Model-based real-time monitoring of large-scale urban traffic networks for decision making.

ilustraciones, diagramas, mapas

Autores:: Portilla Caicedo, Christian Roviro

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

id	UNACIONAL2_60f333c40eb33bd1a14bd1f581de4623
oai_identifier_str	oai:repositorio.unal.edu.co:unal/80257
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Model-based real-time monitoring of large-scale urban traffic networks for decision making.
dc.title.translated.spa.fl_str_mv	Monitoreo en tiempo real basado en modelos de redes de tráfico urbano a gran escala para la toma de decisiones.
title	Model-based real-time monitoring of large-scale urban traffic networks for decision making.
spellingShingle	Model-based real-time monitoring of large-scale urban traffic networks for decision making. 620 - Ingeniería y operaciones afines::624 - Ingeniería civil Tráfico urbano City traffic Urban traffic Urban traffic monitoring system Flow estimation Collaborative information Parameter cloning Parameter estimation Monitoreo de tráfico urbano Información colaborativa Clonación de parámetros Estimación de parámetros Estimación de flujo
title_short	Model-based real-time monitoring of large-scale urban traffic networks for decision making.
title_full	Model-based real-time monitoring of large-scale urban traffic networks for decision making.
title_fullStr	Model-based real-time monitoring of large-scale urban traffic networks for decision making.
title_full_unstemmed	Model-based real-time monitoring of large-scale urban traffic networks for decision making.
title_sort	Model-based real-time monitoring of large-scale urban traffic networks for decision making.
dc.creator.fl_str_mv	Portilla Caicedo, Christian Roviro
dc.contributor.advisor.none.fl_str_mv	Espinosa Oviedo, Jairo José Sarmiento Ordosgoitia, Ivan Reinaldo
dc.contributor.author.none.fl_str_mv	Portilla Caicedo, Christian Roviro
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Automática de la Universidad Nacional GAUNAL
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::624 - Ingeniería civil
topic	620 - Ingeniería y operaciones afines::624 - Ingeniería civil Tráfico urbano City traffic Urban traffic Urban traffic monitoring system Flow estimation Collaborative information Parameter cloning Parameter estimation Monitoreo de tráfico urbano Información colaborativa Clonación de parámetros Estimación de parámetros Estimación de flujo
dc.subject.lemb.spa.fl_str_mv	Tráfico urbano
dc.subject.lemb.eng.fl_str_mv	City traffic Urban traffic
dc.subject.proposal.eng.fl_str_mv	Urban traffic monitoring system Flow estimation Collaborative information Parameter cloning Parameter estimation
dc.subject.proposal.spa.fl_str_mv	Monitoreo de tráfico urbano Información colaborativa Clonación de parámetros Estimación de parámetros
dc.subject.proposal.zho.fl_str_mv	Estimación de flujo
description	ilustraciones, diagramas, mapas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-09-22T15:44:49Z
dc.date.available.none.fl_str_mv	2021-09-22T15:44:49Z
dc.date.issued.none.fl_str_mv	2021-09
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80257
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80257 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	[1]Kapileswar Nellore and Gerhard Hancke. A Survey on Urban Traffic Management System Using Wireless Sensor Networks. Sensors, 16(2):157, 2016. [2]António Nélson Rodrigues da Silva, Marcela da Silva Costa, and Márcia Helena Macedo. Multiple views of sustainable urban mobility: The case of brazil. Transport Policy, 15(6):350-360, 2008. [3]Z. Zhou, B. De Schutter, S. Lin, and Y. Xi. Two-level hierarchical model-based predictive control for large-scale urban traffic network. IEEE Transactions on Control Systems Technology, 25(2):496-508, March 2017. [4]J. W C Van Lint and Serge P. Hoogendoorn. A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways. Computer-Aided Civil and Infrastructure Engineering, 25(8):596-612, 2010. [5]Pan Zhang, Lanlan Rui, Xuesong Qiu, and Ruichang Shi. A New Fusion Structure Model for Real-time Urban Traffic State Estimation by multisource traffic data fusion. 2016. [6]Chris Bachmann, Baher Abdulhai, Matthew J. Roorda, and Behzad Moshiri. A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling. Transportation Research Part C: Emerging Technologies, 26:33-48, 2013. [7]J. Barceló, L. Montero, M. Bullejos, O. Serch, and C. Carmona. A kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent od matrices. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 17(2):123-141, 2013. [8]Michael G McNally. The four-step model. Emerald Group Publishing Limited, 2007. [9]Gitakrishnan Ramadurai and Satish V Ukkusuri. Dynamic traffic equilibrium: Theoretical and experimental network game results in single-bottleneck model. Transportation research record, 2029(1):1-13, 2007. [10]Balázs Kulcsár, István Varga, and József Bokor. Constrained split rate estimation by moving horizon. IFAC Proceedings Volumes (IFAC-PapersOnline), 16(c):78-83, 2005. [11]Michael G.H. Bell. The estimation of origin-destination matrices by constrained generalised least squares. Transportation Research Part B: Methodological, 25(1):13-22, 1991. [12]Hamideh Etemadnia and Khaled Abdelghany. A distributed origin-destination demand estimation approach for real-time traffic network management. Transportation Planning and Technology, 34(3):217-230, 2011. [13]H Inoue. Test of accuracy of od survey and its correction by screen line survey. Traffic Engineering, 12(6):11-19, 1977. [14]Hiroshi INOUYE. Statistical estimation of traffic demand using traffic census results. In Proceedings of the Japan Society of Civil Engineers, volume 1983, pages 85-94. Japan Society of Civil Engineers, 1983. [15] Ennio Cascetta. Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator. Transportation Research Part B: Methodological, 18(4-5):289-299, 1984. [16] Michael J Maher. Inferences on trip matrices from observations on link volumes: a bayesian statistical approach. Transportation Research Part B: Methodological, 17(6):435-447, 1983. [17] Pen´elope G´omez, Monica Men´endez, and Enrique M´erida-Casermeiro. Evaluation of trade-offs between two data sources for the accurate estimation of origin-destination matrices. Transportmetrica B: Transport Dynamics, 3(3):222-245, 2015. [18] Yasuo Asakura, Eiji Hato, and Masuo Kashiwadani. Origin-destination matrices estimation model using automatic vehicle identification data and its application to the han-shin expressway network. Transportation, 27(4):419-438, 2000. [19] Jing Liu, Fangfang Zheng, Henk J. van Zuylen, and Jie Li. A dynamic od prediction approach for urban networks based on automatic number plate recognition data. Transportation Research Procedia, 47:601 - 608, 2020. 22nd EURO Working Group on Transportation Meeting, EWGT 2019, 18th - 20th September 2019, Barcelona, Spain. [20] Md. Shahadat Iqbal, Charisma F. Choudhury, Pu Wang, and Marta C. Gonz´alez. Development of origin-destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies, 40:63 - 74, 2014. [21] J. White and I. Wells. Extracting origin destination information from mobile phone data. In Eleventh International Conference on Road Transport Information and Control, 2002. (Conf. Publ. No. 486), pages 30-34, 2002. [22] Francesco Calabrese, Giusy Di Lorenzo, Liang Liu, and Carlo Ratti. Estimating origindestination flows using opportunistically collected mobile phone location data from one million users in boston metropolitan area. 2011. [23] Jaume Barcel¨o, Lidin Montero, Laura Marqu´es, and Carlos Carmona. Travel time forecasting and dynamic origin-destination estimation for freeways based on bluetooth traffic monitoring. Transportation Research Record, 2175(1):19-27, 2010. [24] Jaime Barcel´o Bugeda, L´ıdia Montero Mercad´e, Manuel Bullejos, Oriol Serch, and Carlos Carmona Bautista. Dynamic od matrix estimation exploiting bluetooth data in urban networks. In Proceedings of the International Conference, pages 116-121, 2012. [25] Madhav V. Chitturi, John W. Shaw, IV John R. Campbell, and David A. Noyce. Validation of origin-destination data from bluetooth reidentification and aerial observation. Transportation Research Record, 2430(1):116-123, 2014. [26] Gabriel Etienne Michau, Alfredo Nantes, Edward Chung, Patrice Abry, and Pierre Borgnat. Retrieving dynamic origin-destination matrices from bluetooth data. In Transportation Research Board (TRB) 93rd Annual Meeting Compendium of Papers, pages 1-11. Transportation Research Board (TRB), 2014. [27] Gabriel Michau, Nelly Pustelnik, Pierre Borgnat, Patrice Abry, Ashish Bhaskar, and Edward Chung. Combining traffic counts and bluetooth data for link-origin-destination matrix estimation in large urban networks: The brisbane case study, 2019. [28] Samiul Hasan and Satish V Ukkusuri. Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C: Emerging Technologies, 44:363-381, 2014. [29] Karl F. Petty, Peter Bickel, Michael Ostland, John Rice, Frederic Schoenberg, Jiming Jiang, and Ya’acov Ritov. Accurate estimation of travel times from single-loop detectors. Transportation Research Part A: Policy and Practice, 32(1):1 - 17, 1998. [30] Hesham Rakha and Wang Zhang. Estimating traffic stream space mean speed and reliability from dual- and single-loop detectors. Transportation Research Record, 1925(1):38-47, 2005. [31] Chi Xie, Ruey Long Cheu, and Der-Horng Lee. Calibration-free arterial link speed estimation model using loop data. Journal of Transportation Engineering, 127(6):507-514, 2001. [32] Janusz Gajda, Ryszard Sroka, Marek Stencel, Andrzej Wajda, and Tadeusz Zeglen. A vehicle classification based on inductive loop detectors. In IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No. 01CH 37188), volume 1, pages 460-464. IEEE, 2001. [33] Benjamin Coifman. Improved velocity estimation using single loop detectors. Transportation Research Part A: Policy and Practice, 35(10):863 - 880, 2001. [34] Yinhai Wang and Nancy L. Nihan. Freeway traffic speed estimation with single-loop outputs. Transportation Research Record, 1727(1):120-126, 2000. [35] Yinhai Wang and Nancy L. Nihan. Can single-loop detectors do the work of dual-loop detectors? Journal of Transportation Engineering, 129(2):169-176, 2003. [36] Benjamin Coifman and SeoungBum Kim. Speed estimation and length based vehicle classification from freeway single-loop detectors. Transportation Research Part C: Emerging Technologies, 17(4):349 - 364, 2009. [37] Qing-Jie Kong Qing-Jie Kong, Zhipeng Li Zhipeng Li, Yikai Chen Yikai Chen, and Yuncai Liu Yuncai Liu. An Approach to Urban Traffic State Estimation by Fusing Multisource Information. IEEE Transactions on Intelligent Transportation Systems, 10(3):499-511, 2009. [38] Y Byon, Amer Shalaby, and Baher Abdulhai. Travel time collection and traffic monitoring via gps technologies. In 2006 IEEE Intelligent Transportation Systems Conference, pages 677-682. IEEE, 2006. [39] Timothy Hunter, Ryan Herring, Pieter Abbeel, and Alexandre Bayen. Path and travel time inference from gps probe vehicle data. NIPS Analyzing Networks and Learning with Graphs, 12(1):2, 2009. [40] Dawn Woodard, Galina Nogin, Paul Koch, David Racz, Moises Goldszmidt, and Eric Horvitz. Predicting travel time reliability using mobile phone gps data. Transportation Research Part C: Emerging Technologies, 75:30-44, 2017. [41] Xianyuan Zhan, Yu Zheng, Xiuwen Yi, and Satish V. Ukkusuri. Citywide traffic volume estimation using trajectory data. IEEE Transactions on Knowledge and Data Engineering, 29(2):272-285, 2017. [42] Maria Martchouk, Fred Mannering, and Darcy Bullock. Analysis of freeway travel time variability using bluetooth detection. 137(10):697-704, 2011. Journal of transportation engineering, [43] Bahar Namaki Araghi, Jonas Hammershøj Olesen, Rajesh Krishnan, Lars Tørholm Christensen, and Harry Lahrmann. Reliability of bluetooth technology for Bibliograf´ıa 153 travel time estimation. Journal of Intelligent Transportation Systems, 19(3):240-255, 2015. [44] Ali Haghani, Masoud Hamedi, Kaveh Farokhi Sadabadi, Stanley Young, and Philip Tarnoff. Data collection of freeway travel time ground truth with bluetooth sensors. Transportation Research Record, 2160(1):60-68, 2010. [45] Alexander M Hainen, Jason S Wasson, Sarah ML Hubbard, Stephen M Remias, Grant D Farnsworth, and Darcy M Bullock. Estimating route choice and travel time reliability with field observations of bluetooth probe vehicles. Transportation research record, 2256(1):43-50, 2011. [46] Jean-Luc Ygnace, Chris Drane, YB Yim, and Renaud De Lacvivier. Travel time estimation on the san francisco bay area network using cellular phones as probes. 2000. [47] Charith D Chitraranjan, Anne M Denton, and Amal S Perera. A complete observation model for tracking vehicles from mobile phone signal strengths and its potential in travel-time estimation. In 2016 IEEE 84th Vehicular Technology Conference (VTCFall), pages 1-7. IEEE, 2016. [48] Gary Bradski and Adrian Kaehler. Opencv. Dr. Dobb’s journal of software tools, 3, 2000. [49] Jorge Ernesto Espinosa Oviedo. Detection and tracking of motorcycles in urban environments by using video sequences with high level of oclussion. PhD thesis, 2019. [50] J. E. Espinosa, S. A. Velast´ın, and J. W. Branch. Detection of motorcycles in urban traffic using video analysis: A review. IEEE Transactions on Intelligent Transportation Systems, pages 1-16, 2020. [51] Xianyuan Zhan, Ruimin Li, and Satish V. Ukkusuri. Lane-based real-time queue length estimation using license plate recognition data. Transportation Research Part C: Emerging Technologies, 57:85-102, 2015. [52] Xianyuan Zhan, Ruimin Li, and Satish V. Ukkusuri. Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data. Transportation Research Part C: Emerging Technologies, 117:102660, 2020. [53] Jeffrey A Burke, Deborah Estrin, Mark Hansen, Andrew Parker, Nithya Ramanathan, Sasank Reddy, and Mani B Srivastava. Participatory sensing. 2006. [54] Waze. Waze traffic data: Specification document. Technical Report Version 2.8, 2017. 154 Bibliograf´ıa [55] Shoshana Vasserman, Michal Feldman, and Avinatan Hassidim. Implementing the wisdom of waze. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015. [56] Thiago H Silva, Pedro OS Vaz De Melo, Aline Carneiro Viana, Jussara M Almeida, Juliana Salles, and Antonio AF Loureiro. Traffic condition is more than colored lines on a map: characterization of waze alerts. In International Conference on Social Informatics, pages 309-318. Springer, 2013. [57] Mostafa Amin-Naseri, Pranamesh Chakraborty, Anuj Sharma, Stephen B. Gilbert, and Mingyi Hong. Evaluating the reliability, coverage, and added value of crowdsourced traffic incident reports from waze. Transportation Research Record, 2672(43):34-43, 2018. [58] Hugh F. Durrant-Whyte and Thomas C. Henderson. Multisensor Data Fusion. Springer Handbook of Robotics, pages 585-610, 2008. [59] H. B. Mitchell. Multi-Sensor Data Fusion. Berlin, Heidelberg, 2007. [60] Bahador Khaleghi, Alaa Khamis, Fakhreddine O Karray, and Saiedeh N Razavi. Multisensor data fusion : A review of the state-of-the-art. Information Fusion, 14(1):28-44, 2013. [61] Nour-Eddin El Faouzi, Henry Leung, and Ajeesh Kurian. Data fusion in intelligent transportation systems: Progress and challenges - A survey. 12(1):4-10, 2011. Information Fusion, [62] R. Boudjemaa and A.B. Forbes. Parameter estimation methods for data fusion. Technical report, National Physical Laboratory Report No. CMSC 38-04, 2004. [63] Hugh F Durrant-Whyte. Sensor models and multisensor integration. The international journal of robotics research, 7(6):97-113, 1988. [64] D. Bellot, A. Boyer, and F. Charpillet. A new definition of qualified gain in a data fusion process: application to telemedicine. In Proceedings of the Fifth International Conference on Information Fusion, volume 2, pages 865-872, July 2002. [65] N. A. Carlson. Federated square root filter for decentralized parallel processors. IEEE Transactions on Aerospace and Electronic Systems, 26(3):517-525, May 1990. [66] G. Shafer. A Mathematical Theory of Evidence. Princeton, NJ: Princeton Univ, 1976. [67] Keechoo Choi and YounShik Chung. A Data Fusion Algorithm for Estimating Link Travel Time. Journal of Intelligent Transportation Systems, 7(3-4):235-260, 2002. Bibliograf´ıa 155 [68] I. Olkin. Meta-analysis: Methodes for combining independent studies. Statistical Science, pages 226-236, 1992. [69] Ruey Long Cheu, Der-Horng Lee, and Chi Xie. An arterial speed estimation model fusing data from stationary and mobile sensors. In ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), pages 573-578, 