Kanok Boriboonsomsin

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.

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%.

Dynamic ECO-driving for arterial corridors

There are a variety of strategies that are now being considered to reduce fuel consumption and carbon dioxide (CO2) emissions from the transportation sector. One strategy that is gaining interest worldwide is known as “eco-driving”. Eco-driving typically consists of changing a persons 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 CO2 improvements of around 12%.

Dynamic Eco-Driving for Signalized Arterial Corridors and Its Indirect Network-Wide Energy/Emissions Benefits

There are various strategies being considered to reduce fuel consumption and emissions from the transportation sector. From the transportation operations perspective, one strategy that has gained interest worldwide is eco-driving. Eco-driving typically consists of changing a persons driving behavior based on providing general advice to the driver, such as accelerating slowly, driving smoothly, and reducing high speeds. More advanced dynamic eco-driving provides real-time advice to drivers based on changing traffic and infrastructure conditions for even greater fuel and emission savings. The 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. This article considers dynamic eco-driving in an arterial corridor with traffic control signals, where signal phase and timing (SPaT) information of traffic lights is provided to the vehicle as it drives down the corridor. The vehicle can then adjust its velocity while traveling through the corridor with the goal of minimizing fuel consumption and emissions. A dynamic eco-driving velocity planning methodology has been developed and is described herein. This algorithm has then been extensively tested in simulation, showing individual vehicle fuel consumption and CO2 reductions of around 10–15%, depending on corridor parameters including traffic volume, traffic speed, and other factors. This 10–15% improvement is realized directly by the vehicle that is equipped with this dynamic eco-driving technology and can be accomplished with very little time loss. We have also carried out an extensive analysis of the entire traffic stream under different traffic volumes and different penetration rates of the dynamic eco-driving technology. It is found that there are also significant indirect network-wide energy and emissions benefits on the overall traffic, even at low penetration rates of the technology-equipped vehicles. Based on the simulation results, the maximum fuel saving and emission reduction occur during medium traffic volumes (corresponding to traffic volume of 300 vehicles/hr/link) and with low penetration rates (5%, 10%, and 20%). Under these conditions, the total traffic energy/emissions savings typically triple what is saved from the technology-equipped vehicles alone (e.g., total 4% savings compared to 1.3% savings). This is due primarily to the eco-driving velocity planning affecting nonequipped vehicles that follow behind.

An energy and emissions impact evaluation of intelligent speed adaptation

Excessive vehicle speed on todays roadways often results in accidents, high fuel consumption rates, and excessive pollutant emissions. Traditional methods of limiting speed have only been moderately effective. Using the latest intelligent transportation technology, speed enforcement can be enhanced through vehicle speed management programs, often referred to as intelligent speed adaptation (ISA). An ISA system monitors the location and speed of the vehicle, compares it to a defined set speed, and takes corrective action such as advising the driver and/or governing the top speed of the vehicle. ISA is an active research field in Europe where it is currently being evaluated. In addition to safety improvements, ISA has the potential to mitigate congestion by smoothing traffic flow during congested conditions, which may also lead to lower fuel consumption and pollutant emissions. In this paper, the energy and emissions impacts of ISA are investigated in detail using both simulation tools and real-world experimentation. This research makes use of state-of-the-art transportation/emissions modeling tools. The simulation analysis is focused on examining different speed management strategies under varying freeway congestion conditions. A set of limited real-world experiments have also been performed using real-time traffic information provided to an ISA-equipped vehicle driving in traffic. Results are compared to another non-equipped-ISA vehicle acting as a control, representing the general traffic flow. Preliminary results indicate that significant reductions are possible for both fuel consumption and emissions without drastically affecting travel time

Development and Evaluation of an Intelligent Energy-Management Strategy for Plug-in Hybrid Electric Vehicles

