Davis, Gary A.

Effective use of finite roadway improvement budgets to accommodate an increasing number of older drivers requires that we be able to identify those locations where older drivers appear to have a heightened accident risk. Ideally, the accident records from a location (such as a particular intersection) should provide the information needed to assess the risk experienced there by a given group of drivers, but the lack of location and age-specific measures of exposure coupled with the relatively small accident samples available for particular locations makes the standard methods of high-hazard identification inapplicable. In this paper it is first shown how, by using an induced exposure approach, it is possible to test for the equality of group-specific accident rates at a given site by testing for the equality of two binomial probabilities which arise from a particular type of contingency table.

In 2009, the FHWA’s Manual on Uniform Traffic Control Devices (MUTCD) introduced the flashing yellow arrow (FYA) traffic signal as an alternative to circular green (CG) to indicate permitted left turns. The FYA is arguably a more intuitive indication that left turns are permitted but not protected and, in addition, the FYA signal heads can support time-of-day changes between protective and permissive left -turn phasing. In 2019, a Research Needs Statement stated that “Research is needed to examine driver comprehension of flashing yellow arrows in different light arrangements and the role of signage.” Our objective in this project was to assess drivers’ understanding of FYA signal indications and to see if the presence or absence of “Left Turn Yield” signs affect gap acceptance. This was accomplished by conducting an online survey of drivers regarding their understanding of FYA signals and by carrying out a field study of drivers’ gap acceptance at a set of Twin Cities intersections.

This report describes the development of a data-driven methodology for estimating the mean daily traffic (MDT) for different vehicle classes from short classification-count samples. Implementation of the methodology requires that an agency maintain a small number of permanent classification counters (PCC), whose output is used to estimate parameters describing their monthly and day-of-week variation patterns and covariance characteristics. The probability of a match between a short classification count sample and each of the PCCs is computed, as well as the estimates of the short-counts site's MDTs which would arise if the short-count site had variation patterns identical to each of the PCCs. The final MDT estimates are then simply the weighted averages of these component MDTs, with the matching probabilities providing the weights. Empirical evaluation of the methods using data collected at the Long Term Pavement Performance Project sites in Minnesota indicated that a reliable match of a short-count site could be made using a sample consisting of a one-day classification count from each month of the year. An evaluation of two-day classification count samples indicated that a two-day count is not sufficient to reliably match the site to a factor group, justifying estimation of MDT using weighted averages. For estimating combination vehicle MDT, these samples should be taken between May and October, and between Tuesday and Thursday. In this case the estimated MDT differed on average by about 10% - 12% compared to estimates based on full year's worth of counts, and differed by less than 26%, 95% of the time.

In 2019 the Minnesota Legislature amended that state’s statutes to allow cities to set speed limits on city-owned streets. In February 2021 we surveyed 33 cities within the Twin Cities metro area and identified the city of St. Louis Park as planning to implement a city-wide change in speed limits, with a default speed limit of 20 mph but with selected roads being signed for limits ranging from 25 mph to 35 mph. Speed data was collected using road tube traffic recorders in the summer of 2021, 2-4 months before the speed limit change, and in the summer of 2022, 6-8 months after the change. There was considerable variability regarding what was seen at individual locations, with before/after differences in mean speed ranging from a decrease of 7 mph to an increase of 2.4 mph. On average, mean speeds were slightly lower (1-2 mph) in the after period, both on streets where the speed limit was lowered and on streets where the limit was unchanged. This pattern, modest reductions in mean speeds following a reduction in speed limit, with possible spillover, was consistent with what has been seen in other cities in North America and Great Britain.

This report describes the application of Bayesian statistical methods to several related problems arising in the estimation of mean daily traffic for roadway locations lacking permanent automatic traffic recorders. A lognormal regression model is fit to daily count data obtained from automatic traffic recorders, and this model is then used to develop (1) a heuristic algorithm for developing traffic sampling plans which minimize the likelihood of assigning a site to an incorrect factor group, (2) an empirical Bayes method for assigning a short-count site to a factor group using the information in a sample of traffic counts, and (3) an empirical Bayes estimator of mean daily traffic which allows for uncertainty concerning the appropriate factors to be used in adjusting a sample count. An evaluation of these methods confirmed results reported in other work, in which a sample consisting of two, 1-week counts was found to be adequate for overcoming prior uncertainty concerning the correct adjustments for a site. The empirical Bayes method produced sample-based estimates of mean daily traffic that on the average differed by 5%-6% from estimates based on daily counts for an entire year. The report concludes with suggestions for agencies wishing to implement these methods.

This presentation pertains to report 2015-18, Implementation of Traffic Data Quality Verification for WIM Sites.

Any proposed traffic management action is essentially a forecast that the action will result in certain traffic conditions, but uncertainty concerning the amount and distribution of traffic demand will introduce random error between what is expected and what actually occurs. This report treats the problem of forecasting whether or not a given set of freeway on-ramp volumes are likely to cause over-capacity demand at some point in the freeway mainline. The main source of uncertainty in these forecasts concerns the freeway's origin-destination matrix, and four different methods for estimating this matrix from loop detector data are evaluated using Monte Carlo simulation. Only the method which explicitly modeled freeway traffic flow produced reasonably unbiased and efficient estimates, and it was concluded that successful estimation must be coupled with a good model of freeway traffic flow.

A 4-3 conversion involves changing a four-lane undivided road into one with two general travel lanes separated by a two-way left turn lane. A commonly-used guideline states that a 4-3 conversion can be considering as long as the road’s average annual daily traffic (AADT) volume does not exceed 15,000 vehicles/day but opinions vary, from lowering the AADT threshold to 10,000 vehicles/day to anecdotal evidence for successful 4-3 conversions with AADTs as high as 20,000. The main objective of this project was to identify conditions where 4-3 conversions might be feasible at AADTs greater than 15,000. After reviewing the literature, we conducted simulation studies on three different roads to identify combinations of major and minor road flow where three-lane configurations provided acceptable levels of service. Eight intersections, with 16 approaches, were then selected to represent our findings. These results were presented as summary tables that practitioners could use to make initial assessments regarding 4-3 conversion feasibility.

These tool instructions appear as chapter 5 in report 2015-27, Development of Guidelines for Permitted Left-Turn Phasing Using Flashing Yellow Arrows. The instructions pertain to an Excel macro file that may be accessed from https://edocs-public.dot.state.mn.us/edocs_public/DMResultSet/download?docId=29248594

There are several ways that a traffic signal system copes with left turns at an intersection: The “permitted phase” allows drivers to choose a safe gap in oncoming traffic; the “protected phase” provides turning vehicles with an exclusive turn phase and the “permitted/protected phase” combines the two to accommodate different left-turn and through patterns. The Mn/DOT Signal Design Manual provides guidance for implementing safe and efficient left-turn phasing.