Search

Search Results

• 1. [Article] Analysis of Transit Signal Priority Using Archived TriMet Bus Dispatch System Data

  This paper presents findings on changes in bus running times, on-time performance, and excess passenger wait times following implementation of transit signal priority in select bus corridors in the Portland ...

  Citation × Citation Citation Abstract Copy Title: Analysis of Transit Signal Priority Using Archived TriMet Bus Dispatch System Data Author: Kimpel, Thomas J., Strathman, James G., Bertini, Robert L., Callas, Steve Year: 2005 This paper presents findings on changes in bus running times, on-time performance, and excess passenger wait times following implementation of transit signal priority in select bus corridors in the Portland metropolitan region. Analysis of the effectiveness of transit signal priority is often undertaken using simulation techniques or empirical studies that are either limited in scope and/or data availability. The present research uses an abundance of trip-level data collected from TriMet’s Bus Dispatch System. The study focuses on the most common performance measures of interest to both transit operators and passengers. Our study shows that the expected benefits of TSP are not consistent across routes and time periods, nor are they consistent across the various performance measures. We believe that benefits of transit signal priority will accrue only as the result of extensive evaluation and adjustment initial deployment. In most cases, an ongoing performance monitoring and adjustment program should be implemented in order to maximize the benefits of transit signal priority. Title: Analysis of Transit Signal Priority Using Archived TriMet Bus Dispatch System Data Author: Kimpel, Thomas J., Strathman, James G., Bertini, Robert L., Callas, Steve Year: 2005 This paper presents findings on changes in bus running times, on-time performance, and excess passenger wait times following implementation of transit signal priority in select bus corridors in the Portland metropolitan region. Analysis of the effectiveness of transit signal priority is often undertaken using simulation techniques or empirical studies that are either limited in scope and/or data availability. The present research uses an abundance of trip-level data collected from TriMet’s Bus Dispatch System. The study focuses on the most common performance measures of interest to both transit operators and passengers. Our study shows that the expected benefits of TSP are not consistent across routes and time periods, nor are they consistent across the various performance measures. We believe that benefits of transit signal priority will accrue only as the result of extensive evaluation and adjustment initial deployment. In most cases, an ongoing performance monitoring and adjustment program should be implemented in order to maximize the benefits of transit signal priority. 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