Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Primary Literature
Patents information at www.depatisnet.de
Abdulhai B, Porwal H, Recker W (1999) Short term freeway traffic flow prediction using genetically-optimized time-delay-based neural networks. In: Proceedings 78th annual meeting Transportation Research Board. National Academies Press, Washington, DC
Acha-Daza JA, Hall FL (1993) A graphical comparison of the predictions for speed given by catastrophe theory and some classic models. Transp Res Rec 1398:119–124
Ahmed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transp Res Rec 722:1–9
Arem BV, Kirby HR, Van Der Vlist MJM, Whittaker JC (1997) Recent advances and applications in the field of short-term traffic forecasting. Int J Forecast 13:1–12
Becker M, Fastenrath U (1998) Method for transmitting local data and measurement data from a terminal, including a telematic terminal, to a central traffic control unit. German Patent Publication DE 197 55 875 A1, USA: US6426709B1
Ben-Akiva M, Cuneo D, Hasan M, Jha M, Yang Q (2003) Evaluation of freeway control using a microscopic simulation laboratory. Transp Res C Emerg Technol 11(1):29–50
Boker G, Lunze J (2001) State estimation in freeway traffic with floating car data. Automatisierungstechnik 49(11):497–504
Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco
Brockwell PJ, Davis RA (2002) Introduction to time series and forecasting, 2nd edn. Springer, New York
Burrus CS, Gopinath RA, Guo HT (1998) Introduction to wavelets and wavelet transforms: a primer. Prentice Hall, Upper Saddle River
Cetin M, Comert G (2006) Short-term traffic flow prediction with regime switching models. Transp Res Rec 1965:23–31
Chatfield C (2001) Time-series forecasting. Chapman & Hall/CRC, London
Chen M, Chien SIJ (2001) Dynamic freeway travel-time prediction with probe vehicle data – link based versus path based. Transp Res Rec 1768:157–161
Chen H, Grant-Muller S (2001) Use of sequential learning for short-term traffic flow forecasting. Transp Res C 9:319–336
Chen H, Grant-Muller S, Mussone L, Montgomery F (2001) A study of hybrid neural network approaches and the effects of missing data on traffic forecasting. Neural Comput Appl 10:277–286
Chen Y, Bell MGH, Bogenberger K (2007) Reliable multipath planning and dynamic adaptation for a centralized road navigation system. IEEE Trans ITS 8(1):14–20
Chickering DM, Heckerman D, Meek C (1997) A Bayesian approach to learning Bayesian networks with local structure. In: Proceedings 13th conference on uncertainty in artificial intelligence, Rhode Island, pp 80–89
Chien SIJ, Kuchipudi CM (2003) Dynamic travel time prediction with real-time and historic data. J Transp Eng ASCE 129(6):608–616
Chrobok R, Wahle J, Schreckenberg M (2001) Traffic forecast using simulations of large scale networks. In: Stone B, Conroy P, Broggi A (eds) 4th international IEEE conference on intelligent transportation systems. IEEE, Oakland, pp 434–439
Cremer M (1979) Traffic flow on freeways. Springer, Berlin. (in German)
D’Angelo MP, Al-Deek HM, Wang MC (1999) Travel-time prediction for freeway corridors. Transp Res Rec 1676:184–191
Daganzo CF (1994) The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp Res B 28(4):269–287
Daganzo CF (1995) The cell transmission model, part II: network traffic. Transp Res B 29(2):79–93
Daganzo CF (1997) Fundamentals of transportation and traffic operations. Elsevier Science, Oxford, UK
Daganzo CF (1999) The lagged cell-transmission model. In: Ceder A (ed) Proceedings of the 14th international symposium on transportation and traffic theory. Elsevier Science, Jerusalem, Israel, pp 81–104
Davis GA, Nihan NL (1991) Nonparametric regression and short-term freeway traffic forecasting. J Transp Eng 117(2):178
de Rham C, Lange R (2000) Short term forecast and evaluation for intelligent VMS settings. In: Proceedings of the 7th world congress on ITS, Torino
Dharia A, Adeli H (2003) Neural network model for rapid forecasting of freeway link travel time. Eng Appl Artif Intell 16(7–8):617–613
