Parameter estimation for Gipps’ car following model in a Bayesian framework
Car following model is an important part in traffic modelling and has attracted a lot of attentions in the literature. As the proposed car following models become more complex with more components, reliably estimating their parameters becomes crucial to enhance model predictive performance. While mo...
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| Vydáno v: | Physica A Ročník 639; s. 129671 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.04.2024
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| ISSN: | 0378-4371, 1873-2119 |
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| Abstract | Car following model is an important part in traffic modelling and has attracted a lot of attentions in the literature. As the proposed car following models become more complex with more components, reliably estimating their parameters becomes crucial to enhance model predictive performance. While most studies adopt an optimisation-based approach for parameters estimation, we present a statistically rigorous method that quantifies uncertainty of the estimates. We present a Bayesian approach to estimate parameters using the popular Gipps’ car following model as demonstration, which allows proper uncertainty quantification and propagation. Since the parameters of the car following model enter the statistical model through the solution of a delay-differential equation, the posterior is analytically intractable so we implemented an adaptive Markov Chain Monte Carlo algorithm to sample from it. Our results show that predictive uncertainty using a point estimator versus a full Bayesian approach are similar with sufficient data. In the absence of adequate data, the former can make over-confident predictions while such uncertainty is more appropriately incorporated in a Bayesian framework. Furthermore, we found that the congested flow parameters in the Gipps’ car following model are strongly correlated in the posterior, which not only causes issues for sampling efficiency but more so suggests the potential ineffectiveness of a point estimator in an optimisation-based approach. Lastly, an application of the Bayesian approach to a car following episode in the NGISM dataset is presented.
•Inference for Gipps’ car following model is performed using Bayesian methods.•An adaptive Metropolis-within-Gibbs sampler is implemented.•Strong correlation between congested flow behavioural parameters was found.•Parameter uncertainty in Bayesian approach is beneficial if data is not abundant. |
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| AbstractList | Car following model is an important part in traffic modelling and has attracted a lot of attentions in the literature. As the proposed car following models become more complex with more components, reliably estimating their parameters becomes crucial to enhance model predictive performance. While most studies adopt an optimisation-based approach for parameters estimation, we present a statistically rigorous method that quantifies uncertainty of the estimates. We present a Bayesian approach to estimate parameters using the popular Gipps’ car following model as demonstration, which allows proper uncertainty quantification and propagation. Since the parameters of the car following model enter the statistical model through the solution of a delay-differential equation, the posterior is analytically intractable so we implemented an adaptive Markov Chain Monte Carlo algorithm to sample from it. Our results show that predictive uncertainty using a point estimator versus a full Bayesian approach are similar with sufficient data. In the absence of adequate data, the former can make over-confident predictions while such uncertainty is more appropriately incorporated in a Bayesian framework. Furthermore, we found that the congested flow parameters in the Gipps’ car following model are strongly correlated in the posterior, which not only causes issues for sampling efficiency but more so suggests the potential ineffectiveness of a point estimator in an optimisation-based approach. Lastly, an application of the Bayesian approach to a car following episode in the NGISM dataset is presented.
