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Dynamic Traffic Routing, Assignment, and Assessment of Traffic Networks

  • Hesham Rakha
  • Aly Tawfik
Reference work entry
Part of the Encyclopedia of Complexity and Systems Science Series book series (ECSSS)

Glossary

Car-following model

A mathematical representation (traffic flow model) for driver longitudinal motion behavior.

Dynamic traffic assignment

Traffic assignment considering the temporal dimension of the problem.

Link or arc

A roadway segment with homogeneous traffic and roadway characteristics (e.g. same number of lanes, base lane capacity, free-flow speed, speed-at-capacity, and jam density). Typically networks are divided into links for traffic modeling purposes.

Marginal link travel time

The increase in a link’s travel time resulting from an assignment of an additional vehicle to this link.

Road pricing

Road pricing is an economic concept in which drivers are charged for the use of the road facility.

Route or path

A sequence of roadway segments (links or arcs) used by a driver to travel from his/her point of origin to his/her destination.

Static traffic assignment

Traffic assignment ignoring the temporal dimension of the problem.

Synthetic O-D estimation

The procedure that...

Bibliography

  1. Abdel-Aty MA, Kitamura R, Jovanis PP (1997) Using stated preference data for studying the effect of advanced traffic information on drivers’ route choice. Transp Res Part C Emerg Technol 5(1):39–50CrossRefGoogle Scholar
  2. Abdelfatah AS, Mahmassani HS (2001) A simulation-based signal optimization algorithm within a dynamic traffic assignment framework. In: IEEE intelligent transportation systems proceedings, IEEE conference on intelligent transportation systems, Proceedings, ITSC 2001, OaklandGoogle Scholar
  3. Abdelghany KF, Mahmassani HS (2001) Dynamic trip assignment-simulation model for intermodal transportation networks. Transp Res Rec 1771:52–60CrossRefGoogle Scholar
  4. Abdelghany KF, Valdes DM et al (1999) Real-time dynamic traffic assignment and path-based signal coordination: application to network traffic management. Transp Res Rec 1667:67–76CrossRefGoogle Scholar
  5. Abdelghany AF, Abdelghany KF et al (2000) Dynamic traffic assignment in design and evaluation of high-occupancy toll lanes. Transp Res Rec 1733:39–48CrossRefGoogle Scholar
  6. Abdulhai B, Porwal H, Recker W (2002) Short-term freeway traffic flow prediction using genetically optimized time delay-based neural networks. ITS J Intell Transp Syst J 7(1):3–41zbMATHCrossRefGoogle Scholar
  7. Ahmed M, Cook AR (1982) Analysis of freeway traffic time series data by using Box-Jenkins techniques. Transp Res Rec 722:1–9Google Scholar
  8. Ahn K, Rakha H et al (2002) Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. J Transp Eng 128(2):182–190CrossRefGoogle Scholar
  9. Ahn K, Rakha H et al (2004) Microframework for modeling of high-emitting vehicles. Transp Res Rec 1880:39–49CrossRefGoogle Scholar
  10. Akcelik R, Rouphail NM (1994) Overflow queues and delays with random and platooned arrivals at signalized intersections. J Adv Transp 28(3):227–251CrossRefGoogle Scholar
  11. Allen RW, Stein AC, Rosenthal TJ, Ziedman D, Torres JF, Halati A (1991) Human factors simulation investigation of driver route diversion and alternate route selection using in-vehicle navigation systems. In: Vehicle navigation & information systems conference, Dearborn, 20–23 Oct 1991. Proceedings Part 1 (of 2) Society of Automotive Engineers. SAE, Warrendale, pp 9–26Google Scholar
  12. Anastassopoulos I (2000) Fault-tolerance and incident detection using Fourier transforms. Purdue University, WestlafayetteGoogle Scholar
  13. Arafeh M, Rakha H (2005) Genetic algorithm approach for locating automatic vehicle identification readers. In: IEEE intelligent transportation system conference, Vienna, 2005. Proceedings ITSV‘05 IEEE intelligent conference on transportations systems, pp 1153–1158Google Scholar
  14. Arnott R, de Palma A, Lindsey R (1991) Does providing information to drivers reduce traffic congestion? Transp Res Part A (General) 25A(5):309CrossRefGoogle Scholar
  15. Arrow KJ (1951) Alternative approaches to the theory of choice in risk-taking situations. Econometrica 19(4):404–437MathSciNetzbMATHCrossRefGoogle Scholar
  16. Ashok K (1996) Estimation and prediction of time-dependent origin-destination flows. PhD thesis, Massachusetts Institute of Technology, BostonGoogle Scholar
  17. Ashok K, Ben-Akiva ME (1993) Dynamic origin-destination matrix estimation and prediction for real-time traffic management systems. In: Daganzo CF (ed) 12th international symposium on transportation and traffic theory. Elsevier, New York, pp 465–484Google Scholar
  18. Ashok K, Ben-Akiva ME (2000) Alternative approaches for realtime estimation and prediction of time-dependent origin destination flows. Transp Sci 34(1):21–36zbMATHCrossRefGoogle Scholar
  19. Balakrishna R, Koutsopoulos HN et al (2005) Simulation-based evaluation of advanced traveler information systems. Transp Res Rec 1910:90–98CrossRefGoogle Scholar
  20. Barth M, An F et al (2000) Comprehensive modal emission model (CMEM): version 2.0 user’s guide. University of California, RiversideGoogle Scholar
  21. Bell M, Iida Y (1997) Transportation network analysis. Iida Y translator. Wiley, Chichester/New YorkGoogle Scholar
  22. Ben-Akiva M, Kroes E et al (1992) Real-time prediction of traffic congestion. Vehicle Navigation and Information Systems, IEEE, New YorkGoogle Scholar
  23. Ben-Akiva M, Bolduc D et al (1993) Estimation of travel choice models with randomly distributed values of time. Transp Res Rec 1413:88–97Google Scholar
  24. Ben-Akiva M, Bierlaire M, Bottom J, Koutsopoulos H et al (1997) Development of a route guidance generation system for real-time application. In: 8th international federation of automatic control symposium on transportation systems, Chania, 16–18 June 1997Google Scholar
  25. Ben-Akiva M, Bierlaire M et al (1998a) DynaMIT: a simulation based system for traffic prediction. DACCORD Short Term Forecasting Workshop, Delft, February 1998Google Scholar
