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Erschienen in:
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2022 | OriginalPaper | Buchkapitel

1. Traffic Monitoring and Reconstruction

verfasst von : Margarita Martínez-Díaz

Erschienen in: The Evolution of Travel Time Information Systems

Verlag: Springer International Publishing

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Abstract

In traffic engineering, as in so many other disciplines, any good analysis requires data. Regardless of whether the most powerful software is available, it will not produce good results if it does not receive the necessary inputs. It is generally accepted that the more data available, the better results can be achieved. Omitting data-driven techniques, this is true only if the data is adequate and, of course, more or less accurate. In this sense, the equipment that collects the data also plays a fundamental role, since it will determine what data can be collected and in what amount. This chapter provides a simple but very useful classification of the most commonly used sensors and explains the data they can collect. It also gives a brief and simplified introduction to the reconstruction of traffic conditions from these data using the most common techniques. Both aspects will be discussed in more detail throughout this book.

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Literatur
Andersen J, Sutcliffe S (2000) Intelligent Transport Systems (ITS)—an overview. Proceedings of the International Federation of Automatic Control (IFAC), Technology Transfer in Developing Countries, Pretoria, South Africa 33(18):99–106
Azevedo CL, Cardoso JL, Ben-Akiva M, Costeira JP, Marques M (2014) Automatic vehicle trajectory extraction by aerial remote sensing. Proc Soc Behav Sci 111:849–858CrossRef
Bachmann C (2011) Multi-sensor data fusion for traffic speed and travel time estimation. PhD dissertation. University of Toronto
Bando M, Hasebe K, Nakyaama A, Shibata A, Sugiyama Y (1995) Dynamical model of traffic congestion and numerical simulation. Phys Rev E 51:1035–1042CrossRef
Banham R (2002) The ford century: ford motor company and the innovations that shaped the world. New York: Artisan Publishers. ISBN-13: 9781579652012
Barceló J, Kuwahara M (2010) Traffic data collection and its standardization. Springer, BerlinCrossRef
Barceló J, Montero L, Marquès L (2010) Travel time forecasting and dynamic OD estimation in freeways based on Bluetooth traffic monitoring. Proceedings of the 89th Annual Meeting of the Transportation Research Board, 10–14 January 2010, Washington DC
Barceló J, Montero L, Bullejos M, Serch O, Carmona C (2013) A kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices. J Intell Transp Syst 17(2):123–141CrossRef
Barmpounakis EN, Vlahogianni EI, Golias JC (2016) Extracting kinematic characteristics from unmanned aerial vehicles. Proceedings of the 95th Annual Meeting of the Transportation Research Board, January 2016. Washington, USA
Berenguer A, Goncalves J, Hosio S, Ferreira D, Anagnostopoulos T, Kostakos V (2017) Are smartphones Ubiquitous?: an in-depth survey of smartphone adoption by seniors. IEEE Cons Elect Magaz 6(1):104–110CrossRef
Bolic M, Simplot-Ryl D, Stojmenović I (2010) RFID systems: research trends and challenges. Wiley, New JerseyCrossRef
Cáceres N, Romero LM, Benitez FG, del Castillo JM (2012) Traffic flow estimation models using cellular phone data. IEEE Trans Intell Transp Syst 13(3):1430–1441CrossRef
Chakroborty P, Kikuchi S (1999) Evaluation of the general motors based car-following models and a proposed fuzzy inference model. Trans Res Part c: Emerg Technol 7(4):209–235CrossRef
Chu L, Oh S, Recker W (2005) Adaptive Kalman filter based freeway travel time estimation. Transportation Research Board 84th Annual Meeting, 2005, Washington, DC
Coifman B (2001) Improved velocity estimation using single loop detectors. Transp Res Part a: Policy Pract 35(10):863–880
