Abstract
The dynamic change of urban road travel time was analyzed using video image detector data, and it showed cyclic variation, so the signal cycle length at the upstream intersection was conducted as the basic unit of time window; there was some evidence of bimodality in the actual travel time distributions; therefore, the fitting parameters of the travel time bimodal distribution were estimated using the EM algorithm. Then the weighted average value of the two means was indicated as the travel time estimation value, and the Modified Buffer Time Index (MBIT) was expressed as travel time variability; based on the characteristics of travel time change and MBIT along with different time windows, the time window was optimized dynamically for minimum MBIT, requiring that the travel time change be lower than the threshold value and traffic incidents can be detected real time; finally, travel times on Shandong Road in Qingdao were estimated every 10 s, 120 s, optimal time windows, and 480 s and the comparisons demonstrated that travel time estimation in optimal time windows can exactly and steadily reflect the real-time traffic. It verifies the effectiveness of the optimization method.
Highlights
The time window determines the period of time that should be considered when estimating the current traffic information [1]
Most travel time estimation or prediction method adopted a constant time window subjectively: the time window was set as 2 min in the TransGuide algorithm [2]; TranStar algorithm estimated travel times every 30 s; Transmit algorithm used a time window of 15 min [3]; Ma and Koutsopoulos demonstrated that a larger time window gave smoother travel time with longer delay and a smaller one reduced delay with volatile travel time [4]
A new travel time reliability index, the Modified Buffer Time Index, is proposed on the basis of the characteristic of the bimodal distribution and its common ground with the unimodal distribution as follows: (1) When travel times are bimodal-distributed, the travel time estimation and MBIT are computed as presented in TT = ω1μ1 + ω2μ2, MBIT = μ2 + σ2 − (μ1 − σ1), (5)
Summary
The time window determines the period of time that should be considered when estimating the current traffic information [1]. Yang et al demonstrated that a constant bimodal distribution could not always provide good fit for travel times of interrupted flow on different urban roads or during different periods because of the traffic state on the road, the delay at the downstream intersection, peak hours, and so on. They modeled six types of bimodal distributions which applied normal and lognormal distribution as typical distribution to fit RFID (Ratio Frequency Identification) data in Nanjing. The remainder of the paper is organized as follows: the second section outlines the traffic data source and travel time calculation; the third section proposes a new index reflecting travel time reliability in accord with the bimodal distribution; in the fourth section, an optimization method is presented to obtain the optimal time window; the fifth section gives numerical experiments to demonstrate effectiveness of the optimization method and the optimal time window
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