In practice, traffic signal timings are derived using macroscopic tools that are essentially deterministic. Traffic flows, signal phasing, and street geometry are processed to deliver optimized signal timings. Objective functions strive for efficiency through minimizing measures such as delay and journey time. We now have traffic microsimulation tools that model traffic by imitating its stochastic nature. This paper looks at microsimulation as a means of testing optimized signal timings. We assess the suitability of evaluating signal timings optimized macroscopically through microsimulation. We analyze a range of traffic demand and traffic control scenarios. A real-world arterial with 12 signalized intersections serves as a test bed for the experiments. The results show that when macroscopically optimized signal timings are subject to extensive evaluation through microsimulation, their efficiency is shown to be inconsistent. The paper concludes that the traffic microsimulation tools cannot always be relied upon to evaluate macroscopically optimized traffic signal timings because these timings sometimes perform worse, in microsimulation, than the nonoptimized signal timings.

Adequate sight distance is an important factor needed to provide safe and efficient operation of left-turn movements at signalized intersections. This factor becomes more crucial for left turners attempting this maneuver during the permitted phase of the traffic signal. The large size median of the major road is commonly designed to accommodate an exclusive left-turn lane. However, studies have proved that if the median of that major road is relatively wide, the simultaneously turning vehicles in the opposite left-turn lanes often restrict driver’s sight distances for each other. In this study, a field observation was conducted to analyze the changes of left-turn gap acceptance parameters associated with drivers’ restricted sight distances. Theoretical models were developed and used to quantify the negative effect of the restricted sight problem on the left-turn capacity. The results show that the sight-distance problem may contribute to significant increments of the critical gap and follow-up time. This influence on driver’s gap-acceptance behavior may result in a 70% reduction in the left-turn capacity when the opposing-through traffic volume increases up to $\mathrm{1,800}\text{vehicles}∕\mathrm{h}$ . The capacity reduction rate increases with the increase in both the opposing-through traffic volume and the volume-to-capacity ratio of the opposing left-turn traffic.

Public transit managers strive to attain multiple goals with tightly constrained resources. Ratio analysis has evolved into a powerful tool for dealing with these goals and constraints. Ratio analysis provides analytical methods for comparing the performance of multiple agencies, as well as the performance of subunits within a particular agency, in order to identify opportunities for improvement. One ratio analysis procedure that has become increasingly popular is data envelopment analysis (DEA). DEA yields a single, comprehensive measure of performance, the ratio of the aggregated, weighted outputs to aggregated, weighted inputs. This paper makes two contributions to the practice of transit performance evaluation using DEA. First, instead of using DEA to compare the performance of multiple transit systems, it uses DEA to compare the performance of multiple bus routes of one transit system. Second, it introduces a new procedure for adjusting the raw DEA scores that modifies these scores to account for the environmental influences that are beyond the control of the transit agency.

In order to identify the improvement needs on transportation facilities and the associated budget, the transportation agencies need to have a reasonable estimate of the facilities’ future condition. Using asphalt concrete overlay over fractured portland cement concrete pavement as an example, this paper shows how to use the existing pavement condition and other recorded information in a pavement management system to predict the future condition of pavements. A comprehensive pavement performance indicator, condition points, was used as the response variable of the prediction model. A dynamic panel data prediction method was employed to make the prediction model reflect the influence of the existing pavement condition, exogenous variables, and the heterogeneity of different pavement sections. The predicted condition points were compared with the actual values and the results indicated that the prediction accuracy was reasonable.

Concern over collisions between passenger cars and sport utility vehicles (SUVs) is growing, as more SUVs enter the market. However, little is known about the different roles that car drivers and SUV drivers play in such collisions or about the roadway locations where the most harmful car–SUV collisions occur. This paper explores the effect of driver and roadway factors as they relate to physical and monetary harm. The analysis is based on a $3\text{-}\text{year}$ sample of crashes in the Crashworthiness Data System. A sample of 539 car–SUV collisions are analyzed, representing 402,306 weighted cases. The analysis of physical harm, based on the most severe injury, corroborates the analysis of monetary harm, which is based on costs incurred by all occupants. Cars and SUVs contribute different amounts to the average cost; the monetary harm to cars and their occupants is about twice as high as for SUVs, reflecting the mass differentials and incompatibility of the two vehicle platforms. Furthermore, higher monetary costs are associated with side-angle collisions occurring at intersections on undivided roadways. The most harmful maneuver involves left turning cars encroaching into oncoming traffic (SUV lane). Other risky situations and implications are discussed in the paper.

This technical note proposes and applies a methodology to analyze the level of service at departure lounges of airport passenger terminals, according to user perceptions. One hundred and twenty passengers were observed and interviewed at São Paulo International Airport in Brazil during May 2004. Two techniques were employed to assist in the data analysis: the successive categories method and regression analysis. The first technique was used to transform qualitative passenger responses into quantitative level of service (LOS) ratings. Regression analysis provided a relationship between these ratings and the characteristics of the facility (space available per passenger and number of seats per passenger). LOS standards that are useful for Brazilian airports for planning, design, and management were obtained. The proposed methodology can be used at any airport or any group of airports, if local data are collected in order to recalibrate the models.