  26. Ben-Akiva MMB, Koutsopoulos H, Mishalani R (1998b) DynaMIT: a simulation-based system for traffic prediction. DACCORD Short Term Forecasting Workshop, DelftzbMATHGoogle Scholar
  27. Bierlaire M, Crittin F (2004) An efficient algorithm for real-time estimation and prediction of dynamic OD tables. Oper Res 52(1):116–127CrossRefGoogle Scholar
  28. Birge JR, Ho JK (1993) Optimal flows in stochastic dynamic networks with congestion. Oper Res 41(1):203–216MathSciNetzbMATHCrossRefGoogle Scholar
  29. Bolland JD, Hall MD et al (1979) SATURN: simulation and assignment of traffic in urban road networks. In: International conference on traffic control systems, BerkeleyGoogle Scholar
  30. Boyce DE, Ran B, Leblanc LJ (1995) Solving an instantaneous dynamic user-optimal route choice model. Transp Sci 29(2):128–142zbMATHCrossRefGoogle Scholar
  31. Braess D (1968) Über ein Paradoxon der Verkehrsplanung. Unternehmensforschung 12:258–268MathSciNetzbMATHGoogle Scholar
  32. Brilon W (1995) Delays at oversaturated unsignalized intersections based on reserve capacities. Transp Res Rec 1484:1–8Google Scholar
  33. Brilon W, Wu N (1990) Delays at fixed-time traffic signals under time-dependent traffic conditions. Traffic Eng Control 31(12):8Google Scholar
  34. Burell JE (1968) Multipath route assignment and its application to capacity-restraint. In: Fourth international symposium on the theory of traffic flow, KarlsruheGoogle Scholar
  35. Burell JE (1976) Multipath route assignment: a comparison of two methods. In: Florian M (ed) Traffic equilibrium methods. Lecture notes in economics and mathematical systems, vol 118. Springer, New York, pp 210–239Google Scholar
  36. Busemeyer JR, Townsend JT (1993) Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychol Rev 100(3):432CrossRefGoogle Scholar
  37. Byung-Wook Wie TRL, Friesz TL, Bernstein D (1995) A discrete time, nested cost operator approach to the dynamic network user equilibrium problem. Transp Sci 29(1):79–92zbMATHCrossRefGoogle Scholar
  38. Cantarella GE, Cascetta ES (1995) Dynamic processes and equilibrium in transportation networks: towards a unifying theory. Transp Sci 29(4):305–329zbMATHCrossRefGoogle Scholar
  39. Carey M (1986) Constraint qualification for a dynamic traffic assignment model. Transp Sci 20(1):55–58CrossRefGoogle Scholar
  40. Carey M (1987) Optimal time-varying flows on congested networks. Oper Res 35(1):58–69MathSciNetzbMATHCrossRefGoogle Scholar
  41. Carey M (1992) Nonconvexity of the dynamic traffic assignment problem. Transp Res Methodol 26B(2):127MathSciNetCrossRefGoogle Scholar
  42. Carey M, Subrahmanian E (2000) An approach to modelling time-varying flows on congested networks. Transp Res Methodol 34B(3):157CrossRefGoogle Scholar
  43. Cascetta E, Marquis G (1993) Dynamic estimators of origin-destination matrices using traffic counts. Transp Sci 27(4):363–373zbMATHCrossRefGoogle Scholar
  44. Cassidy MJ, Han LD (1993) Proposed model for predicting motorist delays at two-lane highway work zones. J Transp Eng 119(1):27–42CrossRefGoogle Scholar
  45. Cassidy MJ, Rudjanakanoknad J (2005) Increasing the capacity of an isolated merge by metering its on-ramp. Transp Res Part B Methodol 39(10):896–913CrossRefGoogle Scholar
  46. Cassidy MJ, Windover JR (1995) Methodology for assessing dynamics of freeway traffic flow. Transp Res Rec 1484:73–79Google Scholar
  47. Cassidy MJ, Son Y et al (1994) Estimating motorist delay at two-lane highway work zones. Transp Res Part A Policy Pract 28(5):433–444CrossRefGoogle Scholar
  48. Castillo E, Menendez JM, Jimenez P (2008) Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations. Transp Res Part B Methodol 42(5):455–481CrossRefGoogle Scholar
  49. Catling I (1977) A time-dependent approach to junction delays. Traffic Eng Control 18(11):520–523, 526Google Scholar
  50. Chang GL, Mahmassani HS (1988) Travel time prediction and departure time adjustment behavior dynamics in a congested traffic system. Transp Res Part B Methodol 22B(3):217–232CrossRefGoogle Scholar
  51. Chang GL, Tao X (1999) Integrated model for estimating time varying network origin-destination distributions. Transp Res Part A Policy Pract 33(5):381–399CrossRefGoogle Scholar
  52. Chen SQ (2000) Comparing probabilistic and fuzzy set approaches for design in the presence of uncertainty. In: Aerospace and ocean engineering. PhD, Polytechnic Institute and State University, BlacksburgGoogle Scholar
  53. Chiu YC, Mahmassani HS (2001) Toward hybrid dynamic traffic assignment-models and solution procedures. In: IEEE intelligent transportation systems proceedings, IEEE conference on intelligent transportation systems, Proceedings, ITSC 2001, OaklandGoogle Scholar
  54. Coifman B (1998) New algorithm for vehicle reidentification and travel time measurement on freeways. In: Proceedings of the 1998 5th international conference on applications of advanced technologies in transportation, Newport Beach, Proceedings of the international conference on applications of advanced technologies in transportation engineering. ASCE, RestonGoogle Scholar
  55. Coifman B, Banerjee B (2002) Vehicle reidentification and travel time measurement on freeways using single loop detectors-from free flow through the onset of congestion. In: Proceedings of the seventh international conference on: applications of advanced technology in transportation, Cambridge, 5–7 Aug 2002. Proceedings of the international conference on applications of advanced technologies in transportation engineering. American Civil EngineersGoogle Scholar
  56. Coifman B, Cassidy M (2001) Vehicle reidentification and travel time measurement, Part I: Congested freeways. In: IEEE intelligent transportation systems proceedings, Conference IEEE on intelligent transportation systems, Proceedings, ITSC 2001, OaklandGoogle Scholar
  57. Coifman B, Ergueta E (2003) Improved vehicle reidentification and travel time measurement on congested freeways. J Transp Eng 129(5):475–483CrossRefGoogle Scholar