Coifman B, Ergueta E (2003) Improved vehicle reidentification and travel time measurement on congested freeways. ASCE J Transp Eng 129(5):475–483CrossRef
Daganzo CF (1994) The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp Res Part b: Methodol 28(4):269–287CrossRef
Daganzo CF (1995) The cell transmission model, part II: network traffic. Transp Res Part b: Methodol 29(2):79–93CrossRef
Daganzo CF (1999a). A Behavioral Theory of Multi-Lane Traffic Flow Part I: Long Homogeneous Freeway Sections. Report. California: ITS Berkeley Institute for Transportation Studies, UC Berkeley
Daganzo CF (1999b) A behavioral theory of multi-lane traffic flow part II: merges and the Onset of Congestion. Report. California: ITS Berkeley Institute for Transportation Studies, UC Berkeley
Doberstein D (2011) Fundamentals of GPS receivers: a hardware approach. Springer, New York
Edie LC (1965) Discussion of traffic stream measurements and definitions. Proc. 2nd International Symposium on the Theory of Traffic Flow, OECD, Paris, pp 139–154
Federal Highway Administration (2006) Traffic detector handbook, vol. 1. Report FHWA-HRT-06–108. Virginia: US Department of Transportation
Forbes TW, Zagorski HJ, Holshouser EL, Deterline WA (1958) Measurement of driver reactions to tunnel conditions. Highway Res Board Proce 37:60–66
Forbes TW (1963) Human factor considerations in traffic flow theory. Highway Res Record 15:60–66
Gazis DC, Herman R, Potts RB (1959) Car-following theory of steady state flow. Operat Res 7(4):499–505
Gazis D, Knapp C (1971) On-line estimation of traffic densities from time-series of flow and speed data. Transp Sci 5(3):282–301CrossRef
Ge Q, Fukuda D (2016) Updating origin-destination matrices with aggregated data of GPS traces. Transp Res Part c: Emerg Technol 69:291–312CrossRef
Godunov S (1959) A difference method for numerical calculation of discontinuous solutions of the equations of hydrodynamics. Matematicheskii Sbornik 47(3):271–306MathSciNetMATH
Hansapalangkul T, Keeratiwintakorn P, Pattaraatikom W (2007) Detection and estimation of road congestion using cellular phones. Proceedings of the 7th International Conference on ITS Telecommunications, 2007, Sophia Antipolis, pp 1–4
Hellinga B (2002) Improving freeway speed estimates from single loop detectors. J Transp Eng 128(1):58–67CrossRef
Herman R, Potts RB (1959) Single lane traffic flow theory and experiment. Proceedings of the Symposium on the Theory of Traffic Flow, 1959, Research Labs, General Motors, pp 147–157. New York: Elsevier
Herman R, Montroll EW, Potts RB, Rothery RW (1959) Traffic dynamics: analysis of stability in car-following. Oper Res 7(1):86–106MathSciNetMATHCrossRef
Herrera JC, Bayen AM (2010) Incorporation of Lagrangian measurements in freeway traffic state estimation. Transp Res Part b: Methodol 44:460–481CrossRef
Herrera JC, Work D, Ban X, Herring R, Jacobson Q, Bayen A (2010) Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment. Transp Res Part c: Emerg Technol 18:568–583CrossRef
Highway Research Board (1950) Highway capacity manual: practical applications of research. Washington DC: National Research Council
Ilie-Zudor E, Kemény Zs, Egri P, Monostori L (2006) The RFID technology and its current applications. Proceedings of the 8th International Conference on The Modern Information Technology in the Innovation Processes of the Industrial Enterprises, 2006, Laboratory of Engineering and Management Intelligence, Computer and Automation Research Institute (SZTAKI), Budapest, Hungary, pp 29–36
Janecek A, Valerio D, Hummel KA, Ricciato F, Hlavacs H (2012) Cellular data meet vehicular traffic theory: Location area updates and cell transitions for travel time estimation. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 5–8 September 2012, Pittsburgh, Pennsylvania, pp 361–370