2001. [70] Chee-Yee Chong and Shozo Mori. Convex Combination and Covariance Intersection Algorithms in Distributed Fusion. Proc. of the 4th International Conference on Information Fusion, 3300, 2001. [71] Yingjie Xia, Xiumei Li, and Zhenyu Shan. Parallelized fusion on multisensor transportation data: A case study in cyberits. International Journal of Intelligent Systems, 28(6):540-564, 2013. [72] Ennio Cascetta, Domenico Inaudi, and G´erald Marquis. Dynamic estimators of origindestination matrices using traffic counts. Transportation Science, 27(4):363-373, 1993. [73] Chris Bachmann, Matthew J. Roorda, Baher Abdulhai, and Behzad Moshiri. Fusing a bluetooth traffic monitoring system with loop detector data for improved freeway traffic speed estimation. Journal of Intelligent Transportation Systems, 17(2):152-164, 2013. [74] Qing-Jie Kong, Zhipeng Li, Yikai Chen, and Yuncai Liu. An approach to urban traffic state estimation by fusing multisource information. IEEE Transactions on Intelligent Transportation Systems, 10(3):499-511, 2009. [75] Chris Bachmann, Baher Abdulhai, Matthew J. Roorda, and Behzad Moshiri. A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling. Transportation Research Part C: Emerging Technologies, 26:33 - 48, 2013. [76] Asha Anand, Gitakrishnan Ramadurai, and Lelitha Vanajakshi. Data fusion-based traffic density estimation and prediction. Journal of Intelligent Transportation Systems, 18(4):367-378, 2014. [77] Asha Anand, L. Vanajakshi, and S. C. Subramanian. Traffic density estimation under heterogeneous traffic conditions using data fusion. In 2011 IEEE Intelligent Vehicles Symposium (IV), pages 31-36, 2011. [78] Dehuai Zeng, Jianmin Xu, and Gang Xu. Data fusion for traffic incident detection using ds evidence theory with probabilistic svms. Journal of computers, 3(10):36-43, 2008. 156 Bibliograf´ıa [79] Christian Bachmann. Multi-sensor data fusion for traffic speed and travel time estimation. PhD thesis, 2011. [80] Soknath Mil and Mongkut Piantanakulchai. Modified bayesian data fusion model for travel time estimation considering spurious data and traffic conditions. Applied Soft Computing, 72:65 - 78, 2018. [81] Taiwo Adetiloye and Anjali Awasthi. Multimodal big data fusion for traffic congestion prediction. In Multimodal Analytics for Next-Generation Big Data Technologies and Applications, pages 319-335. Springer, 2019. [82] T. Darwish and K. Abu Bakar. Traffic density estimation in vehicular ad hoc networks: A review. Ad Hoc Networks, 24(PA):337-351, 2015. [83] Arash Olia, Hossam Abdelgawad, Baher Abdulhai, and Saiedeh N. Razavi. Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 21(4):296-309, 2017. [84] Alessandra Pascale, Monica Nicoli, and Umberto Spagnolini. Cooperative bayesian estimation of vehicular traffic in large-scale networks. IEEE Transactions on Intelligent Transportation Systems, 15(5):2074-2088, 2014. [85] Asha Anand, Gitakrishnan Ramadurai, and Lelitha Vanajakshi. Data fusion-based traffic density estimation and prediction. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 18(4):367-378, 2014. [86] Arnav Thakur and Reza Malekian. Fog Computing for Detecting Vehicular Congestion, an Internet of Vehicles Based Approach: A Review. IEEE Intelligent Transportation Systems Magazine, 11(2):8-16, 2019. [87] Yangxin Lin, Ping Wang, and Meng Ma. Intelligent Transportation System(ITS): Concept, Challenge and Opportunity. In Proceedings - 3rd IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2017, 3rd IEEE International Conference on High Performance and Smart Computing, HPSC 2017 and 2nd IEEE International Conference on Intelligent Data and Securit, pages 167-172, 2017. [88] F. Wang. Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 11(3):630-638, 2010. [89] R. Morris and T. Cherrett. The use of scoot outputs at romanse in southampton. In Proceedings of the ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), pages 50-54, 2001. Bibliograf´ıa 157 [90] Christina Diakaki, Markos Papageorgiou, and Tom McLean. Integrated trafficresponsive urban corridor control strategy in glasgow, scotland: Application and evaluation. Transportation Research Record: Journal of the Transportation Research Board, (1727):101-111, 2000. [91] M. van den Berg, A. Hegyi, B. De Schutter, and J. Hellendoorn. A macroscopic traffic flow model for integrated control of freeway and urban traffic networks. In Proceedings of the 42nd IEEE Conference on Decision and Control, pages 2774-2779, Maui, Hawaii, 2003. [92] Christina Diakaki, Markos Papageorgiou, and Kostas Aboudolas. A multivariable regulator approach to traffic-responsive network-wide signal control. Control Engineering Practice, 10(2):183-195, 2002. [93] Marco Tiriolo, Ludovica Adacher, and Ernesto Cipriani. An urban traffic flow model to capture complex flow interactions among lane groups for signalized intersections. Procedia - Social and Behavioral Sciences, 111:839-848, 2014. [94] L. Adacher and M. Tiriolo. A new methodology to calibrate the congestion wave for the cell transmission model for urban traffic. In Proceedings of the 18th IEEE International Conference on Intelligent Transportation Systems, pages 606-611, 2015. [95] Rui Zhu, Man Sing Wong, Eric Guilbert, and Pak Wai Chan. Understanding heat ´ patterns produced by vehicular flows in urban areas. Scientific Reports, 7(1):1-14, 2017. [96] Warren Re´ategui-Romero, Od´on R. S´anchez-Ccoyllo, Mar´ıa de Fatima Andrade, and Aldo Moya-Alvarez. PM2.5 Estimation with the WRF/Chem Model, Produced by Vehicular Flow in the Lima Metropolitan Area. Open Journal of Air Pollution, 07(03):215-243, 2018. [97] Zihan Kan, Man Sing Wong, and Rui Zhu. Understanding space-time patterns of vehicular emission flows in urban areas using geospatial technique. Computers, Environment and Urban Systems, 79(August 2019):101399, 2020. [98] Luis Bravo-Moncayo, Miguel Ch´avez, Virginia Puyana, Jos´e Lucio-Naranjo, Christiam Garz´on, and Ignacio Pav´on-Garc´ıa. A cost-effective approach to the evaluation of traffic noise exposure in the city of quito, ecuador. Case Studies on Transport Policy, 7(1):128 - 137, 2019. [99] Arnaud Can, Ludovic Leclercq, and Jo¨el Lelong. Dynamic estimation of urban traf fic noise: Influence of traffic and noise source representations. 69(10):858 - 867, 2008. Applied Acoustics, 158 Bibliograf´ıa [100] S. Lin, B. De Schutter, Y. Xi, and H. Hellendoorn. Integrated urban traffic control for the reduction of travel delays and emissions. IEEE Transactions on Intelligent Transportation Systems, 14(4):1609-1619, 2013. [101] A. Jamshidnejad, S. Lin, Y. Xi, and B. De Schutter. Erratum to \Integrated urban traffic control for the reduction of travel delays and emissions"[IEEE Transactions on ITS, 14 (2013), 1609-1619]. IEEE Transactions on Intelligent Transportation Systems, 2018. [102] SIMENS. Stratos, 2021. https://assets.new.siemens.com/siemens/assets/api/uuid:cbdb23c9- 07f6-4a2c-930d-b2d499a4aae2/stratos-brochure.pdf. [103] AIMSUN. Aimsun.live, 2021. https://www.aimsun.com/aimsun-live/. [104] HIKOB. Counting and traffic monitoring, 2021. https://www.hikob.com/en/application/traffic-monit. [105] SWARCO. Urban traffic management solutions, 2021. https://www.swarco.com/solutions/traffic-management/urban-traffic-management. [106] DATAFROMSKY. Flow, 2021. https://datafromsky.com/flow/. [107] URBIOTICA. Inter-urban traffic monitoring, 2021. https://www.urbiotica.com. [108] ISKRA. Urban traffic automation, 2021. https://www.iskra.eu/en/Urban-trafficautomation/Urban-Traffic-Automation-Solutions/. [109] D. I. Robertson and R. D. Bretherton. Optimizing networks of traffic signals in real time-the scoot method. IEEE Transactions on Vehicular Technology, 40(1):11-15, 1991. [110] A. G. Sims and K. W. Dobinson. The sydney coordinated adaptive traffic (scat) system philosophy and benefits. IEEE Transactions on Vehicular Technology, 29(2):130-137, 1980. [111] Dennis I Robertson. ’tansyt’method for area traffic control. Traffic Engineering & Control, 8(8), 1969. [112] Hajime Sakakibara, Tsutomu Usami, Seiji Itakura, and Teruyuki Tajima. Moderato (management by origin-destination related adaptation for traffic optimization). In Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No. 99TH8383), pages 38-43. IEEE, 1999. [113] Lily Ageliki Elefteriadou. The highway capacity manual 6th edition: A guide for multimodal mobility analysis. ITE Journal, 86(4), 2016. Bibliograf´ıa 159 [114] B. S. Kerner. Introduction to modern traffic flow theory and control: the long road to three-phase traffic theory. Springer, Heidelberg, Germany, 2009. [115] T. Pinochet. An´alisis de modelos de capacidad y demora en intersecciones prioritarias., 2012. [116] Daniel Krajzewicz, Jakob Erdmann, Michael Behrisch, and Laura Bieker. Recent Development and Applications of SUMO - Simulation of Urban MObility. International Journal On Advances in Systems and Measurements, 5(3&4):128-138, December 2012. [117] S. Lin, B. De Schutter, Y. Xi, and H. Hellendoorn. Efficient network-wide modelbased predictive control for urban traffic networks. Transportation Research Part C, 24:122-140, 2012. [118] A. Jamshidnejad, I. Papamichail, M. Papageorgiou, and B. De Schutter. Sustainable model-predictive control in urban traffic networks: Efficient solution based on general smoothening methods. IEEE Transactions on Control Systems Technology, 26(3):813- 827, May 2018. [119] Christian Portilla, David S´anchez, Andres Acosta, Juan Nore~na, and Jairo Espinosa. FLEXI, 2019. Register 13-76-334. [120] J G WARDROP and J I WHITEHEAD. Correspondence. some theoretical aspects of road traffic research. Proceedings of the Institution of Civil Engineers, 1(5):767-768, 1952. [121] TRB. Highway Capacity Manual. Transportation Research Board, Washington, D.C., 1950. [122] Andr´es Acosta, Jorge E. Espinosa, and Jairo Espinosa. Developing tools for building simulation scenarios for SUMO based on the SCRUM methodology. In Proceedings of the 3rd SUMO User Conference SUMO 2015, pages 23-35, Berlin-Adlershof, May 2015. Deutsches Zentrum f¨ur Luft - und Raumfahrt e.V. [123] Christian Portilla, Andres Acosta, and Jairo Espinosa. TrafficSensors, 2016. Register 13-53-185. [124] Krishna Saw, BK Katti, and G Joshi. Literature review of traffic assignment: static and dynamic. International Journal of Transportation Engineering, 2(4):339-347, 2015. [125] Zheng Li. User Equilibrium Solution, August 2019. Github repository https://github.com/ZhengLi95/User-Equilibrium-Solution. 160 Bibliograf´ıa [126] Bureau of Public Roads. Traffic assignment manual for application with a large, high speed computer, volume 2. US Department of Commerce, Bureau of Public Roads, Office of Planning, Urban Planning Division., 1964. [127] TPD Ingenier´ıa S.A. Estudio de pre inversi´on de movilidad para determinar la viabilidad de peatonalizaci´on total o parcial de algunos tramos viales del centro de la ciudad de Medell´ın bajo el concepto de tr´afico lento y de supermanzanas. Technical report, 2014. [128] Martin J Beckmann, Charles B McGuire, and Christopher B Winsten. Studies in the economics of transportation. 1955. [129] Semaria Ruiz, Jairo Espinosa, Ivan Sarmiento, Daniel Krajzewicz, Mirko Goletz, and Alain Schengen. Construction of a traffic test scenario in the Aburr´a Valley. Transportation Planning and Technology, 2020. [130] Felipe Valencia, Jos´e D L´opez, Alfredo Nu~nez, Christian Portilla, Luis G Cortes, Jairo Espinosa, and Bart De Schutter. Congestion management in motorways and urban networks through a bargaining-game-based coordination mechanism. In Game Theoretic Analysis of Congestion, Safety and Security, pages 1-40. Springer, 2015. [131] Christian Portilla, Felipe Valencia, Jairo Espinosa, Alfredo Nu~nez, and Bart De Schutter. Model-based predictive control for bicycling in urban intersections. Transportation research part C: emerging technologies, 70:27-41, 2016. [132] C Portilla, J Espinosa, and B De Schutter. A multi-class urban traffic model considering heterogeneous vehicle composition: An extension of the s model. Transportation Research Part C: Emerging Technologies, 115:102613, 2020. [133] Christian Portilla, Alejandro Marquez, and Jairo Espinosa. A mathematical model of pedestrians in signalized intersections. In Proceedings of 2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC), pages 1-6, Manizales, Colombia, 2015. [134] Christian Portilla, Andr´es Acosta, Alejandro Marquez, and Jairo Espinosa. A comparison between macroscopic and microscopic urban traffic simulation including motorcycle dynamics. In Proceedings of XVII Congreso Latinoamericano de Control Autom´atico (CLCA 2016), page 6, Medell´ın, Colombia, 2016. [135] Laura Nore~na, Ana Sarrazola, Alejandro Marquez, Esteban Jimenez, Christian Portilla, Oscar Jaramillo, and Jairo Espinosa. Model predictive control of an urban traffic intersection based on the modify multiclass queuing networks models. In Proceedings of the 9th Triennial Symposium on Transportation Analysis (TRISTAN IX), page 4, Oranjestad, Aruba, 2016. Bibliograf´ıa 161 [136] Christian Portilla, Andr´es Acosta, Jorge Espinosa, and Jairo Espinosa. Scitraffic: A macroscopic simulator for estimation and control of urban traffic. In Proceedings of the SUMO User Conference 2017, pages 80-88, Berlin, Adlershof, 2017. [137] Christian Portilla, Andr´es Acosta, and Jairo Espinosa. Origin-destination matrix estimation based on microsimulation and optimization. In Proceedings of MOVICIMOYCOT 2018: Joint Conference for Urban Mobility in the Smart City, Medell´ınColombia, 2018. [138] Semaria Ruiz, Nicolas Arroyo, Andres Acosta, Christian Portilla, and Jairo Espinosa. An optimal battery charging and schedule control strategy for electric bus rapid transit. In Proceedings of MOVICI-MOYCOT 2018: Joint Conference for Urban Mobility in the Smart City, Medell´ın-Colombia, 2018. [139] Andr´es Acosta, Christian Portilla, and Jairo Espinosa. Virtual sensors for monitoring emissions in urban traffic networks, taking advantage of available traffic measurements. In Proceedings of Congreso Panamericano de Ingenier´ıa de Tr´ansito, Transporte y Log´ıstica 2018, page 6, Medell´ın, Colombia, 2018. [140] Christian Portilla, Andr´es Acosta, Ricardo Ram´ırez, Jose Jim´enez, Iv´an Sarmiento, and Jairo Espinosa. An´alisis del impacto medioambiental a trav´es de microsimulaci´on en intervenciones urban´ısticas y de movilidad: un caso de estudio sobre un corredor urbano de la ciudad de medell´ın. In in Proceedings of Congreso Colombiano de Transporte y Tr´ansito CCTT 2019, Cartagena, Colombia, 2019. [141] Andres Acosta, Juan Nore~na, Christian Portilla, Anna Sarrazola, Laura Nore~na, Eliana Mej´ıa, and Jairo Espinosa. MOSTRO, 2019. Register 13-76-360. [142] Christian Portilla, David S´anchez, Esteban Lage, Andr´es Acosta, and Jairo Espinosa. Real-time urban traffic monitoring system using flexi (flow estimation based on collaborative information): A case study in Medellín-Colombia. Transportationresearch part C: emerging technologies, 2021. Submitted. [143] Juan Nore~na, Andr´es Osorio, Andr´es Acosta, Christian Portilla, and Jairo Espinosa. A methodology for traffic lights planning in urban intersections using a nonlinear formulation of the multi-objective system for traffic optimization (mostro). Transportationresearch part C: emerging technologies, 2021. Submitted. [144] Eliana Meja Estrada, Laura Norena, Anna Sarrazola, and Jairo Espinosa. MOSTRO: A multi-objective system for traffic optimization. In 2017
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	xix, 161 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.coverage.country.none.fl_str_mv	Colombia
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Doctorado en Ingeniería - Ingeniería Civil
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería Civil
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/80257/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/80257/2/1037583637.2021.pdf https://repositorio.unal.edu.co/bitstream/unal/80257/3/1037583637.2021.pdf.jpg
bitstream.checksum.fl_str_mv	cccfe52f796b7c63423298c2d3365fc6 1dd68fa1a7ade8f03a222eec6a5c05cc 6003dabfa931ed7a7f0014aa4909efae
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089738714349568
spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Espinosa Oviedo, Jairo Joséad115e960b2989299b2018dae59e6ec2Sarmiento Ordosgoitia, Ivan Reinaldo633245eae36e8ec2abae5d37031ca30a600Portilla Caicedo, Christian Roviro1ee47d46e6c41c82601ca3edf38ea1f7600Grupo de Automática de la Universidad Nacional GAUNAL2021-09-22T15:44:49Z2021-09-22T15:44:49Z2021-09https://repositorio.unal.edu.co/handle/unal/80257Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapasThis thesis proposes and implements a real-time urban traffic monitoring system for decision-making as an alternative to classical solutions based on on-road sensors, which often implies a large installation, operation, and maintenance cost. The main purpose of this system is to improve the mobility and environmental conditions of cities. This solution takes advantage of data generated by Waze (Free Community-based GPS, Maps, and Traffic Navigation App) through smartphones carried by users traveling in the cities. FLEXI (Flow Estimation based on Collaborative Information) was developed as part of the monitoring system and is based on a mathematical model that transforms the mean speed and delay given by Waze into more usable traffic variables such as demand and queues. Moreover, the monitoring system fuses online, offline, and collaborative information to improve estimation accuracy. The monitoring system was implemented in real-time in Medell´ın - Colombia, including 38 signalized intersections. To this end, FLEXI was calibrated using data collected in the field, achieving a mean relative error of 15 % and a mean absolute error less than 1 veh/min/lane for the estimation of the flow and queue of vehicles.En esta tesis se propone y se implementa un sistema de monitoreo de tráfico urbano en tiempo real para la toma de decisiones oportunas, que impacten positivamente en la movilidad y en la calidad del aire de la ciudad. Este sistema surge como una alternativa a las soluciones basadas en sensores instalados en las vías, pues aprovecha los datos generados por Waze, aplicación móvil usada para la navegación dentro de las ciudades, a través de los teléfonos inteligentes de las personas que usan la infraestructura vial. Como parte del sistema de monitoreo de tráfico fue desarrollado FLEXI (por sus siglas en inglés de estimación de flujo basado en datos colaborativos), el cual transforma la velocidad media y demora reportadas por Waze, en flujos y colas de vehículos a través de un modelo matemático. Además, el sistema de monitoreo de tráfico fusiona datos en línea, fuera de línea y colaborativos con el fin de mejorar la precisión de las estimaciones. El sistema de monitoreo, fue implementado en tiempo real para una zona importante de la ciudad de Medellín - Colombia, la cual incluye 38 intersecciones semaforizadas. Dicha implementación requirió la toma de datos en campo para la validación del sistema de monitoreo, con el cual se obtuvo un error medio relativo de 15 % y un error medio absoluto de menos de 1 veh/min/carril para la estimación del flujo vehicular y del tamaño de la cola de vehículos. (Texto tomado de la fuente)DoctoradoDoctor en IngenieríaDocMatemáticas aplicadasxix, 161 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - Ingeniería CivilDepartamento de Ingeniería CivilFacultad de MinasMedellínUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::624 - Ingeniería civilTráfico urbanoCity trafficUrban trafficUrban traffic monitoring systemFlow estimationCollaborative informationParameter cloningParameter estimationMonitoreo de tráfico urbanoInformación colaborativaClonación de parámetrosEstimación de parámetrosEstimación de flujoModel-based real-time monitoring of large-scale urban traffic networks for decision making.Monitoreo en tiempo real basado en modelos de redes de tráfico urbano a gran escala para la toma de decisiones.Trabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDColombia[1]Kapileswar Nellore and Gerhard Hancke. A Survey on Urban Traffic Management System Using Wireless Sensor Networks. Sensors, 16(2):157, 2016. [2]António Nélson Rodrigues da Silva, Marcela da Silva Costa, and Márcia Helena Macedo. Multiple views of sustainable urban mobility: The case of brazil. Transport Policy, 15(6):350-360, 2008. [3]Z. Zhou, B. De Schutter, S. Lin, and Y. Xi. Two-level hierarchical model-based predictive control for large-scale urban traffic network. IEEE Transactions on Control Systems Technology, 25(2):496-508, March 2017. [4]J. W C Van Lint and Serge P. Hoogendoorn. A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways. Computer-Aided Civil and Infrastructure Engineering, 25(8):596-612, 2010. [5]Pan Zhang, Lanlan Rui, Xuesong Qiu, and Ruichang Shi. A New Fusion Structure Model for Real-time Urban Traffic State Estimation by multisource traffic data fusion. 2016. [6]Chris Bachmann, Baher Abdulhai, Matthew J. Roorda, and Behzad Moshiri. A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling. Transportation Research Part C: Emerging Technologies, 26:33-48, 2013. [7]J. Barceló, L. Montero, M. Bullejos, O. Serch, and C. Carmona. A kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent od matrices. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 17(2):123-141, 2013. [8]Michael G McNally. The four-step model. Emerald Group Publishing Limited, 2007. [9]Gitakrishnan Ramadurai and Satish V Ukkusuri. Dynamic traffic equilibrium: Theoretical and experimental network game results in single-bottleneck model. Transportation research record, 2029(1):1-13, 2007. [10]Balázs Kulcsár, István Varga, and József Bokor. Constrained split rate estimation by moving horizon. IFAC Proceedings Volumes (IFAC-PapersOnline), 16(c):78-83, 2005. [11]Michael G.H. Bell. The estimation of origin-destination matrices by constrained generalised least squares. Transportation Research Part B: Methodological, 25(1):13-22, 1991. [12]Hamideh Etemadnia and Khaled Abdelghany. A distributed origin-destination demand estimation approach for real-time traffic network management. Transportation Planning and Technology, 34(3):217-230, 2011. [13]H Inoue. Test of accuracy of od survey and its correction by screen line survey. Traffic Engineering, 12(6):11-19, 1977. [14]Hiroshi INOUYE. Statistical estimation of traffic demand using traffic census results. In Proceedings of the Japan Society of Civil Engineers, volume 1983, pages 85-94. Japan Society of Civil Engineers, 1983. [15] Ennio Cascetta. Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator. Transportation Research Part B: Methodological, 18(4-5):289-299, 1984. [16] Michael J Maher. Inferences on trip matrices from observations on link volumes: a bayesian statistical approach. Transportation Research Part B: Methodological, 17(6):435-447, 1983. [17] Pen´elope G´omez, Monica Men´endez, and Enrique M´erida-Casermeiro. Evaluation of trade-offs between two data sources for the accurate estimation of origin-destination matrices. Transportmetrica B: Transport Dynamics, 3(3):222-245, 2015. [18] Yasuo Asakura, Eiji Hato, and Masuo Kashiwadani. Origin-destination matrices estimation model using automatic vehicle identification data and its application to the han-shin expressway network. Transportation, 27(4):419-438, 2000. [19] Jing Liu, Fangfang Zheng, Henk J. van Zuylen, and Jie Li. A dynamic od prediction approach for urban networks based on automatic number plate recognition data. Transportation Research Procedia, 47:601 - 608, 2020. 22nd EURO Working Group on Transportation Meeting, EWGT 2019, 18th - 20th September 2019, Barcelona, Spain. [20] Md. Shahadat Iqbal, Charisma F. Choudhury, Pu Wang, and Marta C. Gonz´alez. Development of origin-destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies, 40:63 - 74, 2014. [21] J. White and I. Wells. Extracting origin destination information from mobile phone data. In Eleventh International Conference on Road Transport Information and Control, 2002. (Conf. Publ. No. 486), pages 30-34, 2002. [22] Francesco Calabrese, Giusy Di Lorenzo, Liang Liu, and Carlo Ratti. Estimating origindestination flows using opportunistically collected mobile phone location data from one million users in boston metropolitan area. 2011. [23] Jaume Barcel¨o, Lidin Montero, Laura Marqu´es, and Carlos Carmona. Travel time forecasting and dynamic origin-destination estimation for freeways based on bluetooth traffic monitoring. Transportation Research Record, 2175(1):19-27, 2010. [24] Jaime Barcel´o Bugeda, L´ıdia Montero Mercad´e, Manuel Bullejos, Oriol Serch, and Carlos Carmona Bautista. Dynamic od matrix estimation exploiting bluetooth data in urban networks. In Proceedings of the International Conference, pages 116-121, 2012. [25] Madhav V. Chitturi, John W. Shaw, IV John R. Campbell, and David A. Noyce. Validation of origin-destination data from bluetooth reidentification and aerial observation. Transportation Research Record, 2430(1):116-123, 2014. [26] Gabriel Etienne Michau, Alfredo Nantes, Edward Chung, Patrice Abry, and Pierre Borgnat. Retrieving dynamic origin-destination matrices from bluetooth data. In Transportation Research Board (TRB) 93rd Annual Meeting Compendium of Papers, pages 1-11. Transportation Research Board (TRB), 2014. [27] Gabriel Michau, Nelly Pustelnik, Pierre Borgnat, Patrice Abry, Ashish Bhaskar, and Edward Chung. Combining traffic counts and bluetooth data for link-origin-destination matrix estimation in large urban networks: The brisbane case study, 2019. [28] Samiul Hasan and Satish V Ukkusuri. Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C: Emerging Technologies, 44:363-381, 2014. [29] Karl F. Petty, Peter Bickel, Michael Ostland, John Rice, Frederic Schoenberg, Jiming Jiang, and Ya’acov Ritov. Accurate estimation of travel times from single-loop detectors. Transportation Research Part A: Policy and Practice, 32(1):1 - 17, 1998. [30] Hesham Rakha and Wang Zhang. Estimating traffic stream space mean speed and reliability from dual- and single-loop detectors. Transportation Research Record, 1925(1):38-47, 2005. [31] Chi Xie, Ruey Long Cheu, and Der-Horng Lee. Calibration-free arterial link speed estimation model using loop data. Journal of Transportation Engineering, 127(6):507-514, 2001. [32] Janusz Gajda, Ryszard Sroka, Marek Stencel, Andrzej Wajda, and Tadeusz Zeglen. A vehicle classification based on inductive loop detectors. In IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No. 01CH 37188), volume 1, pages 460-464. IEEE, 2001. [33] Benjamin Coifman. Improved velocity estimation using single loop detectors. Transportation Research Part A: Policy and Practice, 35(10):863 - 880, 2001. [34] Yinhai Wang and Nancy L. Nihan. Freeway traffic speed estimation with single-loop outputs. Transportation Research Record, 1727(1):120-126, 2000. [35] Yinhai Wang and Nancy L. Nihan. Can single-loop detectors do the work of dual-loop detectors? Journal of Transportation Engineering, 129(2):169-176, 2003. [36] Benjamin Coifman and SeoungBum Kim. Speed estimation and length based vehicle classification from freeway single-loop detectors. Transportation Research Part C: Emerging Technologies, 17(4):349 - 364, 2009. [37] Qing-Jie Kong Qing-Jie Kong, Zhipeng Li Zhipeng Li, Yikai Chen Yikai Chen, and Yuncai Liu Yuncai Liu. An Approach to Urban Traffic State Estimation by Fusing Multisource Information. IEEE Transactions on Intelligent Transportation Systems, 10(3):499-511, 2009. [38] Y Byon, Amer Shalaby, and Baher Abdulhai. Travel time collection and traffic monitoring via gps technologies. In 2006 IEEE Intelligent Transportation Systems Conference, pages 677-682. IEEE, 2006. [39] Timothy Hunter, Ryan Herring, Pieter Abbeel, and Alexandre Bayen. Path and travel time inference from gps probe vehicle data. NIPS Analyzing Networks and Learning with Graphs, 12(1):2, 2009. [40] Dawn Woodard, Galina Nogin, Paul Koch, David Racz, Moises Goldszmidt, and Eric Horvitz. Predicting travel time reliability using mobile phone gps data. Transportation Research Part C: Emerging Technologies, 75:30-44, 2017. [41] Xianyuan Zhan, Yu Zheng, Xiuwen Yi, and Satish V. Ukkusuri. Citywide traffic volume estimation using trajectory data. IEEE Transactions on Knowledge and Data Engineering, 29(2):272-285, 2017. [42] Maria Martchouk, Fred Mannering, and Darcy Bullock. Analysis of freeway travel time variability using bluetooth detection. 137(10):697-704, 2011. Journal of transportation engineering, [43] Bahar Namaki Araghi, Jonas Hammershøj Olesen, Rajesh Krishnan, Lars Tørholm Christensen, and Harry Lahrmann. Reliability of bluetooth technology for Bibliograf´ıa 153 travel time estimation. Journal of Intelligent Transportation Systems, 19(3):240-255, 2015. [44] Ali Haghani, Masoud Hamedi, Kaveh Farokhi Sadabadi, Stanley Young, and Philip Tarnoff. Data collection of freeway travel time ground truth with bluetooth sensors. Transportation Research Record, 2160(1):60-68, 2010. [45] Alexander M Hainen, Jason S Wasson, Sarah ML Hubbard, Stephen M Remias, Grant D Farnsworth, and Darcy M Bullock. Estimating route choice and travel time reliability with field observations of bluetooth probe vehicles. Transportation research record, 2256(1):43-50, 2011. [46] Jean-Luc Ygnace, Chris Drane, YB Yim, and Renaud De Lacvivier. Travel time estimation on the san francisco bay area network using cellular phones as probes. 2000. [47] Charith D Chitraranjan, Anne M Denton, and Amal S Perera. A complete observation model for tracking vehicles from mobile phone signal strengths and its potential in travel-time estimation. In 2016 IEEE 84th Vehicular Technology Conference (VTCFall), pages 1-7. IEEE, 2016. [48] Gary Bradski and Adrian Kaehler. Opencv. Dr. Dobb’s journal of software tools, 3, 2000. [49] Jorge Ernesto Espinosa Oviedo. Detection and tracking of motorcycles in urban environments by using video sequences with high level of oclussion. PhD thesis, 2019. [50] J. E. Espinosa, S. A. Velast´ın, and J. W. Branch. Detection of motorcycles in urban traffic using video analysis: A review. IEEE Transactions on Intelligent Transportation Systems, pages 1-16, 2020. [51] Xianyuan Zhan, Ruimin Li, and Satish V. Ukkusuri. Lane-based real-time queue length estimation using license plate recognition data. Transportation Research Part C: Emerging Technologies, 57:85-102, 2015. [52] Xianyuan Zhan, Ruimin Li, and Satish V. Ukkusuri. Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data. Transportation Research Part C: Emerging Technologies, 117:102660, 2020. [53] Jeffrey A Burke, Deborah Estrin, Mark Hansen, Andrew Parker, Nithya Ramanathan, Sasank Reddy, and Mani B Srivastava. Participatory sensing. 2006. [54] Waze. Waze traffic data: Specification document. Technical Report Version 2.8, 2017. 154 Bibliograf´ıa [55] Shoshana Vasserman, Michal Feldman, and Avinatan Hassidim. Implementing the wisdom of waze. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015. [56] Thiago H Silva, Pedro OS Vaz De Melo, Aline Carneiro Viana, Jussara M Almeida, Juliana Salles, and Antonio AF Loureiro. Traffic condition is more than colored lines on a map: characterization of waze alerts. In International Conference on Social Informatics, pages 309-318. Springer, 2013. [57] Mostafa Amin-Naseri, Pranamesh Chakraborty, Anuj Sharma, Stephen B. Gilbert, and Mingyi Hong. Evaluating the reliability, coverage, and added value of crowdsourced traffic incident reports from waze. Transportation Research Record, 2672(43):34-43, 2018. [58] Hugh F. Durrant-Whyte and Thomas C. Henderson. Multisensor Data Fusion. Springer Handbook of Robotics, pages 585-610, 2008. [59] H. B. Mitchell. Multi-Sensor Data Fusion. Berlin, Heidelberg, 2007. [60] Bahador Khaleghi, Alaa Khamis, Fakhreddine O Karray, and Saiedeh N Razavi. Multisensor data fusion : A review of the state-of-the-art. Information Fusion, 14(1):28-44, 2013. [61] Nour-Eddin El Faouzi, Henry Leung, and Ajeesh Kurian. Data fusion in intelligent transportation systems: Progress and challenges - A survey. 