1374232 - Temporal Data Aggregation and Connected Vehicles Effects on Travel Time Reliability

The 6th edition of the Highway Capacity Manual (HCM-6) introduced a Monte Carlo-based methodology for estimating and predicting average travel times of arterials. The estimated travel time distribution (TTD) is then used to determine travel time reliability (TTR) metrics. Previous studies have shown this HCM-6 TTR methodology, when properly calibrated, can replicate the observed TTD. However, this methodology is limited to predicting average travel times at 15-minutes or 60-minutes aggregation levels and cannot be used to estimate corridor reliability when new technologies are introduced. A new TTR estimation and prediction methodology has recently been proposed to overcome these limitations. Unlike the HCM, the new methodology can estimate both the average TTD and the population TTD.
This paper aims to demonstrate the use of the new TTR methodology to predict the effect of new technologies, such as the adoption of automated vehicles (AV), on TTD and TTR metrics. In addition, this paper will test the temporal transferability of the new TTR model and conduct a sensitivity analysis of data aggregation effects on TTR.
The new TTR methodology was used to model a 1.16-mile arterial testbed in Lincoln, Nebraska. It was shown that AV technologies can result in more reliable corridors, under specific conditions. The calibrated new TTR model was used to predict a 12-month average TTD. The resultant average TTD had a 9% error in standard deviation. It is hypothesized that the model temporal transferability accuracy will improve if the sources of variability in travel times could be predicted better.

Keywords: Travel Time Distribution, Reliability, Automated Vehicles