Statistical Analysis of Spatiotemporal Link and Path Flow Variability

In the absence of advanced traveler information systems, commuters tend to select their routes of travel, within a congested network, primarily based on historical average travel times. Typical traffic conditions can be sufficient if a specific day is similar to these average conditions. However, if traffic conditions vary considerably from the norm, historical information may not be sufficient for commuters to make optimum travel decisions. Under these conditions the provision of real-time traffic information could offer significant benefits. Consequently, the proposed research effort attempts to characterize typical variability in traffic conditions using traffic volume data obtained from 31 dual-loop detector stations along a section of 1-66 between Manassas and Vienna, VA during a 3-month period. The detectors logged time-mean speed, volume, and occupancy measurements for each station and lane combination. Using these data, the paper examines the spatiotemporal link and path flow variability on weekdays and weekends. The generation of path flows is made through the use of a synthetic maximum likelihood approach. Statistical analysis of variance (ANOVA) tests are performed on the data. The results demonstrate that in terms of link flows and total traffic demand Mondays and Fridays are similar to core weekdays (Tuesdays, Wednesdays, and Thursdays). In terms of path flows, Fridays appear to be different from core weekdays.