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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

• 2. [Article] Exploiting New Data Sources to Quantify Arterial Congestion and Performance Measures at a Regional Scale

  Transit travel time, operating speed and reliability all influence service attractiveness, operating cost and system efficiency. These metrics have a long-term impact on system effectiveness through a ...

  Citation × Citation Citation Abstract Copy Title: Exploiting New Data Sources to Quantify Arterial Congestion and Performance Measures at a Regional Scale Author: Figliozzi, Miguel, Bertini, Robert L., Glick, Travis B, Stoll, Nicholas B, Feng, Wei, Sidhu, Bobjot S., Pande, Anurag Year: 2017 Transit travel time, operating speed and reliability all influence service attractiveness, operating cost and system efficiency. These metrics have a long-term impact on system effectiveness through a change in ridership. As part of its bus dispatch system (BDS), the Tri-County Metropolitan Transportation District of Oregon (TriMet) has been archiving automatic vehicle location (AVL) and automatic passenger count (APC) data for all bus trips at the stop level since 1997. In 2014, a new and higher-resolution bus AVL data collection system was fully implemented. This new AVL system provides stop-level data as well as five-second resolution (5-SR) bus position data between stops. Rather than relying on interpolation tools to estimate bus trajectories (including stops and delays) between stops, the higher-resolution data shows more precise bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information of several variables on transit travel time. This research project explored potential applications of the new data for assessing transit performance, and for estimating transportation system performance measures for urban streets and arterials. Results suggest that the 5-SR data provides high-resolution time and position information which can be used to determine bus travel speeds between stops, identify speed breakdowns, and estimate intersection signal/queuing delays. Additionally, high-resolution achieved bus data can be used to visualize sources of congestion and delay on urban arterials. A new inter-stop trip time model was developed using the five-second resolution data. This newly developed model resulted in statistically significant and improved results over previous models. The models for overall travel time indicated that dwell time and average speed between stops were the major factors influencing transit travel time. Hence, it was concluded that estimation of average speed between stops is a critical component of the transit trip time models. Using this 5-SR data in the trip time model led to more precise and statistically valid trip time models. The research provides conclusions that can be used by transit agencies to improve operations through improvements such as transit signal priority. More importantly, for transit agencies looking for ways to archive data the research provides recommendations on formatting the data that can be most useful for future analysis. Title: Exploiting New Data Sources to Quantify Arterial Congestion and Performance Measures at a Regional Scale Author: Figliozzi, Miguel, Bertini, Robert L., Glick, Travis B, Stoll, Nicholas B, Feng, Wei, Sidhu, Bobjot S., Pande, Anurag Year: 2017 Transit travel time, operating speed and reliability all influence service attractiveness, operating cost and system efficiency. These metrics have a long-term impact on system effectiveness through a change in ridership. As part of its bus dispatch system (BDS), the Tri-County Metropolitan Transportation District of Oregon (TriMet) has been archiving automatic vehicle location (AVL) and automatic passenger count (APC) data for all bus trips at the stop level since 1997. In 2014, a new and higher-resolution bus AVL data collection system was fully implemented. This new AVL system provides stop-level data as well as five-second resolution (5-SR) bus position data between stops. Rather than relying on interpolation tools to estimate bus trajectories (including stops and delays) between stops, the higher-resolution data shows more precise bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information of several variables on transit travel time. This research project explored potential applications of the new data for assessing transit performance, and for estimating transportation system performance measures for urban streets and arterials. Results suggest that the 5-SR data provides high-resolution time and position information which can be used to determine bus travel speeds between stops, identify speed breakdowns, and estimate intersection signal/queuing delays. Additionally, high-resolution achieved bus data can be used to visualize sources of congestion and delay on urban arterials. A new inter-stop trip time model was developed using the five-second resolution data. This newly developed model resulted in statistically significant and improved results over previous models. The models for overall travel time indicated that dwell time and average speed between stops were the major factors influencing transit travel time. Hence, it was concluded that estimation of average speed between stops is a critical component of the transit trip time models. Using this 5-SR data in the trip time model led to more precise and statistically valid trip time models. The research provides conclusions that can be used by transit agencies to improve operations through improvements such as transit signal priority. More importantly, for transit agencies looking for ways to archive data the research provides recommendations on formatting the data that can be most useful for future analysis. Close