Abstract
In this paper, we explore the characteristics of vehicle speed time series which described the processes that a driver finishing a specific driving task with different driving operations. Three types of vehicle driving behavior like driving towards an intersection for turn-left, driving for turn-right, and driving for go-straight are designed as a set of real vehicle driving experiments to be carried out on an urban road. Similar to the expected, the collected speed time series of all driving behavior types tend to be non-linear and non-stationary. Therefore, empirical mode decomposition (EMD) is introduced to analyze the characteristic values of speed time series intrinsic mode functions (IMF) and residues. After decomposing, there are 4 levels of IMF with a residue exist existed in the speed time series of turn-left driving behavior, as well as 3 levels in turn-right and 5 levels in go-straight. All the first level IMF of three types of vehicle driving behavior have relatively high frequencies which could be regarded as systematic errors of vehicle speed sensors. As the decomposition continued, subsequent IMF frequencies become lower but average amplitudes have different change trends which could help identifying the driving behavior types. All residue curves are firstly monotone increasing and then monotone decreasing, but the occurrence time of residue maximums are inconsistent. Through this research, we can distinguish the driving behavior type between turn-left, turn-right and go-straight with those vehicle driving behavior time series characteristic values and their changing trend. If all those judgment and statistics of characteristic values be implemented by vehicular industrial control computes, it would improve driving behavior recognition or prediction performances of an advanced driving assistance embedded on vehicle.
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References
Eluru N, Chakour V, Chamberlain M, Miranda-Moreno LF (2013) Modeling vehicle operating speed on urban roads in Montreal: a panel mixed ordered probit fractional split model. Accid Anal Prev 59:125–134
Grumert E, Ma X, Tapani A (2015) Analysis of a cooperative variable speed limit system using microscopic traffic simulation. Transp Res Part C 52:173–186
Sun R, Zhuang X, Wu C, Zhao G, Zhang K (2015) The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment. Transp Res Part F 30:97–106
Rossi R, Gastaldi M, Pascucci F (2014) Flow rate effects on vehicle speed at two way-two lane rural roads. Transp Res Procedia 3:932–941
Islam MdT, El-Basyouny K, Ibrahim SE (2014) The impact of lowered residential speed limits on vehicle speed behavior. Saf Sci 62:483–494
Sato T, Akamatsu M (2007) Influence of traffic conditions on driver behavior before making a right turn at an intersection: analysis of driver behavior based on measured data on an actual road. Transp Res Part F Traffic Psychol Behav 10(5):397–413
Spek A, Wieringa P, Janssen W (2006) Intersection approach speed and accident probability. Transp Res Part F Traffic Psychol Behav 9(2):155–171
Ma X, Andreasson I (2005) Dynamic car following data collection and noise cancellation based on the Kalman smoothing. In: 2005 IEEE international conference on vehicular electronics and safety. Beijing, China, pp 35–41
McCall JC, Achler O, Trivedi MM (2004) Design of an instrumented vehicle test bed for developing a human centered driver support system. In: 2004 Proceedings IEEE intelligent vehicles symposium
Bifulco GN, Pariota L, Brackstione M, Mcdonald M (2013) Driving behaviour models enabling the simulation of advanced driving assistance systems: revisiting the action point paradigm. Transp Res Part C 36:352–366
Baek S, Jang J (2015) Implementation of integrated OBD-II connector with external network. Inf Syst 50:69–75
Shi W, Shang P, Wang J (2015) Large deviations estimates for the multiscale analysis of traffic speed time series. Physica A 421:562–570
Sun J, Sun J (2015) A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data. Transp Res Part C, 54:176–186
Fink O, Zio E, Weidmann U (2013) Predicting time series of railway speed restrictions with time-dependent machine learning techniques. Expert Syst Appl 40:6033–6040
Wang J, Shi Q (2013) Short-term traffic speed forecasting hybrid model based on chaos-wavelet analysis- support vector machine theory. Transp Res Part C 27:219–232
Zheng Z, Ahn S, Chen D, Laval J (2011) Applications of wavelet transform for analysis of freeway traffic: bottlenecks, transient traffic, and traffic occillations. Transp Res Part B 45:372–384
Pei Y-L, Li H-P (2006) Research on fractal dimensions of traffic flow time series on expressway. J Highw Transp Res Dev 23(2):115–119, 127
Zhang Y, Guan W (2010) Analysis of multifractal characteristic of traffic-flow time series. Comput Eng Appl 46(29):23–25
Li X, Ding Z (2008) EMD method for multiple time-scale analysis on fluctuation characteristic of natural annual runoff time series of fen river. Water Resour Power 26(1):30–32
Xu T, Li K (2009) Analyzing the dynamic characteristic of the traffic flow using the EMD method. Sci Technol Eng 9(11):3003–3008
Molinari F, Martis RJ, Acharya UR, Meiburger KM, Luca RD, Petraroli G, Liboni W (2015) Empirical mode decomposition analysis of near-infrared spectroscopy muscular signals to assess the effect of physical activity in type 2 diabetic patients. Comput Biol Med 59:1–9
Mao C, Jiang Y, Wang D, Chen X, Tao J (2015) Modeling and simulation of non-stationary vehicle vibration signals based on Hilbert spectrum. Mech Syst Signal Process 50–51:56–69
Kacha A, Grenez F, Schoentgen J (2015) Multiband vocal dysperiodictities analysis using empirical mode decomposition in the log-spectral domain. Biomed Signal Process Control 17:11–20
Huang NE, Shen Z, Long SR, Wu MC, Shin HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys Eng Sci 454:899–955
Zhang L, Zhu H, Chen L, Zheng A, Chu W (2015) Fractal characteristics analysis on driving behavior time series: example with speed data as vehicle driving towards an intersection. In: 2015 Proceedings of the IEEE transportation information and safety international conference, Wuhan, China, pp 126–132
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This paper is supported by the National Natural Science Foundation of China under grant No. 51308426.
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Zhang, L., Pan, B. (2017). Characteristics Analysis on Speed Time Series with Empirical Mode Decomposition as Vehicle Driving Towards an Intersection. In: Balas, V., Jain, L., Zhao, X. (eds) Information Technology and Intelligent Transportation Systems. Advances in Intelligent Systems and Computing, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-319-38789-5_43
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DOI: https://doi.org/10.1007/978-3-319-38789-5_43
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