{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:24:03Z","timestamp":1792171443743,"version":"4.4.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T00:00:00Z","timestamp":1512950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"MOE AcRF Tier 1","award":["M4011261.020"],"award-info":[{"award-number":["M4011261.020"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,5,31]]},"abstract":"<jats:p>Arriving on time and total travel time are two important properties for vehicle routing. Existing route guidance approaches always consider them independently, because they may conflict with each other. In this article, we develop a semi-decentralized multiagent-based vehicle routing approach where vehicle agents follow the local route guidance by infrastructure agents at each intersection, and infrastructure agents perform the route guidance by solving a route assignment problem. It integrates the two properties by expressing them as two objective terms of the route assignment problem. Regarding arriving on time, it is formulated based on the probability tail model, which aims to maximize the probability of reaching destination before deadline. Regarding total travel time, it is formulated as a weighted quadratic term, which aims to minimize the expected travel time from the current location to the destination based on the potential route assignment. The weight for total travel time is designed to be comparatively large if the deadline is loose. Additionally, we improve the proposed approach in two aspects, including travel time prediction and computational efficiency. Experimental results on real road networks justify its ability to increase the average probability of arriving on time, reduce total travel time, and enhance the overall routing performance.<\/jats:p>","DOI":"10.1145\/3078847","type":"journal-article","created":{"date-parts":[[2017,12,13]],"date-time":"2017-12-13T14:50:37Z","timestamp":1513176637000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["A Multiagent-Based Approach for Vehicle Routing by Considering Both Arriving on Time and Total Travel Time"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4499-759X","authenticated-orcid":false,"given":"Zhiguang","family":"Cao","sequence":"first","affiliation":[{"name":"School of Automation, Guangdong University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9836-3090","authenticated-orcid":false,"given":"Hongliang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,12,11]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS\u201912)","author":"do Amarante Maicon"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888913000118"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 3rd International Conference on Advances in System Simulation. 55--60","author":"Behrisch Michael","year":"2011"},{"key":"e_1_2_1_4_1","volume-title":"Convex Optimization","author":"Boyd Stephen"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00041-008-9045-x"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI\u201916)","author":"Cao Zhiguang","year":"2016"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2498160"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2015.2480964"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI\u201917)","author":"Cao Zhiguang","year":"2017"},{"key":"e_1_2_1_10_1","unstructured":"Zhiguang Cao Siwei Jiang Jie Zhang and Hongliang Guo. 2017b. 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