Introduction
A demand analysis for the PPACG region was conducted to provide a data-driven process that identified where bicycle and pedestrian network gaps exists, projects that have been proposed by local jurisdictions, and identify areas that are expected to have higher bicycle and pedestrian activity or need. These analyses show where network improvements should be considered, depicting the places people want to go to should be connected with streets and trails that are comfortable for walking and bicycling.
Demand Analysis
The purpose of the demand analysis is to identify areas where people are likely to be and provide a framework to understand the environment for walking and bicycling. Demand is based on a set of factors that represent live, work, play, and learn locations.
The demand analysis is represented by an index, built off datasets that represent where people live, work, play, and learn. The analysis is based on measuring the time it takes for a Person/Employee to walk out of the parcel’s front door and into the front door at the corresponding land use in any direction. Employment and population demand is based on how many jobs or residents a person would pass during a 15 minute walk in any direction.
The Demand Index is composed of the following datasets:
- Walk Time to nearest
- School
- Hospital
- Park
- Walk Time to 5th Retail Location
- Employment within 15 minute Walk
- Population within 15 minute Walk
Existing Facilities
An analysis of the existing cycling and pedestrian facilities provides a general understanding of the coverage and type of infrastructure. The facilities are given a buffer to show the reasonable access area for the existing infrastructure. The existing facilities map is show in Figure 2.
Reviewed Past Plans
The list represents the various local and regional plans that this project reviewed for projects, priorities, and standards that could be included or blended into a regional approach for this plan. Below are a few highlights from the jurisdictions that provided specific guidance for the ATP, while the full review of the documents listed can be found in Appendix 2.
- PPACG
- Regional Nonmotorized Plan
- Joint Land Use Study
- Financial Plan 2050 LRTP
- Congestion Mitigation Plan
- Tri-County Plan
- EL PASO COUNTY
- Major Transportation Corridor Plan
- Parks, Trails, Open Spaces Map
- Park assets
- Parks Master Plan (Trails)
- COLORADO SPRINGS
- Urban Trail Map
- Bike Map (4 maps)
- Bike Master Plan
- State of Bicycling in Colorado Springs
- Legacy Loop Project
- Envision Shooks Run
- Parks System Master Plan
- PlanCOS
- Downtown Plan
- ConnectCOS + Active Transportation Network
- FOUNTAIN
- Parks, Trails & Open Space Map
- Parks and Trails Master Plan
- Comprehensive Plan
- GREEN MOUNTAIN FALLS
- Comprehensive Plan
- MANITOU SPRINGS
- Parks, Open Space, Trails Master Plan
- Parks and Trails Map
- Community Master Plan
- Transportation & Mobility Master Plan
- MONUMENT
- Parks, Trails, and Open
- Space Master Plan
- Comprehensive Plan
- PALMER LAKE
- Comprehensive Plan
- Three Mile Plan
- WOODLAND PARK
- Parks, Trails, Open Space Master Plan
- Parks, Trails and Open Space Map
- Comprehensive Plan
- CDOT & STATEWIDE
- Colorado Bicycle & Byways Map
- Colorado Trail Explorer
PPACG
The previous Regional Nonmotorized plan provided context for active transportation planning within the region. The image above represents the “Primary Improvement Corridors” identified for regional investment in that plan. Many of these corridors continue to regionally important in the active transportation network.
Colorado Springs & El Paso County
Colorado Springs and El Paso County have undertaken efforts specific to active transportation, trails, and other facilities than other jurisdictions in the region. These jurisdictions have defined cross sections, trail typologies and language differences, as well as other examples for the reviewing bodies to weigh when discussing the corridors, and other recommendations identified in this plan. Examples shown below.
Non-Motorized Crash Analysis
This section reviews data (2017-20211) for crashes involving pedestrians and bicyclists in the PPACG region, as reported by the PPACG Crash Dashboard.2
Number of Crashes
Figure 4 indicates there are approximately twice as many pedestrian crashes as bicyclist crashes annually, which have resulted in 884 or more3 injuries and 79 fatalities over the course of five years. Bicyclist crashes in appear to be trending downwards, with 121, 94, 91, and 71 crashes in 2020, perhaps reflecting the fact that bicycling there has been an increase in infrastructure. At the time of this report the 2021 bicycle data was not available to analyze if that trend continued past COVID. Pedestrian crashes peaked in 2019 with 181. Additional data on the number of bicycle and pedestrian trips that took place each year would be needed to understand if the crash rate (i.e., crashes per bicycle trip) is going up or down.
Severity of Crashes
The following table provides a summary of the bicycle and pedestrian crashes, by severity.
Table 1: Number and Severity of Bicyclist and Pedestrian Crashes (2017-2021)
| SEVERITY | BICYCLE | PEDESTRIAN | TOTAL |
|---|---|---|---|
| Fatal Injury | 12 | 67 | 79 |
| Serious Injury | 24 | 138 | 162 |
| Minor Injury | 229 | 414 | 643 |
| Non-Injury/Property Damage Only | 112 | 174 | 286 |
| Grand Total | 377 | 793 | 1,170 |
Day of Week
Pedestrian and bicyclist crashes happen throughout the week, likely indicating that people walk and bicycle for both recreational and utilitarian purposes. Collision activity appears to be lower on weekends, with the lowest frequency being Sunday.
