The research reported in this paper quantifies the impact of inclement weather (precipitation and visibility) on traffic stream behavior and key traffic stream parameters, including free-flow speed, speed at capacity, capacity, and jam density. The analysis is conducted using weather data (precipitation and visibility) and loop detector data (speed, flow, and density) obtained from the Baltimore, Maryland; Minneapolis–Saint Paul, Minnesota; and Seattle, Washington, areas in the United States. The precipitation data included intensities up to 1.6 and 0.33 cm/h for rain and water equivalent of snow intensity, respectively. The paper demonstrates that the traffic stream jam density is not affected by weather conditions. Snow results in larger reductions in traffic stream free-flow speed and capacity when compared with rain. Reductions in roadway capacity are not affected by the precipitation intensity except in the case of snow. Reductions in free-flow speed and speed at capacity increase as the rain and snow intensities increase. Finally, the paper also develops free-flow speed, speed-at-capacity, and capacity weather adjustment factors that are multiplied by the base clear-condition variables to compute inclement weather parameters. These adjustment factors vary as a function of the precipitation type, precipitation intensity, and visibility level. It is intended that these adjustment factors be incorporated into the Highway Capacity Manual.
The research reported in this paper develops a heuristic automated tool (SPD_CAL) for calibrating steady-state traffic stream and car-following models using loop detector data. The performance of the automated procedure is then compared to off-the-shelf optimization software parameter estimates, including the MINOS and Branch and Reduce Optimization Navigator (BARON) solvers. The model structure and optimization procedure is shown to fit data from different roadway types and traffic regimes (uncongested and congested conditions) with a high quality of fit (within 1% of the optimum objective function). Furthermore, the selected functional form is consistent with multiregime models, without the need to deal with the complexities associated with the selection of regime breakpoints. The heuristic SPD_CAL solver, which is available for free, is demonstrated to perform better than the MINOS and BARON solvers in terms of execution time (at least 10 times faster), computational efficiency (better match to field data), and algorithm robustness (always produces a valid and reasonable solution).
The estimation of path or trip travel-time reliability is critical to any Advanced Traveler Information System. The state-of-practice procedures for estimating path travel-time reliability assumes that travel times follow a normal distribution and requires a measure of trip travel-time variance. The study analyzes AVI data from San Antonio and demonstrates through goodness-of-fit tests that the assumption of normality is, from a theoretical standpoint, inconsistent with field travel-time observations and that a lognormal distribution is more representative of roadway travel times. However, visual inspection of the data demonstrates that the normality assumption may be sufficient from a practical standpoint given its computational simplicity. The paper then proposes five methods for the estimation of path travel-time variance from its component segment travel-time variances. The analysis demonstrates that computing the trip travel-time coefficient of variation as the conditional expectation over all realizations of roadway segments provides estimates within 13% of field observations for both uncongested and congested conditions.
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