•Inference for Gipps’ car following model is performed using Bayesian methods.•An adaptive Metropolis-within-Gibbs sampler is implemented.•Strong correlation between congested flow behavioural parameters was found.•Parameter uncertainty in Bayesian approach is beneficial if data is not abundant. |
| ArticleNumber | 129671 |
| Author | Ting, Samson Lymburn, Thomas Stemler, Thomas Small, Michael Sun, Yuchao |
| Author_xml | – sequence: 1 givenname: Samson orcidid: 0000-0002-7165-0444 surname: Ting fullname: Ting, Samson email: samson.ting@research.uwa.edu.au organization: The Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Perth, Western Australia, Australia – sequence: 2 givenname: Thomas orcidid: 0000-0002-3208-9383 surname: Lymburn fullname: Lymburn, Thomas organization: The Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Perth, Western Australia, Australia – sequence: 3 givenname: Thomas orcidid: 0000-0003-2485-6666 surname: Stemler fullname: Stemler, Thomas organization: The Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Perth, Western Australia, Australia – sequence: 4 givenname: Yuchao orcidid: 0000-0001-8165-3932 surname: Sun fullname: Sun, Yuchao organization: The Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Perth, Western Australia, Australia – sequence: 5 givenname: Michael orcidid: 0000-0001-5378-1582 surname: Small fullname: Small, Michael organization: The Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Perth, Western Australia, Australia |
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| Cites_doi | 10.1198/jasa.2009.tm08393 10.1016/j.physa.2022.128196 10.3141/2088-16 10.1214/aoap/1034625254 10.1093/biomet/57.1.97 10.1146/annurev-statistics-022513-115540 10.3141/2315-02 10.1016/0191-2615(81)90037-0 10.1016/j.physa.2019.122967 10.1016/j.physa.2023.129324 10.1007/s11116-007-9156-2 10.3141/2124-04 10.3141/1855-10 10.1177/0361198105193400102 10.1016/j.physa.2023.129259 10.3141/1876-07 10.3141/1852-17 10.1239/jap/1183667414 10.1016/j.trc.2021.103165 10.1177/0361198105193400122 10.2307/3318737 10.1201/b10905-6 10.1109/TPAMI.1984.4767596 10.3141/1852-16 10.3141/2088-13 10.3141/2315-10 10.1063/1.1699114 10.1103/PhysRevE.62.1805 |
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| Keywords | Adaptive Markov chain Monte Carlo Uncertainty quantification Bayesian inference Model calibration and validation |
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| References | Kim, Rilett (b6) 2003; 1855 Ciuffo, Punzo, Montanino (b16) 2012; 2315 Hollander, Liu (b9) 2008; 35 Ting, Lymburn, Stemler, Sun, Small (b13) 2023 Ossen, Hoogendoorn (b18) 2008; 2088 Martin, Frazier, Robert (b21) 2023; 1 Craiu, Rosenthal, Yang (b31) 2009; 104 Peng, Liu, Dennis (b1) 2020; 538 Ossen, Hoogendoorn (b7) 2005; 1934 Treiber, Hennecke, Helbing (b37) 2000; 62 U.S. Department of Transportation Federal Highway Administration (b36) 2016 Gelman, Rubin (b28) 1992 Haario, Saksman, Tamminen (b29) 2001 Craiu, Rosenthal (b24) 2014; 1 Park, Qi (b8) 2005; 1934 Brockfeld, Kühne, Wagner (b4) 2004; 1876 Hourdakis, Michalopoulos, Kottommannil (b5) 2003; 1852 R.M. Neal, et al., Mcmc using hamiltonian dynamics, in: Handbook of markov chain monte carlo, Vol. 2, 2011, p. 2, (11). Gelman, Carlin, Stern, Dunson, Vehtari, Rubin (b23) 2013 Bradbury, Frostig, Hawkins, Johnson, Leary, Maclaurin, Necula, Paszke, VanderPlas, Wanderman-Milne, Zhang (b35) 2018 Gipps (b14) 1981; 15 Geman, Geman (b27) 1984 Punzo, Zheng, Montanino (b11) 2021; 128 Ossen, Hoogendoorn (b19) 2009; 2124 Roberts, Rosenthal (b30) 2007; 44 Pan, Zhang, Tian, Cui, Wang (b12) 2023; 632 Punzo, Ciuffo, Montanino (b20) 2012; 2315 Cui, Wang, Ci, Yang, Yao (b3) 2023; 630 Hoffman, Gelman (b34) 2014; 15 Lücken (b15) 2019 Wang, Liu, Ci, Wu (b2) 2022; 607 Kesting, Treiber (b10) 2008; 2088 Metropolis, Rosenbluth, Rosenbluth, Teller, Teller (b25) 1953; 21 Brockfeld, Kühne, Skabardonis, Wagner (b17) 