{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T13:04:02Z","timestamp":1778159042646,"version":"3.51.4"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC02-05CH11231"],"award-info":[{"award-number":["DE-AC02-05CH11231"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>The current trend toward urbanization and adoption of flexible and innovative mobility technologies will have complex and difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the increasingly uncertain future state of the urban mobility landscape do not yet exist. Furthermore, few approaches have enabled the massive ingestion of urban data in planning tools capable of offering the flexibility of scenario-based design.<\/jats:p>\n          <jats:p>This article introduces Berkeley Integrated System for Transportation Optimization (BISTRO), a new open source transportation planning decision support system that uses an agent-based simulation and optimization approach to anticipate and develop adaptive plans for possible technological disruptions and growth scenarios. The new framework was evaluated in the context of a machine learning competition hosted within Uber Technologies, Inc., in which over 400 engineers and data scientists participated. For the purposes of this competition, a benchmark model, based on the city of Sioux Falls, South Dakota, was adapted to the BISTRO framework. An important finding of this study was that in spite of rigorous analysis and testing done prior to the competition, the two top-scoring teams discovered an unbounded region of the search space, rendering the solutions largely uninterpretable for the purposes of decision-support. On the other hand, a follow-on study aimed to fix the objective function. It served to demonstrate BISTRO\u2019s utility as a human-in-the-loop cyberphysical system: one that uses scenario-based optimization algorithms as a feedback mechanism to assist urban planners with iteratively refining objective function and constraints specification on intervention strategies. The portfolio of transportation intervention strategy alternatives eventually chosen achieves high-level regional planning goals developed through participatory stakeholder engagement practices.<\/jats:p>","DOI":"10.1145\/3384344","type":"journal-article","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T16:41:20Z","timestamp":1593016880000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["BISTRO"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4626-4194","authenticated-orcid":false,"given":"Sidney A.","family":"Feygin","sequence":"first","affiliation":[{"name":"Uber Technologies, Inc."}]},{"given":"Jessica R.","family":"Lazarus","sequence":"additional","affiliation":[{"name":"University of California, Berkeley; Uber Technologies, Inc."}]},{"given":"Edward H.","family":"Forscher","sequence":"additional","affiliation":[{"name":"University of California, Berkeley; Uber Technologies, Inc."}]},{"given":"Valentine","family":"Golfier-Vetterli","sequence":"additional","affiliation":[{"name":"Uber Technologies, Inc."}]},{"given":"Jonathan W.","family":"Lee","sequence":"additional","affiliation":[{"name":"Uber Technologies, Inc."}]},{"given":"Abhishek","family":"Gupta","sequence":"additional","affiliation":[{"name":"Uber Technologies, Inc."}]},{"given":"Rashid A.","family":"Waraich","sequence":"additional","affiliation":[{"name":"Lawrence Berkeley National Laboratory"}]},{"given":"Colin J. R.","family":"Sheppard","sequence":"additional","affiliation":[{"name":"Lawrence Berkeley National Laboratory"}]},{"given":"Alexandre M.","family":"Bayen","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science, Berkeley; Institute of Transportation Studies"}]}],"member":"320","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"key":"e_1_2_1_2_1","volume-title":"A primer for agent-based simulation and modeling in transportation applications. Development","author":"Zheng Hong","year":"2013","unstructured":"Hong Zheng . 2013. A primer for agent-based simulation and modeling in transportation applications. Development ( 2013 ), 75. Hong Zheng. 2013. A primer for agent-based simulation and modeling in transportation applications. Development (2013), 75."},{"key":"e_1_2_1_3_1","volume-title":"Axhausen","author":"Horni Andreas","year":"2016","unstructured":"Andreas Horni , Kai Nagel , and Kay W . Axhausen . 2016 . The Multi-agent Transport Simulation MATSim. Ubiquity Press London . Andreas Horni, Kai Nagel, and Kay W. Axhausen. 2016. The Multi-agent Transport Simulation MATSim. Ubiquity Press London."