Month of Year
Similar to the distribution across the week, pedestrian and bicyclist crashes occur throughout the year, though the levels are somewhat lower in March and April, and spike in September, both are likely caused by weather and daylight hours.
Time of Day
The crash data shows a significant peak in pedestrian crashes at the noon hour, with a more general increase in crashes reported in the afternoon and evening. The spike in 12:00pm crashes could be caused by default data inputs when reporting.
Contributing Factors
The contributing factors for pedestrian collisions during this period were identified for only ten percent of the collisions. This data is extremely important for the development of effective education and enforcement programs. Of the 67 fatal pedestrian crashes, only one had a contributing factor associated with it, Driver Inexperience, while all others were denoted as ‘Unknown’ or ‘No Apparent Contributing Factor.’ There may be opportunities to improve reporting of the contributing factor of crashes involving pedestrians or bicyclists to better understand and address safety concerns.
- More than 57% of both bicycle and pedestrian crashes listed the contributing factor as ‘No Apparent Contributing Factor’ while another 31% listed the factor as ‘Unknown.’
- The most common categories for pedestrian crashes were associated with distracted driving, including ‘Driver Preoccupied’ and ‘Driver Emotionally Upset.’
- The most common factor for bicyclist crashes was inexperience or unfamiliarity.
| Factor | Cyclist Collisions |
|---|---|
| Driver unfamiliar with area | 8 |
| Driver preoccupied | 14 |
| Driver inexperience | 23 |
| Factor | Pedestrian Crashes |
|---|---|
| Evading law enforcement | 1 |
| Physical disability | 2 |
| Illness | 3 |
| Unfamiliar with area | 4 |
| Driver inexperience | 19 |
| Fatigue | 1 |
| Asleep at the wheel | 2 |
| Districted by passenger | 2 |
| Emotionally upset | 4 |
| Preoccupied | 26 |
Location on Roadway
As illustrated in Figure 10, pedestrian and bicyclist crashes locations exhibit similar but different trends:
Bicyclist crashes occurred overwhelmingly at intersections or were intersection related (70%). Nineteen percent took place away from intersections, and nine percent took place at driveways.
Pedestrian crashes were also commonly at intersections (60%), however 35% occurred at non-intersection locations (e.g., mid- block locations).
Figure 10
| Location | Bicycle | Pedestrian |
|---|---|---|
| Intersection | 64% | 51% |
| Non-intersection | 19% | 35% |
| Intersection Related | 6% | 9% |
| Driveway | 9% | 5% |
| In Alley | 2% | 0% |
Vehicle Action
Table 2 identifies the vehicle action during the crash. Most crashes involve vehicles traveling straight, including 71% of pedestrian collisions and 53% of bicyclist collisions. The single most common vehicle action involved a vehicle going straight away from an intersection, representing 45% of all pedestrian collisions. Vehicles turning left or right at intersections accounted for 11% of pedestrian crashes and 27% of bicyclist crashes.
Table 2 – Summary of Location and Vehicle Action
| Vehicle Action | Location: Driveway Access | Location: Intersection | Location: Intersection Relation | Location: Non-Intersection | Location: Other | Location: Total |
|---|---|---|---|---|---|---|
| Pedestrian Crashes | ||||||
| Going Straight | 2% | 16% | 3% | 17% | 1% | 38% |
| Making Left Turn | 1% | 14% | 1% | 0% | 0% | 16% |
| Making Right Turn | 1% | 10% | 2% | 0% | 0% | 13% |
| Other | 1% | 11% | 2% | 18% | 1% | 33% |
| Total | 5% | 51% | 8% | 35% | 1% | 100% |
| Bicycle Crashes | ||||||
| Going Straight | 2% | 34% | 3% | 10% | 1% | 51% |
| Making Left Turn | 3% | 11% | 1% | 1% | 0% | 16% |
| Making Right Turn | 3% | 11% | 1% | 1% | 0% | 16% |
| Other | 2% | 7% | 1% | 2% | 1% | 17% |
| Total | 10% | 64% | 6% | 19% | 2% | 100% |
Opportunities For Improved Crash Data
The crash data provides only limited information to understand the nature of crashes involving pedestrians and bicyclists. Below are three categories that could be improved or added to the data to provide greater clarity and increase the ability to match appropriate countermeasures with particular safety issues:
- There may be opportunities to change and increase use of the contributing factor field for collision reports. More than 40% of crashes listed the contributing factor as ‘none’ while another 15% listed the factor as ‘unknown’. Common contributing factors are ‘careless/prohibited driving’ and ‘failure to yield,’ which yield little insight.
- Opportunity to increase fidelity of bicycle/ped crash data to include actions (e.g. pedestrian crossing at mid block, crossing at non-marked location, bicyclist taking left through intersection, etc.)
- Opportunity to collect additional data, such as demographic information, to understand disproportionate impacts or concerns.
Figure 11: Bicycle Crash Map: 2017–2021 Crash Data
