A Probabilistic Traffic-Theoretic Network Loading Model Suitable for Large-Scale Network Analysis

References

• Boel R, Mihaylova L (2006) A compositional stochastic model for real time freeway traffic simulation. Transportation Res. Part B: Methodological 40(4):319–334.Crossref, Google Scholar
• Daganzo C (2005) A variational formulation of kinematic waves: Basic theory and complex boundary conditions. Transportation Res. Part B: Methodological 39(2):187–196.Crossref, Google Scholar
• Deng W, Lei H, Zhou X (2013) Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach. Transportation Res. Part B: Methodological 57:132–157.Crossref, Google Scholar
• Dumont AG, Bert E (2006) Simulation de l’agglomération lausannoise, SIMLO. Report, Laboratoire des voies de circulation, ENAC, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. http://web.mit.edu/osorioc/www/papers/dumont06BertRapport.pdf.Google Scholar
• Endres DM, Schindelin JE (2003) A new metric for probability distributions. IEEE Trans. Inform. Theory 49(7):1858–1860.Crossref, Google Scholar
• Flötteröd G, Osorio C (2017) Stochastic network link transmission model. Transportation Res. Part B: Methodological 102:180–209.Crossref, Google Scholar
• Heidemann D (2001) A queueing theory model of nonstationary traffic flow. Transportation Sci. 35(4):405–412.Link, Google Scholar
• Jabari SE (2012) A stochastic model of macroscopic traffic flow: Theoretical foundations. Doctoral thesis, University of Minnesota, Minneapolis.Google Scholar
• Jabari SE, Liu HX (2012) A stochastic model of traffic flow: Theoretical foundations. Transportation Res. Part B: Methodological 46(1):156–174.Crossref, Google Scholar
• Jabari SE, Liu HX (2013) A stochastic model of traffic flow: Gaussian approximation and estimation. Transportation Res. Part B: Methodological 47:15–41.Crossref, Google Scholar
• Jabari SE, Zheng J, Liu HX (2014) A probabilistic stationary speed-density relation based on Newell’s simplified car-following model. Transportation Res. Part B: Methodological 68:205–223.Crossref, Google Scholar
• Kullback S, Leibler RA (1951) On information and sufficiency. Ann. Math. Statist. 22(1):79–86.Crossref, Google Scholar
• Lam WH, Shao H, Sumalee A (2008) Modeling impacts of adverse weather conditions on a road network with uncertainties in demand and supply. Transportation Res. Part B: Methodological 42(10):890–910.Crossref, Google Scholar
• Larson RC, Odoni AR (1981) Urban Operations Research (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
• Laval JA, Castrillón F (2015) Stochastic approximations for the macroscopic fundamental diagram of urban networks. Transportation Res. Part B: Methodological 81:904–916.Crossref, Google Scholar
• Laval JA, Chilukuri BR (2014) The distribution of congestion on a class of stochastic kinematic wave models. Transportation Sci. 48(2):217–224.Link, Google Scholar
• Lighthill M, Whitham J (1955) On kinematic waves. II. A theory of traffic flow on long crowded roads. Proc. Roy. Soc. London A 229(1178):317–345.Crossref, Google Scholar
• MATLAB (2016) Optimization Toolbox: User’s Guide (R2016a) (MathWorks, Natick, MA).Google Scholar
• Morse PM (1958) Queues, Inventories and Maintenance: The Analysis of Operational Systems with Variable Demand and Supply (John Wiley & Sons, New York).Crossref, Google Scholar
• Newell G (1993) A simplified theory of kinematic waves in highway traffic, part I: General theory. Transportation Res. Part B: Methodological 27(4):281–287.Crossref, Google Scholar
• Osorio C (2010) Mitigating network congestion: Analytical models, optimization methods and their applications. Doctoral thesis, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.Google Scholar
• Osorio C, Chong L (2015) A computationally efficient simulation-based optimization algorithm for large-scale urban transportation problems. Transportation Sci. 49(3):623–636.Link, Google Scholar
• Osorio C, Flötteröd G (2015) Capturing dependency among link boundaries in a stochastic dynamic network loading model. Transportation Sci. 49(2):420–431.Link, Google Scholar
• Osorio C, Wang C (2017) On the analytical approximation of joint aggregate queue-length distributions for traffic networks: A stationary finite capacity Markovian network approach. Transportation Res. Part B: Methodological 95:305–339.Crossref, Google Scholar
• Osorio C, Yamani J (2017) Analytical and scalable analysis of transient tandem Markovian finite capacity queueing networks. Transportation Sci. 51(3):823–840.Link, Google Scholar
• Osorio C, Flötteröd G, Bierlaire M (2011) Dynamic network loading: A stochastic differentiable model that derives link state distributions. Transportation Res. Part B: Methodological 45(9):1410–1423.Crossref, Google Scholar
• Osorio C, Chen X, Gao J, Talas M, Marsico M (2015) On the control of highly congested urban networks with intricate traffic patterns: A New York City case study. Technical report, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge. http://web.mit.edu/osorioc/www/papers/osoChenNYCDOTOfflineSO.pdf.Google Scholar
• Reibman A (1991) A splitting technique for Markov chain transient solution. Stewart WJ, ed. Numerical Solution of Markov Chains (Marcel Dekker, New York), 373–400.Google Scholar
• Richards PI (1956) Shock waves on highways. Oper. Res. 4(1):42–51.Link, Google Scholar
• Stafford R (2006) Random vectors with fixed sum. Accessed June 1,2015. http://www.mathworks.com/matlabcentral/fileexchange/9700.Google Scholar
• Sumalee A, Zhong RX, Pan TL, Szeto WY (2011) Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment. Transportation Res. Part B: Methodological 45(3):507–533.Crossref, Google Scholar
• Trafficware (2011) Synchro Studio 8 User Guide (Trafficware, Sugar Land, TX).Google Scholar
• Transport for London (2010) Traffic modelling guidelines: TfL traffic manager and network performance best practice, version 3.0. Technical report, Transport for London, London.Google Scholar
• Transport Simulation Systems (2014) AIMSUN 8.1 Microsimulator User’s Manual (Transport Simulation Systems, Barcelona).Google Scholar
• U.S. Department of Transportation (2008) Transportation vision for 2030. Technical report, Research and Innovative Technology Administration, U.S. Department of Transportation, Washington, DC.Google Scholar
• Yperman I, Tampere C, Immers B (2007) A kinematic wave dynamic network loading model including intersection delays. Transportation Res. Board Annual Meeting, Washington DC.Google Scholar