  58. Colyar JD, Rouphail NM (2003) Measured distributions of control delay on signalized arterials. Transp Res Rec 1852:1–9CrossRefGoogle Scholar
  59. Cremer M, Keller H (1987) New class of dynamic methods for the identification of origin-destination flows. Transp Res Part B Methodol 21(2):117–132CrossRefGoogle Scholar
  60. Cronje WB (1983a) Analysis of existing formulas for delay, overflow, and stops. Transp Res Rec 905:89–93Google Scholar
  61. Cronje WB (1983b) Derivation of equations for queue length, stops, and delay for fixed-time traffic signals. Transp Res Rec 905:93–95Google Scholar
  62. Cronje WB (1983c) Optimization model for isolated signalized traffic intersections. Transp Res Rec 905:80–83Google Scholar
  63. Cronje WB (1986) Comparative analysis of models for estimating delay for oversaturated conditions at fixed-time traffic signals. Transp Res Record 1091:48–59Google Scholar
  64. Dafermos S (1980) Traffic equilibrium and variational inequalities. Transp Sci 14(1):42–54MathSciNetCrossRefGoogle Scholar
  65. Daganzo CF, Laval JA (2005) On the numerical treatment of moving bottlenecks. Transp Res Part B Methodol 39(1):31–46CrossRefGoogle Scholar
  66. Daniel J, Fambro DB et al (1996) Accounting for nonrandom arrivals in estimate of delay at signalized intersections. Transp Res Rec 1555:9–16CrossRefGoogle Scholar
  67. Dantzig GB (1957) The shortest route problem. Oper Res 5:270–273CrossRefGoogle Scholar
  68. Dial R (1971) A probabilistic multipath traffic assignment model which obviates path enumeration. Transp Res 5:83–111CrossRefGoogle Scholar
  69. Dijkstra EW (1959) A note on two problems in connection with graphics. Numer Math 1:209–271CrossRefGoogle Scholar
  70. Dion F, Rakha H (2006) Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates. Transp Res Part B 40:745–766CrossRefGoogle Scholar
  71. Dion F, Rakha H et al (2004a) Comparison of delay estimates at under-saturated and over-saturated pre-timed signalized intersections. Transp Res Part B Methodol 38(2):99–122CrossRefGoogle Scholar
  72. Dion F, Rakha H et al (2004b) Evaluation of potential transit signal priority benefits along a fixed-time signalized arterial. J Transp Eng 130(3):294–303CrossRefGoogle Scholar
  73. Elefteriadou L, Fang C et al (2005) Methodology for evaluating the operational performance of interchange ramp terminals. Transp Res Rec 1920:13–24CrossRefGoogle Scholar
  74. Engelbrecht RJ, Fambro DB et al (1996) Validation of generalized delay model for oversaturated conditions. Transp Res Rec 1572:122–130CrossRefGoogle Scholar
  75. Evans JL, Elefteriadou L et al (2001) Probability of breakdown at freeway merges using Markov chains. Transp Res Part B Methodol 35(3):237–254CrossRefGoogle Scholar
  76. Fambro DB, Rouphail NM (1996) Generalized delay model for signalized intersections and arterial streets. Transp Res Rec 1572:112–121CrossRefGoogle Scholar
  77. Fang FC, Elefteriadou L et al (2003) Using fuzzy clustering of user perception to define levels of service at signalized intersections. J Transp Eng 129(6):657–663CrossRefGoogle Scholar
  78. Fisk C (1979) More paradoxes in the equilibrium assignment problem. Transp Res 13B:305–309CrossRefGoogle Scholar
  79. Flannery A, Kharoufeh JP et al (2005) Queuing delay models for single-lane roundabouts. Civ Eng Environ Syst 22(3):133–150CrossRefGoogle Scholar
  80. Frank M (1981) The Braess paradox. Math Program 20:283–302MathSciNetzbMATHCrossRefGoogle Scholar
  81. Frank M, Wolfe P (1956) An algorithm of quatdratic programming. Nav Res Logist 3:95–110CrossRefGoogle Scholar
  82. Friesz TL, Luque J, Tobin RL, Wie B-W (1989) Dynamic network traffic assignment considered as a continuous time optimal control problem. Oper Res 37(6):893–901MathSciNetzbMATHCrossRefGoogle Scholar
  83. Friesz TL, Bernstein D, Smith TE, Tobin RL, Wie BW (1993) A variational inequality formulation of the dynamic network user equilibrium problem. Oper Res 41(1):179–191MathSciNetzbMATHCrossRefGoogle Scholar
  84. Ghali MO, Smith MJ (1995) A model for the dynamic system optimum traffic assignment problem. Transp Res 29B(3):155–170CrossRefGoogle Scholar
  85. Greenshields BD (1934) A study of traffic capacity. Proc Highway Res Board 14:448–477Google Scholar
  86. Hagring O, Rouphail NM et al (2003) Comparison of capacity models for two-lane roundabouts. Transp Res Rec 1852:114–123CrossRefGoogle Scholar
  87. Hall MD, Van Vliet D et al (1980) SATURN a simulation assignment model of the evaluation of traffic management schemes. Traffic Eng Control 4:167–176Google Scholar
  88. Hawas YE (1995) A decentralized architecture and local search procedures for real-time route guidance in congested vehicular traffic networks. University of Texas, AustinGoogle Scholar
  89. Hawas YE (2004) Development and calibration of route choice utility models: neuro-fuzzy approach. J Transp Eng 130(2):171–182MathSciNetCrossRefGoogle Scholar
  90. Hawas YE, Mahmassani HS (1995) A decentralized scheme for real-time route guidance in vehicular traffic networks. In: Second world congress on intelligent transport systems, Yokohama, 1995, pp 1965–1963Google Scholar
  91. Hawas YE, Mahmassani HS (1997) Comparative analysis of robustness of centralized and distributed network route control systems in incident situations. Transp Res Rec 1537:83–90CrossRefGoogle Scholar
  92. Hawas YE, Mahmassani HS, Chang GL, Taylor R, Peeta S, Ziliaskopoulos A (1997) Development of dynasmart-X software for real-time dynamic traffic assignment. Center for Transportation Research, The University of Texas, AustinGoogle Scholar
  93. Hellinga BR, Van Aerde M (1998) Estimating dynamic O-D demands for a freeway corridor using loop detector data. Canadian Society for Civil Engineering, HalifaxGoogle Scholar
  94. Ho JK (1980) A successive linear optimization approach to the dynamic traffic assignment problem. Transp Sci 14(4):295–305MathSciNetCrossRefGoogle Scholar
  95. Hu SR, Madanat SM, Krogmeier JV, Peeta S (2001) Estimation of dynamic assignment matrices and OD demands using adaptive Kalman filtering. Intell Transp Syst J 6:281–300zbMATHGoogle Scholar
