In many applications of directed graph theory, it is desired to obtain a list of the simple cycles of the graph. In this paper, a new search algorithm for finding the simple cycles of any finite directed graph is presented, and the validity of the algorithm is proven. The algorithm has been implemented experimentally in Snobol3, and tests indicate that the algorithm is reasonably fast. (The simple cycles of a 193 vertex graph were obtained in 6.8 seconds on an IBM 7094 computer.)
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A new procedure for estimating the emissions of heavy-duty vehicles (HDVs) is presented. This procedure combines second-by-second data on actual in-use speed and acceleration of HDVs with data on average emissions rates of HDVs operating at corresponding speeds and acceleration rates. The initial implementation of this procedure used a limited amount of newly collected emissions data and a somewhat larger amount of previously collected HDV activity data. Validation tests provide a reasonable level of confidence about the validity of the nitrogen oxide (NOx) emissions factors produced using this initial implementation. However, these tests also indicate that the small amount of emissions data used in the initial implementation is insufficient to produce meaningful estimates of emissions factors for carbon monoxide or particulate matter. The research, the procedure that was developed, the validation tests, the results for NOx emissions, and NOx speed correction factors derived from these results are briefly described. The speed correction factors are of particular interest. The minimum values for these factors occur at speeds higher than those currently used by the U.S. Environmental Protection Agency, and the factors grow more slowly at higher speeds than do the factors generated by MOBILE.
Vehicle classification data are an important component of traffic-monitoring programs. Although most vehicle classification conducted in the United States is axle based, some applications could be supplemented or replaced by length-based data. The typically higher deployment cost and reliability issues associated with collecting axle-based data as compared with length-based data present a challenge. This paper reports on analyses of alternative length-based vehicle classification schemes and appropriate length bin boundaries. The primary analyses use data from a set of 13 Long-Term Pavement Performance weigh-in-motion sites, all in rural areas; additional analyses are conducted with data from 11 Michigan Department of Transportation weigh-in-motion sites located in rural and small urban areas and one site located in an urbanized area. For most states, the recommended length-based vehicle classification scheme is a four-bin scheme (motorcycles, short, medium, and long) with an optional very long bin recommended for use by states in which significant numbers of longer combination vehicles operate.
Procedures developed by FHWA for “factoring” short-duration traffic counts for seasonal and day-of-week variations in traffic volumes are capable of producing estimates of annual average daily traffic (AADT) that are quite accurate. Moreover, there is virtually no bias in these estimates, so AADT estimates for a set of road sections can be used to produce unbiased estimates of total vehicle miles traveled (VMT) for systems of roads. Unfortunately, corresponding procedures are not generally used for estimating AADT by vehicle class, and the less sophisticated procedures that are commonly used contribute to substantial overestimates of truck AADT and VMT. Current procedures apparently overestimate VMT by 25 to 40 percent for combination trucks and possibly more for single-unit trucks. Modified versions of the FHWA factoring procedure that are capable of producing substantially improved estimates of truck VMT and of AADT of combination trucks are presented. These procedures use seasonal and day-of-week factoring to reduce the errors in truck AADT estimates and to eliminate the upward bias in truck VMT estimates that result from the use of unfactored weekday classification counts.
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