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Development of precise active traffic control strategies urgently requires real-time estimation for operational metrics in transportation systems satisfying the level of smaller spatial granularity simultaneously. This paper proposed a probability approach to estimate real-time lane-based queue length using license plate recognition (LPR) data. The method first developed a nested logit model to depict initial lane selection and then improved a lane changing model, which can obtain lane-based equivalent cumulative arrival-departure curve. Thus queue length is obtained combined with input-output model. Finally, validation results show that the proposed approach can capture the real-time variations of queue length and achieve reasonable estimation accuracy for maximum cycle queue length.

Recent studies have indicated that there are significant changes in physiological signals among multiple emotions. This work aimed to study the differences of heart rate variability (HRV) indices among multiple emotions, and thus to explore the consistency of the differences within and without individuals. Sixty healthy students were involved in this study. Electrocardiography (ECG) from each subject was recorded among six different emotions: neutral, fear, sadness, happiness, anger and disgust. Variability series (RR interval time series) were abstracted from the ECG signals. Six HRV indices of the RR interval time series were calculated, including SDNN and RMSSD in time-domain, LF, HF, and LF/HF in frequency-domain, and SampEn in nonlinear domain. The results showed that there were significant differences of the above HRV indices among different emotions. No notable consistency of these differences was acquired, as no similar reference group orders were found, and the inconsistency measure (IM) of each single index was approximately normal distribution.

Heart rate variability (HRV) has been subject to high attention in the field of emotion recognition as it can reflect inherent information objectively and accurately. Various methods of nonlinear dynamics and complexity measures have been utilized for HRV analysis, including fractal dimension, Lyapunov exponents and entropies. Permutation entropy proposed by Bandt is a simple and fast algorithm to investigate complex dynamical systems. It symbolizes the time series and focus on the relative magnitude of data. Therefore, equalities in series should be considered carefully. In this paper, two methods of dealing with equalities have been applied to RRI time series in three emotional states: ranking equal values according to their order of appearance, or eliminate equalities by adding a little random noise. Results show that PE values derived from these two methods are significantly different. The significant differences are demonstrated under all three emotional states.