High resolution bus lane performance evaluation from real time update data
Bus priority measures such as bus lanes are designed to enhance bus performance and increase ridership. Traditionally, benefits have been evaluated at an aggregate level. Newer data sources, however, enable the tracking of micro delays and their relation to detailed bus priority data. Given schedule...
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| Language: | English |
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Elsevier
2025-07-01
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| Series: | Transportation Research Interdisciplinary Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198225001526 |
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| author | Tingsen (Tim) Xian John D. Nelson Emily Moylan |
| author_facet | Tingsen (Tim) Xian John D. Nelson Emily Moylan |
| author_sort | Tingsen (Tim) Xian |
| collection | DOAJ |
| description | Bus priority measures such as bus lanes are designed to enhance bus performance and increase ridership. Traditionally, benefits have been evaluated at an aggregate level. Newer data sources, however, enable the tracking of micro delays and their relation to detailed bus priority data. Given schedule adjustments for bus priority measures, we anticipate minimal impacts on expected delay at the route-segment level, with the primary benefit being reduced delay variability relative to the schedule.This study analyzes real bus arrival data to examine the impact of stop-to-stop route characteristics on marginal delay. The analysis uses pooled, between-, and within- effects panel regression models to predict average and standard deviation of marginal delay for each stop-to-stop segment within rolling windows of 30 arrivals. Independent variables include priority measures, traffic signals, traffic volumes, scheduled travel time, stop-to-stop link length, scheduled travel speed, cross-traffic turns, precipitation, weekends, holidays, and the COVID stringency index.Findings reveal that bus-taxi lanes and bus-HOV lanes reduce marginal delay by 6–7 s per kilometer. While the direct impact on marginal delay is minimal due to schedule adjustments, these lanes significantly reduce the variability of delay, saving 5–20 s of standard deviation of delay per kilometer. The study also highlights the substantial impact of traffic signals and cross-traffic turns on bus performance reliability. These findings support the effectiveness of bus priority measures in improving bus service reliability. |
| format | Article |
| id | doaj-art-eb4473ec71a94570a68f320fa3737ad0 |
| institution | Kabale University |
| issn | 2590-1982 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj-art-eb4473ec71a94570a68f320fa3737ad02025-08-22T04:57:32ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822025-07-013210147310.1016/j.trip.2025.101473High resolution bus lane performance evaluation from real time update dataTingsen (Tim) Xian0John D. Nelson1Emily Moylan2The School of Civil Engineering (J05), The University of Sydney, Darlington, 2008, NSW, Australia; Corresponding author.The Institute of Transport and Logistics Studies (ITLS), The University of Sydney Business School, Darlington, 2008, NSW, AustraliaThe School of Civil Engineering (J05), The University of Sydney, Darlington, 2008, NSW, AustraliaBus priority measures such as bus lanes are designed to enhance bus performance and increase ridership. Traditionally, benefits have been evaluated at an aggregate level. Newer data sources, however, enable the tracking of micro delays and their relation to detailed bus priority data. Given schedule adjustments for bus priority measures, we anticipate minimal impacts on expected delay at the route-segment level, with the primary benefit being reduced delay variability relative to the schedule.This study analyzes real bus arrival data to examine the impact of stop-to-stop route characteristics on marginal delay. The analysis uses pooled, between-, and within- effects panel regression models to predict average and standard deviation of marginal delay for each stop-to-stop segment within rolling windows of 30 arrivals. Independent variables include priority measures, traffic signals, traffic volumes, scheduled travel time, stop-to-stop link length, scheduled travel speed, cross-traffic turns, precipitation, weekends, holidays, and the COVID stringency index.Findings reveal that bus-taxi lanes and bus-HOV lanes reduce marginal delay by 6–7 s per kilometer. While the direct impact on marginal delay is minimal due to schedule adjustments, these lanes significantly reduce the variability of delay, saving 5–20 s of standard deviation of delay per kilometer. The study also highlights the substantial impact of traffic signals and cross-traffic turns on bus performance reliability. These findings support the effectiveness of bus priority measures in improving bus service reliability.http://www.sciencedirect.com/science/article/pii/S2590198225001526GTFS-RStop-to-stop marginal delayBus performanceBus lanesBus priorityOn-time running |
| spellingShingle | Tingsen (Tim) Xian John D. Nelson Emily Moylan High resolution bus lane performance evaluation from real time update data Transportation Research Interdisciplinary Perspectives GTFS-R Stop-to-stop marginal delay Bus performance Bus lanes Bus priority On-time running |
| title | High resolution bus lane performance evaluation from real time update data |
| title_full | High resolution bus lane performance evaluation from real time update data |
| title_fullStr | High resolution bus lane performance evaluation from real time update data |
| title_full_unstemmed | High resolution bus lane performance evaluation from real time update data |
| title_short | High resolution bus lane performance evaluation from real time update data |
| title_sort | high resolution bus lane performance evaluation from real time update data |
| topic | GTFS-R Stop-to-stop marginal delay Bus performance Bus lanes Bus priority On-time running |
| url | http://www.sciencedirect.com/science/article/pii/S2590198225001526 |
| work_keys_str_mv | AT tingsentimxian highresolutionbuslaneperformanceevaluationfromrealtimeupdatedata AT johndnelson highresolutionbuslaneperformanceevaluationfromrealtimeupdatedata AT emilymoylan highresolutionbuslaneperformanceevaluationfromrealtimeupdatedata |