{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/network-learning-via-multi-agent-inverse","title":"Network learning via multi-agent inverse transportation problems","arxiv_id":"1609.04117","date":"2016-09-14","proceeding":null,"authors":["Susan Jia Xu","Mehdi Nourinejad","Xuebo Lai","Joseph Y. J. Chow"],"abstract":"Despite the ubiquity of transportation data, methods to infer the state\nparameters of a network either ignore sensitivity of route decisions, require\nroute enumeration for parameterizing descriptive models of route selection, or\nrequire complex bilevel models of route assignment behavior. These limitations\nprevent modelers from fully exploiting ubiquitous data in monitoring\ntransportation networks. Inverse optimization methods that capture network\nroute choice behavior can address this gap, but they are designed to take\nobservations of the same model to learn the parameters of that model, which is\nstatistically inefficient (e.g. requires estimating population route and link\nflows). New inverse optimization models and supporting algorithms are proposed\nto learn the parameters of heterogeneous travelers' route behavior to infer\nshared network state parameters (e.g. link capacity dual prices). The inferred\nvalues are consistent with observations of each agent's optimization behavior.\nWe prove that the method can obtain unique dual prices for a network shared by\nthese agents in polynomial time. Four experiments are conducted. The first one,\nconducted on a 4-node network, verifies the methodology to obtain heterogeneous\nlink cost parameters even when multinomial or mixed logit models would not be\nmeaningfully estimated. The second is a parameter recovery test on the\nNguyen-Dupuis network that shows that unique latent link capacity dual prices\ncan be inferred using the proposed method. The third test on the same network\ndemonstrates how a monitoring system in an online learning environment can be\ndesigned using this method. The last test demonstrates this learning on real\ndata obtained from a freeway network in Queens, New York, using only real-time\nGoogle Maps queries.","url_abs":"http://arxiv.org/abs/1609.04117v4","url_pdf":"http://arxiv.org/pdf/1609.04117v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"network-learning-via-multi-agent-inverse","repo_url":"https://github.com/BUILTNYU/Network-learning-via-multi-agent-inverse-transportation-problems","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"descriptive","task_name":"Descriptive"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}