Kaufmann S, Kerner BS, Rehborn H, Koller M, Klenov SL (2018) Aerial observations of moving synchronized flow patterns in over-saturated city traffic. Transp Res Part c: Emerg Technol 86:393–406CrossRef
Lajunen T, Summala H (2003) Can we trust self-reports of driving? Effects of impression management on driver behaviour questionnaire responses. Transport Res f: Traffic Psychol Behav 6(2):97–107CrossRef
Lamb H (1895) Hydrodynamics. Cambridge University Press, UKMATH
Lighthill M, Whitham G (1955) On kinematic waves II. A theory of traffic flow on long crowded roads. Proc Royal Soc A 229(1178):317–345
Lin Y, Wang P, Ma M (2017) Intelligent Transportation System (ITS): Concept, Challenge and Opportunity. 2017 IEEE 3rd International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing and IEEE International Conference on Intelligent Data and Security, 2017, Beijing, pp 167–172
Lv M, Chen L, Wu X, Chen G (2015) A road congestion detection system using undedicated mobile phones. IEEE Trans Intell Transp Syst 16(6):3060–3072CrossRef
Nanthawichit C, Nakatsuji T, Suzuki H (2003) Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a freeway. Transp Res Rec 1855:49–59CrossRef
Newell GF (1993a) A simplified theory of kinematic waves in highway traffic. Part I: General Theory. Transp Res Part B: Methodol 27(4):281–287
Newell GF (1993b) A simplified theory of kinematic waves in highway traffic. Part II: Queuing at freeway bottlenecks. Transp Res Part B: Methodol 27(4):289–303
Newell GF (1993c) A simplified theory of kinematic waves in highway traffic. Part III: Multi-destination flows. Transp Res Part B: Methodol 27(4):305–313
Oberauer C, Stottan T, Wagner R (2011) Requirements of processing extended floating car data in a large scale environment. In Advanced Microsystems for Automotive Applications, pp 335–342. Berlin: Springer
Ozguven EE, Ozbay K (2013) A secure and efficient inventory management system for disasters. Transp Res Part c: Emerg Technol 29:171–196CrossRef
Ozguven EE, Ozbay K (2015) An RFID-based inventory management framework for emergency relief operations. Transp Res Part c: Emerg Technol 57:166–187CrossRef
Paek J, Kim J, Govindan R (2010) Energy-efficient rate-adaptive GPS-based positioning for smartphones. Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, 15–18 June 2010; San Francisco, CA, pp 299–314
Palen J (1997) The need for surveillance in intelligent transportation systems. Intellimotion 6(1):1–3. University of California PATH, Berkeley, CA
Pipes LA (1967) Car-following models and the fundamental diagram of road traffic. Transp Res b: Methodol 1:21–29CrossRef
Prasanna R, Hemalatha M (2012) RFID GPS and GSM based logistics vehicle load balancing and tracking mechanism. Proceedings of the International Conference on Communication Technology and System Design, 8–10 December 2012, Beijing, China, pp 726–729
Rahman M, Chowdhury M, Xie Y, He Y (2013) Review of microscopic lane-changing models and future research opportunities. IEEE Trans Intell Transp Syst 14(4):1942–1956CrossRef
Rose G (2006) Mobile phones as traffic probes: practices, prospects and issues. Trans Rev 26(3):275–291CrossRef
Salvo G, Caruso L, Scordo A, Guido G, Vitale A (2017) Traffic data acquirement by unmanned aerial vehicle. Euro J Remote Sens 50(1):343–351CrossRef
Sanaullah I, Quddus M, Enoch M (2016) Developing travel time estimation methods using sparse GPS data. J Intell Transp Syst 20(6):532–544CrossRef
Shladover SE (1990) Advanced Vehicle Control Systems (AVCS) (1990). Vehicle Electronics in the 90s: Proceedings of the International Congress on Transportation Electronics, 1990, Warrendale, US, pp 103–112
Soriguera F (2016) Highway travel time estimation with data fusion. Springer Tracts on Transportation and Traffic, 11, 212 pages. Berlin: Springer-Verlag Berlin Heidelberg
Sun X, Muñoz L, Horowitz R (2004) Mixture Kalman filter based highway congestion mode and vehicle density estimator and its application. Proceedings of the 2004 American Control Conference, 2004, Boston, MA, 2098–2103