},{"key":"e_1_2_1_4_1","volume-title":"Implementation of an autonomous taxi service in a multi-modal traffic simulation using MATSim Master thesis in Complex Adaptive Systems. June","author":"H\u00f6rl Sebastian","year":"2016","unstructured":"Sebastian H\u00f6rl . 2016. Implementation of an autonomous taxi service in a multi-modal traffic simulation using MATSim Master thesis in Complex Adaptive Systems. June ( 2016 ). DOI:http:\/\/dx.doi.org\/10.13140\/RG.2.1.2060.9523 Sebastian H\u00f6rl. 2016. Implementation of an autonomous taxi service in a multi-modal traffic simulation using MATSim Master thesis in Complex Adaptive Systems. June (2016). DOI:http:\/\/dx.doi.org\/10.13140\/RG.2.1.2060.9523"},{"key":"e_1_2_1_5_1","volume-title":"et\u00a0al","author":"Grant Michael","year":"2013","unstructured":"Michael Grant , Janet D\u2019Ignazio , Alexander Bond , Alanna McKeeman , et\u00a0al . 2013 . Performance-based Planning and Programming Guidebook.Technical Report. United States. Federal Highway Administration . Michael Grant, Janet D\u2019Ignazio, Alexander Bond, Alanna McKeeman, et\u00a0al. 2013. Performance-based Planning and Programming Guidebook.Technical Report. United States. Federal Highway Administration."},{"key":"e_1_2_1_6_1","unstructured":"United States Department of Transportation. 2012. Trends in Statewide Long-Range Transportation Plans: Core and Emerging Topics. Retrieved from https:\/\/www.planning.dot.gov\/documents\/State.  United States Department of Transportation. 2012. Trends in Statewide Long-Range Transportation Plans: Core and Emerging Topics. Retrieved from https:\/\/www.planning.dot.gov\/documents\/State."},{"key":"e_1_2_1_7_1","volume-title":"Public Engagement: Case Studies and Notable Practices.","author":"Federal Highway Administration\/Federal Transit Administration","year":"2019","unstructured":"Federal Highway Administration\/Federal Transit Administration . 2019 . Public Engagement: Case Studies and Notable Practices. Retrieved from https:\/\/planning.dot.gov\/focus_caseStudies.aspx. Federal Highway Administration\/Federal Transit Administration. 2019. Public Engagement: Case Studies and Notable Practices. Retrieved from https:\/\/planning.dot.gov\/focus_caseStudies.aspx."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/01944363.2017.1322526"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.07.017"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2016.08.518"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1108\/TG-09-2013-0030"},{"key":"e_1_2_1_14_1","volume-title":"Transportation Research Board 97th Annual Meeting.","author":"Azevedo Carlos Lima","year":"2018","unstructured":"Carlos Lima Azevedo , Ravi Seshadri , Song Gao , Bilge Atasoy , Arun Prakash Akkinepally , Eleni Christofa , Fang Zhao , Jessika Trancik , and Moshe Ben-Akiva . 2018 . Tripod: Sustainable travel incentives with prediction, optimization, and personalization . In Transportation Research Board 97th Annual Meeting. Carlos Lima Azevedo, Ravi Seshadri, Song Gao, Bilge Atasoy, Arun Prakash Akkinepally, Eleni Christofa, Fang Zhao, Jessika Trancik, and Moshe Ben-Akiva. 2018. Tripod: Sustainable travel incentives with prediction, optimization, and personalization. In Transportation Research Board 97th Annual Meeting."},{"key":"e_1_2_1_15_1","volume-title":"Agents meet traffic simulation, control and management: A review of selected recent contributions. CEUR Workshop Proceedings 1664","author":"Postorino Maria Nadia","year":"2016","unstructured":"Maria Nadia Postorino and Giuseppe M. L. Sarn\u00e9 . 2016 . Agents meet traffic simulation, control and management: A review of selected recent contributions. CEUR Workshop Proceedings 1664 ( 2016 ), 112--117. Maria Nadia Postorino and Giuseppe M. L. Sarn\u00e9. 2016. Agents meet traffic simulation, control and management: A review of selected recent contributions. CEUR Workshop Proceedings 1664 (2016), 112--117."},{"key":"e_1_2_1_16_1","unstructured":"Association of Metropolitan Planning Organizations. 2019. ActivitySim | An open platform for activity-based travel modeling. Retrieved from https:\/\/activitysim.github.io\/.  Association of Metropolitan Planning Organizations. 2019. ActivitySim | An open platform for activity-based travel modeling. Retrieved from https:\/\/activitysim.github.io\/."},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Joe Castiglione Mark Bradley and John Gliebe. 2016. Activity-Based Travel Demand Models: A Primer. 7--8 pages. DOI:http:\/\/dx.doi.org\/10.17226\/22357  Joe Castiglione Mark Bradley and John Gliebe. 2016. Activity-Based Travel Demand Models: A Primer. 7--8 pages. DOI:http:\/\/dx.doi.org\/10.17226\/22357","DOI":"10.17226\/22357"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","unstructured":"L. Smith R. Beckman and K. Baggerly. 