12(1):4-10, 2011. Information Fusion, [62] R. Boudjemaa and A.B. Forbes. Parameter estimation methods for data fusion. Technical report, National Physical Laboratory Report No. CMSC 38-04, 2004. [63] Hugh F Durrant-Whyte. Sensor models and multisensor integration. The international journal of robotics research, 7(6):97-113, 1988. [64] D. Bellot, A. Boyer, and F. Charpillet. A new definition of qualified gain in a data fusion process: application to telemedicine. In Proceedings of the Fifth International Conference on Information Fusion, volume 2, pages 865-872, July 2002. [65] N. A. Carlson. Federated square root filter for decentralized parallel processors. IEEE Transactions on Aerospace and Electronic Systems, 26(3):517-525, May 1990. [66] G. Shafer. A Mathematical Theory of Evidence. Princeton, NJ: Princeton Univ, 1976. [67] Keechoo Choi and YounShik Chung. A Data Fusion Algorithm for Estimating Link Travel Time. Journal of Intelligent Transportation Systems, 7(3-4):235-260, 2002. Bibliograf´ıa 155 [68] I. Olkin. Meta-analysis: Methodes for combining independent studies. Statistical Science, pages 226-236, 1992. [69] Ruey Long Cheu, Der-Horng Lee, and Chi Xie. An arterial speed estimation model fusing data from stationary and mobile sensors. In ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), pages 573-578, 2001. [70] Chee-Yee Chong and Shozo Mori. Convex Combination and Covariance Intersection Algorithms in Distributed Fusion. Proc. of the 4th International Conference on Information Fusion, 3300, 2001. [71] Yingjie Xia, Xiumei Li, and Zhenyu Shan. Parallelized fusion on multisensor transportation data: A case study in cyberits. International Journal of Intelligent Systems, 28(6):540-564, 2013. [72] Ennio Cascetta, Domenico Inaudi, and G´erald Marquis. Dynamic estimators of origindestination matrices using traffic counts. Transportation Science, 27(4):363-373, 1993. [73] Chris Bachmann, Matthew J. Roorda, Baher Abdulhai, and Behzad Moshiri. Fusing a bluetooth traffic monitoring system with loop detector data for improved freeway traffic speed estimation. Journal of Intelligent Transportation Systems, 17(2):152-164, 2013. [74] Qing-Jie Kong, Zhipeng Li, Yikai Chen, and Yuncai Liu. An approach to urban traffic state estimation by fusing multisource information. IEEE Transactions on Intelligent Transportation Systems, 10(3):499-511, 2009. [75] Chris Bachmann, Baher Abdulhai, Matthew J. Roorda, and Behzad Moshiri. A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling. Transportation Research Part C: Emerging Technologies, 26:33 - 48, 2013. [76] Asha Anand, Gitakrishnan Ramadurai, and Lelitha Vanajakshi. Data fusion-based traffic density estimation and prediction. Journal of Intelligent Transportation Systems, 18(4):367-378, 2014. [77] Asha Anand, L. Vanajakshi, and S. C. Subramanian. Traffic density estimation under heterogeneous traffic conditions using data fusion. In 2011 IEEE Intelligent Vehicles Symposium (IV), pages 31-36, 2011. [78] Dehuai Zeng, Jianmin Xu, and Gang Xu. Data fusion for traffic incident detection using ds evidence theory with probabilistic svms. Journal of computers, 3(10):36-43, 2008. 156 Bibliograf´ıa [79] Christian Bachmann. Multi-sensor data fusion for traffic speed and travel time estimation. PhD thesis, 2011. [80] Soknath Mil and Mongkut Piantanakulchai. Modified bayesian data fusion model for travel time estimation considering spurious data and traffic conditions. Applied Soft Computing, 72:65 - 78, 2018. [81] Taiwo Adetiloye and Anjali Awasthi. Multimodal big data fusion for traffic congestion prediction. In Multimodal Analytics for Next-Generation Big Data Technologies and Applications, pages 319-335. Springer, 2019. [82] T. Darwish and K. Abu Bakar. Traffic density estimation in vehicular ad hoc networks: A review. Ad Hoc Networks, 24(PA):337-351, 2015. [83] Arash Olia, Hossam Abdelgawad, Baher Abdulhai, and Saiedeh N. Razavi. Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 21(4):296-309, 2017. [84] Alessandra Pascale, Monica Nicoli, and Umberto Spagnolini. Cooperative bayesian estimation of vehicular traffic in large-scale networks. IEEE Transactions on Intelligent Transportation Systems, 15(5):2074-2088, 2014. [85] Asha Anand, Gitakrishnan Ramadurai, and Lelitha Vanajakshi. Data fusion-based traffic density estimation and prediction. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 18(4):367-378, 2014. [86] Arnav Thakur and Reza Malekian. Fog Computing for Detecting Vehicular Congestion, an Internet of Vehicles Based Approach: A Review. IEEE Intelligent Transportation Systems Magazine, 11(2):8-16, 2019. [87] Yangxin Lin, Ping Wang, and Meng Ma. Intelligent Transportation System(ITS): Concept, Challenge and Opportunity. In Proceedings - 3rd IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2017, 3rd IEEE International Conference on High Performance and Smart Computing, HPSC 2017 and 2nd IEEE International Conference on Intelligent Data and Securit, pages 167-172, 2017. [88] F. Wang. Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 11(3):630-638, 2010. [89] R. Morris and T. Cherrett. The use of scoot outputs at romanse in southampton. In Proceedings of the ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), pages 50-54, 2001. Bibliograf´ıa 157 [90] Christina Diakaki, Markos Papageorgiou, and Tom McLean. Integrated trafficresponsive urban corridor control strategy in glasgow, scotland: Application and evaluation. Transportation Research Record: Journal of the Transportation Research Board, (1727):101-111, 2000. [91] M. van den Berg, A. Hegyi, B. De Schutter, and J. Hellendoorn. A macroscopic traffic flow model for integrated control of freeway and urban traffic networks. In Proceedings of the 42nd IEEE Conference on Decision and Control, pages 2774-2779, Maui, Hawaii, 2003. [92] Christina Diakaki, Markos Papageorgiou, and Kostas Aboudolas. A multivariable regulator approach to traffic-responsive network-wide signal control. Control Engineering Practice, 10(2):183-195, 2002. [93] Marco Tiriolo, Ludovica Adacher, and Ernesto Cipriani. An urban traffic flow model to capture complex flow interactions among lane groups for signalized intersections. Procedia - Social and Behavioral Sciences, 111:839-848, 2014. [94] L. Adacher and M. Tiriolo. A new methodology to calibrate the congestion wave for the cell transmission model for urban traffic. In Proceedings of the 18th IEEE International Conference on Intelligent Transportation Systems, pages 606-611, 2015. [95] Rui Zhu, Man Sing Wong, Eric Guilbert, and Pak Wai Chan. Understanding heat ´ patterns produced by vehicular flows in urban areas. Scientific Reports, 7(1):1-14, 2017. [96] Warren Re´ategui-Romero, Od´on R. S´anchez-Ccoyllo, Mar´ıa de Fatima Andrade, and Aldo Moya-Alvarez. PM2.5 Estimation with the WRF/Chem Model, Produced by Vehicular Flow in the Lima Metropolitan Area. Open Journal of Air Pollution, 07(03):215-243, 2018. [97] Zihan Kan, Man Sing Wong, and Rui Zhu. Understanding space-time patterns of vehicular emission flows in urban areas using geospatial technique. Computers, Environment and Urban Systems, 79(August 2019):101399, 2020. [98] Luis Bravo-Moncayo, Miguel Ch´avez, Virginia Puyana, Jos´e Lucio-Naranjo, Christiam Garz´on, and Ignacio Pav´on-Garc´ıa. A cost-effective approach to the evaluation of traffic noise exposure in the city of quito, ecuador. Case Studies on Transport Policy, 7(1):128 - 137, 2019. [99] Arnaud Can, Ludovic Leclercq, and Jo¨el Lelong. Dynamic estimation of urban traf fic noise: Influence of traffic and noise source representations. 69(10):858 - 867, 2008. Applied Acoustics, 158 Bibliograf´ıa [100] S. Lin, B. De Schutter, Y. Xi, and H. Hellendoorn. Integrated urban traffic control for the reduction of travel delays and emissions. IEEE Transactions on Intelligent Transportation Systems, 14(4):1609-1619, 2013. [101] A. Jamshidnejad, S. Lin, Y. Xi, and B. De Schutter. Erratum to \Integrated urban traffic control for the reduction of travel delays and emissions"[IEEE Transactions on ITS, 14 (2013), 1609-1619]. IEEE Transactions on Intelligent Transportation Systems, 2018. [102] SIMENS. Stratos, 2021. https://assets.new.siemens.com/siemens/assets/api/uuid:cbdb23c9- 07f6-4a2c-930d-b2d499a4aae2/stratos-brochure.pdf. [103] AIMSUN. Aimsun.live, 2021. https://www.aimsun.com/aimsun-live/. [104] HIKOB. Counting and traffic monitoring, 2021. https://www.hikob.com/en/application/traffic-monit. [105] SWARCO. Urban traffic management solutions, 2021. https://www.swarco.com/solutions/traffic-management/urban-traffic-management. [106] DATAFROMSKY. Flow, 2021. https://datafromsky.com/flow/. [107] URBIOTICA. Inter-urban traffic monitoring, 2021. https://www.urbiotica.com. [108] ISKRA. Urban traffic automation, 2021. https://www.iskra.eu/en/Urban-trafficautomation/Urban-Traffic-Automation-Solutions/. [109] D. I. Robertson and R. D. Bretherton. Optimizing networks of traffic signals in real time-the scoot method. IEEE Transactions on Vehicular Technology, 40(1):11-15, 1991. [110] A. G. Sims and K. W. Dobinson. The sydney coordinated adaptive traffic (scat) system philosophy and benefits. IEEE Transactions on Vehicular Technology, 29(2):130-137, 1980. [111] Dennis I Robertson. ’tansyt’method for area traffic control. Traffic Engineering & Control, 8(8), 1969. [112] Hajime Sakakibara, Tsutomu Usami, Seiji Itakura, and Teruyuki Tajima. Moderato (management by