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Abstract

Biking is becoming mainstream and more popular in the United States since the introduction of bike-sharing systems (BSS). These systems enhance mobility and health in cities and provide a low-cost, sustainable mode of transportation to communities. Bicycle riders are now primary road users with dedicated bike paths for their safety and convenience. BSSs are becoming integrated into public transit services. The bike-sharing industry shows no sign of stopping. However, there are public debates on the road behavior of cyclists. To investigate the reckless cycling behavior of cyclists, SURTC is conducting a two-phase study. The first phase is a survey of U.S. residents, and the second involves a reckless cycling behaviors simulation.

Objectives

• Conduct a national survey to study what cycling behaviors are perceived as reckless and risky from U.S. residents' perspective and the perceived magnitude of each risky behavior.
• Simulate risky behaviors to evaluate and develop a new method for capturing such risky behaviors using sensors embedded in mobile phones.

There are several strategies for enforcing and monitoring the bicycling riding public, but existing approaches are not comprehensive enough to capture all the riding behaviors at all locations. For example, it is not possible to monitor an entire commuting route with surveillance cameras. Additionally, there are some concerns that enforcement may be biased toward some users. This research focuses on a model that captures all possible riding behaviors via a set of sensors embedded in a mobile device. The core technology is "human behavior signal processing" blended with several machine learning algorithms. Our system will consist of processing and recognition of biking behaviors, including, but not limited to, accelerating, abrupt changes in direction, sudden braking.

The study will attempt to answer these questions:

• What are the cycling maneuvers that are perceived to be risky from the community members' perspective?
• What are the enforcement strategies that may help to discourage reckless cycling?
• What are the best methods to detect such reckless cycling behaviors?