2003; 1852 Givens, Hoeting (b22) 2012 Hastings (b26) 1970; 57 Gelman, Gilks, Roberts (b32) 1997; 7 Hollander (10.1016/j.physa.2024.129671_b9) 2008; 35 U.S. Department of Transportation Federal Highway Administration (10.1016/j.physa.2024.129671_b36) 2016 Gelman (10.1016/j.physa.2024.129671_b28) 1992 Bradbury (10.1016/j.physa.2024.129671_b35) 2018 Gipps (10.1016/j.physa.2024.129671_b14) 1981; 15 Gelman (10.1016/j.physa.2024.129671_b23) 2013 Kesting (10.1016/j.physa.2024.129671_b10) 2008; 2088 Brockfeld (10.1016/j.physa.2024.129671_b17) 2003; 1852 Peng (10.1016/j.physa.2024.129671_b1) 2020; 538 Ossen (10.1016/j.physa.2024.129671_b18) 2008; 2088 Hourdakis (10.1016/j.physa.2024.129671_b5) 2003; 1852 Ciuffo (10.1016/j.physa.2024.129671_b16) 2012; 2315 Geman (10.1016/j.physa.2024.129671_b27) 1984 Park (10.1016/j.physa.2024.129671_b8) 2005; 1934 Roberts (10.1016/j.physa.2024.129671_b30) 2007; 44 Ossen (10.1016/j.physa.2024.129671_b19) 2009; 2124 Punzo (10.1016/j.physa.2024.129671_b11) 2021; 128 Hastings (10.1016/j.physa.2024.129671_b26) 1970; 57 Ossen (10.1016/j.physa.2024.129671_b7) 2005; 1934 Lücken (10.1016/j.physa.2024.129671_b15) 2019 Pan (10.1016/j.physa.2024.129671_b12) 2023; 632 Givens (10.1016/j.physa.2024.129671_b22) 2012 Craiu (10.1016/j.physa.2024.129671_b24) 2014; 1 Martin (10.1016/j.physa.2024.129671_b21) 2023; 1 Punzo (10.1016/j.physa.2024.129671_b20) 2012; 2315 Haario (10.1016/j.physa.2024.129671_b29) 2001 Kim (10.1016/j.physa.2024.129671_b6) 2003; 1855 Ting (10.1016/j.physa.2024.129671_b13) 2023 Craiu (10.1016/j.physa.2024.129671_b31) 2009; 104 10.1016/j.physa.2024.129671_b33 Cui (10.1016/j.physa.2024.129671_b3) 2023; 630 Brockfeld (10.1016/j.physa.2024.129671_b4) 2004; 1876 Wang (10.1016/j.physa.2024.129671_b2) 2022; 607 Gelman (10.1016/j.physa.2024.129671_b32) 1997; 7 Hoffman (10.1016/j.physa.2024.129671_b34) 2014; 15 Metropolis (10.1016/j.physa.2024.129671_b25) 1953; 21 Treiber (10.1016/j.physa.2024.129671_b37) 2000; 62 |
| References_xml | – volume: 15 start-page: 1593 year: 2014 end-page: 1623 ident: b34 article-title: The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo publication-title: J. Mach. Learn. Res. – volume: 2088 start-page: 117 year: 2008 end-page: 125 ident: b18 article-title: Validity of trajectory-based calibration approach of car-following models in presence of measurement errors publication-title: Transp. Res. Rec. – volume: 57 start-page: 97 year: 1970 end-page: 109 ident: b26 article-title: Monte carlo sampling methods using markov chains and their applications publication-title: Biometrika – start-page: 457 year: 1992 end-page: 472 ident: b28 article-title: Inference from iterative simulation using multiple sequences publication-title: Stat. Sci. – volume: 104 start-page: 1454 year: 2009 end-page: 1466 ident: b31 article-title: Learn from thy neighbor: Parallel-chain and regional adaptive mcmc publication-title: J. Amer. Statist. Assoc. – year: 2023 ident: b13 article-title: Model calibration and validation from a statistical inference perspective – volume: 1934 start-page: 13 year: 2005 end-page: 21 ident: b7 article-title: Car-following behavior analysis from microscopic trajectory data publication-title: Transp. Res. Rec. – volume: 1934 start-page: 208 year: 2005 end-page: 217 ident: b8 article-title: Development and evaluation of a procedure for the calibration of simulation models publication-title: Transp. Res. Rec. – volume: 630 year: 2023 ident: b3 article-title: Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow publication-title: Physica A – volume: 35 start-page: 347 year: 2008 end-page: 362 ident: b9 article-title: The principles of calibrating traffic microsimulation models publication-title: Transportation – volume: 128 year: 2021 ident: b11 article-title: About calibration of car-following dynamics of automated and human-driven vehicles: Methodology, guidelines and codes publication-title: