Browsing by Author "Wang, Zhuojin"

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    A Cellular Automata Approach to Estimate Incident-Related Travel Time on Interstate 66 in Near Real Time
    Wang, Zhuojin; Murray-Tuite, Pamela M. (Virginia Center for Transportation Innovation and Research, 2010-03-01)
    Incidents account for a large portion of all congestion and a need clearly exists for tools to predict and estimate incident effects. This study examined (1) congestion back propagation to estimate the length of the queue and travel time from upstream locations to the incident location and (2) queue dissipation. Shockwave analysis, queuing theory, and cellular automata were initially considered. Literature indicated that shockwave analysis and queuing theory underestimate freeway travel time under some conditions. A cellular automata simulation model for I-66 eastbound between US 29 and I-495 was developed. This model requires inputs of incident location, day, time, and estimates of duration, lane closures and timing, and driver re-routing by ramp. The model provides estimates of travel times every 0.2 mile upstream of the incident at every minute after the start of the incident and allows for the determination of queue length over time. It was designed to be used from the beginning of the incident and performed well for normal conditions and incidents, but additional calibration was required for rerouting behavior. We recommend that the Virginia Department of Transportation (1) further pursue cellular automata approaches for near-real time applications along freeways; and (2) consider adopting an approach to address detector failures and errors. Adopting these recommendations should improve VDOT's freeway real-time travel time estimation and other applications based on detector data.
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    Incident-Related Travel Time Estimation Using a Cellular Automata Model
    Wang, Zhuojin (Virginia Tech, 2009-06-04)
    The purpose of this study was to estimate the drivers' travel time with the occurrence of an incident on freeway. Three approaches, which were shock wave analysis, queuing theory and cellular automata models, were initially considered, however, the first two macroscopic models were indicated to underestimate travel time by previous literature. A microscopic simulation model based on cellular automata was developed to attain the goal. The model incorporated driving behaviors on the freeway with the presence of on-ramps, off-ramps, shoulder lanes, bottlenecks and incidents. The study area was a 16 mile eastbound section of I-66 between US-29 and I-495 in northern Virginia. The data for this study included loop detector data and incident data for the road segment for the year 2007. Flow and speed data from the detectors were used for calibration using quantitative and qualitative techniques. The cellular automata model properly reproduced the traffic flow under normal conditions and incidents. The travel time information was easily obtained from the model. The system is promising for travel time estimation in near real time.