Dia H (2001) An object oriented neural network approach to short term traffic forecasting. Eur J Oper Res 131:253–261
Ding A, Zhao X, Jiao L (2002) Traffic flow time series prediction based on statistics learning theory. In: IEEE 5th international conference on intelligent transportation systems, Singapore, pp 727–730
Dion F, Rakha H, Kang YS (2004) Comparison of delay estimates at under-saturated and 38 over-saturated pre-timed signalized intersections. Transp Res B Methodol 38(2):99–122
Disbro JE, Frame M (1989) Traffic flow theory and chaotic behaviour. Transp Res Rec 1225:109–125
Dougherty M (1995) A review of neural networks applied to transport. Transp Res C 3(4):247–260
Edie LC, Foote RS (1960) Effect of shock waves on tunnel traffic flow. In: Highway Research Board – proceedings 39. National Research Council, Washington, DC, pp 492–505
Fallah-Tafti M (2001) The application of artificial neural networks to anticipate the average journey time of traffic in the vicinity of merges. Knowl-Based Syst 14:203–211
Fastenrath U (1998) Method for determining traffic data and traffic information exchange. German Patent Publication DE 197 37 440 A1, USA: US 6329932B1
Fuller WA (1996) Introduction to statistical time series, 2nd edn. Wiley, New York
Gartner NH, Stamatiadis C (2008) Optimization and control of urban traffic networks. This encyclopedia. Springer, New York
Gazis D, Knapp C (1971) Online estimation of traffic densities from time series of traffic and speed data. Transp Sci 5:283–301
Geroliminis N, Skabardonis A (2011) Identification and analysis of queue spillovers in city street networks. IEEE Trans Intell Transp Syst 12(4):1107–1115
Gipps PGA (1981) A behavioural car-following model for computer simulation. Transp Res B 15:105–111
Grenander U (1996) Elements of pattern theory. Johns Hopkins University Press, Baltimore
Hamed MM, Al-Masaeid HR, Bani Said ZM (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng 121(3):249–254
Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River
Head LK (1995) Event-based short-term traffic flow prediction model. Transp Res Rec 1510:45–52
Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, Reading
Heidemann D, Wimber P (1982) Types of traffic flow rate time series based on clustering methods. In: Straßenverkehrszählungen, vol 26. BASt, Germany
Helbing D (1997) Traffic dynamics: new modelling concepts in physics. Springer, Berlin/Heidelberg. (in German)
Hemmerle P, Koller M, Rehborn H, Kerner BS, Schreckenberg M (2016a) Fuel consumption in empirical synchronised flow in urban traffic. IET Intell Transp Syst 10(2):122–129
Hemmerle P, Koller M, Hermanns G, Schreckenberg M, Rehborn H, Kerner BS (2016b) Impact of synchronised flow in oversaturated city traffic on energy efficiency of conventional and electrical vehicles. In: Knoop V, Daamen W (eds) Traffic and granular flow ’15. Springer, Cham
Hemmerle P, Koller M, Hermanns G, Rehborn H, Kerner BS, Schreckenberg M (2016c) Impact of synchronised flow in oversaturated city traffic on energy efficiency of conventional and electrical vehicles. Collect Dyn 1:1–27
Hermanns G, Hemmerle P, Rehborn H, Koller M, Kerner BS, Schreckenberg M (2015) Microscopic simulation of synchronized flow in oversaturated city traffic: effect of drivers speed adaptation. Transp Res Rec J Transp Res Board 2490:47–55
Hermanns G, Hemmerle P, Rehborn H, Kerner BS, Schreckenberg M (2016) Microscopic simulations of oversaturated city traffic: features of synchronised flow patterns. In: Knoop V, Daamen W (eds) Traffic and granular flow ’15. Springer, Cham
Highway Capacity Manual 2000 (2000) Transportation Research Board. National Research Council, Washington, DC
Horvitz E, Apacible J, Sarin R, Liao L (2005) Prediction, expectation, and surprise: methods, designs, and study of a deployed traffic forecasting service. In: Proceedings of the conference on uncertainty and artificial intelligence 2005. AUAI Press, Edinburgh, Scotland
