Ward 3Ward 4Ward 5Ward 6Ward 7Ward 8
Network Mileage
Percentage of Network by LTS by Ward
LTS 1LTS 2LTS 3LTS 4
2024 LTS by Ward

26. 26
Level of Traffic Stress Takeaways
•Most D.C. streets (76%) are Level of Traffic
Stress 1 or 2 (i.e., low stress).
•Begintosee islands of low stress, and
barriers.
•Real value of LTS analysis is in its role in
Network Analysis.

27. 27
Bicycle Network Analysis
Preliminary Findings

28. 28
Bicycle Network Analysis
Trip
Potential
Network
Density and
Coverage
Centrality
Directness
Access
Equity
Network
Assessment

29. 29
•Trip Potential analysis measures the
potential for increased bike trips if the
quality of streets was not a factor.
•Trip Potential favors places with higher
densities of active land uses like
residencies, employment, transit
stops, schools, and grocery stores.
Darker red areas have higher Trip
Potential.
•Because it does not account for the
street network, simply the location of
trip generators, high stress streets in
high potential areas may see high
usage if facilities were improved.
Trip Potential

30. 30
Centrality
•Centrality measures the importance of a
street segment by looking at how
many shortest paths travel through it
relative to other streets.
•Building bike facilities on streets with
higher centrality serves more trips to
more destinations.
•ForD.C., because the density of the
street network varies significantly, we
adjusted the centrality for the density
around each street segment. As we’d
expect, avenues tend to have higher
centrality since they serve trips in a
more direct manner than local streets.

31. 31
Directness (Intersection Pairs
Connected)
•IntroducesLevel of Traffic Stress
•Considers how high stress streets might
affect someone who wants to travel by
bicycle but isn’t willing to ride on high
stress streets.
•It measures locations that are no
longer accessible, and the detour
required to reach a location without
using a high stress connection.
•This map shows the percentage of
intersections on the full street network
that are still connected to each other
when high stress network links are
removed. Areas that are darker blue
have better connectivity.

32. 32
•This map shows the increase in
average trip distance for someone
only using low stress links instead of
the full street network, which may
require taking a detour.
•People in the darker red areas have to
travel further to reach their destinations
on low stress streets
•Because some intersections are no
longer connected at all when high
stress streets are removed, the analysis
only compares trips between
intersections connected on both
networks, which may distort the average
increase, since longer trips are more
likely to be disconnected.
Directness (Trip Distances)

33. 33
The Access analysis looks at how the full street network and low stress
street networks allow people across the District to access various
destinations.
Destinations analyzed:
•Libraries
•Schools
•Parks
•Grocery Stores
•Metro Stations
Access

34. 34
Access Example:
Libraries
•The map shows the distribution of
public libraries in Washington, DC.

35. 35
Access Example:
Libraries
•Areas in the District within 1 mile of
a library by biking distance using
only low stress streets.

36. 36
Access Example:
Libraries
•Incremental increase in access to
libraries if all streets were low stress

37. 37
Equity Emphasis Areas
•42% of DC is in an Equity Emphasis Area
(EEA), as determined by MWCOG using
2016-2020 ACS data
•Currently 29% of DC has safe access to a
library by bicycle while 27% of EEAs within
DC have safe access to a library by bicycle.
•EEAsare slightly more negatively affectedby
high stress streets:30% of EEAswould have
access if all streets were low stress while 26%
of DC as a whole wouldhave access.
•EEAs haveless access to libraries by bike.
Whilesome of this may be due to the
location of libraries across the District, the
distribution of high stress streets and low
stress connections also plays a role in limiting
access
.

38. 38
Case Studies
3 projects
•Kenyon St NW(residential)
•9
th
St NW(downtown)
•C Street NE(capital const project)4-6 pageproject summaries
•History
•Planning
•Design
•Construction
•Public Engagement
•Lessons Learned

39. 39
Stakeholder Engagement
•BAC Meeting –July 10
•DDOT Workshop –July 24
•Outside Stakeholder Workshop (ANCs, etc.) –August

40. 40
Next Steps
•Complete Network Analysis
•Prepare Existing Conditions Report
•Additional Stakeholder Involvement
•Complete Case Studies