  96. Chen H-K, Hsueh C-F (1998) A model and an algorithm for the dynamic user-optimal route choice problem. Transp Res Part B Methodol 32B(3):219–234CrossRefGoogle Scholar
  97. Ishak S, Al-Deek H (2003) Performance evaluation of a shortterm freeway traffic prediction model. Transportation Research Board 82nd annual meeting, Washington, DCGoogle Scholar
  98. Janson BN (1991a) Convergent algorithm for dynamic traffic assignment. Transp Res Rec 1328:69–80Google Scholar
  99. Janson BN (1991b) Dynamic traffic assignment for urban road networks. Transp Res Part B Methodol 25B:2–3Google Scholar
  100. Jayakrishnan R, Mahmassani HS (1990) Dynamic simulation assignment methodology to evaluate in-vehicle information strategies in urban traffic networks. In: Winter simulation conference proceedings, New Orleans 1990. 90 Winter simulation conference winter simulation conference proceedings. IEEE, Piscataway (IEEE cat n 90CH2926–4)Google Scholar
  101. Jayakrishnan R, Mahmassani HS (1991) Dynamic modelling framework of real-time guidance systems in general urban traffic networks. In: Proceedings of the 2nd international conference on applications of advanced technologies in transportation engineering, Minneapolis. ASCE, New YorkGoogle Scholar
  102. Jayakrishnan R, Mahmassani HS et al (1993) User-friendly simulation model for traffic networks with ATIS/ATMS. In: Proceedings of the 5th international conference on computing in civil and building engineering V ICCCBE, Anaheim 1993. ASCE, New YorkGoogle Scholar
  103. Jeffery DJ (1981) The potential benefits of route guidance. TRRL, Department of Transportation, CrowthorneGoogle Scholar
  104. Jha M, Madanat S, Peeta S (1998) Perception updating and day-to-day travel choice dynamics in traffic networks with information provision. Transp Res Part C Emerg Technol 6C(3):189–212CrossRefGoogle Scholar
  105. Katsikopoulos KV, Duse-Anthony Y et al (2000) The framing of drivers’ route choices when travel time information is provided under varying degrees of cognitive load. J Hum Factors Ergon Soc 42(3):470–481CrossRefGoogle Scholar
  106. Kerner BS (2004a) The physics of traffic. Springer, BerlinCrossRefGoogle Scholar
  107. Kerner BS (2004b) Three-phase traffic theory and highway capacity. Physica A 333(1–4):379–440MathSciNetCrossRefGoogle Scholar
  108. Kerner BS (2005) Control of spatiotemporal congested traffic patterns at highway bottlenecks. Physica A 355(2–4):565–601CrossRefGoogle Scholar
  109. Kerner BS, Klenov SL (2006) Probabilistic breakdown phenomenon at on-ramp bottlenecks in three-phase traffic theory: congestion nucleation in spatially non-homogeneous traffic. Physica A 364:473–492CrossRefGoogle Scholar
  110. Kerner BS, Rehborn H et al (2004) Recognition and tracking of spatial-temporal congested traffic patterns on freeways. Transp Res Part C Emerg Technol 12(5):369–400CrossRefGoogle Scholar
  111. Khattak AJ, Schofer JL, Koppelman FS (1993) Commuters’ enroute diversion and return decisions: analysis and implications for advanced traveler information systems. Transp Res Policy Pract 27A(2):101CrossRefGoogle Scholar
  112. Kim H, Baek S et al (2001) Origin-destination matrices estimated with a genetic algorithm from link traffic counts. Transp Res Rec 1771:156–163CrossRefGoogle Scholar
  113. Koutsopoulos HN, Polydoropoulou A et al (1995) Travel simulators for data collection on driver behavior in the presence of information. Transp Res Part C Emerg Technol 3(3):143CrossRefGoogle Scholar
  114. Krishnamurthy S, Coifman B (2004) Measuring freeway travel times using existing detector infrastructure. In: Proceedings 7th international IEEE conference on intelligent transportation systems, ITSC, Washington, DCGoogle Scholar
  115. Laval JA, Daganzo CF (2006) Lane-changing in traffic streams. Transp Res Part B Methodol 40(3):251–264CrossRefGoogle Scholar
  116. Lawson TW, Lovell DJ et al (1996) Using input-output diagram to determine spatial and temporal extents of a queue upstream of a bottleneck. Transp Res Rec 1572:140–147CrossRefGoogle Scholar
  117. LeBlanc LJ (1975) An algorithm for discrete network design problem. Transp Sci 9:183–199CrossRefGoogle Scholar
  118. LeBlanc LJ, Abdulaal M (1970) A comparison of user-optimum versus system-optimum traffic assignment in transportation network design. Transp Res 18B:115–121MathSciNetGoogle Scholar
  119. LeBlanc LJ, Morlok EK et al (1974) An accurate and efficient approach to equilibrium traffic assignment on congested networks. Transp Res Rec 491:12–23Google Scholar
  120. Lee S, Fambro D (1999) Application of the subset ARIMA model for short-term freeway traffic volume forecasting. Transp Res Rec 1678:179–188CrossRefGoogle Scholar
  121. Leonard DR, Tough JB et al (1978) CONTRAM a traffic assignment model for predicting flows and queues during peak periods. TRRL SR 568. Transport Research Laboratory, CrowthomeGoogle Scholar
  122. Lertworawanich P, Elefteriadou L (2001) Capacity estimations for type B weaving areas based on gap acceptance. Transp Res Rec 1776:24–34CrossRefGoogle Scholar
  123. Lertworawanich P, Elefteriadou L (2003) A methodology for estimating capacity at ramp weaves based on gap acceptance and linear optimization. Transp Res Part B Methodol 37(5):459–483CrossRefGoogle Scholar
  124. Li Y (2001) Development of dynamic traffic assignment models for planning applications. Northwestern University, EvanstonGoogle Scholar
  125. Li J, Rouphail NM et al (1994) Overflow delay estimation for a simple intersection with fully actuated signal control. Transp Res Rec 1457:73–81Google Scholar
  126. Lighthill MJ, Witham GB (1955) On kinematic waves. I: Flood movement in long rivers, II A theory of traffic flow on long crowded roads. Proc R Soc Lond A 229:281–345CrossRefGoogle Scholar
  127. Lorenz MR, Elefteriadou L (2001) Defining freeway capacity as function of breakdown probability. Transp Res Rec 1776:43–51CrossRefGoogle Scholar
