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Dwell-time trends are not well established in the literature despite the ubiquity of large sets of automated passenger count (APC) data. Additionally, the impact of dwell-time variability on TSP performance is not well studied, particularly with field-collected dwell-time data. This study first analyzes trends and distributions inherent in dwell-time data deduced from APC data. Dwell times vary from stop to stop and for each stop by time of day (TOD). Stops with the highest proportions of non-zero dwell time also had the highest dwell-time magnitudes and variability. For most stops, dwell-time data was closely fitted by inverse Gaussian, log-normal, power log-normal, Fisk (log-logistic), and Johnson\u2019s SU distributions. The second part of the study used a simulation environment to evaluate the impact of dwell-time magnitude and variability on TSP performance at both far-side and near-side bus stops. For far-side bus stops, dwell time significantly impacted the bus arrival profile at the check-in detector and thus the selected TSP strategy. Higher dwell-time magnitudes and variability led to a higher share of an Early Green Phase (EG) which is not as effective as a Green Phase Extension (GE). At near-side bus stops, dwell-time variability induced more uncertainty in an estimated time of arrival (ETA) and significantly reduced TSP effectiveness especially for GE. TSP performance in terms of GE success, bus travel time, and side-street traffic delay was significantly better at far-side stops compared to near-side stops.<\/jats:p>","DOI":"10.1007\/s12469-025-00393-y","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T08:54:53Z","timestamp":1744793693000},"page":"311-333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Analysis of bus dwell times from automated passenger count data and the impact of dwell-time variability on the performance of transit signal priority"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2642-7825","authenticated-orcid":false,"given":"Dickness Kakitahi","family":"Kwesiga","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6949-5126","authenticated-orcid":false,"given":"Angshuman","family":"Guin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0307-9127","authenticated-orcid":false,"given":"Michael","family":"Hunter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"393_CR1","doi-asserted-by":"publisher","first-page":"100027","DOI":"10.1016\/j.trip.2019.100027","volume":"2","author":"T AlHadidi","year":"2019","unstructured":"AlHadidi T, Rakha HA (2019) Modeling bus passenger boarding\/alighting times: a stochastic approach. 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The study uses data from a public transit agency in Atlanta, USA. The collection of the data by the agency does not involve interaction with or identification of human subjects.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}