A study of travel time and reliability on arterial routes

This study is concerned with travel time and operational reliability on arterial routes. Reliability is viewed in terms of the consistency of operation of the route under investigation and defined in terms of the inverse of the standard deviation of the travel time distribution.Under certain assumptions, travel time behavior on an arterial route is seen to closely follow a gamma distribution; the reliability measure can be derived accordingly. Utilizing arterial travel time data from the Chicago area, both a regression and a statistical model are show to serve as efficient techniques in predicting reliability. The prediction models are evaluated.     

Evaluation of a public transportation level of service concept

The level of service of a bus line is evaluated by its operational characteristics, particularly by the ratio between average bus travel time on a given route and the average passenger car travel time on the shortest distance between the origin and the destination of the bus in question. It is shown that the level-of-service measure may be predicted by such independent variables as route length, average distance between bus stations, number of signalized and unsignalized intersections, and the ratio between such intersections. It is hypothesized that use of other independent variables such as boarding and alighting passengers, or volume to capacity ratio on the route concerned, could improve the predictive power of the suggested models. Further research is recommended on the effect of these latter variables and other operational variables which might influence bus level of service, and also on the comparison between direct bus lines and lines which use transfer points.     

A new,non-canonical Poisson regression model for the prediction of crashes on low-volume rural roads

The purpose of the study was to compare the prediction power of a simplified non-canonical Poisson crash-prediction model to other model types. The model, fitted to serious and fatal crash data from 86 two-lane low-volume rural highway segments, showed a good fit, which was not significantly different from that of a negative binomial model. The application of the present model uses the linear form of the non-canonical Poisson model. Hence the simplification of the model versus other models results from the finding that the expected number of crashes per 1 km is directly proportional to the daily volume, unlike logarithmic functions in other models. In the non-canonical model, it is necessary to estimate only one parameter, whereas estimations of more parameters are needed in the negative binomial model.     

Evaluation of congestion trends on chicago expressways

The growth in congestion on Chicago area expressways has had a major effect on almost all aspects of the region's economy, travel behavior and land-use development patterns. The objective of this study was to evaluate congestion trends on Chicago's expressways during the last decade (1980 to 1989) by quantifying the magnitude and tendencies of congestion for the various expressways. This study concentrated on an analysis of the minute-miles of congestion (MMC), a measure that provides an averaged section congestion during a given time period and that is continuously collected by the Illinois Department of Transportation for all Chicago-area expressways. The MMC offers a relatively accurate and unbiased estimate of the quantity and duration of congestion. In order to compare congestion trends on the area's ten major expressways in the 1980's it was decided to evaluate the yearly Cumulative Distribution Function of congestion. This analysis was conducted for both the inbound and outbound directions and for the A.M. and P.M. daily peak periods. The magnitude of congestion was found to increase considerably with the years from 1980 to 1989. The increase in congestion for “reverse commuting”, trips to the suburbs in the morning peak and from the suburbs in the evening peak, grew more substantially than the increase in the regular “city commute.” The reason for this phenomenon was the rise in job opportunities in the suburbs, resulting partially from increased congestion and reduced accessibility. Additional analysis showed that the duration of the peak period, defined by the time in which congestion exceeded a predetermined threshold level, was generally longer for the “city commuting” than for the “reverse commuting.” Comparison of congestion among seasons, showed that the most congested season was usually the spring (May 1 to June 15). The system analysis showed that the by 2000, the expected system congestion will be more than twice the 1980 system congestion.     

Estimating percent-time-spent-following on two-lane rural highways

This study concentrated on estimating the percent-time-spent-following (PTSF) on two-lane highways. This measure is a key estimate of level-of-service in traffic engineering applications. Its evaluation to date has been based on simulations that yielded over-estimated values. The present study shows how to estimate this variable from easily obtained field data based on queuing theory. The estimates accord with opinions on yielding significantly lower values of PTSF that are expressed in the relevant traffic literature. An improved relationship between PTSF and two-way flow is provided by fitting the new estimates by means of the least-squares method.