Sunderrajan A, Viswanathan V, Cai W, Knoll A (2016) Traffic state estimation using floating car data. Proc Comp Sci 80:2008–2018CrossRef
Sussman J (2005) Perspectives on intelligent transportation systems (ITS). Springer Science and Business Media, Berlin
Szeto M, Gazis D (1972) Application of Kalman filtering to the surveillance and control of traffic systems. Transp Sci 6(4):419–439CrossRef
Tao S, Manolopoulos V, Rodriguez S, Rusu A (2012) Real-time urban traffic state estimation with A-GPS mobile phones as probes. J Trans Technol 2(1):22–31
Traffic Detector Handbook (1991) Washington DC: Institute of Transportation Engineers
Treiber M, Helbing D (2002) Reconstructing the spatio-temporal traffic dynamics from stationary detector data. Cooper@tive Tr@nsport@tion Dyn@mics, 1, 3.1–3.24
Treiber M, Kesting A, Wilson RE (2011) Reconstructing the traffic state by fusion of heterogeneous data. Comput Aided Civ Infrastructure Eng 26(6):408–419
Troullinos D, Papamichail I, Chalkiadakis G, Papageorgiou M (2021) Collaborative Multiagent Decision Making for Lane- Free Autonomous Driving. In Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Online, May 3–7, 2021, IFAAMAS, p 9
Turner SM, Eisele WL, Benz RJ, Holdener DJ (1998) Travel time data collection handbook. Research Report FHWA-PL-98–035. Washington, DC: Federal Highway Administration, Office of Highway Information Management
Vaidya NH, Das SR (2008) RFID-based networks: exploiting diversity and redundancy. Mobile Comput Commun Rev 12:2–14CrossRef
Wang T, Fang T, Han J, Wu J (2010) Traffic monitoring using floating car data in Hefei. Proceedings of the International Symposium in Intelligence Information Processing and Trusted Computing (IPTC), 28–29 October 2010, Huanggang, China, 122–124
Wei P, Cao Y, Sun D (2013) Total unimodularity and decomposition method for large-scale air traffic cell transmission model. Transp Res Part b: Methodol 53:1–16CrossRef
Weiland RJ, Purser LB (2000) Intelligent Transportation Systems. In: Transportation in the New Millennium: State of the Art and Future Directions, Perspectives from Transportation Research Board Standing Committees. Washington, DC: Transportation Research Board
Woodard D, Nogin G, Koch P, Racz D, Goldszmidt M, Horvitz E (2017) Predicting travel time reliability using mobile phone GPS data. Transp Res Part C Emerg Technol 75:30–44CrossRef
Xia C, Cochrane C, DeGuire J, Fan G, Holmes E, McGuirl M, Murphy P, Palmer J, Carter P, Slivinsiki L, Sandstede B (2017) Assimilating eulerian and lagrangian data in traffic-flow models. Physica D 346:59–72MathSciNetMATHCrossRef
Ygnace JL, Benguigui C, Delannoy V (2001) Travel Time/Speed Estimates on the French Rhone Corridor Network Using Cellular Phones as Probes. Lyon: INRETS. Final Report of the SERTI V Program
Yoon J, Noble B, Liu M (2007) Surface street traffic estimation. Proceedings of the 5th International Conference on Mobile Systems, Applications, and Services (MobiSys 2007), June 11–13, 2007, San Juan, Puerto Rico, 22–31
Young S (2007) Real-time traffic operations data using vehicle probe technology. Proceedings of the 2007 Mid-Continent Transportation Research Symposium, August 2007, Ames, Iowa
Yuan J, Zheng Y, Xie X, Sun G (2011) Driving with knowledge from the physical world. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 21–24 August 2011, San Diego, CA, USA, 316–324
Zhang HM (1998) A theory of non-equilibrium traffic flow. Trans Res Part b: Methodol 32(7):485–498CrossRef
Metadaten
Titel
Traffic Monitoring and Reconstruction
verfasst von
Margarita Martínez-Díaz
Copyright-Jahr
2022
Buch
The Evolution of Travel Time Information Systems

Print ISBN: 978-3-030-89671-3

Electronic ISBN: 978-3-030-89672-0

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-89672-0_1

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