Transp. Res. C – volume: 632 year: 2023 ident: b12 article-title: Analysis of car–following behaviors based on data–driven and theory–driven car–following models: Heterogeneity and asymmetry publication-title: Physica A – volume: 1 start-page: 179 year: 2014 end-page: 201 ident: b24 article-title: Bayesian computation via markov chain monte carlo publication-title: Annu. Rev. Stat. Appl. – volume: 607 year: 2022 ident: b2 article-title: Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis publication-title: Physica A – volume: 1852 start-page: 130 year: 2003 end-page: 139 ident: b5 article-title: Practical procedure for calibrating microscopic traffic simulation models publication-title: Transp. Res. Rec. – year: 2018 ident: b35 article-title: JAX: composable transformations of python+numpy programs – volume: 1 start-page: 1 year: 2023 end-page: 17 ident: b21 article-title: Computing bayes: From then ‘til now publication-title: Statist. Sci. – start-page: 223 year: 2001 end-page: 242 ident: b29 article-title: An adaptive metropolis algorithm publication-title: Bernoulli – year: 2016 ident: b36 article-title: Next generation simulation (NGSIM) vehicle trajectories and supporting data, provided by ITS DataHub through data.transportation.gov – volume: 15 start-page: 105 year: 1981 end-page: 111 ident: b14 article-title: A behavioural car-following model for computer simulation publication-title: Transp. Res. B – reference: R.M. Neal, et al., Mcmc using hamiltonian dynamics, in: Handbook of markov chain monte carlo, Vol. 2, 2011, p. 2, (11). – volume: 62 start-page: 1805 year: 2000 ident: b37 article-title: Congested traffic states in empirical observations and microscopic simulations publication-title: Phys. Rev. E – volume: 2315 start-page: 89 year: 2012 end-page: 99 ident: b16 article-title: Thirty years of gipps’ car-following model: Applications, developments, and new features publication-title: Transp. Res. Rec. – year: 2019 ident: b15 article-title: Resolving collisions for the gipps car-following model – volume: 1876 start-page: 62 year: 2004 end-page: 70 ident: b4 article-title: Calibration and validation of microscopic traffic flow models publication-title: Transp. Res. Rec. – volume: 2315 start-page: 11 year: 2012 end-page: 24 ident: b20 article-title: Can results of car-following model calibration based on trajectory data be trusted? publication-title: Transp. Res. Rec. – volume: 21 start-page: 1087 year: 1953 end-page: 1092 ident: b25 article-title: Equation of state calculations by fast computing machines publication-title: J. Chem. Phys. – year: 2013 ident: b23 article-title: Bayesian Data Analysis – volume: 538 year: 2020 ident: b1 article-title: An improved car-following model with consideration of multiple preceding and following vehicles in a driver’s view publication-title: Phys. A – volume: 2088 start-page: 148 year: 2008 end-page: 156 ident: b10 article-title: Calibrating car-following models by using trajectory data: Methodological study publication-title: Transp. Res. Rec. – year: 2012 ident: b22 publication-title: Computational Statistics – volume: 1855 start-page: 80 year: 2003 end-page: 89 ident: b6 article-title: Simplex-based calibration of traffic microsimulation models with intelligent transportation systems data publication-title: Transp. Res. Rec. – volume: 2124 start-page: 36 year: 2009 end-page: 44 ident: b19 article-title: Reliability of parameter values estimated using trajectory observations publication-title: Transp. Res. Rec. – volume: 7 start-page: 110 year: 1997 end-page: 120 ident: b32 article-title: Weak convergence and optimal scaling of random walk metropolis algorithms publication-title: Ann. Appl. Probab. – volume: 1852 start-page: 124 year: 2003 end-page: 129 ident: b17 article-title: Toward benchmarking of microscopic traffic flow models publication-title: Transp. Res. Rec. – start-page: 721 