Matthew Barth

Real-World Carbon Dioxide Impacts of Traffic Congestion

Transportation plays a significant role in carbon dioxide (CO2) emissions, accounting for approximately a third of the U.S. inventory. To reduce CO2 emissions in the future, transportation policy makers are planning on making vehicles more efficient and increasing the use of carbon-neutral alternative fuels. In addition, CO2 emissions can be lowered by improving traffic operations, specifically through the reduction of traffic congestion. Traffic congestion and its impact on CO2 emissions were examined by using detailed energy and emission models, and they were linked to real-world driving patterns and traffic conditions. With typical traffic conditions in Southern California as an example, it was found that CO2 emissions could be reduced by up to almost 20% through three different strategies: congestion mitigation strategies that reduce severe congestion, allowing traffic to flow at better speeds; speed management techniques that reduce excessively high free-flow speeds to more moderate conditions; and shock wave suppression techniques that eliminate the acceleration and deceleration events associated with the stop-and-go traffic that exists during congested conditions.

Battery state-of-charge estimation

This paper discusses the problem of lead acid battery state-of-charge estimation for (hybrid) electric vehicles. The problem is to accurately estimate the remaining battery capacity for both driver notification and automated energy management. The article presents a review of the problem, existing solution methods, presentation of a new solution method, and experimental analysis of the performance of that method.

Simulation model performance analysis of a multiple station shared vehicle system

As an alternative transportation paradigm, shared vehicle systems have become increasingly popular in recent years. Shared vehicle systems typically consist of a fleet of vehicles that are used several times each day by diAerent users. One of the main advantages of shared vehicle systems is that they reduce the number of vehicles required to meet total travel demand. An added energy/emissions benefit comes when lowpolluting (e.g., electric) vehicles are used in the system. In order to evaluate operational issues such as vehicle availability, vehicle distribution, and energy management, a unique shared vehicle system computer simulation model has been developed. As an initial case study, the model was applied to a resort community in Southern California. The simulation model has a number of input parameters that allow for the evaluation of numerous scenarios. Several measures of eAectiveness have been determined and are calculated to characterize the overall system performance. For the case study, it was found that the most eAective number of vehicles (in terms of satisfying customer wait time) is in the range of 3‐6 vehicles per 100 trips in a 24 h day. On the other hand, if the number of relocations also is to be minimized, there should be approximately 18‐24 vehicles per 100 trips. Various inputs to the model were varied to see the overall system response. The model shows that the shared vehicle system is most sensitive to the vehicle-to-trip ratio, the relocation algorithm used, and the charging scheme employed when electric vehicles are used. A preliminary cost analysis was also performed, showing that such a system can be very competitive with present transportation systems (e.g., rental cars, taxies, etc.). ” 1999 Elsevier Science Ltd. All rights reserved.

Real-time differential carrier phase GPS-aided INS

This article describes the implementation and experimental results of a real-time carrier phase differential Global Positioning System (GPS) aided inertial navigation system (INS). The implementation is the result of a study to analyze the capabilities of such a system relative to the requirements of advanced vehicle control and safety systems for intelligent transportation systems. Such navigation systems have many application possibilities (e,g., aviation and precision flight, automated mining, precision farming, dredging, satellite attitude control). Advantages and disadvantages of the GPS, INS, and differential GPS-aided INS approaches are discussed. The implementation achieves 100-Hz vehicle state estimates with position accuracies at the centimeter level through the use of differential carrier phase GPS techniques.

Shared-Use Vehicle Systems Framework for Classifying Carsharing, Station Cars, and Combined Approaches

In recent years, shared-use vehicle systems have garnered a great deal of interest and activity internationally as an innovative mobility solution. In general, shared-use vehicle systems consist of a fleet of vehicles that are used by several different individuals throughout the day. Shared-use vehicles offer the convenience of the private automobile and more flexibility than public transportation alone. These systems are attractive since they offer the potential to lower a user’s transportation costs; reduce the need for parking spaces in a community; improve overall air quality; and facilitate access to and encourage use of other transportation modes such as rail transit. Shared-use vehicle systems take many forms, ranging from neighborhood carsharing to classic station car models. Given the recent proliferation in system approaches, it is useful to establish a classification system or framework for characterizing these programs. The classification system presented here outlines key program elements that can help policy makers and practitioners characterize and evaluate various aspects of this rapidly evolving field. Further, it helps researchers analyze and compare the various models, including their similarities, differences, and benefits. A shared-use vehicle classification system is provided that describes existing and evolving models; examples are provided of each. It is argued that carsharing and station car concepts can be viewed as two ends of a continuum, sharing many similarities, rather than as separate concepts. Indeed, many existing shared-use vehicle systems can be viewed as hybrid systems, exhibiting key characteristics of both concepts.

