{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T22:23:10Z","timestamp":1777069390600,"version":"3.51.4"},"reference-count":79,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. In the first fold, we provide a background about the potential benefits of MLLMs in transportation applications and conduct a comprehensive review of current MLLM technologies in previous studies. We highlight their effectiveness and limitations in object detection within various transportation scenarios. The second fold involves providing an overview of the taxonomy of end-to-end object detection in transportation applications and future directions. Building on this, we proposed empirical analysis for testing MLLMs on three real-world transportation problems that include object detection tasks, namely, road safety attribute extraction, safety-critical event detection, and visual reasoning of thermal images. Our findings provide a detailed assessment of MLLM performance, uncovering both strengths and areas for improvement. Finally, we discuss practical limitations and challenges of MLLMs in enhancing object detection in transportation, thereby offering a roadmap for future research and development in this critical area.<\/jats:p>","DOI":"10.3390\/computation13060133","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T03:57:18Z","timestamp":1748923038000},"page":"133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6835-8338","authenticated-orcid":false,"given":"Huthaifa I.","family":"Ashqar","sequence":"first","affiliation":[{"name":"AI and Data Science Department, Arab American University, 13 Zababdeh, Jenin P.O. Box 240, Palestine"},{"name":"Artificial Intelligence Program, Fu Foundation School of Engineering and Applied Science, Columbia University, 500 W 120th Street, New York, NY 10027, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5493-2530","authenticated-orcid":false,"given":"Ahmed","family":"Jaber","sequence":"additional","affiliation":[{"name":"Department of Transport Technology and Economics, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., H-1111 Budapest, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6388-0904","authenticated-orcid":false,"given":"Taqwa I.","family":"Alhadidi","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, Al-Ahliyya Amman University, Al-Saro Al-Salt, Amman 19111, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2634-4576","authenticated-orcid":false,"given":"Mohammed","family":"Elhenawy","sequence":"additional","affiliation":[{"name":"CARRS-Q, Queensland University of Technology, 130 Victoria Park Rd, Kelvin Grove, Brisbane, QLD 4059, Australia"},{"name":"Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Al-Saro Al-Salt, Amman 19111, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101350","DOI":"10.1016\/j.compenvurbsys.2019.101350","article-title":"Detecting and mapping traffic signs from Google Street View images using deep learning and GIS","volume":"77","author":"Campbell","year":"2019","journal-title":"Comput. 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