Hoyer R, Chrobok R, Feldges M, Folkerts G, Friedrich B, Huber W, Kates R, Kemper C, Kirschfink H, Lange R, Listl G, Mathias P, Offermann F, Pinkofsky L, Rehborn H, Schlichting B, Stieler P, Thiemann O, Vortisch P (2003) Advice for data completion and data aggregation in traffic management applications. Hinweispapier der Forschungsgesellschaft für Straßen- und Verkehrswesen, FGSV-Papier, vol 382. (in German)
Huang SH, Ran B (2003) An application of neural network on traffic speed prediction under adverse weather condition. In: 82nd TRB annual meeting. National Academies Press, Washington, DC
Huisken G, Van Berkum EC (2003) A comparative analysis of short-range travel time prediction methods. In: 82nd TRB annual meeting Transportation Research Board. National Academies Press, Washington, DC
Hunt PB, Robertson DI, Bretherton RD, Winton RI (1981) SCOOT – a traffic responsive method of coordinating signals. TRRL report no. LR1014, Transport and Road Research Laboratory, Crowthorne
Innama S (2001) Short term prediction of highway travel time using MLP neural networks. In: 8th world congress on intelligent transportation systems, Sydney, pp 1–12
Ishak S, Al-Deek H (2002) Performance evaluation of short term time series traffic prediction model. J Transp Eng ASCE 128(6):490–498
Ishak S, Alecsandru C (2004) Optimizing traffic prediction performance of neural networks under various topological, input, and traffic condition settings. J Transp Eng 130:452–465
Jiang X, Adeli H (2004) Wavelet packet-autocorrelation function method for traffic flow pattern analysis. Comput Aided Civ Infrastruct Eng 19:324–337
Kaumann O, Froese K, Chrobok R, Wahle J, Neubert L, Schreckenberg M (2000) Online simulation of the freeway network of NRW. In: Helbing D, Hermann HJ, Schreckenberg M, Wolf DE (eds) Traffic and granular flow ’99. Springer, Berlin Heidelberg, pp 351–356
Kaysi I, Ben-Akiva M, Koutsopoulos H (1993) An integrated approach to vehicle routing and congestion prediction for real-time driver guidance. Transp Res Rec 1408:66–74
Kerner BS (1998) Experimental features of self-organization in traffic flow. Phys Rev Lett 81:3797
Kerner BS (1999a) Traffic prediction method for road network with traffic controlled network nodes. German Patent DE 199 40 957 C2. (in German)
Kerner BS (1999b) Method for monitoring the condition of traffic for a traffic network comprising effective narrow points. German Patent DE 199 44 075 C2, USA Patent: US6813555B1; Japan Patent: JP 2002117481
Kerner BS (1999c) Congested traffic flow: observations and theory. Transp Res Rec 1678:160–167
Kerner BS (1999d) Theory of congested traffic flow: self-organization without bottlenecks. In: 14th international symposium on transportation and traffic theory. Jerusalem, Israel, pp 147–171
Kerner BS (2002) Empirical macroscopic features of spatial-temporal traffic patterns at highway bottlenecks. Phys Rev E 65:046138
Kerner BS (2004) The physics of traffic. Springer, Berlin/New York
Kerner BS (2007) On-ramp metering based on three-phase traffic theory. Traffic Eng Control 48(1):28–35
Kerner BS (2008) Modelling approaches to traffic congestion. This encyclopaedia. Springer, New York
Kerner BS (2009) Introduction to modern traffic flow theory and control. Springer, Berlin/New York
Kerner BS (2014) Cumulated vehicle acceleration. Traffic Eng Control 55(4):139–141
Kerner BS (2017) Breakdown in traffic networks: fundamentals of transportation science. Springer, Berlin
Kerner BS, Herrtwich RGH (2001) Traffic forecasting. Automatisierungstechnik 49:505–511
Kerner BS, Klenov SL (2002) A microscopic model for phase transitions in traffic flow. J Phys A Math Gen 35(3):L31–L43
Kerner BS, Klenov SL (2003) A microscopic theory of spatial-temporal congested traffic patterns at highway bottlenecks. Phys Rev E 68(3):036130
Kerner BS, Klenov SL (2006) Deterministic microscopic three-phase traffic flow models. J Phys A Math Gen 39:1775–1809
Kerner BS, Konhäuser P (1994) Structure and parameters of clusters in traffic flow. Phys Rev E 50(1):54
