Figliozzi et al 2012a - Revision history

Scipediacontent at 19:13, 5 March 2021

2021-03-05T19:13:06Z

Scipediacontent at 19:12, 5 March 2021

2021-03-05T19:12:17Z

Scipediacontent: Scipediacontent moved page Draft Content 357603636 to Figliozzi et al 2012a

2021-02-11T13:25:55Z

Scipediacontent moved page Draft Content 357603636 to Figliozzi et al 2012a

Scipediacontent: Created page with " == Abstract == healthy and efficient public transit system is indispensable to reduce congestion, emissions, energy consumption, and car dependency in urban areas. The obje..."

2021-02-11T13:25:51Z

Created page with " == Abstract == healthy and efficient public transit system is indispensable to reduce congestion, emissions, energy consumption, and car dependency in urban areas. The obje..."

New page

== Abstract ==

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. The authors first determined the frequency of each cause (expressed as percentages) meeting pre-determined thresholds. Next, they performed a sensitivity analysis to demonstrate how cause percentage results change using varying difficulty levels of bus bunching thresholds. Finally, the authors 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.

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.15760/trec.107 http://dx.doi.org/10.15760/trec.107]

* [https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1095&context=cengin_fac https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1095&context=cengin_fac]

* [https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1095&context=cengin_fac https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1095&context=cengin_fac],
: [https://rosap.ntl.bts.gov/view/dot/24701 https://rosap.ntl.bts.gov/view/dot/24701],
: [https://pdxscholar.library.pdx.edu/cengin_fac/96 https://pdxscholar.library.pdx.edu/cengin_fac/96],
: [https://core.ac.uk/display/37776754 https://core.ac.uk/display/37776754],
: [https://works.bepress.com/miguel_figliozzi/55 https://works.bepress.com/miguel_figliozzi/55],
: [https://trid.trb.org/view/1147623 https://trid.trb.org/view/1147623],
: [https://works.bepress.com/miguel_figliozzi/55/download https://works.bepress.com/miguel_figliozzi/55/download],
: [https://academic.microsoft.com/#/detail/1557616572 https://academic.microsoft.com/#/detail/1557616572]

* [ ]

← Older revision		Revision as of 19:13, 5 March 2021
Line 20:		Line 20:
	: [https://works.bepress.com/miguel_figliozzi/55/download https://works.bepress.com/miguel_figliozzi/55/download],		: [https://works.bepress.com/miguel_figliozzi/55/download https://works.bepress.com/miguel_figliozzi/55/download],
	: [https://academic.microsoft.com/#/detail/1557616572 https://academic.microsoft.com/#/detail/1557616572]		: [https://academic.microsoft.com/#/detail/1557616572 https://academic.microsoft.com/#/detail/1557616572]
−
−	* [ ]

@@ Line 2: / Line 2: @@
 == Abstract ==
- 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. The authors first determined the frequency of each cause (expressed as percentages) meeting pre-determined thresholds. Next, they performed a sensitivity analysis to demonstrate how cause percentage results change using varying difficulty levels of bus bunching thresholds. Finally, the authors 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.
+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. The authors first determined the frequency of each cause (expressed as percentages) meeting pre-determined thresholds. Next, they performed a sensitivity analysis to demonstrate how cause percentage results change using varying difficulty levels of bus bunching thresholds. Finally, the authors 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.
-−
 == Original document ==

← Older revision	Revision as of 13:25, 11 February 2021
(No difference)