  128. Lotan T (1997) Effects of familiarity on route choice behavior in the presence of information. Transp Res Part C Emerg Technol 5(3–4):225–243CrossRefGoogle Scholar
  129. Mahmassani H, Jou R-C (2000) Transferring insights into commuter behavior dynamics from laboratory experiments to ®eld surveys. Transp Res Part A Policy Pract 34A(4):243–260CrossRefGoogle Scholar
  130. Mahmassani H, Peeta S (1992) System optimal dynamic assignment for electronic route guidance in a congested traffic network. In: Gartner NH, Improta G (eds) Urban traffic networks. Dynamic flow modelling and control. Springer, Berlin, pp 3–37Google Scholar
  131. Mahmassani HS, Peeta S (1993) Network performance under system optimal and user equilibrium dynamic assignments: implications for ATIS. Transp Res Rec 1408:83–93Google Scholar
  132. Mahmassani HS, Peeta S (1995) System optimal dynamic assignment for electronic route guidance in a congested traffic network. In: Gartner NH, Improta G (eds) Urban traffic networks: dynamic flow modeling and control. Springer, Berlin, pp 3–37zbMATHCrossRefGoogle Scholar
  133. Mahmassani HS, Peeta S, Hu T, Ziliaskopoulos A (1993) Algorithm for dynamic route guidance in congested networks with multiple user information availability groups. In: 26th international symposium on automotive technology and automation, AachenGoogle Scholar
  134. Mahmassani HS, Chiu Y-C, Chang GL, Peeta S, Ziliaskopoulos A (1998a) Off-line laboratory test results for the DYNASMARTX real-time dynamic traffic assignment system. Center for Transportation Research, The University of Texas, AustinGoogle Scholar
  135. Mahmassani HS, Hawas Y, Abdelghany K, Abdelfatah A, Chiu Y-C, Kang Y, Chang GL, Peeta S, Taylor R, Ziliaskopoulos A (1998b) DYNASMART-X, vol II: Analytical and algorithmic aspects. Center for Transportation Research, The University of Texas, AustinGoogle Scholar
  136. Mahmassani HS, Hawas Y, Hu T-Y, Ziliaskopoulos A, Chang G-L, Peeta S, Taylor R (1998c) Development of Dynasmart-X software for real-time dynamic traffic assignment. Technical report ST067-85-Tast E (revised) submitted to Oak Ridge National Laboratory under subcontract 85X-SU565CGoogle Scholar
  137. Matsoukis EC (1986) Road traffic assignment, a review. Part I: Non-equilibrium methods. Transp Plan Technol 11:69–79CrossRefGoogle Scholar
  138. Matsoukis EC, Michalopolos PC (1986) Road traffic assignment, a review. Part II: Equilibrium methods. Transp Plan Technol 11:117–135CrossRefGoogle Scholar
  139. Mekky A (1995) Toll revenue and traffic study of highway 407 in Toronto. Transp Res Rec 1498:5–15Google Scholar
  140. Mekky A (1996) Modeling toll pricing strategies in greater Toronto areas. Transp Res Rec 1558:46–54CrossRefGoogle Scholar
  141. Mekky A (1998) Evaluation of two tolling strategies for highway 407 in Toronto. Transp Res Rec 1649:17–25CrossRefGoogle Scholar
  142. Merchant DK, Nemhauser GL (1978a) A model and an algorithm for the dynamic traffic assignment problems. Transp Sci 12(3):183–199CrossRefGoogle Scholar
  143. Merchant DK, Nemhauser GL (1978b) Optimality conditions for a dynamic traffic assignment model. Transp Sci 12(3):200–207CrossRefGoogle Scholar
  144. Minderhoud MM, Elefteriadou L (2003) Freeway weaving: comparison of highway capacity manual 2000 and Dutch guidelines. Transp Res Rec 1852:10–18CrossRefGoogle Scholar
  145. Moskowitz K (1956) California method for assigning directed traffic to proposed freeways. Bull Highw Res Board 130:1–26Google Scholar
  146. Munnich LW Jr, Hubert HH et al (2007) L-394 MnPASS high-occupancy toll lanes planning and operational issues and outcomes (lessons learning in year 1). Transp Res Rec 1996:49–57CrossRefGoogle Scholar
  147. Murchland JD (1970) Braess’s paradox of traffic flow. Transp Res 4:391–394CrossRefGoogle Scholar
  148. Nagel K (1996) Particle hopping model and traffic flow theory. Phys Rev E 53(5):4655–4672CrossRefGoogle Scholar
  149. Nagel K, Schrekenberg M (1992) Cellular automaton model for freeway traffic. J Phys 2(20):2212–2229Google Scholar
  150. Nagel K, Schrekenberg M (1995) Traffic jam dynamics in stochastic cellular automata. US D Energy, Los Alamos National Laboratory, LA-UR-95-2132, Los AlamosGoogle Scholar
  151. Nakayama S, Kitamura R (2000) Route choice model with inductive learning. Transp Res Rec 1725:63–70CrossRefGoogle Scholar
  152. Nakayama S, Kitamura R et al (2001) Drivers’ route choice rules and network behavior: do drivers become rational and homogeneous through learning? Transp Res Rec 1752:62–68CrossRefGoogle Scholar
  153. Newell GF (1965) Approximation methods for queues with application to the fixed-cycle traffic light. SIAM Rev 7:223–240MathSciNetzbMATHCrossRefGoogle Scholar
  154. Newell GF (1999) Delays caused by a queue at a freeway exit ramp. Transp Res Part B Methodol 33(5):337–350CrossRefGoogle Scholar
  155. Nguyen S (1969) An algorithm for the assignment problem. Transp Sci 8:203–216CrossRefGoogle Scholar
  156. Nie Y, Zhang HM et al (2005) Inferring origin-destination trip matrices with a decoupled GLS path flow estimator. Transp Res Part B Methodol 39(6):497–518CrossRefGoogle Scholar
  157. Noonan J, Shearer O (1998) Intelligent transportation systems field operational test: cross-cutting study advance traveler information systems. US Department of Transportation, Federal Highways Administration, Intelligent Transportation System, Washington, DCGoogle Scholar
  158. Okutani I (1987) The Kalman filtering approaches in some transportation and traffic problems. In: Proceedings of the tenth international symposium on transportation and traffic theory. Elsevier, New YorkGoogle Scholar
  159. Park B (2002) Hybrid neuro-fuzzy application in short-term freeway traffic volume forecasting. Transp Res Rec 1802:190–196CrossRefGoogle Scholar
  160. Park S, Rakha H (2006) Energy and environmental impacts of roadway grades. Transp Res Rec 1987:148–160CrossRefGoogle Scholar
  161. Park D, Rilett LR (1998) Forecasting multiple-period freeway link travel times using modular neural networks. Transp Res Rec 1617:163–170CrossRefGoogle Scholar
