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Determining the required probe vehicle size for real-time travel time estimation is an important issue in probe-data-based applications, such as traffic monitoring on a roadway network. This paper provides an innovative approach for the determination of proper sample sizes with the aid of a simulation model. The model, which was built for El Camino Real, a major arterial in the California San Francisco Bay Area, helps to provide the necessary detailed data to verify the proposed methodology. Our proposed approach is based on the findings that the distribution of travel time estimate error can be constructed with the generalized Pareto distribution function. Considering that travel times vary within a signal cycle and among different movements (through, left-turning and right-turning) at intersections, our study uses data in 15-s intervals to examine the travel time variations associated with different intersection movements. Our findings show that when the penetration rate of probe vehicles is 40%, the probability that the relative error will be less than 0.10 within the time period is 90%, whereas a penetration rate greater than 60% is needed to attain a 0.95 confidence level at the same error rate. For left-turning vehicles, a 10% penetration of probe vehicles is sufficient for estimating their travel times with relative errors less than 0.10 at a 90% confidence level, whereas through vehicles and right-turning vehicles require substantially higher penetration rates of approximately 50% and 30%, respectively, to attain the same confidence level.

Determining the Required Probe Vehicle Size for Real-Time Travel Time Estimation on Signalized Arterial