Surface transportation consumes a vast quantity of fuel and accounts for about a third of the US carbon dioxide (CO2) emissions. In addition to the use of more fuel-efficient vehicles and carbon-neutral alternative fuels, fuel consumption and CO2 emissions can be lowered through a variety of strategies that reduce congestion, smooth traffic flow, and reduce excessive vehicle speeds. Eco-driving is one such strategy. It typically consists of changing a person's driving behavior by providing general static advice to the driver (e.g. do not accelerate too quickly, reduce speeds, etc.). In this study, we investigate the concept of dynamic eco-driving, where advice is given in real-time to drivers changing traffic conditions in the vehicle's vicinity. This dynamic strategy takes advantage of real-time traffic sensing and telematics, allowing for a traffic management system to monitor traffic speed, density, and flow, and then communicates advice in real-time back to the vehicles. By providing dynamic advice to drivers, approximately 10-20% in fuel savings and lower CO2 emissions are possible without a significant increase in travel time. Based on simulations, it was found that in general, higher percentage reductions in fuel consumption and CO2 emission occur during severe compared to less congested scenarios. Real-world experiments have also been carried out, showing similar reductions but to a slightly smaller degree.

Energy and emissions impacts of a freeway-based dynamic eco-driving system

Control of connected and automated vehicles: State of the art and future challenges

The objective here is to quantify the variability in emissions of selected light duty gasoline vehicles by routes, time of day, road grade, and vehicle with a focus on the impact of routes and road grade. Field experiments using a portable emission measurement system were conducted under real-world driving cycles. The study area included two origin/destination pairs, each with three alternative routes. Total emissions varied from trip to trip and from route to route due to variations in average speed and travel time. On an average trip basis, the total NO emissions differed by 24% when comparing alternative routes and by 19% when comparing congested travel time with less congested traffic time. Positive road grades were associated with an approximately 20% increase in localized emissions rates, while negative road grades were associated with a similar relative decrease. The average vehicle-specific power based NO modal emission rates differed by more than 2 orders of magnitude when comparing different vehicles. The results demonstrate that alternative routing can significantly impact trip emissions. Furthermore, road grade should be taken into account for localized emissions estimation. Vehicle-specific models are needed to capture episodic effects of emissions for near-road short-term human exposure assessment.

Fuel use and emissions comparisons for alternative routes, time of day, road grade, and vehicles based on in-use measurements.

Vehicle fuel consumption and emissions are directly related to the acceleration/deceleration patterns and the idling period. In order to reduce emissions and improve fuel economy, sharp acceleration/deceleration and idling should be avoided as much as possible. Unlike on freeways, traffic on signalized corridors suffers from increased fuel consumption and emissions due to idling and acceleration/deceleration maneuvers at traffic signals. By taking advantage of the recent developments in communication technology between vehicles and roadside infrastructure, it is possible for vehicles to receive the signal phase and timing information well in advance of approaching a signalized intersection. Based on this traffic signal information, we have developed arterial velocity planning algorithms that give dynamic speed advice to the driver so that the probability of having a green light is maximized when approaching signalized intersections. The algorithms are aimed at minimizing the acceleration/deceleration rates while ensuring that the vehicle never exceeds the speed limit, and that it will pass through intersections without coming to a stop. Using a stochastic simulation technique, the algorithms are used to generate sample vehicle velocity profiles along a 10-intersection signalized corridor. The resulting vehicle fuel consumption and emissions from these velocity profiles are calculated using a modal emissions model, and then compared with those from a typical velocity profile of vehicles without velocity planning. The energy/emission savings for vehicles with velocity planning are found to be 12–14%.

Arterial velocity planning based on traffic signal information under light traffic conditions

There are a variety of strategies that are now being considered to reduce fuel consumption and carbon dioxide (CO 2 ) emissions from the transportation sector. One strategy that is gaining interest worldwide is known as “eco-driving”. Eco-driving typically consists of changing a person's driving behavior based on general (static) advice to the driver, such as accelerating slowly, driving smoothly, reducing high speeds, etc. Taking this one-step further, it is possible to provide realtime advice to drivers based on changing traffic and infrastructure conditions for even greater fuel and emission savings. This concept of dynamic eco-driving takes advantage of real-time traffic sensing and infrastructure information, which can then be communicated to a vehicle with a goal of reducing fuel consumption and emissions. In this paper, we consider dynamic eco-driving in an arterial corridor with traffic signals, where signal phase and timing information of a traffic light is provided to the vehicle. The vehicle can then adjust its velocity while traveling through a signalized corridor with the goal of minimizing fuel consumption and emissions. A dynamic eco-driving velocity planning algorithm has been developed and is described herein. This algorithm has then been tested in simulation, showing initial fuel economy and CO 2 improvements of around 12%.

