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• 1. [Article] Evaluation of Advanced Traffic Control Systems

  Researchers from Portland State University develop performance measures and algorithms for evaluation of state-of-the-art traffic signal control technologies..

  Citation × Citation Citation Abstract Copy Title: Evaluation of Advanced Traffic Control Systems Author: Figliozzi, Miguel Year: 2014 Researchers from Portland State University develop performance measures and algorithms for evaluation of state-of-the-art traffic signal control technologies.. Title: Evaluation of Advanced Traffic Control Systems Author: Figliozzi, Miguel Year: 2014 Researchers from Portland State University develop performance measures and algorithms for evaluation of state-of-the-art traffic signal control technologies.. Close

• 2. [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

• 3. [Article] A Model for Better Crosswalk Safety

  NITC researchers from Portland State University developed models to find what factors affected vehicles’ compliance with pedestrian safety laws. The results suggest that treatments discouraging accelerating ...

  Citation × Citation Citation Abstract Copy Title: A Model for Better Crosswalk Safety Author: Figliozzi, Miguel Year: 2017 NITC researchers from Portland State University developed models to find what factors affected vehicles’ compliance with pedestrian safety laws. The results suggest that treatments discouraging accelerating toward a crosswalk could help increase pedestrian safety. Title: A Model for Better Crosswalk Safety Author: Figliozzi, Miguel Year: 2017 NITC researchers from Portland State University developed models to find what factors affected vehicles’ compliance with pedestrian safety laws. The results suggest that treatments discouraging accelerating toward a crosswalk could help increase pedestrian safety. Close

• 4. [Article] Crowdsourcing Cycling Safety and Route Data with the “ORcycle” Smartphone App

  ORcycle is a new smartphone application (for both Android and iOS) developed by Transportation, Technology, and People (TTP) lab researchers at Portland State University as part of an Oregon Department ...

  Citation × Citation Citation Abstract Copy Title: Crowdsourcing Cycling Safety and Route Data with the “ORcycle” Smartphone App Author: Figliozzi, Miguel Year: 2015 ORcycle is a new smartphone application (for both Android and iOS) developed by Transportation, Technology, and People (TTP) lab researchers at Portland State University as part of an Oregon Department of Transportation (ODOT) research project. ORcycle collects user, route, infrastructure, crash, and safety data. ORcycle was successfully launched in early November 2014 and presents many improvements over existing or similar apps. Initial data findings and insights will be presented. Lessons learned as well as opportunities and challenges associated with smartphone data collection methods will be discussed. More information about the app can be found here: http://www.pdx.edu/transportation-lab/orcycle. Title: Crowdsourcing Cycling Safety and Route Data with the “ORcycle” Smartphone App Author: Figliozzi, Miguel Year: 2015 ORcycle is a new smartphone application (for both Android and iOS) developed by Transportation, Technology, and People (TTP) lab researchers at Portland State University as part of an Oregon Department of Transportation (ODOT) research project. ORcycle collects user, route, infrastructure, crash, and safety data. ORcycle was successfully launched in early November 2014 and presents many improvements over existing or similar apps. Initial data findings and insights will be presented. Lessons learned as well as opportunities and challenges associated with smartphone data collection methods will be discussed. More information about the app can be found here: http://www.pdx.edu/transportation-lab/orcycle. Close

• 5. [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

• 6. [Article] Identficaton and Characterization of Pollutant Hot Spots Integratng Probe Vehicle, Traffic and Land Use Data

  Identification and Characterization of PM2.5 and VOC Hot Spots on Arterial Corridor by Integrating Probe Vehicle, Traffic, and Land Use Data: The purpose of this study is to explore the use of integrated ...

  Citation × Citation Citation Abstract Copy Title: Identficaton and Characterization of Pollutant Hot Spots Integratng Probe Vehicle, Traffic and Land Use Data Author: Bell, Katherine Eleanor, Figliozzi, Miguel Year: 2014 Identification and Characterization of PM2.5 and VOC Hot Spots on Arterial Corridor by Integrating Probe Vehicle, Traffic, and Land Use Data: The purpose of this study is to explore the use of integrated probe vehicle, traffic and land use data to identify and characterize fine particulate matter (PM2.5) and volatile organic compound (VOC) hot spot locations on urban arterial corridors. An emission hot spot is defined as a fixed location along a corridor in which the mean pollutant concentrations are consistently above the 85th percentile of pollutant concentrations when considering all other locations along the corridor during the same time period. In order to collect data for this study, an electric vehicle was equipped with instruments designed to measure PM2.5 and VOC concentrations. Second-by-second measurements were performed for each pollutant from both the right and left sides of the vehicle. Detailed meteorological, traffic and land use data is also available for this research. The results of a statistical analysis are used to better understand which data sources are most valuable in estimating PM2.5 and VOC hot spot locations consistent with empirical data, as well as which variables have the greatest impact on emissions and pollutant levels at a microscale level. This research highlights the importance of considering both consistency and peak emission levels when identifying hot spot locations. An objective of this research is to develop a method to identify urban arterial hot spot locations that provides a balance of efficiency (in terms of capital expenses, time, resources, expertise requirements, etc.) and accuracy. Title: Identficaton and Characterization of Pollutant Hot Spots Integratng Probe Vehicle, Traffic and Land Use Data Author: Bell, Katherine Eleanor, Figliozzi, Miguel Year: 2014 Identification and Characterization of PM2.5 and VOC Hot Spots on Arterial Corridor by Integrating Probe Vehicle, Traffic, and Land Use Data: The purpose of this study is to explore the use of integrated probe vehicle, traffic and land use data to identify and characterize fine particulate matter (PM2.5) and volatile organic compound (VOC) hot spot locations on urban arterial corridors. An emission hot spot is defined as a fixed location along a corridor in which the mean pollutant concentrations are consistently above the 85th percentile of pollutant concentrations when considering all other locations along the corridor during the same time period. In order to collect data for this study, an electric vehicle was equipped with instruments designed to measure PM2.5 and VOC concentrations. Second-by-second measurements were performed for each pollutant from both the right and left sides of the vehicle. Detailed meteorological, traffic and land use data is also available for this research. The results of a statistical analysis are used to better understand which data sources are most valuable in estimating PM2.5 and VOC hot spot locations consistent with empirical data, as well as which variables have the greatest impact on emissions and pollutant levels at a microscale level. This research highlights the importance of considering both consistency and peak emission levels when identifying hot spot locations. An objective of this research is to develop a method to identify urban arterial hot spot locations that provides a balance of efficiency (in terms of capital expenses, time, resources, expertise requirements, etc.) and accuracy. Close