  162. Park D, Rilett LR (1999) Forecasting freeway link travel times with a multilayer feedforward neural network. Comput-Aided Civ Infrastruct Eng 14(5):357–367CrossRefGoogle Scholar
  163. Park D, Rilett LR et al (1998) Forecasting multiple-period freeway link travel times using neural networks with expanded input nodes. In: Proceedings of the 1998 5th international conference on applications of advanced technologies in transportation, Newport Beach and Proceedings of the international conference on applications of advanced technologies in transportation engineering 1998, ASCE, RestonGoogle Scholar
  164. Park D, Rilett LR et al (1999) Spectral basis neural networks for real-time travel time forecasting. J Transp Eng 125(6):515–523CrossRefGoogle Scholar
  165. Pavlis Y, Papageorgiou M (1999) Simple decentralized feedback strategies for route guidance in traffic networks. Transp Sci 33(3):264–278zbMATHCrossRefGoogle Scholar
  166. Peeta S (1994) System optimal dynamic traffic assignment in congested networks with advanced information systems. University of Texas, AustinGoogle Scholar
  167. Peeta S, Bulusu S (1999) Generalized singular value decomposition approach for consistent on-line dynamic traffic assignment. Transp Res Rec 1667:77CrossRefGoogle Scholar
  168. Peeta S, Mahmassani HS (1995a) Multiple user classes real-time traffic assignment for online operations: a rolling horizon solution framework. Transp Res Part C Emerg Technol 3C(2):83CrossRefGoogle Scholar
  169. Peeta S, Mahmassani HS (1995b) System optimal and user equilibrium time-dependent traffic assignment in congested networks. Ann Oper Res 60:81–113zbMATHCrossRefGoogle Scholar
  170. Peeta S, Paz A (2006) Behavior-consistent within-day traffic routing under information provision. In: IEEE intelligent transportation systems conference, Toronto, pp 212–217Google Scholar
  171. Peeta S, Ramos JL (2006) Driver response to variable message signs-based traffic information. Intell Transp Syst 153(1):2–10Google Scholar
  172. Peeta S, Yang T-H (2000) Stability of large-scale dynamic traffic networks under on-line control strategies. In: 6th international conference on applications of advanced technologies in transportation engineering, Singapore, paper no. 11 (eProceedings on CD), p 9Google Scholar
  173. Peeta S, Yang T-H (2003) Stability issues for dynamic traffic assignment. Automatica 39(1):21–34MathSciNetzbMATHCrossRefGoogle Scholar
  174. Peeta S, Yu JW (2004) Adaptability of a hybrid route choice model to incorporating driver behavior dynamics under information provision. IEEE Trans Syst Man Cybern Part A Syst Humans 34(2):243–256CrossRefGoogle Scholar
  175. Peeta S, Yu JW (2006) Behavior-based consistency-seeking models as deployment alternatives to dynamic traffic assignment models. Transp Res Part C Emerg Technol 14(2):114–138CrossRefGoogle Scholar
  176. Peeta S, Zhou C (1999a) On-line dynamic update heuristics for robust guidance. In: International conference modeling and management in transportation, Cracow, October 1999Google Scholar
  177. Peeta S, Zhou C (1999b) Robustness of the off-line a priori stochastic dynamic traffic assignment solution for on-line operations. Transp Res Part C Emerg Technol 7C(5):281–303CrossRefGoogle Scholar
  178. Peeta S, Ziliaskopoulos AK (2001) Foundations of dynamic traffic assignment: the past, the present and the future. Netw Spat Econ 1(3–4):233CrossRefGoogle Scholar
  179. Peeta S, Mahmassani HS et al (1991) Effectiveness of real-time information strategies in situations of non-recurrent congestion. In: Proceedings of the 2nd international conference on applications of advanced technologies in transportation engineering, Minneapolis. ASCE, New YorkGoogle Scholar
  180. Peeta S, Ramos JL, Pasupathy R (2000) Content of variable message signs and on-line driver behavior. Transp Res Rec 1725:102–108CrossRefGoogle Scholar
  181. Rakha H (1990) An evaluation of the benefits of user and system optimised route guidance strategies. Civil Engineering, Queen’s University, KingstonGoogle Scholar
  182. Rakha H, Ahn K (2004) Integration modeling framework for estimating mobile source emissions. J Transp Eng 130(2):183–193CrossRefGoogle Scholar
  183. Rakha H, Arafeh M (2007) Tool for calibrating steady-state traffic stream and car-following models. In: Transportation research board annual meeting, Washington, DC, 22–25 Jan 2008Google Scholar
  184. Rakha H, Crowther B (2002) Comparison of greenshields, pipes, and van aerde car-following and traffic stream models. Transp Res Rec 1802:248–262CrossRefGoogle Scholar
  185. Rakha H, Lucic I (2002) Variable power vehicle dynamics model for estimating maximum truck acceleration levels. J Transp Eng 128(5):412–419CrossRefGoogle Scholar
  186. Rakha HA, Van Aerde MW (1996) Comparison of simulation modules of TRANSYT and integration models. Transp Res Rec 1566:1–7CrossRefGoogle Scholar
  187. Rakha H, Zhang Y (2004a) INTEGRATION 2.30 framework for modeling lane-changing behavior in weaving sections. Transp Res Rec 1883:140–149CrossRefGoogle Scholar
  188. Rakha H, Zhang Y (2004b) Sensitivity analysis of transit signal priority impacts on operation of a signalized intersection. J Transp Eng 130(6):796–804CrossRefGoogle Scholar
  189. Rakha H, Van Aerde M et al (1989) Evaluating the benefits and interactions of route guidance and traffic control strategies using simulation. In: First vehicle navigation and information systems conference VNIS ‘89, Toronto. IEEE, PiscatawayGoogle Scholar
  190. Rakha H, Van Aerde M et al (1998) Construction and calibration of a large-scale microsimulation model of the Salt Lake area. Transp Res Rec 1644:93–102CrossRefGoogle Scholar
  191. Rakha H, Medina A et al (2000) Traffic signal coordination across jurisdictional boundaries: field evaluation of efficiency, energy, environmental, and safety impacts. Transp Res Rec 1727:42–51CrossRefGoogle Scholar
  192. Rakha H, Kang Y-S et al (2001a) Estimating vehicle stops at undersaturated and oversaturated fixed-time signalized intersections. Transp Res Rec 1776:128–137CrossRefGoogle Scholar
