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• 1. [Article] Modeling and Analyzing the Impact of Advanced Technologies on Transit Performance Measures in Arterial Corridors

  Transportation and transit agencies have implemented advanced technologies like transit signal priority (TSP) and Sydney Coordinated Adaptive Traffic System (SCATS) to reduce travel times and improve reliability. ...

  Citation × Citation Citation Abstract Copy Title: Modeling and Analyzing the Impact of Advanced Technologies on Transit Performance Measures in Arterial Corridors Author: Figliozzi, Miguel A., Feng, Wei Year: 2014 Transportation and transit agencies have implemented advanced technologies like transit signal priority (TSP) and Sydney Coordinated Adaptive Traffic System (SCATS) to reduce travel times and improve reliability. However, due to the lack of detailed empirical data, the joint impact of these factors and improvement strategies on bus travel time has not been studied at the stop-to-stop segment level. With the aim of assessing the performance of an existing TSP/SCATS system, this study had access to a unique set of high-resolution bus and traffic signal data. Novel algorithms and performance measures to measure TSP performance are proposed. Results indicate that a timely and effective TSP system requires a high degree of sophistication, monitoring and maintenance. Empirical data suggest that most TSP phase adjustments were granted in time (i.e., within a cycle) to buses that requested priority, but that only a small proportion resulted in reduced delay. In this study, many green extension phases were granted late, making them less effective than early green signal phases. Despite this, the TSP system did not increase delays for passengers and vehicles when side-street traffic is also considered. Title: Modeling and Analyzing the Impact of Advanced Technologies on Transit Performance Measures in Arterial Corridors Author: Figliozzi, Miguel A., Feng, Wei Year: 2014 Transportation and transit agencies have implemented advanced technologies like transit signal priority (TSP) and Sydney Coordinated Adaptive Traffic System (SCATS) to reduce travel times and improve reliability. However, due to the lack of detailed empirical data, the joint impact of these factors and improvement strategies on bus travel time has not been studied at the stop-to-stop segment level. With the aim of assessing the performance of an existing TSP/SCATS system, this study had access to a unique set of high-resolution bus and traffic signal data. Novel algorithms and performance measures to measure TSP performance are proposed. Results indicate that a timely and effective TSP system requires a high degree of sophistication, monitoring and maintenance. Empirical data suggest that most TSP phase adjustments were granted in time (i.e., within a cycle) to buses that requested priority, but that only a small proportion resulted in reduced delay. In this study, many green extension phases were granted late, making them less effective than early green signal phases. Despite this, the TSP system did not increase delays for passengers and vehicles when side-street traffic is also considered. Close

• 2. [Article] Evaluation of Transportation Microenvironments Through Assessment of Cyclysts' Exposure to Traffic-Related Particulate Matter

  It is well established that vehicles powered by carbon-based fuels (e.g. gasoline, diesel) have a negative impact on air quality, especially in urban centers. Traditionally, air quality conformity studies ...

