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• 1. [Article] Utilizing Egocentric Video and Sensors to Conduct Naturalistic Bicycling Studies

  Existing data collection methods are mostly designed for videos captured by stationary cameras and are not designed to follow cyclists along a route or to integrate other sensor data. The goals of this ...

  Citation × Citation Citation Abstract Copy Title: Utilizing Egocentric Video and Sensors to Conduct Naturalistic Bicycling Studies Author: Liu, Feng, Figliozzi, Miguel Year: 2016 Existing data collection methods are mostly designed for videos captured by stationary cameras and are not designed to follow cyclists along a route or to integrate other sensor data. The goals of this research are: a) to develop a platform to collect naturalistic video bicycling data, b) to develop a methodology to integrate video data with other sensors that measure cyclists’ position and comfort levels, and c) to apply the platform and data collection methodology to a real-world route. This research effort has successfully integrated video and sensor data to describe cyclists’ comfort levels along a route. It was found that stress levels while riding during peak hours averaged 1.75 times higher than while riding at offpeak hours on the same routes and facilities. Separated bicycle infrastructure, such as multiuse paths, during peak and off-peak hours showed the lowest stress levels. Signalized intersections were hotspots for cyclists’ stress. All these results are statistically significant. The results indicate that integrating video and sensor data allows for a more detailed understanding of cyclists’ perceptions along a route. Rather than having an average measure for the whole route or path, it is possible to precisely identify the places and/or situations that trigger a change in experience or stress. By measuring how different facility types and riding conditions affect the distribution of stress levels among users, transportation engineers and planners may in the future incorporate video and detailed sensor data to evaluate the real-world performance of different types of facilities Title: Utilizing Egocentric Video and Sensors to Conduct Naturalistic Bicycling Studies Author: Liu, Feng, Figliozzi, Miguel Year: 2016 Existing data collection methods are mostly designed for videos captured by stationary cameras and are not designed to follow cyclists along a route or to integrate other sensor data. The goals of this research are: a) to develop a platform to collect naturalistic video bicycling data, b) to develop a methodology to integrate video data with other sensors that measure cyclists’ position and comfort levels, and c) to apply the platform and data collection methodology to a real-world route. This research effort has successfully integrated video and sensor data to describe cyclists’ comfort levels along a route. It was found that stress levels while riding during peak hours averaged 1.75 times higher than while riding at offpeak hours on the same routes and facilities. Separated bicycle infrastructure, such as multiuse paths, during peak and off-peak hours showed the lowest stress levels. Signalized intersections were hotspots for cyclists’ stress. All these results are statistically significant. The results indicate that integrating video and sensor data allows for a more detailed understanding of cyclists’ perceptions along a route. Rather than having an average measure for the whole route or path, it is possible to precisely identify the places and/or situations that trigger a change in experience or stress. By measuring how different facility types and riding conditions affect the distribution of stress levels among users, transportation engineers and planners may in the future incorporate video and detailed sensor data to evaluate the real-world performance of different types of facilities Close