Kerner BS, Rehborn H (1996a) Experimental properties of complexity in traffic flow. Phys Rev E 53:R4257
Kerner BS, Rehborn H (1996b) Experimental features and characteristics of traffic jams. Phys Rev E 53:1297
Kerner BS, Rehborn H (1997) Experimental properties of phase transitions in traffic flow. Phys Rev Lett 79:4030
Kerner BS, Rehborn H (1998) Traffic surveillance method and vehicle flow control in a road network. German Patent Publication DE 198 35 979 A1, USA Patent: US 6587779B1
Kerner BS, Rehborn H, Kirschfink H (1998) Method for the automatic monitoring of traffic including the analysis of back-up dynamics. German Patent DE 196 47 127 C2, Dutch Patent: NL1007521C, USA Patent US 5861820
Kerner BS, Aleksic M, Denneler U (1999) Traffic condition supervision in traffic network, undertaking inquiry of current position and/or prognosis of future position of flank between area of free traffic and area of synchronized traffic continuously. German Patent DE 199 44 077 C1
Kerner BS, Rehborn H, Aleksic M, Haug A (2004) Recognition and tracing of spatial-temporal congested traffic patterns on freeways. Transp Res C 12:369–400
Kerner BS, Rehborn H, Haug A, Aleksic M (2005) Traffic prediction in vehicles. In: Proceedings 8th IEEE conference on intelligent transportation systems, Vienna, pp 251–256
Kerner BS, Rehborn H, Palmer J, Klenov SL (2011) Using probe vehicle data to generate jam warning messages. Traffic Eng Control 3:141–148
Kerner BS, Klenov SL, Hermanns G, Hemmerle P, Rehborn H, Schreckenberg M (2013a) Synchronized flow in oversaturated city traffic. Phys Rev E 88(5):054801
Kerner BS, Rehborn H, Schäfer RP, Klenov SL, Palmer J, Lorkowski S, Witte N (2013b) Traffic dynamics in empirical probe vehicle data studied with three-phase theory: spatiotemporal reconstruction of traffic phases and generation of jam warning messages. Phys A Stat Mech Appl 392(1):221–251
Kerner BS, Hemmerle P, Koller M, Hermanns G, Klenov SL, Rehborn H, Schreckenberg M (2014) Empirical synchronized flow in oversaturated city traffic. Phys Rev E 90(3):032810
Kirby HR, Watson SM, Dougherty MS (1997) Should we use neural networks or statistical models for short-term motorway traffic forecasting? Int J Forecast 13:43–50
Kirschfink H (1999) Collective traffic control in motorways. Tutorial at the 11th EURO-mini conference on AI in transportation systems and science, Helsinki
Kirschfink H, Hernández J, Boero M (2000) Intelligent traffic management models. In: Proceedings of the European symposium on intelligent techniques (ESIT). Helsinki, Finland
Kisgyorgy L, Rilett LR (2002) Travel time prediction by advanced neural network. Period Polytech Ser Civ Eng 46(1):15–32
Kitamura K, Kuwahara M (eds) (2005) Simulation approaches in transportation analysis: recent advances and challenges. Operations research/computer science interfaces series, vol 31. Springer, US
Kniss HC (2000) Evaluation of ASDA/FOTO in traffic control centre Hessen (internal report, in German)
Koller M, Hemmerle P, Rehborn H, Hermanns G, Kerner BS, Schreckenberg M (2014) Increased consumption in synchronized flow in oversaturated city traffic. In: 10th ITS European congress, Helsinki, proceedings
Koller M, Hemmerle P, Rehborn H, Kerner BS, Kaufmann S (2016) Traffic phase dependent fuel consumption. In: Knoop V, Daamen W (eds) Traffic and granular flow ’15. Springer, Cham
Koshi M, Iwasaki M, Ohkura I (1983) Some findings and an overview on vehicular flow characteristics. In: Proceedings 8th international symposium on transportation and traffic theory. Toronto, Canada, p 403
Kuchipudi CM, Chien SIJ (2003) Development of a hybrid model for dynamic travel time prediction. In: 82nd annual meeting Transportation Research Board, Washington, DC
Kwon J, Coifman B, Bickel P (2000) Day-to-day travel-time trends and travel-time prediction from loop-detector data. Transp Res Rec 1717:120–129
Lan CJ, Miaou SP (1999) Real-time prediction of traffic flows using dynamic generalized linear models. Transp Res Rec 1678:168–178