year: 1984 end-page: 741 ident: b27 article-title: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 44 start-page: 458 year: 2007 end-page: 475 ident: b30 article-title: Coupling and ergodicity of adaptive markov chain monte carlo algorithms publication-title: J. Appl. Probab. – year: 2012 ident: 10.1016/j.physa.2024.129671_b22 – volume: 104 start-page: 1454 issue: 488 year: 2009 ident: 10.1016/j.physa.2024.129671_b31 article-title: Learn from thy neighbor: Parallel-chain and regional adaptive mcmc publication-title: J. Amer. Statist. Assoc. doi: 10.1198/jasa.2009.tm08393 – year: 2016 ident: 10.1016/j.physa.2024.129671_b36 – volume: 607 year: 2022 ident: 10.1016/j.physa.2024.129671_b2 article-title: Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis publication-title: Physica A doi: 10.1016/j.physa.2022.128196 – year: 2018 ident: 10.1016/j.physa.2024.129671_b35 – year: 2023 ident: 10.1016/j.physa.2024.129671_b13 – volume: 2088 start-page: 148 issue: 1 year: 2008 ident: 10.1016/j.physa.2024.129671_b10 article-title: Calibrating car-following models by using trajectory data: Methodological study publication-title: Transp. Res. Rec. doi: 10.3141/2088-16 – start-page: 457 year: 1992 ident: 10.1016/j.physa.2024.129671_b28 article-title: Inference from iterative simulation using multiple sequences publication-title: Stat. Sci. – volume: 1 start-page: 1 issue: 1 year: 2023 ident: 10.1016/j.physa.2024.129671_b21 article-title: Computing bayes: From then ‘til now publication-title: Statist. Sci. – volume: 7 start-page: 110 issue: 1 year: 1997 ident: 10.1016/j.physa.2024.129671_b32 article-title: Weak convergence and optimal scaling of random walk metropolis algorithms publication-title: Ann. Appl. Probab. doi: 10.1214/aoap/1034625254 – volume: 57 start-page: 97 issue: 1 year: 1970 ident: 10.1016/j.physa.2024.129671_b26 article-title: Monte carlo sampling methods using markov chains and their applications publication-title: Biometrika doi: 10.1093/biomet/57.1.97 – volume: 1 start-page: 179 year: 2014 ident: 10.1016/j.physa.2024.129671_b24 article-title: Bayesian computation via markov chain monte carlo publication-title: Annu. Rev. Stat. Appl. doi: 10.1146/annurev-statistics-022513-115540 – volume: 2315 start-page: 11 issue: 1 year: 2012 ident: 10.1016/j.physa.2024.129671_b20 article-title: Can results of car-following model calibration based on trajectory data be trusted? publication-title: Transp. Res. Rec. doi: 10.3141/2315-02 – volume: 15 start-page: 1593 issue: 1 year: 2014 ident: 10.1016/j.physa.2024.129671_b34 article-title: The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo publication-title: J. Mach. Learn. Res. – volume: 15 start-page: 105 issue: 2 year: 1981 ident: 10.1016/j.physa.2024.129671_b14 article-title: A behavioural car-following model for computer simulation publication-title: Transp. Res. B doi: 10.1016/0191-2615(81)90037-0 – volume: 538 year: 2020 ident: 10.1016/j.physa.2024.129671_b1 article-title: An improved car-following model with consideration of multiple preceding and following vehicles in a driver’s view publication-title: Phys. A doi: 10.1016/j.physa.2019.122967 – volume: 632 year: 2023 ident: 10.1016/j.physa.2024.129671_b12 article-title: Analysis of car–following behaviors based on data–driven and theory–driven car–following models: Heterogeneity and asymmetry publication-title: Physica A doi: 10.1016/j.physa.2023.129324 – year: 2013 ident: 10.1016/j.physa.2024.129671_b23 – volume: 35 start-page: 347 issue: 3 year: 2008 ident: 10.1016/j.physa.2024.129671_b9 article-title: The principles of calibrating traffic microsimulation models publication-title: Transportation doi: 10.1007/s11116-007-9156-2 – volume: 2124 start-page: 36 issue: 1 year: 2009 ident: 10.1016/j.physa.2024.129671_b19 article-title: Reliability of parameter values estimated using trajectory observations publication-title: Transp. Res. Rec. doi: 