Eco-Routing Navigation System Based on Multisource Historical and Real-Time Traffic Information

Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin-destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed.

Next-Generation Automated Vehicle Location Systems: Positioning at the Lane Level

The majority of todays automated vehicle location (AVL) systems use Global Positioning System (GPS) technology, which can provide position information with an accuracy of approximately 15 m. Recently, low-cost Differential GPS (DGPS) receivers, which have a positioning accuracy of approximate 2-3 m, have become available. With this increased accuracy, it is now possible to perform AVL down to specific roadway lanes. In this paper, a vehicle-lane-determining system is described, consisting of an onboard DGPS receiver that is connected with a wireless communications channel, a unique lane-level digital roadway database, a developed lane-matching algorithm, and a real-time vehicle location display. Lane-level positioning opens up the door for a number of new intelligent transportation system applications such as better fleet management, lane-based traffic measurements from probe vehicles, and lane-level navigation. The developed low-cost system has been tested on a number of roadways and has performed very well when used with accurately surveyed map data. Based on more than 100 000 s, it has correctly determined the lane 97% of the time.

Real-Time Computer Vision/DGPS-Aided Inertial Navigation System for Lane-Level Vehicle Navigation

Many intelligent transportation system (ITS) applications will increasingly rely on lane-level vehicle positioning that requires high accuracy, bandwidth, availability, and integrity. Lane-level positioning methods must reliably work in real time in a wide range of environments, spanning rural to urban areas. Traditional positioning sensors such as the Global Navigation Satellite Systems may have poor performance in dense urban areas, where obstacles block satellite signals. This paper presents a sensor fusion technique that uses computer vision and differential pseudorange Global Positioning System (DGPS) measurements to aid an inertial navigation system (INS) in challenging environments where GPS signals are limited and/or unreliable. To supplement limited DGPS measurements, this method uses mapped landmarks that were measured through a priori observations (e.g., traffic light location data), taking advantage of existing infrastructure that is abundant within suburban/urban environments. For example, traffic lights are easily detected by color vision sensors in both day and night conditions. A tightly coupled estimation process is employed to use observables from satellite signals and known feature observables from a camera to correct an INS that is formulated as an extended Kalman filter. A traffic light detection method is also outlined, where the projected feature uncertainty ellipse is utilized to perform data association between a predicted feature and a set of detected features. Real-time experimental results from real-world settings are presented to validate the proposed localization method.

Impacts of Road Grade on Fuel Consumption and Carbon Dioxide Emissions Evidenced by Use of Advanced Navigation Systems

Recently, advanced navigation systems have been developed that provide users the ability to select not only a shortest-distance route and even the shortest-duration route (on the basis of real-time traffic congestion information) but also routes that minimize fuel consumption as well as greenhouse gas and pollutant emissions. In these ecorouting systems, fuel consumption and emission attributes are estimated for roadway links on the basis of the measured traffic volume, density, and average speed. Instead of standard travel time or distance attributes, these link attributes are then used as cost factors when an optimal route for any particular trip is selected. In addition to roadway congestion attributes, road grade factors also have an effect on fuel consumption and emissions. This study evaluated the effect of road grade on vehicle fuel consumption (and thus carbon dioxide [CO2] emissions). The real-world experimental results show that road grade does have significant effects on the fuel economy of light-duty vehicles both at the roadway link level and at the route level. Comparison of the measured fuel economy between a flat route and example hilly routes revealed that the vehicle fuel economy of the flat route is superior to that of the hilly routes by approximately 15% to 20%. This road grade effect will certainly play a significant role in advanced ecorouting navigation algorithms, in which the systems can guide drivers away from steep roadways to achieve better fuel economy and reduce CO2 emissions.

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

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%.