Lee S, Kim D, Kim J, Cho B (1998) Comparison of models for predicting short-term travel speeds. In: 5th world congress on intelligent transport systems, Seoul
Leutzbach W (1988) Introduction to the theory of traffic flow. Springer, Berlin
Lieu HC (2000) Traffic estimation and prediction system. Transp Res News 208:3–6
Lindveld CDR, Thijs R, Bovy PHL, Van der Zijpp NJ (2000) Evaluation of online travel time estimators and predictors. Transp Res Rec 1719:45–53
Lingras P, Sharma S, Zhong M (2002) Prediction of recreational travel using genetically designed regression and time-delay neural network models. Transp Res Rec 1805:16–24
Lu J (1990) Prediction of traffic flow by an adaptive prediction system. Transp Res Rec 1287:13–20
Maerivoet S, De Moor B (2005) Cellular automata models of road traffic. Phys Rep 419:1–64
Matsui H, Fujita M (1998) Travel time prediction for freeway traffic information by neural network driven fuzzy reasoning. In: Himanen V, Nijkamp P, Reggiani A, Raito J (eds) Neural networks in transport applications. Ashgate Publishers, Burlington, pp 355–364
May AD (1990) Traffic flow fundamentals. Prentice Hall, Englewood Cliffs
Middelham F (2001) Predictability: some thoughts on modelling. Futur Gener Comput Syst 17(5):627–636
Miyata S, Noda M, Usami T (1995) STREA. In: Proceedings of the 2nd world congress on intelligent transport systems, Yokohama, vol 1, pp 289–297
Moorthy CK, Ratcliffe BG (1998). Short term traffic forecasting using time series methods. Transp Plan Technol 12(1):45–56
Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I Fr 2:2221–2229
Nam DH, Drew DR (1996) Traffic dynamics: method for estimating freeway travel times in real time from flow measurements. J Transp Eng 122(3):186–191
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. In: Proceedings 82nd annual meeting Transportation Research Board, Washington, DC
Newell GF (1965) Approximation methods for queues with application to the fixed-cycle traffic light. SIAM Rev 7(2):223–240
Newell GF (1982) Applications of queuing theory. Chapman & Hall, London
Nicholson H, Swann CD (1974) The prediction of traffic flow volumes based on spectral analysis. Transp Res 8:533–538
Nihan NL, Holmesland KO (1980) Use of the Box and Jenkins time series technique in traffic forecasting. Transportation 9:125–14372
Nikovski D, Nishiuma N, Goto Y, Kumazawa H (2005) Univariate short-term prediction of road travel times. In: International IEEE conference on intelligent transportation systems (ITSC), Vienna
Ober-Sundermeier A, Zackor H (2001) Prediction of congestion due to road works on freeways. In: Proceedings IEEE intelligent transportation systems, Oakland, pp 240–244
Oda T (1990) An algorithm for prediction of travel time using vehicle sensor data. In: IEEE 3rd international conference on road traffic control. London, pp 40–44
Oh C, Ritchie SG, Oh JS (2005) Exploring the relationship between data aggregation and predictability toward providing better predictive traffic information. Transp Res Rec 1935:28–36
Ohba Y, Koyama T, Shimada S (1997) Online learning type of travelling time prediction model in expressway. In: IEEE conference on intelligent transport systems, Boston, pp 350–355
Okutani I, Stephanedes YI (1984) Dynamic prediction of traffic volume through Kalman filtering theory. Transp Res B 18B(1):1–11
Palmer J, Rehborn H (2008) ASDA/FOTO based on Kerner’s three-phase traffic theory in North-Rhine Westfalia. Straßenverkehrstechnik. (in German) 8:463–470
Pancratz A (1991) Forecasting with dynamic regression models. Wiley-InterScience, New York
Papageorgiou M (1983) Application of automatic control concepts in traffic flow modelling and control. Springer, Berlin/New York
Park B, Messer CJ, Urbanik T II (1998) Short term traffic volume forecasting using radial basis function neural network. Transp Res Rec 1651:39–47
Park DJ, Rilett LR, Han G (1999) Spectral basis neural networks for real-time travel time forecasting. J Transp Eng 125(6):515–523