10.3141/2124-04 – volume: 1855 start-page: 80 issue: 1 year: 2003 ident: 10.1016/j.physa.2024.129671_b6 article-title: Simplex-based calibration of traffic microsimulation models with intelligent transportation systems data publication-title: Transp. Res. Rec. doi: 10.3141/1855-10 – volume: 1934 start-page: 13 issue: 1 year: 2005 ident: 10.1016/j.physa.2024.129671_b7 article-title: Car-following behavior analysis from microscopic trajectory data publication-title: Transp. Res. Rec. doi: 10.1177/0361198105193400102 – volume: 630 year: 2023 ident: 10.1016/j.physa.2024.129671_b3 article-title: Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow publication-title: Physica A doi: 10.1016/j.physa.2023.129259 – volume: 1876 start-page: 62 issue: 1 year: 2004 ident: 10.1016/j.physa.2024.129671_b4 article-title: Calibration and validation of microscopic traffic flow models publication-title: Transp. Res. Rec. doi: 10.3141/1876-07 – volume: 1852 start-page: 130 issue: 1 year: 2003 ident: 10.1016/j.physa.2024.129671_b5 article-title: Practical procedure for calibrating microscopic traffic simulation models publication-title: Transp. Res. Rec. doi: 10.3141/1852-17 – volume: 44 start-page: 458 issue: 2 year: 2007 ident: 10.1016/j.physa.2024.129671_b30 article-title: Coupling and ergodicity of adaptive markov chain monte carlo algorithms publication-title: J. Appl. Probab. doi: 10.1239/jap/1183667414 – volume: 128 year: 2021 ident: 10.1016/j.physa.2024.129671_b11 article-title: About calibration of car-following dynamics of automated and human-driven vehicles: Methodology, guidelines and codes publication-title: Transp. Res. C doi: 10.1016/j.trc.2021.103165 – volume: 1934 start-page: 208 issue: 1 year: 2005 ident: 10.1016/j.physa.2024.129671_b8 article-title: Development and evaluation of a procedure for the calibration of simulation models publication-title: Transp. Res. Rec. doi: 10.1177/0361198105193400122 – start-page: 223 year: 2001 ident: 10.1016/j.physa.2024.129671_b29 article-title: An adaptive metropolis algorithm publication-title: Bernoulli doi: 10.2307/3318737 – ident: 10.1016/j.physa.2024.129671_b33 doi: 10.1201/b10905-6 – year: 2019 ident: 10.1016/j.physa.2024.129671_b15 – start-page: 721 issue: 6 year: 1984 ident: 10.1016/j.physa.2024.129671_b27 article-title: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.1984.4767596 – volume: 1852 start-page: 124 issue: 1 year: 2003 ident: 10.1016/j.physa.2024.129671_b17 article-title: Toward benchmarking of microscopic traffic flow models publication-title: Transp. Res. Rec. doi: 10.3141/1852-16 – volume: 2088 start-page: 117 issue: 1 year: 2008 ident: 10.1016/j.physa.2024.129671_b18 article-title: Validity of trajectory-based calibration approach of car-following models in presence of measurement errors publication-title: Transp. Res. Rec. doi: 10.3141/2088-13 – volume: 2315 start-page: 89 issue: 1 year: 2012 ident: 10.1016/j.physa.2024.129671_b16 article-title: Thirty years of gipps’ car-following model: Applications, developments, and new features publication-title: Transp. Res. Rec. doi: 10.3141/2315-10 – volume: 21 start-page: 1087 issue: 6 year: 1953 ident: 10.1016/j.physa.2024.129671_b25 article-title: Equation of state calculations by fast computing machines publication-title: J. Chem. Phys. doi: 10.1063/1.1699114 – volume: 62 start-page: 1805 issue: 2 year: 2000 ident: 10.1016/j.physa.2024.129671_b37 article-title: Congested traffic states in empirical observations and microscopic simulations publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.62.1805 |
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| SubjectTerms | Adaptive Markov chain Monte Carlo Bayesian inference Model calibration and validation Uncertainty quantification |
| Title | Parameter estimation for Gipps’ car following model in a Bayesian framework |
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