Ihab El-Shawarby

Characterizing Driver Behavior on Signalized Intersection Approaches at the Onset of a Yellow-Phase Trigger

This paper involves a field test on 60 test participants to characterize driver behavior (perception-reaction time (PRT) and stopping/running decisions) at the onset of a yellow phase. Driver behavior is analyzed for five trigger distances that are measured from the vehicle position at the start of the yellow indication to the stop bar. This paper demonstrates that the 1.0-s 85th-percentile PRT that is recommended in traffic-signal-design procedures is valid and consistent with the field observations. Furthermore, this paper clearly shows that brake PRTs are impacted by the vehicles time to intersection (TTI) at the onset of a yellow-indication introduction. This paper also demonstrates that either a lognormal or a beta distribution is sufficient to model the stochastic nature of the brake PRT. In terms of stopping decisions, this paper demonstrates that the probability of stopping varies from 100% at a TTI of 5.5 s to 9% at a TTI of 1.6 s. This paper also indicates a decrease in the probability of stopping for male drivers when compared with female drivers. Furthermore, this study suggests that drivers 65 years of age and older are significantly less likely to clear the intersection at short yellow-indication trigger distances when compared with other age groups. The dilemma zone for the less than 40 year old group is found to range from 3.9 to 1.85 s, whereas the dilemma zone for the greater than 70 year old group is found to range from 3.2 to 1.5 s.

Estimating Path Travel-Time Reliability

The estimation of path or trip travel-time reliability is critical to any advanced traveler information system. The state-of-practice procedures for estimating path travel-time reliability assumes that travel times follow a normal distribution and requires a measure of trip travel-time variance. The study analyzes AVI data from San Antonio and demonstrates through goodness-of-fit tests that the assumption of normality is, from a theoretical standpoint, inconsistent with field travel-time observations and that a lognormal distribution is more representative of roadway travel times. However, visual inspection of the data demonstrates that the normality assumption may be sufficient from a practical standpoint given its computational simplicity. The paper then proposes five methods for the estimation of path travel-time variance from its component segment travel-time variances. The analysis demonstrates that computing the trip travel-time coefficient of variation as the conditional expectation over all realizations of roadway segments provides estimates within 13% of field observations for both uncongested and congested conditions

Trip Travel-Time Reliability: Issues and Proposed Solutions

The estimation of path or trip travel-time reliability is critical to any advanced traveler information system. The state-of-practice procedures for estimating path travel-time reliability assume that travel times follow a normal distribution and that segment travel times are independent (i.e., trip variance is a summation of segment variances). The present study analyzes Automatic Vehicle Identification (AVI) data from San Antonio, Texas, and simulated data to demonstrate through goodness-of-fit tests that a log-normal travel-time distribution is valid only under steady-state conditions, whereas a normal distribution is not valid. In the present article, the authors propose five methods for the estimation of path travel-time variance from its component segment travel-time variances. The analysis demonstrates that computing the trip travel-time coefficient of variation as the conditional expectation over all realizations of roadway segments provides estimates within 70% of trip travel-time variance for both uncongested and congested conditions.

Evaluation of Driver Deceleration Behavior at Signalized Intersections

This study analyzes field data gathered from 60 test subjects to characterize driver deceleration rates at the onset of a yellow-phase transition on high-speed signalized intersection approaches using an in-vehicle Global Positioning System. The driver rate of deceleration is analyzed for five yellow-phase trigger times to stop line (1.6, 2.7, 3.3, 4.4, and 5.6 s) as drivers approach the intersection at a speed of 72 km/h (45 mph). Results of the study, based on a sample of 821 deceleration events, indicate that driver deceleration rates range between 1.51 and 7.47 m/s2 (5–24.5 ft/s2) with a mean value of 3.27 m/s2 (10.7 ft/s2). Mean deceleration rates varying from 2.2 m/s2 (7.2 ft/s2) for the longest time to stop line (5.6 s) to 5.9 m/s2 (19.4 ft/s2) for the shortest time to stop line (1.6 s) demonstrate that drivers use more time to decelerate if they are sufficiently away from the intersection. Statistical analyses were used to investigate the effects of the time to stop line, gender, age group, and grade on the average deceleration rates. Results demonstrate that male drivers appear to show slightly higher rates of deceleration when compared with female drivers. This difference increases as the trigger time to stop line decreases. Younger drivers (younger than 40 years old) and older drivers (60 years of age or older) exhibit greater deceleration rates when compared with drivers in the 40- to 59-year age group.

Modeling Driver Behavior Within a Signalized Intersection Approach Decision-Dilemma Zone

This study uses data gathered in a field test on 60 test participants to develop models that characterize driver brake perception–reaction times (PRTs), brake times, and stop–go decisions at the onset of a yellow indication at a high-speed signalized intersection approach. The study demonstrates that driver PRTs are influenced only by the drivers time to intersection (TTI) at the onset of the yellow indication. The driver PRT is found to increase linearly with TTI and is not affected by the vehicle speed (in the range of 54 to 88 km/h), driver gender, or driver age. In the case of stop–go behavior, the older driver (≥65 years of age) dilemma zone is wider, ranging from a TTI of 4.81 to 1.66 s versus 4.90 to 2.87 s for the younger age group. Female drivers are more likely to stop than male drivers and tend to have a dilemma zone that is closer to the intersection. Finally, the study demonstrates that dilemma zone control systems should consider a dilemma zone from 5.0 to 1.5 s instead of the current state of practice of 5.5 to 2.5 s to capture all potential driver age and gender groups.