Petty KF, Bickel P, Ostland M, Rice J, Schoenberg F, Jiang J, Ritov Y (1998) Accurate estimation of travel times from single loop detectors. Transp Res A 32(1):1–17
Pinkofsky L (2002) Types of time series. In: Verkehrsentwicklung auf Bundesfernstraßen 2002. Bericht der Bundesanstalt für Straßenwesen, Reihe Verkehrstechnik, vol V99. Bergisch Gladbach. Bundesanstalt für Straßenwesen (BASt) (in German)
Qiao F, Wang X, Yu L (2003) Optimizing aggregation level for ITS data based on wavelet decomposition. In: Proceedings 82nd annual meeting Transportation Research Board. National Academies Press, Washington, DC
Rakha H, Crowther B (2003) Comparison and calibration of FRESIM and INTEGRATION steady-state car-following behaviour. Transp Res A 37:1–27
Ran R, Boyce D (1996) Modelling dynamic transportation networks. Springer, Berlin
Rehborn H, Haug A, Aleksic M, Kerner BS, Fastenrath U (2002) Statistical analysis of traffic message archives as decision support for road construction up to traffic management. Straßenverkehrstechnik 9:478–485. (in German)
Rehborn H, Haug A, Kerner BS, Aleksic M, Fastenrath U (2003) Floating car data and methods for recognition and tracking of spatiotemporal traffic patterns. Straßenverkehrstechnik 9:461–468. (in German)
Rehborn H, Klenov SL, Palmer J (2011) An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany. Phys A Stat Mech Appl 390(23):4466–4485
Rice J, Van Zwet E (2001) A simple and effective method for predicting travel times on freeways. In: Proceedings of the IEEE conference on intelligent transportation systems, Oakland, pp 227–232
Riegelhuth G, Kirschfink H (2003) Management with decision support of road works for traffic flow optimization on freeways. In: Proceedings of ITS world congress, paper no. 2255T
Rilett LR, Park D (2001) Direct forecasting of freeway corridor travel times using spectral basis neural networks. Transp Res Rec 1752:140–147
Robertson DI (1969) TRANSYT: a traffic network study tool. TRRL report no. LR 253, Transportation and Road Research Laboratory, Crowthorne
Rumelhart DE, McClelland JL (1986) Parallel distributed processing: exploration in the microstructure of cognition. MIT Press, Cambridge, MA
Schönhof M, Helbing D (2007) Empirical features of congested traffic states and their implications for traffic modeling. Transp Sci 41(2):135–166
Schrader CC, Kornhauser AL, Friese LM (2004) Using historical information in forecasting travel times. In: 82nd annual meeting Transportation Research Board. National Academies Press, Washington, DC
Smith BL, Demetsky MJ (1994) Short term traffic flow prediction: neural network approach. Transp Res Rec 1453:98–104
Smith BL, Demetsky MJ (1997) Traffic flow forecasting: comparison of modeling approaches. J Transp Eng 123(4):261–266
Smith BL, Oswald KR (2003) Meeting real-time traffic flow forecasting requirements with imprecise computations. Comput Aided Civ Infrastruct Eng 18:201–213
Smith BL, Williams BM, Oswald KR (2002) Comparison of parametric and non-parametric models for traffic flow forecasting. Transp Res C 10(4):303–321
Stathopoulos A, Karlaftis MG (2003) A multivariate state-space approach for urban traffic flow modeling and prediction. Transp Res C 11:121–135
Sun H, Liu HX, Xiao H, He RR, Ran B (2003) Short-term traffic forecasting using the local linear regression model. J Transp Res Board 1836:143–150
Sun H, Xiao HX, Yang F, Ran B, Tao Y, Oh Y (2004) Wavelet preprocessing for local linear traffic prediction. In: 83rd Transportation Research Board annual meeting, Washington, DC
Teng H, Qi Y (2003) Application of wavelet technique to freeway incident detection. Transp Res C 11(3–4):289
Traffic Flow Theory 2006 (2006) Monograph with 22 papers on the subject of traffic flow theory. Transportation Research Record 1965. Transportation Research Board, Washington
Treiterer J (1975) Investigations of traffic dynamics by aerial photogrammetry. Ohio State University Technical Report PB 246 094, Columbus