  193. Rakha H, Lucic I et al (2001b) Vehicle dynamics model for predicting maximum truck acceleration levels. J Transp Eng 127(5):418–425CrossRefGoogle Scholar
  194. Rakha H, Ahn K et al (2004a) Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions. Transp Res Part D Transp Environ 9(1):49–74CrossRefGoogle Scholar
  195. Rakha H, Pasumarthy P et al (2004b) Modeling longitudinal vehicle motion: issues and proposed solutions. In: Transport science and technology congress, Athens, Sep 2004Google Scholar
  196. Rakha H, Pasumarthy P et al (2004c) The INTEGRATION framework for modeling longitudinal vehicle motion. TRANSTEC, AthensGoogle Scholar
  197. Rakha H, Snare M et al (2004d) Vehicle dynamics model for estimating maximum light-duty vehicle acceleration levels. Transp Res Rec 1883:40–49CrossRefGoogle Scholar
  198. Rakha H, Flintsch AM et al (2005a) Evaluating alternative truck management strategies along interstate 81. Transp Res Rec 1925:76–86CrossRefGoogle Scholar
  199. Rakha H, Paramahamsan H et al (2005b) Comparison of static maximum likelihood origin-destination formulations. Transportation and traffic theory: flow, dynamics and human interaction. In: Proceedings of the 16th international symposium on transportation and traffic theory (ISTTT16), pp 693–716Google Scholar
  200. Ran B, Boyce DE (1996) A link-based variational inequality formulation of ideal dynamic user-optimal route choice problem. Res Part C Emerg Technol 4C(1):1–12Google Scholar
  201. Ran B, Shimazaki T (1989a) A general model and algorithm for the dynamic traffic assignment problems. In: Fifth world conference on transport research, transport policy, management and technology towards, Yokohama, 2001Google Scholar
  202. Ran B, Shimazaki T (1989b) Dynamic user equilibrium traffic assignment for congested transportation networks. In: Fifth world conference on transport research, Yokohama, 1989Google Scholar
  203. Ran B, Boyce DE, LeBlanc LJ (1993) A new class of instantaneous dynamic user-optimal traffic assignment models. Oper Res 41(1):192–202zbMATHCrossRefGoogle Scholar
  204. Ran B, Hall RW, Boyce DE (1996) A link-based variational inequality model for dynamic departure time/route choice. Transp Res Methodol 30B(1):31–46CrossRefGoogle Scholar
  205. Randle J (1979) A convergence probabilistic road assignment model. Traffic Eng Control 11:519–521Google Scholar
  206. Richards PI (1956) Shock waves on the highway. Oper Res 4:42–51MathSciNetCrossRefGoogle Scholar
  207. Rilett L, Aerde V (1993) Modeling route guidance using the integration model. In: Proceedings of the pacific rim trans tech conference, Seattle, 1993 and Proceedings of the ASCE international conference on applications of advanced technologies in transportation engineering. ASCE, New YorkGoogle Scholar
  208. Rilett L, Van Aerde M (1991a) Routing based on anticipated travel times. In: Proceedings of the 2nd international conference on applications of advanced technologies in transportation engineering, Minneapolis. ASCE, New YorkGoogle Scholar
  209. Rilett LR, van Aerde MW (1991b) Modelling distributed realtime route guidance strategies in a traffic network that exhibits the Braess paradox. In: Vehicle navigation & information systems conference proceedings part 2 (of 2). Dearborn, 1991. Proceedings society of automotive engineers n P-253. SAE, WarrendaleGoogle Scholar
  210. Rilett LR, Van Aerde M et al (1991) Simulating the TravTek route guidance logic using the integration traffic model. In: Vehicle navigation & information systems conference proceedings part 2 (of 2). Dearborn, 1991. In: Proceedings society of automotive engineers n P-253, SAE. WarrendaleGoogle Scholar
  211. Rouphail NM (1988) Delay models for mixed platoon and secondary flows. J Transp Eng 114(2):131–152CrossRefGoogle Scholar
  212. Rouphail NM, Akcelik R (1992) Preliminary model of queue interaction at signalised paired intersections. In: Proceedings of the 16th ARRB conference, Perth, 9–12 November 1992. Congestion management proceedings conference of the Australian Road Research Board. Australian Road Research Board, NunawadingGoogle Scholar
  213. Schofer AJKFSKJL (1993) Stated preferences for investigating commuters’ diversion propensity. Transportation 20(2):107–127CrossRefGoogle Scholar
  214. Sheffi Y (1985) Urban transportation networks: equilibrium analysis with mathematical programming methods. Prentice Hall, Englewood CliffsGoogle Scholar
  215. Sheffi Y, Powell W (1981) A comparison of stochastic and deterministic traffic assignment over congested networks. Transp Res 15B:65–88Google Scholar
  216. Shen W, Nie Y et al (2006) Path-based system optimal dynamic traffic assignment models: formulations and solution methods. In: IEEE intelligent transportation systems conference IEEE, Toronto, pp 1298–1303Google Scholar
  217. Sherali HD, Arora N, Hobeika AG (1997) Parameter optimization methods for estimating dynamic origin-destination triptables. Transp Res Part B Methodol 31B(2):141–157CrossRefGoogle Scholar
  218. Sherali HD, Desai J et al (2006) A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times. Transp Res Part B 40:857–871CrossRefGoogle Scholar
  219. Simon HA (1947) Administrative behavior. Am Polit Sci Rev 41(6)Google Scholar
  220. Simon HA (1955) A behavioral model of rational choice. Q J Econ 69(1):99–118CrossRefGoogle Scholar
  221. Simon H (1957) Models of man, social and rational. Adm Sci Q 2(2)Google Scholar
  222. Sivanandan R, Dion F et al (2003) Effect of variable-message signs in reducing railroad crossing impacts. Transp Res Rec 1844:85–93CrossRefGoogle Scholar
  223. Smock R (1962) An iterative assignment approach to capacity-restraint on arterial networks. Bull Highw Res Board 347:226–257Google Scholar
  224. Srinivasan KK, Mahmassani HS (2000) Modeling inertia and compliance mechanisms in route choice behavior under realtime information. Transp Res Rec 1725:45–53CrossRefGoogle Scholar
  225. Steinberg R, Zangwill WI (1983) The prevalence of braess’ paradox. Transp Sci 17:301–318CrossRefGoogle Scholar
