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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] 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

• 3. [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