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• 1. [Article] Analyses of Bus Travel Time Reliability and Transit Signal Priority at the Stop-To-Stop Segment Level

  Transit travel time is affected by many factors including traffic signals and traffic condition. Transit agencies have implemented strategies such as transit signal priority (TSP) to reduce transit travel ...

  Citation × Citation Citation Abstract Copy Title: Analyses of Bus Travel Time Reliability and Transit Signal Priority at the Stop-To-Stop Segment Level Author: Feng, Wei Year: 2014 Transit travel time is affected by many factors including traffic signals and traffic condition. Transit agencies have implemented strategies such as transit signal priority (TSP) to reduce transit travel time and improve service reliability. However, due to the lack of 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. This study utilizes and integrates three databases available along an urban arterial corridor in Portland, Oregon. Data sources include stop-level bus automatic vehicle location (AVL) and automatic passenger count (APC) data provided by the Tri-County Metropolitan Transportation District of Oregon (TriMet), the Sydney Coordinated Adaptive Traffic System (SCATS) signal phase log data, and intersection vehicle count data provided by the City of Portland. Based on the unique collection and integration of these fine granularity empirical data, this research utilizes multiple linear regression models to understand and quantify the joint impact of intersection signal delay, traffic conditions and bus stop location on bus travel time and its variability at stop-to-stop segments. Results indicate that intersection signal delay is the key factor that affects bus travel time variability. The amount of signal delay is nearly linearly associated with intersection red phase duration. Results show that the effect of traffic conditions (volumes) on bus travel time varies significantly by intersection and time of day. This study also proposed new and useful performance measures for evaluating the effectiveness of TSP systems. Relationships between TSP requests (when buses are late) and TSP phases were studied by comparing TSP phase start and end times with bus arrival times at intersections. Results show that green extension phases were rarely used by buses that requested TSP and that most green extension phases were granted too late. Early green effectiveness (percent of effective early green phases) is much higher than green extension effectiveness. The estimated average bus and passenger time savings from an early green phase are also greater compared to the average time savings from a green extension phase. On average, the estimated delay for vehicles on the side street due to a TSP phase is less than the time saved for buses and automobiles on the major street. Results from this study can be used to inform cities and transit agencies on how to improve transit operations. Developing appropriate strategies, such as adjusting bus stop consolidation near intersections and optimizing bus operating schedules according to intersection signal timing characteristics, can further reduce bus travel time delay and improve TSP effectiveness. Title: Analyses of Bus Travel Time Reliability and Transit Signal Priority at the Stop-To-Stop Segment Level Author: Feng, Wei Year: 2014 Transit travel time is affected by many factors including traffic signals and traffic condition. Transit agencies have implemented strategies such as transit signal priority (TSP) to reduce transit travel time and improve service reliability. However, due to the lack of 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. This study utilizes and integrates three databases available along an urban arterial corridor in Portland, Oregon. Data sources include stop-level bus automatic vehicle location (AVL) and automatic passenger count (APC) data provided by the Tri-County Metropolitan Transportation District of Oregon (TriMet), the Sydney Coordinated Adaptive Traffic System (SCATS) signal phase log data, and intersection vehicle count data provided by the City of Portland. Based on the unique collection and integration of these fine granularity empirical data, this research utilizes multiple linear regression models to understand and quantify the joint impact of intersection signal delay, traffic conditions and bus stop location on bus travel time and its variability at stop-to-stop segments. Results indicate that intersection signal delay is the key factor that affects bus travel time variability. The amount of signal delay is nearly linearly associated with intersection red phase duration. Results show that the effect of traffic conditions (volumes) on bus travel time varies significantly by intersection and time of day. This study also proposed new and useful performance measures for evaluating the effectiveness of TSP systems. Relationships between TSP requests (when buses are late) and TSP phases were studied by comparing TSP phase start and end times with bus arrival times at intersections. Results show that green extension phases were rarely used by buses that requested TSP and that most green extension phases were granted too late. Early green effectiveness (percent of effective early green phases) is much higher than green extension effectiveness. The estimated average bus and passenger time savings from an early green phase are also greater compared to the average time savings from a green extension phase. On average, the estimated delay for vehicles on the side street due to a TSP phase is less than the time saved for buses and automobiles on the major street. Results from this study can be used to inform cities and transit agencies on how to improve transit operations. Developing appropriate strategies, such as adjusting bus stop consolidation near intersections and optimizing bus operating schedules according to intersection signal timing characteristics, can further reduce bus travel time delay and improve TSP effectiveness. 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] Transit Bus Fleet Age and Replacement Type Optimization

  Due to recent budget and fiscal constraints, it is ever more imperative for transit agencies to manage their fleets in an optimal way. Fleet data have consistently shown that bus operational and maintenance ...