  226. Stewart N (1980) Equilibrium versus system-optimal flow: some examples. Transp Res 14A:81–84CrossRefGoogle Scholar
  227. Talaat H, Abdulhai B (2006) Modeling driver psychological deliberation during dynamic route selection processes. In: 2006 IEEE intelligent transportation systems conference, Toronto, pp 695–700Google Scholar
  228. Tarko A, Rouphail N et al (1993) Overflow delay at a signalized intersection approach influenced by an upstream signal. An analytical investigation. Transp Res Rec 1398:82–89Google Scholar
  229. Van Aerde M (1985) Modelling of traffic flows, assignment and queueing in integrated freeway/traffic signal networks. In: Civil engineering. PhD, University of Waterloo, WaterlooGoogle Scholar
  230. Van Aerde M, Rakha H (1989) Development and potential of system optimized route guidance strategies. In: IEEE vehicle navigation and information systems conference IEEE, Toronto, pp 304–309Google Scholar
  231. Van Aerde M, Rakha H (2007) INTEGRATION © Release 2.30 for Windows: user’s guide vol I: fundamental model features. M Van Aerde & Assoc, Ltd, BlacksburgGoogle Scholar
  232. Van Aerde M, Yagar S (1988a) Dynamic integrated freeway/traffic signal networks: a routeing-based modelling approach. Transp Res 22A(6):445–453Google Scholar
  233. Van Aerde M, Yagar S (1988b) Dynamic integrated freeway/traffic signal networks: problems and proposed solutions. Transp Res 22A(6):435–443CrossRefGoogle Scholar
  234. Van Aerde, M, Hellinga BR et al (1993) QUEENSOD: a method for estimating time varying origin-destination demands for freeway corridors/networks. In: 72nd annual meeting of the transportation research board, Washington, DCGoogle Scholar
  235. Van Aerde M, Rakha H et al (2003) Estimation of origin-destination matrices: relationship between practical and theoretical considerations. Transp Res Rec 1831:122–130CrossRefGoogle Scholar
  236. Van Der Zijpp NJ, De Romph E (1997) A dynamic traffic forecasting application on the Amsterdam beltway. Int J Forecast 13:87–103CrossRefGoogle Scholar
  237. Van Vliet D (1976) Road assignment. Transp Res 10:137–157CrossRefGoogle Scholar
  238. Van Vliet D (1982) SATURN a modern assignment model. Traffic Eng Control 12:578–581Google Scholar
  239. Van Zuylen JH, Willumsen LG (1980) The most likely trip matrix estimated from traffic counts. Transp Res 14B:281–293CrossRefGoogle Scholar
  240. Walker N, Fain WB et al (1997) Aging and decision making: driving-related problem solving. J Hum Factors Ergon Soc 39(3):438–444(7)CrossRefGoogle Scholar
  241. Waller ST (2000) Optimization and control of stochastic dynamic transportation systems: formulations, solution methodologies, and computational experience. PhD, Northwestern University, EvanstonGoogle Scholar
  242. Waller ST, Ziliaskopoulos AK (2006) A chance-constrained based stochastic dynamic traffic assignment model: analysis, formulation and solution algorithms. Transp Res Part C Emerg Technol 14(6):418–427CrossRefGoogle Scholar
  243. Wardrop J (1952) Some theoretical aspects of road traffic research. Institute of Civil Engineers, pp 325–362Google Scholar
  244. Webster F (1958) Traffic signal settings. HMsSO Road Research Laboratory, LondonGoogle Scholar
  245. Webster FV, Cobbe BM (1966) Traffic signals. HMsSO Road Research Laboratory, LondonGoogle Scholar
  246. Wie BW (1991) Dynamic analysis of user-optimized network flows with elastic travel demand. Transp Res Rec 1328:81–87Google Scholar
  247. Willumsen LG (1978) Estimation of an O-D matrix from traffic counts: a review. Institute for Transport Studies, Working paper no 99, Leeds University, LeedsGoogle Scholar
  248. Wilson AG (1970) Entropy in urban and regional modelling. Pion, LondonGoogle Scholar
  249. Wu J, Chang G-L (1996) Estimation of time-varying origin-destination distributions with dynamic screenline flows. Transp Res Part B Methodol 30B(4):277–290MathSciNetCrossRefGoogle Scholar
  250. Yagar S (1971) Dynamic traffic assignment by individual path minimization and queueing. Transp Res 5:179–196CrossRefGoogle Scholar
  251. Yagar S (1974) Dynamic traffic assignment by individual path minimization and queueing. Transp Res 5:179–196CrossRefGoogle Scholar
  252. Yagar S (1975) CORQ a model for predicting flows and queues in a road corridor. Transp Res 553:77–87Google Scholar
  253. Yagar S (1976) Measures of the sensitivity and effectiveness of the CORQ traffic model. Transp Res Rec 562:38–48Google Scholar
  254. Yang T-H (2001) Deployable stable traffic assignment models for control in dynamic traffic networks: a dynamical systems approach. PhD, Purdue University, West LafayetteGoogle Scholar
  255. Yang Q, Ben-Akiva ME (2000) Simulation laboratory for evaluating dynamic traffic management systems. Transp Res Rec 1710:122–130CrossRefGoogle Scholar
  256. Zhou X, Mahmassani HS (2006) Dynamic origin-destination demand estimation using automatic vehicle identification data. IEEE Trans Intell Transp Syst 7(1):105–114CrossRefGoogle Scholar
  257. Zhou Y, Sachse T (1997) A few practical problems on the application of OD-estimation in motorway networks. TOP 5(1):61–80MathSciNetzbMATHCrossRefGoogle Scholar
  258. Ziliaskopoulos AK (2000) A linear programming model for the single destination system optimum dynamic traffic assignment problem. Transp Sci 34(1):37–49zbMATHCrossRefGoogle Scholar
  259. Ziliaskopoulos AK, Waller ST (2000) An Internet-based geographic information system that integrates data, models and users for transportation applications. Transp Res Part C Emerg Technol 8C:1–6Google Scholar
  260. Ziliaskopoulos A, Wardell W (2000) Intermodal optimum path algorithm for multimodal networks with dynamic arc travel times and switching delays. Eur J Oper Res 125(3):486–502MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag  2009

Authors and Affiliations

  • Hesham Rakha
    • 1
  • Aly Tawfik
    • 1
  1. 1.Center for Sustainable Mobility, Virginia Tech, Transportation InstituteVirginia Polytechnic Institute and State UniversityBlacksburgUSA

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