Dynamic ECO-driving for arterial corridors

Mobile source emissions estimation techniques play a critical role for regional planning and development of emission control strategies. The primary models for mobile source emissions estimation have been the U.S. Environmental Protection Agency's MOBILE model and the California Air Resources Board's EMFAC model. These models work well for large regional areas but are not as well suited for "microscale" evaluation. Over the last several years, the College of Engineering-Center for Environmental Research and Technology (CE-CERT) has been evaluating in-use, light-duty vehicles as part of NCHRP Project 25-11, resulting in the development of a Comprehensive Modal Emissions Model (CMEM). An essential part of any model development process is validating the model. Various validation techniques have been applied to CMEM. This paper describes some of the latest validation work carried out in comparing CMEM results to independent emission testing results (independent in both vehicles and driving cycles). Further, C...

Recent Validation Efforts for a Comprehensive Modal Emissions Model

Intelligent Transportation Systems (ITS) have generated considerable enthusiasm in the transportation community as a potential means to improve roadway safety, reduce congestion, enhance the mobility of people and goods, and reduce energy consumption and vehicle emissions. In order to estimate these potential benefits, new and improved analytical techniques and simulation models are being developed for ITS. In terms of environmental effects, the University of California, Riverside, College of Engineering-Center for Environmental Research and Technology (CE-CERT) has developed a comprehensive modal emissions and energy consumption (CME/EC) model that can be directly used for ITS evaluation. In this project, an examination was performed on the key interface issues between the detailed CME/EC model and other ITS simulation models and analytical techniques developed within the California PATH program. Methodologies for integrating various ITS transportation models/data sets with the CME/EC model were established. These integration issues are not trivial; many ITS simulation models and analytical techniques inherently have different levels of aggregation and detail (e.g., both in time and across various vehicle fleets). Much of the work performed focused on integrating the CME/EC model with PARAMICS. PARAMICS is used throughout CALTRANS and the PATH program for various ITS studies. After successfully completing this integration, two case studies were carried out using this PARAMICS/CME-EC tool. The first case study examined the emissions impact of HOT lanes along the SR-91 corridor in Southern California. The other case study examined the emissions impact associated redesignating uphill lanes on I-60 near Riverside, California. By completing these case studies, the integrated transportation/emissions model was thoroughly debugged. These case studies can serve as examples as how to apply this new tool for creating microscale emission inventories. In this report, background material is first provided on the Comprehensive Modal Emissions/Energy Consumption (CME/EC) model and ITS traffic simulation modeling efforts in the California PATH program. This is followed by a description of the integration methodology between CME/EC and PARAMICS. In the last part of the report, two separate case studies are described, where analyses with the integrated models were carried out.

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Integrating a Comprehensive Modal Emissions Model into ATMIS Transportation Modeling Frameworks

Accurately estimating mobile-source emissions requires a good understanding of vehicle activity and the characteristics of the on-road vehicle fleet. Spatial variability in vehicle activity patterns and vehicle fleet composition can have significant effects on the overall emissions inventory. Simply determining total vehicle miles traveled is insufficient for emissions inventory calculations from the new-generation models of mobile-source emissions. Improvements in emissions-control technology over the past 20 years have led to large decreases in the emissions of light-duty cars and trucks, resulting in large variations in vehicle emissions depending on model year and technology type. In addition, research indicates that the accurate characterization of vehicle activity is necessary in conjunction with better spatial resolution of vehicle fleet characteristics because of the differing modal behavior of the vehicles within various vehicle and technology groups. Vehicle activity and vehicle fleet data were collected in the South Coast Air Basin in southern California. Vehicle activity was characterized primarily using a large second-by-second speed and acceleration data set collected from probe vehicles operated within the flow of traffic. In addition, three sets of vehicle fleet data were collected and used for spatial comparison. The results of the analysis show spatial and temporal differences in vehicle activity patterns and vehicle fleet characteristics; differences in speed and congestion affect the speed-acceleration profiles as well as associated emissions.

Mobile Source Emissions: Analysis of Spatial Variability in Vehicle Activity Patterns and Vehicle Fleet Distributions

Motivated by the need to develop regional air pollution control strategies, a comprehensive field study was conducted throughout California to characterize the unregistration rate of light duty vehicles in the state. Based on an analysis of more than 98,000 vehicle records, the average unregistered rate was found to be 3.38+/-0.13%. This included vehicles unregistered for a period of less than 3 months (2.4% of the total), vehicles unregistered between 3 months and 2 years (0.95% of the total), and vehicles unregistered for more than 2 years (0.03% of the total). About half of the counties had unregistration rates between 2% and 4%, with most counties' rates below 5%. The unregistered fleet was more heavily weighted toward older vehicles than the registered fleet. Department of Motor Vehicles (DMV) unregistration rates were compared with the field study rates. DMV estimates ranged from 6.2% to 7.5%, which were higher than those obtained in the field study. It was also found that 1.7% of the vehicles identified in the survey were registered outside the state or the country.

Carrie Malcolm

Papers

Recent Validation Efforts for a Comprehensive Modal Emissions Model

Integrating a Comprehensive Modal Emissions Model into ATMIS Transportation Modeling Frameworks

Mobile Source Emissions: Analysis of Spatial Variability in Vehicle Activity Patterns and Vehicle Fleet Distributions

Unregistration Rates for On-Road Vehicles in California