Van der Voort M, Dougherty M, Watson S (1996) Combining KOHONEN maps with ARIMA time series models to forecast traffic flow. Transp Res C 4:307–318
Van Lint JWC, Van der Zijpp NJ (2003) Improving a travel time estimation algorithm by using dual loop detectors. Transp Res Rec 1855:41–48
Van Lint JWC, Hoogendoorn P, Van Zuylen HJ (2002) Freeway travel time prediction with state-space neural networks-modeling state-space dynamics with recurrent neural networks. Transp Res Rec 1811:30–39
Venkatanarayana R, Smith BL, Demetsky MJ (2005) Traffic pattern identification using wavelets transforms. In: 84th Transportation Research Board annual meeting, Washington, DC
Vlahogianni EI, Golias JC, Karlaftis MG (2004) Short-term traffic forecasting: overview of objectives and methods. Transp Rev 24(5):533–557
Vlahogianni EI, Karlaftis MG, Golias JC (2006) Statistical methods for detecting non-linearity and non-stationarity in univariate short-term time-series of traffic volume. Transp Res C 14(5):351–367
Wahle J, Bazzan A, Klügl F, Schreckenberg M (2000) Anticipatory traffic forecast using multi-agent techniques. In: Helbing D, Hermann HJ, Schreckenberg M, Wolf DE (eds) Traffic and granular flow ’99. Springer, Berlin Heidelberg, pp 87–92
Wang Y, Papageorgiou M (2005) Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. Transp Res B 39:141–167
Webster FV (1958) Traffic signal settings. Road Research Laboratory technical paper no. 39
Whitham G (1974) Linear and nonlinear waves. Wiley, New York
Wiedemann R (1974) Simulation of traffic flow. Schriftenreihe des Instituts für Verkehrswesen der Universität Karlsruhe, Heft 8. (in German)
Wild D (1997) Short-term forecasting based on a transformation and classification of traffic volume time series. Int J Forecast 13:63–72
Williams BM (2001) Multivariate vehicular traffic flow prediction: an evaluation of ARIMAX modeling. Transp Res Rec 1776:194–200
Williams BM, Hoel LA (2003) Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J Transp Eng 129(6):664–672
Williams JC, Mahmassani HS, Herman R (1987) Urban network flow models. Transp Res Rec 1112:78–88
Xiao H, Sun H, Ran B, Oh Y (2003) Fuzzy-neural network traffic prediction with wavelet decomposition. Transp Res Rec 1836:16–20
Yang F, Sun H, Tao Y, Ran B (2004a) Temporal difference learning with recurrent neural network in multi-step ahead freeway speed prediction. In: 83rd Transportation Research Board annual meeting, Washington, DC
Yang F, Lin Z, Liu HX, Ran B (2004b) Online recursive algorithm for short-term traffic prediction. Transp Res Rec 1879:1–9
Yasdi R (1999) Prediction of road traffic using a neural network. Neural Comput Appl 8:135–142. Springer
Yin H, Wong SC, Xu J (2002) Urban traffic prediction using a fuzzy-neural approach. Transp Res C 10:85–98
Zhang HM (2000) Recursive prediction of traffic conditions with neural networks. J Transp Eng 126(6):472–481
Zhang X, Rice J (2003) Short term travel time prediction. Transp Res C 11:187–210
Zhang G, Patuwo E, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62
Zwahlen HT, Russ A (2002) Evaluation of the accuracy of a real-time travel time prediction system in a freeway construction work zone. Transp Res Rec 1803:87–93
Books and Reviews
Kalman R (1960) A new approach to linear filtering and prediction problems. ASME Basic Eng J 82(1):35–45
Kants H, Schreiber T (2004) Nonlinear time series analysis. Cambridge University Press, Cambridge, UK
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Rehborn, H., Klenov, S.L., Koller, M. (2019). Traffic Prediction of Congested Patterns. In: Kerner, B. (eds) Complex Dynamics of Traffic Management. Encyclopedia of Complexity and Systems Science Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8763-4_564
Download citation
DOI: https://doi.org/10.1007/978-1-4939-8763-4_564
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-8762-7
Online ISBN: 978-1-4939-8763-4
eBook Packages: Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics