Development and evaluation of adaptive transit signal priority control with updated transit delay model
| dc.contributor.author | Han, X. | |
| dc.contributor.author | Li, P. | |
| dc.contributor.author | Sikder, R. | |
| dc.contributor.author | Qiu, Z. | |
| dc.contributor.author | Kim, A. | |
| dc.contributor.other | The research was partially sponsored by the Edmonton Transit System (ETS) of the City of Edmonton. We appreciate the support from Ken Koropeski, Musse Dese, Gurch Lotey, 397 Andrew Gregory, Hefny Mahmoud from ETS; Richard Leclerc from Transportation Planning; and Craig Walbaum, Wai Cheung, Iris Ye from Transportation Operations. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein, and do not necessarily reflect the official views or policies of the sponsoring organizations, nor do the contents constitute a standard, specification, or regulation. | |
| dc.coverage.spatial | Edmonton | |
| dc.date.accessioned | 2025-05-01T21:04:10Z | |
| dc.date.available | 2025-05-01T21:04:10Z | |
| dc.date.issued | 2019-11-12 | |
| dc.description | Transit signal priority (TSP) strategies are widely used to reduce bus travel delay and to increase bus service reliability. State-of-the-art strategies enable dynamic (and optimal), rather than predetermined, TSP plans to reflect real-time traffic conditions. These dynamic plans are called adaptive TSP. Existing adaptive TSP strategies normally use a performance index (PI), which is a weighted summation of all types of delays, to evaluate each candidate TSP plan and the weights to reflect the corresponding priority. The performance of an adaptive TSP depends on three factors: delay estimation, weights determination, and optimization formulation. In this context, there are three key academic contributions: (a) an enhanced bus delay estimation model based on advance detection, (b) a mechanism to adjust the PI weights dynamically to reflect the changing necessity of TSP under different conditions, and (c) TSP optimization formulated into a quadratic programming problem with an enhanced delay-based PI to obtain global optimization with the use of MATLAB solvers. In addition, an adaptive TSP simulation platform using a full-scale signal simulator, ASC/3, in VISSIM was developed. The optimal TSP plans were granted or were rejected on the basis of TSP events, such as check-in, check-out, and multiple TSP requests. Through a case study in VISSIM, this research found that, compared with conventional active TSP strategies, the new adaptive TSP strategy could further reduce bus travel time while maintaining a better balance of service on non-TSP approaches along a 7.4-km bus corridor in Edmonton, Alberta, Canada. | |
| dc.identifier.doi | https://doi.org/10.7939/r3-bp48-9734 | |
| dc.language.iso | en | |
| dc.relation | https://doi.org/10.3141%2F2438-05 | |
| dc.relation.isversionof | Han, X., Li, P., Sikder, R., Qiu, Z., & Kim, A. (2014). Development and evaluation of adaptive transit signal priority control with updated transit delay model. Transportation Research Record, 2438(1), 45-54. https://doi.org/10.3141%2F2438-05 | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Adaptive Transit Signal Priority | |
| dc.subject | Traffic Operations | |
| dc.subject | Optimization | |
| dc.subject | Traffic Signal Control | |
| dc.subject | Traffic Simulation | |
| dc.title | Development and evaluation of adaptive transit signal priority control with updated transit delay model | |
| dc.type | http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_b1a7d7d4d402bcce http://purl.org/coar/version/c_71e4c1898caa6e32 | |
| ual.jupiterAccess | http://terms.library.ualberta.ca/public |
Files
Original bundle
1 - 1 of 1