1995. TRANSIMS: Transportation analysis and simulation system. In Proceedings of the 5th National Conference on Transportation Planning Methods Applications\u2014Volume II: A Compendium of Papers Based on a Conference Held in Seattle Washington in April 1995 by the Transportation Research Board and Washington State Department of Transportation. DOI:http:\/\/dx.doi.org\/10.2172\/88648  L. Smith R. Beckman and K. Baggerly. 1995. TRANSIMS: Transportation analysis and simulation system. In Proceedings of the 5th National Conference on Transportation Planning Methods Applications\u2014Volume II: A Compendium of Papers Based on a Conference Held in Seattle Washington in April 1995 by the Transportation Research Board and Washington State Department of Transportation. DOI:http:\/\/dx.doi.org\/10.2172\/88648","DOI":"10.2172\/88648"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the 3rd International Conference on Advances in System Simulation, SIMUL","author":"Behrisch Michael","year":"2011","unstructured":"Michael Behrisch , Laura Bieker , Jakob Erdmann , and Daniel Krajzewicz . 2011 . SUMO--simulation of urban mobility: An overview . In Proceedings of the 3rd International Conference on Advances in System Simulation, SIMUL 2011. ThinkMind. Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Krajzewicz. 2011. SUMO--simulation of urban mobility: An overview. In Proceedings of the 3rd International Conference on Advances in System Simulation, SIMUL 2011. ThinkMind."},{"key":"e_1_2_1_20_1","volume-title":"Jacopo de Berardinis, Giorgio Forcina, and Andrea Polini.","author":"Castagnari Carlo","year":"2018","unstructured":"Carlo Castagnari , Flavio Corradini , Francesco De Angelis , Jacopo de Berardinis, Giorgio Forcina, and Andrea Polini. 2018 . Tangramob : An agent-based simulation framework for validating urban smart mobility solutions. arXiv preprint arXiv:1805.10906 (2018). Carlo Castagnari, Flavio Corradini, Francesco De Angelis, Jacopo de Berardinis, Giorgio Forcina, and Andrea Polini. 2018. Tangramob: An agent-based simulation framework for validating urban smart mobility solutions. arXiv preprint arXiv:1805.10906 (2018)."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trpro.2018.12.173"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11067-005-2630-5"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1080\/01944360208976274"},{"key":"e_1_2_1_24_1","volume-title":"Latsis Symposium. 12--16","author":"Nicolai T.","unstructured":"T. Nicolai and K. Nagel . 2012. Coupling transport and land use: Investigating accessibility indicators for feedback from a travel to a land-use model . In Latsis Symposium. 12--16 . T. Nicolai and K. Nagel. 2012. Coupling transport and land use: Investigating accessibility indicators for feedback from a travel to a land-use model. In Latsis Symposium. 12--16."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5198\/jtlu.2018.1205"},{"key":"e_1_2_1_26_1","volume-title":"Nicolai and Kai Nagel","author":"Thomas","year":"2010","unstructured":"Thomas W. Nicolai and Kai Nagel . 2010 . Coupling MATSim and UrbanSim: Software Design Issues. Technical Report. SustainCity Working Paper . Thomas W. Nicolai and Kai Nagel. 2010. Coupling MATSim and UrbanSim: Software Design Issues. Technical Report. SustainCity Working Paper."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.04.192"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1287\/trsc.2014.0534"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.3141\/2653-06"},{"key":"e_1_2_1_31_1","volume-title":"Actors: A model for reasoning about open distributed systems. Formal Methods for Distributed Processing: A Survey of Object-oriented Approaches","author":"Agha Gul A.","year":"2001","unstructured":"Gul A. Agha , Prasannaa Thati , Reza Ziaei , H. Bowman , and J. Derrick . 2001 . Actors: A model for reasoning about open distributed systems. Formal Methods for Distributed Processing: A Survey of Object-oriented Approaches (2001), 155--176. Gul A. Agha, Prasannaa Thati, Reza Ziaei, H. Bowman, and J. Derrick. 2001. Actors: A model for reasoning about open distributed systems. Formal Methods for Distributed Processing: A Survey of Object-oriented Approaches (2001), 155--176."},{"key":"e_1_2_1_32_1","volume-title":"Discrete Choice Methods with Simulation","author":"Train Kenneth E.","unstructured":"Kenneth E. Train . 2009. Discrete Choice Methods with Simulation . Cambridge University Press . Kenneth E. Train. 2009. Discrete Choice Methods with Simulation. Cambridge University Press."},{"key":"e_1_2_1_33_1","volume-title":"Lerman","author":"Ben-Akiva Moshe E.","year":"1985","unstructured":"Moshe E. Ben-Akiva and Steven R . Lerman . 