  Citation × Citation Citation Abstract Copy Title: Evaluation of Transportation Microenvironments Through Assessment of Cyclysts' Exposure to Traffic-Related Particulate Matter Author: George, Linda A., Figliozzi, Miguel A., Monsere, Christopher M., Kendrick, Christine M., Bigazzi, Alexander Y, Moore, Adam Year: 2011 It is well established that vehicles powered by carbon-based fuels (e.g. gasoline, diesel) have a negative impact on air quality, especially in urban centers. Traditionally, air quality conformity studies analyze the macroenvironmental impact of transportation corridors, as they relate to regional air quality management concerns. Urban residents spend a considerable amount of outdoor time in transportation microenvironments as pedestrians, bicycle commuters, people waiting to use public transport, residents and workers situated along roadways, and commuters within vehicles. An emerging area of research has shown that human health impacts within transport microenvironments can be considerable, but have not been well-characterized. As urban public policies increasingly encourage the use of multi-mode commuting in dense urban centers, decisionmakers need reliable modeling tools to predict and mitigate the health consequences of vehicle-related emissions in transportation microenvironments. This project proposes unique integration of real-time multi-modal personal exposure monitoring with real-time detailed traffic monitoring in order to develop a computer model that links traffic characteristics, emissions, built environment configuration, mode of travel and health impacts. Existing emission and dispersion models do not take direct account of the dynamic nature of traffic and how emissions impact the health of the diversity of urban dwellers, bus commuters, pedestrians, and bicycle riders. Typical approaches use highly aggregated vehicle data and computed link speeds to assign an emission factor to the traffic, an assumed proportion of which is trucks. In addition, existing methods to assess environmental impacts tend to focus on the production source of the pollutant not the impacts, which require information on population activities and its proximity to vehicle flows. Personal exposure studies document significant air quality impacts within the transportation microenvironment but do not integrate dynamic traffic and emission analysis. Previous research argues for a comprehensive approach. From the perspective of mitigating transportation impacts, it is significant to understand: a) exposure to the pollution, which in turn requires assessment of where people are in relation to the source of the pollutant, and b) the relation between pollution and vehicle flows. This research will target highly polluted high exposure hotspots in Portland, e.g. high traffic flows running through a major trip destination area for shopping, entertainment, education, or health care highly dependent on mode of travel, traffic levels and type, and built environment characteristics. Our approach is to conduct field studies to measure personal exposure to particulate matter as a function of transportation mode through pre-determined routes in the city. Exposure levels, position, time and rate of travel will be recorded. Simultaneously we will collect detailed traffic data that will provide type/number of vehicle, speed of travel and start/stop behavior. GIS analysis of the urban form and land use characteristics will establish built environment parameters. These data will be integrated into a built environment-travel mode-exposure model. The major scientific innovations offered by this project are: 1) integration of detailed and real-time exposure/emissions with a real-time traffic measurement approach, and 2) development of an integrated emissions and transportation model that combines traffic characteristics, emissions, built environment configuration, mode of travel and predicted health impacts. The outcomes of the research will be of importance to the general public, urban communities, departments of transportation charged with managing urban traffic, and public health authorities. Title: Evaluation of Transportation Microenvironments Through Assessment of Cyclysts' Exposure to Traffic-Related Particulate Matter Author: George, Linda A., Figliozzi, Miguel A., Monsere, Christopher M., Kendrick, Christine M., Bigazzi, Alexander Y, Moore, Adam Year: 2011 It is well established that vehicles powered by carbon-based fuels (e.g. gasoline, diesel) have a negative impact on air quality, especially in urban centers. Traditionally, air quality conformity studies analyze the macroenvironmental impact of transportation corridors, as they relate to regional air quality management concerns. Urban residents spend a considerable amount of outdoor time in transportation microenvironments as pedestrians, bicycle commuters, people waiting to use public transport, residents and workers situated along roadways, and commuters within vehicles. An emerging area of research has shown that human health impacts within transport microenvironments can be considerable, but have not been well-characterized. As urban public policies increasingly encourage the use of multi-mode commuting in dense urban centers, decisionmakers need reliable modeling tools to predict and mitigate the health consequences of vehicle-related emissions in transportation microenvironments. This project proposes unique integration of real-time multi-modal personal exposure monitoring with real-time detailed traffic monitoring in order to develop a computer model that links traffic characteristics, emissions, built environment configuration, mode of travel and health impacts. Existing emission and dispersion models do not take direct account of the dynamic nature of traffic and how emissions impact the health of the diversity of urban dwellers, bus commuters, pedestrians, and bicycle riders. Typical approaches use highly aggregated vehicle data and computed link speeds to assign an emission factor to the traffic, an assumed proportion of which is trucks. In addition, existing methods to assess environmental impacts tend to focus on the production source of the pollutant not the impacts, which require information on population activities and its proximity to vehicle flows. Personal exposure studies document significant air quality impacts within the transportation microenvironment but do not integrate dynamic traffic and emission analysis. Previous research argues for a comprehensive approach. From the perspective of mitigating transportation impacts, it is significant to understand: a) exposure to the pollution, which in turn requires assessment of where people are in relation to the source of the pollutant, and b) the relation between pollution and vehicle flows. This research will target highly polluted high exposure hotspots in Portland, e.g. high traffic flows running through a major trip destination area for shopping, entertainment, education, or health care highly dependent on mode of travel, traffic levels and type, and built environment characteristics. Our approach is to conduct field studies to measure personal exposure to particulate matter as a function of transportation mode through pre-determined routes in the city. Exposure levels, position, time and rate of travel will be recorded. Simultaneously we will collect detailed traffic data that will provide type/number of vehicle, speed of travel and start/stop behavior. GIS analysis of the urban form and land use characteristics will establish built environment parameters. These data will be integrated into a built environment-travel mode-exposure model. The major scientific innovations offered by this project are: 1) integration of detailed and real-time exposure/emissions with a real-time traffic measurement approach, and 2) development of an integrated emissions and transportation model that combines traffic characteristics, emissions, built environment configuration, mode of travel and predicted health impacts. The outcomes of the research will be of importance to the general public, urban communities, departments of transportation charged with managing urban traffic, and public health authorities. Close

