Travel time unreliability is an important characterization of transportation systems. The appropriate modeling and reporting of travel time reliability is crucial for individual travelers as well as management agencies. A novel multistate model is proposed for travel time modeling and reporting. The model advances travel time modeling in two aspects. First, the multistate model provides much improved model fitting as compared with single-mode models. Second and more important, the proposed model provides a connection between travel time distributions and the underlying travel time state. This connection allows for the quantitative evaluation of the probability of each travel time state as well as the uncertainty associated with each state, for example, the probability of encountering congestion at a given time of day and the expected travel time if congestion is experienced. A simulation study was conducted to demonstrate the performance of the model. The proposed model was applied to field data collected at San Antonio, Texas. The variation of the model parameters as a function of time of day was also investigated. The simulation study and field data analysis confirmed the superiority of the multistate model over the state-of-practice single-mode travel time reliability models and for ease of interpretation.

A power-based vehicle fuel consumption model, entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM) that was developed in an earlier publication is validated against in-field fuel consumption measurements. The study demonstrates that the VT-CPFMs calibrated using the EPA city and highway fuel economy ratings generally provide reliable fuel consumption estimates with a coefficient of determination in the range of 0.96. More importantly, both estimates and measurements produce very similar behavioral changes depending on engine load conditions. The VT-CPFMs are demonstrated to be easily calibrated using publically available data without the need to gather in-field instantaneous data

The research presented in this paper develops and quantifies the fuel savings associated with an ecodrive system that combines an ecocruise control algorithm and state-of-the-art car-following models. The system makes use of topographic information provided from a high-resolution map, the spacing between the subject and lead vehicle that can be obtained from a radar system or some form of vehicle-to-vehicle communication, a desired (or target) vehicle speed, a minimum and maximum desired speed, and a cruise control spacing threshold. The study demonstrates that the proposed system can significantly improve vehicle fuel efficiency without significantly increasing the intervehicle spacing. Specifically, fuel savings in the range of 27% are achieved with an average vehicle spacing of 47 m along a study section of Interstate 81. The study also demonstrates that regular vehicles can benefit significantly by following a lead ecodrive vehicle.

A vehicle predictive eco-cruise control system is developed that minimizes vehicle fuel consumption levels utilizing roadway topographic information. The predictive eco-cruise control system consists of three components: a fuel consumption model, a powertrain model, and an optimization algorithm. The developed system generates an optimal vehicle control plan using anticipated roadway grade information so that the vehicle can vary its speed within a preset speed window in a fuel-saving manner. The developed system is tested by simulating a vehicle trip on synthetic roadway profiles and compared to a conventional cruise control system performance. Finally, the potential benefits of the predictive eco-cruise control system are quantified for the entire United States.

Although roadway grades are known to affect vehicle fuel consumption and emissions rates, there do not appear to be any systematic evaluations of these impacts in the literature. Consequently, this paper addresses this void by offering a systematic analysis of the impact of roadway grades on vehicle fuel consumption and emissions rates by using the INTEGRATION microscopic traffic simulation software. The energy and emissions impacts are quantified for various cruising speeds, under stop-and-go conditions, and for various traffic signal control scenarios. The study demonstrates that the impact of roadway grade is significant, with increases in vehicle fuel consumption and emissions rates in excess of 9% for a 1% increase in roadway grade. Consequently, a reduction in roadway grades in the range of 1% can offer savings that are equivalent to those offered by various advanced traffic management systems.

The research identifies the steady-state car-following model parameters within state-of-the-practice traffic simulation software that require calibration to reflect inclement weather and roadway conditions. The research then develops procedures for calibrating non-steady state car-following models to capture inclement weather impacts and applies the procedures to the INTEGRATION software on a sample network. The results demonstrate that the introduction of rain precipitation results in a 5% reduction in light-duty vehicle speeds and a 3% reduction in heavy-duty vehicle speeds. An increase in the rain intensity further reduces light-duty vehicle and heavy-duty truck speeds resulting in a maximum reduction of 9.5% and 5.5% at the maximum rain intensity of 1.5 cm/h, respectively. The results also demonstrate that the impact of rain on traffic stream speed increases with the level of congestion and is more significant than speed differences attributed to various traffic operational improvements and thus should be accounted for in the analysis of alternatives. In the case of snow precipitation, the speed reductions are much more significant (in the range of 55%). Furthermore, the speed reductions are minimally impacted by the snow precipitation intensity. The study further demonstrates that precipitation intensity has no impact on the relative merit of various scenarios (i.e. the ranking of the scenario results are consistent across the various rain intensity levels). This finding is important given that it demonstrates that a recommendation on the optimal scenario is not impacted by the weather conditions that are considered in the analysis