1985 . Discrete Choice Analysis: Theory and Application to Travel Demand. Vol. 9 . MIT press . Moshe E. Ben-Akiva and Steven R. Lerman. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. Vol. 9. MIT press."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2013.1226"},{"key":"e_1_2_1_35_1","volume-title":"Vicente","author":"Conn Andrew R.","year":"2009","unstructured":"Andrew R. Conn , Katya Scheinberg , and Luis N . Vicente . 2009 . Introduction to Derivative-free Optimization. Vol. 8 . Siam . Andrew R. Conn, Katya Scheinberg, and Luis N. Vicente. 2009. Introduction to Derivative-free Optimization. Vol. 8. Siam."},{"key":"e_1_2_1_36_1","volume-title":"Barton and Martin Meckesheimer","author":"Russell","year":"2006","unstructured":"Russell R. Barton and Martin Meckesheimer . 2006 . Chapter 18 metamodel-based simulation optimization. Handbooks in Operations Research and Management Science 13, C ( 2006), 535--574. DOI:http:\/\/dx.doi.org\/10.1016\/S0927-0507(06)13018-2 Russell R. Barton and Martin Meckesheimer. 2006. Chapter 18 metamodel-based simulation optimization. Handbooks in Operations Research and Management Science 13, C (2006), 535--574. DOI:http:\/\/dx.doi.org\/10.1016\/S0927-0507(06)13018-2"},{"key":"e_1_2_1_37_1","volume-title":"Summer School on Machine Learning","author":"Rasmussen Carl Edward","unstructured":"Carl Edward Rasmussen . 2003. Gaussian processes in machine learning . In Summer School on Machine Learning . Springer , 63--71. Carl Edward Rasmussen. 2003. Gaussian processes in machine learning. In Summer School on Machine Learning. Springer, 63--71."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Ian Dewancker Michael McCourt and Scott Clark. 2015. Bayesian Optimization Primer. (2015). DOI:http:\/\/dx.doi.org\/10.1016\/j.jss.2013.05.010  Ian Dewancker Michael McCourt and Scott Clark. 2015. Bayesian Optimization Primer. (2015). DOI:http:\/\/dx.doi.org\/10.1016\/j.jss.2013.05.010","DOI":"10.1016\/j.jss.2013.05.010"},{"key":"e_1_2_1_40_1","volume-title":"A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811","author":"Frazier Peter I.","year":"2018","unstructured":"Peter I. Frazier . 2018. A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811 ( 2018 ). Peter I. Frazier. 2018. A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811 (2018)."},{"key":"e_1_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Frank Hutter Holger H. Hoos and Kevin Leyton-Brown. 2011. Sequential model-based optimization for general algorithm configuration. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6683 LNCS (2011) 507--523.  Frank Hutter Holger H. Hoos and Kevin Leyton-Brown. 2011. Sequential model-based optimization for general algorithm configuration. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6683 LNCS (2011) 507--523.","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"e_1_2_1_42_1","unstructured":"James S. Bergstra R\u00e9mi Bardenet Yoshua Bengio and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems. 2546--2554.  James S. Bergstra R\u00e9mi Bardenet Yoshua Bengio and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems. 2546--2554."},{"key":"e_1_2_1_43_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning. 115--123","author":"Bergstra James","unstructured":"James Bergstra , Daniel L. K. Yamins , and D. Cox . 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures . In Proceedings of the 30th International Conference on Machine Learning. 115--123 . http:\/\/jmlr.org\/proceedings\/papers\/v28\/bergstra13.html. James Bergstra, Daniel L. K. Yamins, and D. Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Proceedings of the 30th International Conference on Machine Learning. 115--123. http:\/\/jmlr.org\/proceedings\/papers\/v28\/bergstra13.html."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.1080.0314"},{"key":"e_1_2_1_46_1","unstructured":"Jian Wu Matthias Poloczek Andrew G. Wilson and Peter Frazier. 2017. Bayesian optimization with gradients. In Advances in Neural Information Processing Systems. 5267--5278.  Jian Wu Matthias Poloczek Andrew G. Wilson and Peter Frazier. 2017. Bayesian optimization with gradients. In Advances in Neural Information Processing Systems. 5267--5278."},{"key":"e_1_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Jasper Snoek Hugo Larochelle and Ryan Adams. 2012. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems. 2951--2959. DOI:http:\/\/dx.doi.org\/10.1016\/s2468-2667(17)30214-1  Jasper Snoek Hugo Larochelle and Ryan Adams. 2012. