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Bicycle Detection and Feedback Assessment
Principal Investigator:
Christopher Monsere, Portland State University
Co-Investigators:
- David Hurwitz, Oregon State University
- Sirisha Kothuri, Portland State University
Summary:
Bicycling is increasing in the United States -- the number of trips made by bicycle more than doubled from 1.7 billion trips in 2001 to 4 billion in 2009 (National Household Travel Survey, 2009). With the increase in bicycling rates, there is a critical need for cycling infrastructure, which includes on and off road bicycle lanes and paths, signs, markings, and signals. One of the key links in a b...
Bicycling is increasing in the United States -- the number of trips made by bicycle more than doubled from 1.7 billion trips in 2001 to 4 billion in 2009 (National Household Travel Survey, 2009). With the increase in bicycling rates, there is a critical need for cycling infrastructure, which includes on and off road bicycle lanes and paths, signs, markings, and signals. One of the key links in a bicycle network is signalized crossings of high volume and high speed roadways. At these intersections in Oregon, cyclists are primarily detected by inductive loops, often using the same inductive loops that are used for automobile detection. While vehicles are almost always detected automatically due to their size and predictable stopping location, that is not the case for bicycles. If a cyclist does not position themselves for detection there can be unnecessary delays. These delays lead to a lower quality experience and may lead to increased risk taking behavior (i.e. signal non-compliance). Improved detection for bicycles can be accomplished by proper loop placement, calibration of loop sensitivity, alternative detection technologies, or through the use of pavement markings that communicate the correct stopping location for bicyclists. The MUTCD 9C-7 bicycle stencil has been used to communicate where a person on a bicycle should position themselves. There has been interest in the adoption of bicycle feedback confirmation devices (examples include a blue light confirmation and “wait” countdown timers) to better communicate presence detection and delay to people on bicycles. Research is needed to evaluate the comprehension of these devices, to determine if additional signage is needed, and whether they influence the quality of the cycling experience.
An improvement to the quality of the cycling experience would be presence detection confirmation and/or a countdown timer. These devices are similar in purpose to the confirmation lights commonly used on pedestrian pushbuttons. When a cyclist is detected and the input recognized by the traffic signal controller, the confirmation device is activated. In the case of the countdown timer (used in some European countries), the timer counts down the time until the green display. Countdown timers for other modes (pedestrians and drivers) have been explored in the research literature.
It is hypothesized that confirmation feedback and countdown timers for cyclists might help reduce the level-of-stress for waiting cyclists. Given the relatively low cost of installation, they could be tools for creating infrastructure that promotes mobility, efficiency, and predictability for cyclists. There is limited research on the effect of the confirmation devices, accompanying informational signs, and countdown timers on the behavioral and psychological effects for bicyclists.
Objectives of the Study
This research seeks to:
• explore how well the alternate designs for feedback confirmation devices with or without informational signs are understood by the general public;
• establish quantitative information from the field review about the effect of the confirmation devices (e.g. bicyclist start-up delay, false start, dismounting, positioning)
• qualitatively study how the information provided by the confirmation and feedback device affect the overall cycling experience; and
• provide guidance to practitioners regarding the use of detection confirmation devices for bicycles.
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Project Details
| Project Type: | Research |
|---|---|
| Project Status: | Completed |
| End Date: | June 30, 2020 |