This paper presents the analysis of traffic operations at 34 instances of yellow/red malfunction flash and nine instances of red/red malfunction flash in the Atlanta area. Malfunction flash occurs when a traffic signal’s malfunction monitoring unit detects a problem and automatically initiates a flashing operation. Many of these instances were during periods of high demand, and a high level of driver confusion was documented. The stopping rate of major-street through drivers (i.e., those facing a flashing yellow signal and not required to stop) exceeded 75% at some yellow/red flash intersections. This eliminates many of the operational benefits that yellow/red flash is generally assumed to have over red/red flash and may create a safety hazard. To predict major-street (flashing yellow) stopping rates, logit models were developed. At red/red flash intersections, 10–20% of drivers did not stop before entering the intersection; many of these drivers crept through or “piggybacked” with another vehicle.

This study presents an arterial signal optimization model that can consider queue blockage among intersection lane groups under oversaturated conditions. The proposed model captures traffic dynamics with the cell transmission concept, which takes into account complex flow interactions among different lane groups. With the embedded formulations for forward wave, backward wave, and the horizontal queue, the proposed arterial signal optimization model can yield effective signal plans for both saturated and under-saturated intersections. To evaluate the performance of the proposed model, this study has conducted extensive simulation experiments with a segment of Georgia Avenue intersecting the Capital Beltway in Silver Spring, Maryland. Through comparisons with signal-timing plans from TRANSYT-7F (Release 10), the proposed model shows its promise for signal-timing optimization, particularly under congested conditions.

This research explores factors associated with driver yielding behavior at unsignalized pedestrian crossings and develops predictive models for yielding by using logistic regression. It considers the effect of variables describing driver attributes, pedestrian characteristics, and concurrent conditions at the crosswalk on yield response. Special consideration is given to “vehicle dynamics constraints” that form a threshold for the potential to yield. Similarities to driver reaction in response to the amber indication at a signalized intersection are identified. The logit models were developed from data collected at two unsignalized midblock crosswalks in North Carolina. The data include before and after observations of two pedestrian safety treatments, an in-street pedestrian crossing sign and pedestrian-actuated in-roadway warning lights. The analysis suggests that drivers are more likely to yield to assertive pedestrians who walk briskly in their approach to the crosswalk. In turn, the yield probability is reduced with higher speeds, with deceleration rates, and if vehicles are traveling in platoons. The treatment effects proved to be significant and increased the propensity of drivers to yield, but their effectiveness may be dependent on whether the pedestrian activates the treatment. The results of this research provide new insights into the complex interaction of pedestrians and vehicles at unsignalized intersections and have implications for future work toward predictive models for driver yielding behavior. The developed logit models can provide the basis for representing driver yielding behavior in a microsimulation modeling environment.

State highway agencies have been experimenting with new concrete mixtures and pavement design features for improving the behavior and performance of concrete pavements. This paper presents the results of a field study undertaken in Maryland with alternative concrete mixtures. The objective of the study was to evaluate the in-place material properties for three concrete mixtures: a control, a fiber-reinforced, and a low-shrinkage concrete mix. Field instrumentation was used for monitoring the deflections and strains of the concrete slabs built with these concrete mixtures. Nondestructive testing (NDT) and back-calculation analysis were then used to evaluate the in situ material properties immediately after construction. Although traditional back-calculation analysis is based primarily on deflection measurements, in this study an iterative approach using NDT methods and measured strain and deflection data was employed for estimating the in situ modulus of subgrade reaction, $\mathit{k}$. This analysis provided a base model for future comparisons of the behavior and performance of the pavement test sections after prolonged exposure to in-service loading conditions.

Ohio crash data for 2003–2007 were used to investigate the odds of a motorcyclist being fatally injured in a crash and the risk factors involved. The results show that risk factors for fatality/severe injury significantly increase when the following circumstances apply: the motorcyclist is a female, being the motorcycle rider, use of excessive speeding, use of alcohol and/or drugs, riding without a helmet, being involved in a single-vehicle crash or at a nonintersection location, crashing on horizontal curves or on graded segments, and on major roadways. To reduce the number of fatal crashes, this study indicates that the dangers of excessive speed and operating a motorcycle while intoxicated must be fully stressed to the public and both require an elevated enforcement. The enactment of an Ohio universal helmet law is particularly recommended.

Safety performance is evaluated by using measures obtained experimentally from a videotaping of traffic operations at an urban roundabout situated in Cosenza, Italy. Five different expressions of safety performance from the perspective of “rear-end” vehicle interactions include maximum deceleration rate to avoid a crash (DRAC), time-to-collision (TTC), proportion of stopping distance (PSD), time integrated time-to-collision (TIT), and crash potential index (CPI). Differences in safety performance are discussed with respect to the type of measure, traffic conditions, and variations in roundabout geometry. The results of this analysis suggest that safety performance is highly sensitive to the way it is measured; different measures can highlight different locations or geometric features of the roundabout as posing potential safety problems. This study underscores the usefulness of safety performance measures for providing meaningful experimental indicators of potential safety problems at roundabouts and how these problems can be affected by changing traffic conditions.

In developing Association of Southeast Asian Nations (ASEAN) countries, the motorcycle is a popular means of transportation because it is cheap and provides flexible door-to-door mobility. However, motorcyclists are also highly involved in road accidents. Separating motorcycles from other vehicles in traffic by providing motorcycle lanes is a good engineering measure to improve the safety of motorcyclists. In designing motorcycle lanes, considerations of geometrical elements such as horizontal and vertical curve lengths, stopping distances, and passing sight distances are essential. This study attempts to quantify the eye levels and head levels of motorcycle riders and motorcycle headlight and taillight height characteristics that influenced these geometrical elements. Characteristics of the motorcycles observed along the existing exclusive motorcycle lanes in Selangor state of Malaysia were transcribed from a camcorder, using reference dimension. Findings recommend a design motorcycle eye height of 1,350 mm (5th percentile), headlight height of 800 mm (5th percentile), and taillight height of 625 mm (5th percentile). A motorcyclist height of 1,525 mm (10th percentile) is recommended for the design of sight distance. Recommended heights reduce the cost of motorcycle lane construction with demonstrated safety compared with the current criteria.

This paper presents a case study to explore the truck-trip generation model for hauling containers at a major international seaport. An internal-external truck-trip forecast model is examined. It incorporates influential factors of regional freight activity attributes, economic growth attributes, and natural disaster attributes based on monthly data from 2000–2008. A best-fit truck-trip forecasting model is determined by comparing the prediction accuracy of a multiple regression model, time-series models, and a neural network model. The findings indicate that the back propagation neural network model generates better forecasting performance than the regression and time-series approaches. Additionally, this paper identifies the difference between truck trips and commodity-flow tonnages converted by truck payload factors, which would be significantly affected by truck-trip chains and truck drivers’ route choice behaviors. The analysis also reveals that a port truck-trip forecast model based on commodity flows would be very sensitive to the events of oil price fluctuations and new operation or infrastructure upgrade of competitive ports nearby, once the conversion difference goes up to 30%.