This paper examines a way to synthesize route travel time probability density functions (PDFs) on the basis of segment-level PDFs. Real-world data from 1-5 in Sacramento, California, are employed. The first finding is that careful filtering is required to extract useful travel times from the raw data because trip times, not travel times, are observed (i.e., the movement of vehicles between locations). The second finding is that significant correlations exist between individual vehicle travel times for adjacent segments. Two analyses are done in this regard: one predicts downstream travel times on the basis of upstream travel times, and the second checks for correlations in travel times between upstream and downstream segments. The results of these analyses suggest that strong positive correlations exist. The third finding is that comonotonicity, or perfect positive dependence, can be assumed when route travel time PDFs are generated from segment PDFs. Kolmogorov-Smirnov tests show that travel times synthesized from the segment-specific data are statistically different only under highly congested conditions, and even then, the percentage differences in the distributions of the synthesized and actual travel times are small. The fourth finding, somewhat tangential, is that there is little variation in individual driver travel times under given operating conditions. This is an important finding, because such an assumption serves as the basis for all traffic simulation models.
Synthesizing route travel time distributions from segment travel time distributions