{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:54:59Z","timestamp":1783119299948,"version":"3.54.6"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T00:00:00Z","timestamp":1657584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Capacity drop is the critical phenomenon that triggers traffic congestion, while traffic evolution is very complex during a capacity drop. This study applied the empirical vehicle trajectory data to explore the traffic characteristics during the capacity drop at the tunnel bottleneck section. We first construct a capacity drop analysis model using image processing technology to extract high-precision vehicle trajectories. We then analyze the characteristics of the evolution process of the capacity drop at the bottleneck area. The results show that the capacity drop is a dynamic evolution process from free flow to congested flow where traffic operation is distinct. The capacity drop shows the difference between congested flow and non-congested flow. The driving characteristics of drivers in the two states are also different. The influence of lane-changing behavior on the capacity drop is estimated. In the free flow state, the disturbance caused by lane-changing can be quickly eliminated. With the increase in vehicle numbers in the area, the frequent lane-changing behavior accumulates disturbance. When the disturbance reaches a certain degree, congestion will occur, and the vehicle\u2019s speed will drop sharply, resulting in a capacity drop.<\/jats:p>","DOI":"10.3390\/a15070240","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T20:52:41Z","timestamp":1657659161000},"page":"240","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Lu","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"Nanjing Communications Construction &Investment Holdings (Group) Co., Ltd., Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chishe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.trb.2017.11.006","article-title":"Kinematic wave models of sag and tunnel bottlenecks","volume":"107","author":"Jin","year":"2018","journal-title":"Transp. 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