• 3. [Article] A Study of Headway Maintenance for Bus Routes: Causes and Effects of “Bus Bunching” in Extensive and Congested Service Areas

  A healthy and efficient public transit system is indispensable to reduce congestion, emissions, energy consumption, and car dependency in urban areas. The objective of this research is to 1) develop methods ...

  Citation × Citation Citation Abstract Copy Title: A Study of Headway Maintenance for Bus Routes: Causes and Effects of “Bus Bunching” in Extensive and Congested Service Areas Author: Figliozzi, Miguel A., Feng, Wu-chi, Lafferriere, Gerardo, Feng, Wei Year: 2012 A healthy and efficient public transit system is indispensable to reduce congestion, emissions, energy consumption, and car dependency in urban areas. The objective of this research is to 1) develop methods to evaluate and visualize bus service reliability for transit agencies in various temporal and spatial aggregation levels; 2) identify the recurrent unreliability trends of bus routes (focusing on high-frequency service periods) and understand their characteristics, causes and effects; and 3) model service times using linear regression models. This research utilized six months of archived automatic vehicle location (AVL) and automatic passenger count (APC) data from a low-performance route (Route 15) of TriMet, the public transit provider in the Portland metropolitan area. Route 15 has experienced difficulties in terms of schedule adherence and headway regularity. This research developed methods to summarize causes of bus bunching. We first determined the frequency of each cause (expressed as percentages) meeting pre-determined thresholds. Next, we performed a sensitivity analysis to demonstrate how cause percentage results change using varying difficulty levels of bus bunching thresholds. Finally, we investigated how cause percentage results vary spatially along different route segments. This research also developed novel ways to summarize and visualize vast amounts of bus route operations data in an insightful and intuitive manner: 1) a route/stop level visualization performance measure framework using color contour diagrams and 2) a dynamic interactive bus monitoring visualization framework based on a Google Maps platform. Visualizations proposed in this study can aid transit agency managers and operators to identify operational problems and better understand how such problems propagate spatially and temporally across routes. Finally, regression models were estimated to understand the key factors impacting dwell and travel times. Title: A Study of Headway Maintenance for Bus Routes: Causes and Effects of “Bus Bunching” in Extensive and Congested Service Areas Author: Figliozzi, Miguel A., Feng, Wu-chi, Lafferriere, Gerardo, Feng, Wei Year: 2012 A healthy and efficient public transit system is indispensable to reduce congestion, emissions, energy consumption, and car dependency in urban areas. The objective of this research is to 1) develop methods to evaluate and visualize bus service reliability for transit agencies in various temporal and spatial aggregation levels; 2) identify the recurrent unreliability trends of bus routes (focusing on high-frequency service periods) and understand their characteristics, causes and effects; and 3) model service times using linear regression models. This research utilized six months of archived automatic vehicle location (AVL) and automatic passenger count (APC) data from a low-performance route (Route 15) of TriMet, the public transit provider in the Portland metropolitan area. Route 15 has experienced difficulties in terms of schedule adherence and headway regularity. This research developed methods to summarize causes of bus bunching. We first determined the frequency of each cause (expressed as percentages) meeting pre-determined thresholds. Next, we performed a sensitivity analysis to demonstrate how cause percentage results change using varying difficulty levels of bus bunching thresholds. Finally, we investigated how cause percentage results vary spatially along different route segments. This research also developed novel ways to summarize and visualize vast amounts of bus route operations data in an insightful and intuitive manner: 1) a route/stop level visualization performance measure framework using color contour diagrams and 2) a dynamic interactive bus monitoring visualization framework based on a Google Maps platform. Visualizations proposed in this study can aid transit agency managers and operators to identify operational problems and better understand how such problems propagate spatially and temporally across routes. Finally, regression models were estimated to understand the key factors impacting dwell and travel times. Close