Evaluating Alternative Truck Management Strategies Along Interstate 81

The study evaluates lane management strategies along one of the most highly traveled sections of Interstate 81 in the state of Virginia by using the INTEGRATION traffic simulation software. The lane management strategies considered include the separation of heavy-duty trucks from light-duty traffic, the restriction of trucks to specific lanes, and the construction of climbing lanes at strategic locations. Overall, the results demonstrate that a physical separation of heavy-duty trucks from the regular traffic offers the maximum benefits and that restricting trucks from the use of the leftmost lane offers the second-highest benefits in terms of efficiency, energy, and environmental impacts.

Effect of Yellow-Phase Trigger on Driver Behavior at High-Speed Signalized Intersections

The paper characterizes driver behavior at the onset of a yellow-phase transition on high-speed signalized intersection approaches using field data gathered from 60 test subjects (approximately balanced in gender and age). The driver stopping/running decisions are analyzed for five trigger distances as drivers approach the intersection at a speed of 72 km/h (45 mph). The study demonstrates that the probability of stopping varies from 9% at the shortest yellow-phase trigger distance of 32 m to approximately 100% for the longest 111 m trigger distance. The study also demonstrates an increase in the probability of running for male drivers when compared to female drivers. This difference increases as the trigger distance decreases. The data demonstrate that drivers 65 years of age and older are significantly less likely to clear the intersection at short yellow-phase trigger distances when compared to other age groups. Dilemma zone boundaries (distances where 10% to 90% of the vehicles stop) are derived and uncertainty zones for different age groups are also developed

Modeling framework for the evaluation of alternative truck lane management strategies

This paper develops and validates a modeling framework for the evaluation of alternative truck lane management strategies. The framework is used to evaluate alternative truck lane management strategies along a section of Interstate 81, VA. The average light-duty and heavy-duty vehicle speeds produced by the simulation model were found to be consistent with field observations for the base condition. Three scenarios were considered, including: (a) adding a single lane to section 2 (from mileposts 125.0 to 120.7); (b) adding a single lane across sections 1 (from mileposts 128.1 to 125.0), 2, and 3 (from milepost 120.7 to 119.6); (c) combining (a) and (b) to result in four lanes from mileposts 128.1 to 119.6. The results of the analysis indicate that all three scenarios produce savings in travel time; energy; HC, CO, and CO2 emissions; and crash savings relative to the base do-nothing scenario. These benefits increase as the travel demand grows from the base year of 2004 to the horizon year of 2035. A benefit-cost analysis was conducted, and the results demonstrate that the most cost-effective upgrade is to add a third lane to section 2 (benefit-cost ratio of 5.35) followed by the addition of a single lane to sections 1 through 3 (benefit-cost ratio of 2.30). The addition of a fourth lane to section 2, together with an extra lane in sections 1 through 3, still offers advantages with a benefit-cost ratio of 1.60.

Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions

The ability to model driver stop/run behavior at signalized intersections considering the roadway surface condition is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop/run behavior. The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication for different roadway surface conditions. The paper offers two contributions. First, it introduces a new predictor related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Second, it applies well-known artificial intelligence techniques including: adaptive boosting (AdaBoost), random forest, and support vector machine (SVM) algorithms as well as traditional logistic regression techniques on the data in order to develop a model that can be used by traffic signal controllers to predict driver stop/run decisions in a connected vehicle environment. The research demonstrates that by adding the proposed driver aggressiveness predictor to the model, there is a statistically significant increase in the model accuracy. Moreover the false alarm rate is significantly reduced but this reduction is not statistically significant. The study demonstrates that, for the subject data, the SVM machine learning algorithm performs the best in terms of optimum classification accuracy and false positive rates. However, the SVM model produces the best performance in terms of the classification accuracy only.

Impact of Driver and Surrounding Traffic on Vehicle Deceleration Behavior at Onset of Yellow Indication

This paper discusses driver deceleration levels in a controlled field environment at the onset of a yellow indication on high-speed signalized intersection approaches using an in-vehicle differential Global Positioning System. The impacts of driver gender, driver age, roadway grade, mean approach speed, platooning scenarios (leading, following, or alone), and time to intersection (TTI) on driver deceleration levels were analyzed. This information is critical for the efficient and safe design of traffic signal clearance timings. The IntelliDrive initiative can gather information about the driver, subject vehicle, and surrounding traffic conditions to execute safe and customizable traffic signal indication change warnings. The results indicate that driver deceleration levels are significantly higher than the 3-m/s2 deceleration level used in the state-of-the-practice traffic signal design guidelines. The mean deceleration level is 3.6 to 4.1 m/s2. The results can be used to enhance the design of yellow timings and may be integrated with the new IntelliDrive initiative to provide customizable driver warnings. The results demonstrate that driver deceleration levels are higher at shorter TTIs at the onset of yellow. Drivers are willing to exert deceleration levels in excess of 7 m/s2 at short TTIs (less than 2.5 s). Furthermore, older drivers (60 years of age or older) employ greater deceleration levels compared with younger (under 40 years old) and middle-aged (between 40 and 59 years old) drivers. A driver following another vehicle that proceeds legally through an intersection without stopping exerts higher deceleration levels than drivers driving alone on a roadway or leading another vehicle, and drivers leading a platoon of vehicles are not affected by vehicles behind them.