Stochastic Capacity at Freeway Bottlenecks with Application to Travel Time Prediction.
详细信息
文摘
The stochastic nature of freeway bottleneck breakdown and queue discharge are investigated in this study through a comprehensive analysis of sensor data collected at bottleneck sites in the San Francisco Bay Area,California and San Antonio,Texas. A new procedure is proposed to define the stochastic variation of the onset of freeway breakdown and of queue discharge capacity based on time indexed field data of speed-flow profiles. The former is developed as a function of average vehicle time headways preceding observed conditions where both speed is below and density is above locally defined congested flow thresholds. Full year 15-minute data series were used in the demonstration and testing of the procedure,yielding a high degree of statistical confidence in the resulting headway distribution parameter estimates. The statistical analysis indicated that the probability function of freeway bottleneck pre-breakdown flow rates follows a generalized logistic distribution. In addition,a recursive queue discharge model is proposed for bottleneck flows under congested queued) conditions. The proposed queue discharge model is a simple autocorrelated time series recursion that is seeded with the corresponding pre-breakdown flow and dampens to the mean queue discharge rate. The proposed stochastic models are robust and accurate and represent a significant improvement in the understanding and modeling of freeway bottleneck flow. Focusing on investigating underlying reason of stochastic capacity and queue discharge at macro-level analysis,this study subsequently analyzes the individual vehicle trajectory data by using the simplified car following model and demonstrates experimental distributions of stochastic wave speed and jam density. Despite the limitations due to some simplified assumptions,it is reasonable to conclude that the heterogeneity of kjam and w at the micro-level results from individual driving behavior,and the corresponding stochastic capacity and queue discharge rate observed in the field data marco-level) are also generated by the heterogeneity between individual driving behaviors. Furthermore,in order to recognize and capture the stochastic day-to-day travel time evolution process,this study tries to apply several theoretical approaches to explicitly account for the travel time variability caused by stochastic capacity and demonstrate the travel time variability in an empirical study. Comparisons are made between the travel time predictions from stochastic and deterministic HCM level) capacity under the same demand level. The results show to what extent the stochastic capacity contributes to the observed travel time variability.