  Citation × Citation Citation Abstract Copy Title: Transit Bus Fleet Age and Replacement Type Optimization Author: Figliozzi, Miguel A., Feng, Wei, Boudart, Jesse Year: 2013 Due to recent budget and fiscal constraints, it is ever more imperative for transit agencies to manage their fleets in an optimal way. Fleet data have consistently shown that bus operational and maintenance (O&M) per-mile costs increase as buses age. From a purely economic perspective, there is a cost tradeoff between the lower O&M costs of newer fleets and their higher initial capital costs. This tradeoff has a significant impact on the optimal timing of purchase and replacement decisions. Utilizing realistic cost data and an optimization modeling framework, we analyze (a) the impact of purchase timing decisions on fleet per-mile costs and (b) the key factors and variables affecting the optimization of transit diesel and hybrid bus fleets. Given uncertain and hard-to-forecast market variables, multiple scenarios are examined and sensitivity analyses are performed to study the impacts of key variables on optimal replacement policies and costs. In terms of the impact of purchase timing decisions on fleet per-mile costs (a), results indicate that: 1) increases in diesel prices do not affect total bus fleet costs as much as increases in maintenance costs; 2) increases in maintenance costs and utilization per year reduce the optimal replacement age; 3) increases in utilization and fuel economy have a similar impact in terms of total fleet costs; and 4) bus purchase-price changes have a significant impact on the optimal replacement age. In terms of the key factors and variables affecting the optimization of transit diesel and hybrid bus fleets (b), results indicate that: 1) the Federal Transit Administration (FTA) purchase cost subsidy has the highest impact on the optimal replacement policies; 2) without the FTA subsidy, the optimal policy is to choose the diesel bus unless the purchase cost difference is larger than 10%; 3) with an 80% FTA purchase cost subsidy, the hybrid bus is always the best choice unless fuel economy difference between the hybrid and diesel buses is substantial; 4) maintenance costs affect the optimal replacement age but are unlikely to change the optimal bus type when comparing diesel and hybrid technologies; and 5) greenhouse gas emissions costs are not significant and affect neither bus type nor replacement age. Title: Transit Bus Fleet Age and Replacement Type Optimization Author: Figliozzi, Miguel A., Feng, Wei, Boudart, Jesse Year: 2013 Due to recent budget and fiscal constraints, it is ever more imperative for transit agencies to manage their fleets in an optimal way. Fleet data have consistently shown that bus operational and maintenance (O&M) per-mile costs increase as buses age. From a purely economic perspective, there is a cost tradeoff between the lower O&M costs of newer fleets and their higher initial capital costs. This tradeoff has a significant impact on the optimal timing of purchase and replacement decisions. Utilizing realistic cost data and an optimization modeling framework, we analyze (a) the impact of purchase timing decisions on fleet per-mile costs and (b) the key factors and variables affecting the optimization of transit diesel and hybrid bus fleets. Given uncertain and hard-to-forecast market variables, multiple scenarios are examined and sensitivity analyses are performed to study the impacts of key variables on optimal replacement policies and costs. In terms of the impact of purchase timing decisions on fleet per-mile costs (a), results indicate that: 1) increases in diesel prices do not affect total bus fleet costs as much as increases in maintenance costs; 2) increases in maintenance costs and utilization per year reduce the optimal replacement age; 3) increases in utilization and fuel economy have a similar impact in terms of total fleet costs; and 4) bus purchase-price changes have a significant impact on the optimal replacement age. In terms of the key factors and variables affecting the optimization of transit diesel and hybrid bus fleets (b), results indicate that: 1) the Federal Transit Administration (FTA) purchase cost subsidy has the highest impact on the optimal replacement policies; 2) without the FTA subsidy, the optimal policy is to choose the diesel bus unless the purchase cost difference is larger than 10%; 3) with an 80% FTA purchase cost subsidy, the hybrid bus is always the best choice unless fuel economy difference between the hybrid and diesel buses is substantial; 4) maintenance costs affect the optimal replacement age but are unlikely to change the optimal bus type when comparing diesel and hybrid technologies; and 5) greenhouse gas emissions costs are not significant and affect neither bus type nor replacement age. Close

• 4. [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