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems. 2951--2959. DOI:http:\/\/dx.doi.org\/10.1016\/s2468-2667(17)30214-1","DOI":"10.1016\/S2468-2667(17)30214-1"},{"key":"e_1_2_1_48_1","volume-title":"Freeze-thaw Bayesian optimization. arXiv preprint arXiv:1406.3896","author":"Swersky Kevin","year":"2014","unstructured":"Kevin Swersky , Jasper Snoek , and Ryan Prescott Adams . 2014. Freeze-thaw Bayesian optimization. arXiv preprint arXiv:1406.3896 ( 2014 ). Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams. 2014. Freeze-thaw Bayesian optimization. arXiv preprint arXiv:1406.3896 (2014)."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1287\/trsc.2014.0550"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2014.12.007"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1287\/trsc.2016.0717"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/0191-2615(89)90019-2"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2016.12.009"},{"key":"e_1_2_1_54_1","volume-title":"et\u00a0al","author":"Bucci Gregory","year":"2018","unstructured":"Gregory Bucci , Chris Calley , Michael Green , et\u00a0al . 2018 . FHWA Research and Technology Evaluation: Agent- Based Modeling and Simulation. Technical Report. United States. Federal Highway Administration. Office of Corporate Research . Gregory Bucci, Chris Calley, Michael Green, et\u00a0al. 2018. FHWA Research and Technology Evaluation: Agent-Based Modeling and Simulation. Technical Report. United States. Federal Highway Administration. Office of Corporate Research."},{"key":"e_1_2_1_55_1","volume-title":"94th Annual Meeting of the Transportation Research Board 250","author":"Vovsha Peter","year":"2015","unstructured":"Peter Vovsha , James E. Hicks , Binny M. Paul , Vladimir Livshits , Petya Maneva , and Kyunghwi Jeon . 2015 . New features of population synthesis . 94th Annual Meeting of the Transportation Research Board 250 (2015), 1--20. Peter Vovsha, James E. Hicks, Binny M. Paul, Vladimir Livshits, Petya Maneva, and Kyunghwi Jeon. 2015. New features of population synthesis. 94th Annual Meeting of the Transportation Research Board 250 (2015), 1--20."},{"key":"e_1_2_1_56_1","volume-title":"Fourie","author":"Chakirov Artem","year":"2014","unstructured":"Artem Chakirov and Pieter J . Fourie . 2014 . Enriched sioux falls scenario with dynamic and disaggregate demand. Arbeitsberichte Verkehrs-und Raumplanung 978 (2014). Artem Chakirov and Pieter J. Fourie. 2014. Enriched sioux falls scenario with dynamic and disaggregate demand. Arbeitsberichte Verkehrs-und Raumplanung 978 (2014)."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022602019183"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1213847"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1218263"},{"key":"e_1_2_1_60_1","volume-title":"Ten simple rules for reproducible computational research. PLOS Computational Biology 9, 10 (10","author":"Sandve Geir Kjetil","year":"2013","unstructured":"Geir Kjetil Sandve , Anton Nekrutenko , James Taylor , and Eivind Hovig . 2013. Ten simple rules for reproducible computational research. PLOS Computational Biology 9, 10 (10 2013 ), 1--4. DOI:http:\/\/dx.doi.org\/10.1371\/journal.pcbi.1003285 Geir Kjetil Sandve, Anton Nekrutenko, James Taylor, and Eivind Hovig. 2013. Ten simple rules for reproducible computational research. PLOS Computational Biology 9, 10 (10 2013), 1--4. DOI:http:\/\/dx.doi.org\/10.1371\/journal.pcbi.1003285"},{"key":"e_1_2_1_61_1","volume-title":"Unlocking Data to Improve Public Policy. (03","author":"Hastings Justine S.","year":"2019","unstructured":"Justine S. Hastings , Mark Howison , Ted Lawless , John Ucles , and Preston White . 2019. Unlocking Data to Improve Public Policy. (03 2019 ). DOI:http:\/\/dx.doi.org\/10.31219\/osf.io\/28krq Justine S. Hastings, Mark Howison, Ted Lawless, John Ucles, and Preston White. 2019. Unlocking Data to Improve Public Policy. (03 2019). DOI:http:\/\/dx.doi.org\/10.31219\/osf.io\/28krq"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3384344","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3384344","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3384344","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:03:24Z","timestamp":1750197804000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3384344"}},"subtitle":["Berkeley Integrated System for Transportation Optimization"],"short-title":[],"issued":{"date-parts":[[2020,6,24]]},"references-count":57,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,8,31]]}},"alternative-id":["10.1145\/3384344"],"URL":"https:\/\/doi.org\/10.1145\/3384344","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,24]]},"assertion":[{"value":"2019-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-06-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}