[1] ROBERTSON D I. Transyt: A traffic network study tool [J]. Road Research Laboratory Report Lr, 1996, 1969(1969): 1-30.
[2] HUNT P B, ROBERTSON D I, BRETHERTON R D, et al. SCOOT- a traffic responsive method of coordinating signals[R]. Crowthorne: Transport and Road Research Laboratory, 1981.
[3] PACEY G M. The progress of a bunch of vehicles released from a traffic signal[J]. Road Research Laboratory Note RN/2665/GMP, 1956, 1956(1956): 36- 50.
[4] HALL M, WILLUMSEN L G. SATURN-a simulationassignment model for the evaluation of traffic management schemes[J]. Traffic Engineering & Control, 1980, 21(4): 81-94.
[5] LIEBERMAN E B, ANDREWS B J. TRAFLO: A new tool to evaluate transportation system management strategies[M]. Washington: Transportation Research Board, 1980.
[6] WEI M, JIN W Z, SHEN L O. A platoon dispersion model based on a truncated normal distribution of speed[J]. Journal of Applied Mathematics, 2012, 2012 (1): 155-172.
[7] WU W W, JIN W Z, SHEN L O. Mixed platoon flow dispersion model based on speed- truncated gaussian mixture distribution[J]. Journal of Applied Mathematics, 2013, 2013(39): 415-425.
[8] WU W W, SHEN L O, JIN W Z, et al. Density-based mixed platoon dispersion modelling with truncated mixed gaussian distribution of speed[J]. Transportmetrica B: Transport Dynamics, 2015, 3(2): 114-130.
[9] 姚志洪,蒋阳升,吴云霞,等. 基于速度服从混合PH分 布的车队离散模型[J]. 交通运输系统工程与信息, 2016, 16(3): 133-140. [YAO Z H, JIANG Y S, WU Y X, et al. Platoon dispersion model based on mixed phase distribution of speed[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(3): 133-140.]
[10] JIANG Y S, YAO Z H, DING X, et al. Mixed platoon flow dispersion model based on truncated mixed phase distribution of speed[C]. Transportation Research Board of the National Academies, Washington, D.C., 2016. [11] LAM W H K, TANG Y F, TAM M L. Comparison of two non-parametric models for daily traffic forecasting in Hong Kong[J]. Journal of Forecasting, 2006, 25(3): 173- 192.
[12] GHOSH B, BASU B, OMAHONY M. Bayesian timeseries model for short- term traffic flow forecasting[J]. Journal of Transportation Engineering, 2007, 133(3): 180-189.
[13] DIMITRIOU L, TSEKERIS T, STATHOPOULOS A. Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow[J]. Transportation Research Part C Emerging Technologies, 2008, 16(5): 554-573.
[14] GHOSH B, BASU B, OMAHONY M. Random process model for urban traffic flow using a wavelet- bayesian hierarchical technique[J]. Computer-Aided Civil and Infrastructure Engineering, 2010, 25(8): 613-624.
[15] HONG W C, DONG Y, ZHENG F, et al. Hybrid evolutionary algorithms in a SVR traffic flow forecasting model[J]. Applied Mathematics & Computation, 2011, 217(15): 6733-6747.
[16] TCHRAKIAN T T, BASU B, OMAHONY M. Real-time traffic flow forecasting using spectral analysis[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 519-526.
[17] GUO F C, KRISHNAN R, POLAK J. A computationally efficient two-stage method for short-term traffic prediction on urban roads[J]. Transportation Planning & Technology, 2013, 36(1): 62-75.
[18] MAI T, GHOSH B, WILSON S. Short-term traffic-flow forecasting with auto- regressive moving average models[J]. Transport, 2014, 167(4): 232-239.
[19] JLINT J W C V, HOOGENDOORN S P, ZUYLEN H J V. Accurate freeway travel time prediction with statespace neural networks under missing data[J]. Transportation Research Part C Emerging Technologies, 2005, 13(5-6): 347-369.
[20] HODGE V J, KRISHNAN R, AUSTIN J, et al. Shortterm prediction of traffic flow using a binary neural network[J]. Neural Computing & Applications, 2014, 25 (25): 1639-1655.
[21] 廖瑞辉, 周晶. 基于云— 自组织神经网络的交通 流预测模型[J]. 交通运输系统工程与信息, 2014, 14 (4): 154-159. [LIAO R H, ZHOU J. Traffic flow forecasting model based on cloud- self-organizing neural network[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(4): 154-159.]
[22] 吕进,赵祥模,樊海玮,等. 基于生长自组织神经网络 群的交通流预测[J]. 交通运输系统工程与信息, 2013, 13(3): 33-39. [LV J, ZHAO X M, FAN H W, et al. Traffic flow forecasting based on growing self-organized neural network group[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(3): 33-39.]
[23] 杨兆升, 王媛, 管青. 基于支持向量机方法的短时交通 流量预测方法[J]. 吉林大学学报(工学版), 2006, 36 (6): 881-884. [YANG Z S, WANG Y, GUAN Q. Shortterm traffic flow prediction method based on SVM[J]. Journal of Jilin University, 2006, 36(6): 881-884.]
[24] YANG Y N, LU H P. Short-term traffic flow combined forecasting model based on SVM[C]. International Conference on Computational and Information Sciences. Chengdu, China: IEEE Computer Society, 2010.
[25] XIE Y C, ZHANG Y, YE Z. Short-term traffic volume forecasting using kalman filter with discrete wavelet decomposition[J]. Computer-Aided Civil and Infrastructure Engineering, 2007, 22(5): 326-334.
[26] OJEDA L L, KIBANGOU A Y, DEWIT C C. Adaptive kalman filtering for multi- step ahead traffic flow prediction[C]. Washington: IEEE, 2013.
[27] WALLACE C E, COURAGE K G, REAVES D P, et al. TRANSYT-7F user's manual[R]. Gainesville: University of Florida, 1984.
[28] MAKRIDAKIS S G. Forecasting: methods and applications[J]. Journal of the Operational Research Society, 1984, 35(1): 79. |