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建立交通流短时预测状态空间模型, 研究混合状态下城市快速路交通流短时预测. 结合城市快速路自由流状态、拥挤流状态和阻塞流状态下交通流参数的时间和空间分布特性, 基于交通流守恒方程和速度动态模型, 借鉴偏微分方程组求解时空离散的思想, 建立三种状态下交通流短时预测模型; 同时考虑进出口匝道、车道数变更以及道路坡度等因素的影响, 将交通流短时预测模型转化为交通流短时预测状态空间模型, 实现混合状态下交通流短时预测. 研究表明, 该方法能够实现混合状态下道路网内的交通流短时预测, 预测精度可达90.23%. 相同条件下, 经典自回归滑动平均模型的预测精度仅为81%.Short-term traffic flow prediction for multi traffic states on urban expressway network is carried out in this paper. The model for multi traffic states is proposed by integrating the spatial and the temporal distribution characteristics of traffic flow parameters under free traffic, congested traffic and jam traffic respectively. Based on the classical traffic flow conservation equation, the ideology of spatial and temporal dispersions in partial differential equations is adopted to establish short-time traffic flow prediction model. Meanwhile, the impact factors, such as on and off ramp, lane change and road slope are considered, which convertes short-term traffic flow prediction model into short-time traffic flow prediction state space model. Finally, the short-term traffic flow prediction for multi traffic states on urban expressway network is realized. The empirical research shows that compared with the classic ARMA model, the proposed method can not only realize short-term traffic flow prediction for multi traffic states on urban expressway network but also achieve an accuracy of 90.23%. In the same condition, the accuracy of ARMA model is 81%.
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Keywords:
- short-term traffic flow prediction /
- free traffic /
- congested traffic /
- jam traffic
[1] Williams B M, Durvasula P K, Brown D E 1998 Transport. Res. Record 1644 132
[2] Lee S, Fambro D B 1999 Transport. Res. Record 1678 179
[3] Shao C F, Asai K, Nakagawa S 1999 Proceedings of the 99’ Shanghai International Symposium on Urban Transportation Proceedings Shanghai, Novmber 1999 158
[4] Yao Z S, Shao C F, Xiong Z H 2007 Chin. J. Manag. Sci. 15 64 (in Chinese) [姚智胜, 邵春福, 熊志华 2007 中国管理科学 15 64]
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[7] Dong C J, Shao C F, Li X 2009 World Congress on Computer Science and Information Engineering Los Angeles, U.S.A., March 31-April 2, 2009 645
[8] Maschavan D V, Mark D, Susan W 1996 Transport. Res. C 4 307
[9] Park B 2002 Transport. Res. Record 1802 190
[10] Wang D H 2002 Traffic Theory (Beijing: China Communications Press) (in Chinese) [王殿海 2002 交通流理论 (北京: 人民交通出版社)]
[11] Kerner B S 2004 Physica A 333 379
[12] Kerner B S, Klenov S L, Hiller A 2007 Nonlin. Dyn. 49 525
[13] Treiber M, Kesting A, Helbing D 2010 Transport. Res. B 44 983
[14] Wu J J, Xu S Y, Sun H J 2011 Acta Phys. Sin. 60 019502 (in Chinese) [吴建军, 徐尚义, 孙会君 2011 60 019502]
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[1] Williams B M, Durvasula P K, Brown D E 1998 Transport. Res. Record 1644 132
[2] Lee S, Fambro D B 1999 Transport. Res. Record 1678 179
[3] Shao C F, Asai K, Nakagawa S 1999 Proceedings of the 99’ Shanghai International Symposium on Urban Transportation Proceedings Shanghai, Novmber 1999 158
[4] Yao Z S, Shao C F, Xiong Z H 2007 Chin. J. Manag. Sci. 15 64 (in Chinese) [姚智胜, 邵春福, 熊志华 2007 中国管理科学 15 64]
[5] Yao Z S, Shao C F, Gao Y L 2006 J. Beijing Jiaotong Univ. 30 19 (in Chinese) [姚智胜, 邵春福, 高永亮 2006 北京交通大学学报 30 19]
[6] Dong C J, Shao C F, Xiong Z H, Li J 2010 J. Transportat. Sys. Eng. Inform. Technol. 10 145 (in Chinese) [董春娇, 邵春福, 熊志华, 李娟 2010 交通运输系统工程与信息 10 145]
[7] Dong C J, Shao C F, Li X 2009 World Congress on Computer Science and Information Engineering Los Angeles, U.S.A., March 31-April 2, 2009 645
[8] Maschavan D V, Mark D, Susan W 1996 Transport. Res. C 4 307
[9] Park B 2002 Transport. Res. Record 1802 190
[10] Wang D H 2002 Traffic Theory (Beijing: China Communications Press) (in Chinese) [王殿海 2002 交通流理论 (北京: 人民交通出版社)]
[11] Kerner B S 2004 Physica A 333 379
[12] Kerner B S, Klenov S L, Hiller A 2007 Nonlin. Dyn. 49 525
[13] Treiber M, Kesting A, Helbing D 2010 Transport. Res. B 44 983
[14] Wu J J, Xu S Y, Sun H J 2011 Acta Phys. Sin. 60 019502 (in Chinese) [吴建军, 徐尚义, 孙会君 2011 60 019502]
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