origin-destination related adaptation for traffic optimization). In Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No. 99TH8383), pages 38-43. IEEE, 1999. [113] Lily Ageliki Elefteriadou. The highway capacity manual 6th edition: A guide for multimodal mobility analysis. ITE Journal, 86(4), 2016. Bibliograf´ıa 159 [114] B. S. Kerner. Introduction to modern traffic flow theory and control: the long road to three-phase traffic theory. Springer, Heidelberg, Germany, 2009. [115] T. Pinochet. An´alisis de modelos de capacidad y demora en intersecciones prioritarias., 2012. [116] Daniel Krajzewicz, Jakob Erdmann, Michael Behrisch, and Laura Bieker. Recent Development and Applications of SUMO - Simulation of Urban MObility. International Journal On Advances in Systems and Measurements, 5(3&4):128-138, December 2012. [117] S. Lin, B. De Schutter, Y. Xi, and H. Hellendoorn. Efficient network-wide modelbased predictive control for urban traffic networks. Transportation Research Part C, 24:122-140, 2012. [118] A. Jamshidnejad, I. Papamichail, M. Papageorgiou, and B. De Schutter. Sustainable model-predictive control in urban traffic networks: Efficient solution based on general smoothening methods. IEEE Transactions on Control Systems Technology, 26(3):813- 827, May 2018. [119] Christian Portilla, David S´anchez, Andres Acosta, Juan Nore~na, and Jairo Espinosa. FLEXI, 2019. Register 13-76-334. [120] J G WARDROP and J I WHITEHEAD. Correspondence. some theoretical aspects of road traffic research. Proceedings of the Institution of Civil Engineers, 1(5):767-768, 1952. [121] TRB. Highway Capacity Manual. Transportation Research Board, Washington, D.C., 1950. [122] Andr´es Acosta, Jorge E. Espinosa, and Jairo Espinosa. Developing tools for building simulation scenarios for SUMO based on the SCRUM methodology. In Proceedings of the 3rd SUMO User Conference SUMO 2015, pages 23-35, Berlin-Adlershof, May 2015. Deutsches Zentrum f¨ur Luft - und Raumfahrt e.V. [123] Christian Portilla, Andres Acosta, and Jairo Espinosa. TrafficSensors, 2016. Register 13-53-185. [124] Krishna Saw, BK Katti, and G Joshi. Literature review of traffic assignment: static and dynamic. International Journal of Transportation Engineering, 2(4):339-347, 2015. [125] Zheng Li. User Equilibrium Solution, August 2019. Github repository https://github.com/ZhengLi95/User-Equilibrium-Solution. 160 Bibliograf´ıa [126] Bureau of Public Roads. Traffic assignment manual for application with a large, high speed computer, volume 2. US Department of Commerce, Bureau of Public Roads, Office of Planning, Urban Planning Division., 1964. [127] TPD Ingenier´ıa S.A. Estudio de pre inversi´on de movilidad para determinar la viabilidad de peatonalizaci´on total o parcial de algunos tramos viales del centro de la ciudad de Medell´ın bajo el concepto de tr´afico lento y de supermanzanas. Technical report, 2014. [128] Martin J Beckmann, Charles B McGuire, and Christopher B Winsten. Studies in the economics of transportation. 1955. [129] Semaria Ruiz, Jairo Espinosa, Ivan Sarmiento, Daniel Krajzewicz, Mirko Goletz, and Alain Schengen. Construction of a traffic test scenario in the Aburr´a Valley. Transportation Planning and Technology, 2020. [130] Felipe Valencia, Jos´e D L´opez, Alfredo Nu~nez, Christian Portilla, Luis G Cortes, Jairo Espinosa, and Bart De Schutter. Congestion management in motorways and urban networks through a bargaining-game-based coordination mechanism. In Game Theoretic Analysis of Congestion, Safety and Security, pages 1-40. Springer, 2015. [131] Christian Portilla, Felipe Valencia, Jairo Espinosa, Alfredo Nu~nez, and Bart De Schutter. Model-based predictive control for bicycling in urban intersections. Transportation research part C: emerging technologies, 70:27-41, 2016. [132] C Portilla, J Espinosa, and B De Schutter. A multi-class urban traffic model considering heterogeneous vehicle composition: An extension of the s model. Transportation Research Part C: Emerging Technologies, 115:102613, 2020. [133] Christian Portilla, Alejandro Marquez, and Jairo Espinosa. A mathematical model of pedestrians in signalized intersections. In Proceedings of 2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC), pages 1-6, Manizales, Colombia, 2015. [134] Christian Portilla, Andr´es Acosta, Alejandro Marquez, and Jairo Espinosa. A comparison between macroscopic and microscopic urban traffic simulation including motorcycle dynamics. In Proceedings of XVII Congreso Latinoamericano de Control Autom´atico (CLCA 2016), page 6, Medell´ın, Colombia, 2016. [135] Laura Nore~na, Ana Sarrazola, Alejandro Marquez, Esteban Jimenez, Christian Portilla, Oscar Jaramillo, and Jairo Espinosa. Model predictive control of an urban traffic intersection based on the modify multiclass queuing networks models. In Proceedings of the 9th Triennial Symposium on Transportation Analysis (TRISTAN IX), page 4, Oranjestad, Aruba, 2016. Bibliograf´ıa 161 [136] Christian Portilla, Andr´es Acosta, Jorge Espinosa, and Jairo Espinosa. Scitraffic: A macroscopic simulator for estimation and control of urban traffic. In Proceedings of the SUMO User Conference 2017, pages 80-88, Berlin, Adlershof, 2017. [137] Christian Portilla, Andr´es Acosta, and Jairo Espinosa. Origin-destination matrix estimation based on microsimulation and optimization. In Proceedings of MOVICIMOYCOT 2018: Joint Conference for Urban Mobility in the Smart City, Medell´ınColombia, 2018. [138] Semaria Ruiz, Nicolas Arroyo, Andres Acosta, Christian Portilla, and Jairo Espinosa. An optimal battery charging and schedule control strategy for electric bus rapid transit. In Proceedings of MOVICI-MOYCOT 2018: Joint Conference for Urban Mobility in the Smart City, Medell´ın-Colombia, 2018. [139] Andr´es Acosta, Christian Portilla, and Jairo Espinosa. Virtual sensors for monitoring emissions in urban traffic networks, taking advantage of available traffic measurements. In Proceedings of Congreso Panamericano de Ingenier´ıa de Tr´ansito, Transporte y Log´ıstica 2018, page 6, Medell´ın, Colombia, 2018. [140] Christian Portilla, Andr´es Acosta, Ricardo Ram´ırez, Jose Jim´enez, Iv´an Sarmiento, and Jairo Espinosa. An´alisis del impacto medioambiental a trav´es de microsimulaci´on en intervenciones urban´ısticas y de movilidad: un caso de estudio sobre un corredor urbano de la ciudad de medell´ın. In in Proceedings of Congreso Colombiano de Transporte y Tr´ansito CCTT 2019, Cartagena, Colombia, 2019. [141] Andres Acosta, Juan Nore~na, Christian Portilla, Anna Sarrazola, Laura Nore~na, Eliana Mej´ıa, and Jairo Espinosa. MOSTRO, 2019. Register 13-76-360. [142] Christian Portilla, David S´anchez, Esteban Lage, Andr´es Acosta, and Jairo Espinosa. Real-time urban traffic monitoring system using flexi (flow estimation based on collaborative information): A case study in Medellín-Colombia. Transportationresearch part C: emerging technologies, 2021. Submitted. [143] Juan Nore~na, Andr´es Osorio, Andr´es Acosta, Christian Portilla, and Jairo Espinosa. A methodology for traffic lights planning in urban intersections using a nonlinear formulation of the multi-objective system for traffic optimization (mostro). Transportationresearch part C: emerging technologies, 2021. Submitted. [144] Eliana Meja Estrada, Laura Norena, Anna Sarrazola, and Jairo Espinosa. MOSTRO: A multi-objective system for traffic optimization. In 2017 IEEE 3rd Colombian Conference on Automatic Control, CCAC 2017 - Conference Proceedings, volume 2018-Janua, pages 1-6, 2018.COLCIENCIAS - Doctorados Nacionales - Convocatoria 647InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80257/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL1037583637.2021.pdf1037583637.2021.pdftesis de Doctorado en Ingeniería Civilapplication/pdf10685471https://repositorio.unal.edu.co/bitstream/unal/80257/2/1037583637.2021.pdf1dd68fa1a7ade8f03a222eec6a5c05ccMD52THUMBNAIL1037583637.2021.pdf.jpg1037583637.2021.pdf.jpgGenerated Thumbnailimage/jpeg4350https://repositorio.unal.edu.co/bitstream/unal/80257/3/1037583637.2021.pdf.jpg6003dabfa931ed7a7f0014aa4909efaeMD53unal/80257oai:repositorio.unal.edu.co:unal/802572024-07-29 23:12:52.534Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Model-based real-time monitoring of large-scale urban traffic networks for decision making.

Publicaciones similares