Kyoungho Ahn

DEVELOPMENT OF VT-MICRO MODEL FOR ESTIMATING HOT STABILIZED LIGHT DUTY VEHICLE AND TRUCK EMISSIONS

Abstract The paper applies a framework for developing microscopic emission models (VT-Micro model version 2.0) for assessing the environmental impacts of transportation projects. The original VT-Micro model was developed using chassis dynamometer data on nine light duty vehicles. The VT-Micro model is expanded by including data from 60 light duty vehicles and trucks. Statistical clustering techniques are applied to group vehicles into homogenous categories. Specifically, classification and regression tree algorithms are utilized to classify the 60 vehicles into 5 LDV and 2 LDT categories. In addition, the framework accounts for temporal lags between vehicle operational variables and measured vehicle emissions. The VT-Micro model is validated by comparing against laboratory measurements with prediction errors within 17%.

Requirements for Evaluating Traffic Signal Control Impacts on Energy and Emissions Based on Instantaneous Speed and Acceleration Measurements

The evaluation of many transportation network improvements commonly is conducted by first estimating average speeds from a transportation or traffic model and then converting these average speeds into emission estimates based on an environmental model such as MOBILE. Unfortunately, recent research has shown that certainly average speed and perhaps even simple estimates of the amount of delay and the number of stops on a link are insufficient measures to fully capture the impact of intelligent transportation system strategies such as traffic signal coordination. In an attempt to address this limitation, the application of a series of multivariate fuel consumption and emission prediction models is illustrated, both within a traffic simulation model of a signalized arterial and directly to instantaneous speed and acceleration data from floating cars traveling down a similar signalized arterial. The application of these multivariate relationships is illustrated for eight light-duty vehicles, ranging in size from subcompacts to minivans and sport-utilities using data obtained from the Oak Ridge National Laboratory. The objective is to illustrate that the application of these instantaneous models is both feasible and practical and that it produces results that are reasonable in terms of both their absolute magnitude and their relative trends. This research is one step in a more comprehensive modeling framework for dealing with the impacts of intelligent transportation systems on energy consumption and vehicle emissions. Other steps include analyses of traffic diversion and induced demand and validation of the estimated fuel consumption and emissions using direct on-road measurements.

Stochastic Optimization for Sustainable Traffic Signal Control

ABSTRACT The primary research focus of traffic signal control systems has been the development of traffic signal timing plans minimizing vehicular delay and stops. Very little is known if such a strategy would be optimal for a sustainable traffic signal control system minimizing emission and fuel consumption. This paper presents a development of a sustainable traffic signal control system and speed management framework consisting of a microscopic simulation model, a microscopic fuel consumption and emission model, and a genetic algorithm – based optimizer. Based on an implementation of the proposed framework on a case study network, it was found that the proposed framework was very effective in minimizing fuel consumption and emission with moderate trade-offs in delay and stops.

INTEGRATION Framework for Modeling Eco-routing Strategies: Logic and Preliminary Results

The paper presents the INTEGRATION microscopic traffic assignment and simulation framework for modeling eco-routing strategies. Two eco-routing algorithms are developed: one based on vehicle sub-populations (ECO-Subpopulation Feedback Assignment or ECO-SFA) and another based on individual agents (ECO-Agent Feedback Assignment or ECO-AFA). Both approaches initially assign vehicles based on fuel consumption levels for travel at the facility free-flow speed. Subsequently, fuel consumption estimates are refined based on experiences of other vehicles within the same class. The proposed framework is intended to evaluate the network-wide impacts of eco-routing strategies. This stochastic, multi-class, dynamic traffic assignment framework was demonstrated to work for two scenarios. Savings in fuel consumption levels in the range of 15 percent were observed and potential implementation challenges were identified.

Energy and Environmental Assessment of High-Speed Roundabouts

Recently, an increased number of roundabouts have been implemented across the United States to improve intersection efficiency and safety. However, few studies have evaluated their energy and environmental impacts. Consequently, this study quantifies the energy and environmental impact of an isolated roundabout on a high-speed road by using second-by-second speed profiles derived from traffic simulation models in conjunction with microscopic energy and emission models. The study demonstrates that, at the intersection of a high-speed road with a low-speed road, an isolated roundabout does not necessarily reduce vehicle fuel consumption and emissions compared with other forms of intersection control (stop sign and traffic signal control). This case study found that the roundabout reduces the delay and queue lengths on the intersection approaches. However, the roundabout results in a significant increase in vehicle fuel consumption and emission levels compared with a two-way stop. The study demonstrates, for this case study, that the roundabout provides efficient movement of vehicles when the approach traffic volumes are relatively low. However, as demand increases, traffic at the roundabout experiences substantial increases in unnecessary delay in comparison with a strategy that uses signalized intersection control.

MICROFRAMEWORK FOR MODELING OF HIGH-EMITTING VEHICLES

High emitters represent a small fraction of the vehicle fleet, yet they are responsible for a large portion of the total mobile source emissions. Drive-cycle-specific high-emitter cut points for the identification of high-emitter vehicles are first developed. A microscopic model for estimating high-emitter vehicle emissions by using second-by-second emission data is developed subsequently. The proposed model estimates vehicle emissions with a margin of error of 10% when compared with in-laboratory bag emission measurements. The model was incorporated in the INTEGRATION traffic assignment and simulation software for the environmental assessment of alternative traffic operational projects, including emerging intelligent transportation system initiatives.

Predictive eco-cruise control: Algorithm and potential benefits

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.

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.

Field Evaluation of Energy and Environmental Impacts of Driver Route Choice Decisions

The study estimates the energy and air quality impacts of route choice decisions using various fuel consumption and emission models with second-by-second floating-car GPS data. The study investigates two routes: a faster and longer highway route and a slower and shorter arterial route. The study demonstrates that the faster highway route choice is not always the best route from an environmental and energy consumption standpoint. Specifically, the study demonstrates that significant improvements (savings of up to 63, 71, 45, and 20 percent in HC, CO, NOx, and CO2 emissions, respectively) to air quality can be achieved when motorists utilize a slower arterial route although they incur an additional 17 percent in travel time. Moreover, the study demonstrates that energy savings in the range of 23 percent can be achieved by traveling on the slower arterial route. The study also demonstrates that a small portion of the entire trip that involves high engine-load conditions has significant impacts on the total emissions, demonstrating that by minimizing high-emitting driving behavior air quality can be significantly improved.

Simple Vehicle Powertrain Model for Modeling Intelligent Vehicle Applications

This research develops a simple vehicle powertrain model that can be incorporated within microscopic traffic simulation software for the modeling of intelligent vehicle applications. This simple model can be calibrated using vehicle parameters that are publically available without the need for field data collection. The model uses the driver throttle level input to compute the engine speed and, subsequently, the engine torque and power to finally compute the vehicle acceleration, speed, and position. The model is tested using field measurements and is demonstrated to produce vehicle power, fuel consumption, acceleration, speed, and position estimates that are consistent with field observations.