There has been significant interest in plug-in hybrid electric vehicles (PHEVs) as a means to decrease dependence on imported oil and to reduce greenhouse gases as well as other pollutant emissions. One of the critical considerations in PHEV development is the design of its energy-management strategy, which determines how energy in a hybrid powertrain should be produced and utilized as a function of various vehicle parameters. In this paper, we propose an intelligent energy-management strategy for PHEVs. At the trip level, the strategy takes into account a priori knowledge of vehicle location, roadway characteristics, and real-time traffic conditions on the travel route from intelligent transportation system technologies in generating a synthesized velocity trajectory for the trip. The synthesized velocity trajectory is then used to determine batterys charge-depleting control that is formulated as a mixed-integer linear programming problem to minimize the total trip fuel consumption. The strategy can be extended to optimize vehicle fuel consumption at the tour level if a preplanned travel itinerary for the tour and the information about available battery recharging opportunities at intermediate stops along the tour are available. The effectiveness of the proposed strategy, both for the trip- and tour-based controls, was evaluated against the existing binary-mode energy-management strategy using real-world trip/tour examples in southern California. The evaluation results show that the fuel savings of the proposed strategy over the binary-mode strategy are around 10%-15%.

Field operational testing of ECO-approach technology at a fixed-time signalized intersection

As part of a number of Intelligent Transportation (ITS) applications aimed at providing an environmental benefit, eco-approach technology is one that is feasible in the near-term. An eco-approach application uses Signal Phase and Timing (SPaT) and intersection map information of signalized intersections to provide drivers with recommendations in order to encourage “green” driving while approaching, passing through, and departing the intersections. Upon receiving SPaT information, invehicle systems calculate and provide speed advice to the driver, allowing the driver to adapt the vehicles speed to pass through the upcoming signal on green or to decelerate to a stop in the most eco-friendly manner. Eco-approach methods have been proposed and simulated showing promising results. In this study, both simulation experimentation and field operational testing have been carried out to demonstrate the eco-approach application and to quantify its potential fuel and carbon dioxide (CO2) savings. Furthermore, it has been shown that a communication platform based on a 4G/LTE network link and a cloud-based server infrastructure is effective and sufficient for this kind of application. It was found in both the simulation experiment and the field operational testing that on average 14% fuel and CO2 savings can be achieved.

Energy and Emission Benefit Comparison of Stationary and In-Vehicle Advanced Driving Alert Systems

Automobiles powered by fossil fuels are one of the major contributors to both criteria pollutant and greenhouse gas—in particular, carbon dioxide (CO2)—emissions. Previous studies revealed that unnecessary acceleration and hard braking in response to sudden changes of traffic signals may cause a significant amount of wasted energy and increased emissions. Altering the behavior of drivers approaching signalized intersections potentially could reduce energy consumption and emissions from motor vehicles without increasing travel time or delay. Two types of advanced driving alert systems (ADAS) are proposed: stationary ADAS [based on roadside infrastructure, such as changeable message signs (CMSs)] and in-vehicle ADAS [driven by advanced communication technology, such as vehicle-infrastructure integration (VII)]. These systems can help drivers avoid hard braking at intersections by providing real-time information on traffic signal status. A state-of-the-art modal emissions model is used to evaluate and compare the effects of these two types of ADAS on the reduction of vehicle fuel consumption and CO2 emissions. A numerical analysis of a single vehicle shows that ADAS can help reduce vehicle fuel consumption and CO2 emissions by up to 40% in the tested hypothetical conditions. The benefits of these systems are further investigated in a traffic simulation environment with various levels of congestion and posted speed limits. The simulation results reveal that both CMS-based ADAS and VII-driven ADAS can provide fuel and CO2 savings; VII ADAS offer greater savings in most cases.

Mix Design and Benefit Evaluation of High Solar Reflectance Concrete for Pavements

The use of cool paving materials, or cool pavements, has been identified as one strategy that can help mitigate the urban “heat island” effect. One method of creating a cool pavement is to increase the solar reflectance, or albedo, of its surface. This increase can be achieved by many existing paving technologies. This study explores alternative ways of creating high-albedo concrete for use in pavement applications. The key approach is to make concrete whiter by replacing cement with whiter constituents. Fly ash and slag are used as the main constituents because they are environmentally friendly, readily available, and already familiar to the concrete industry. Compared with a conventional concrete mix, concrete mixes containing fly ash have lower albedo, whereas concrete mixes containing slag have higher albedo. Of all mixes tested, the mix with 70% slag as cement replacement achieves the highest albedo of 0.582, which is 71% higher than the conventional mix. It also has better compressive strength as tested at 7 and 28 days and modulus of rupture as tested at 7 days. The production of high solar reflectance concrete consumes 43.5% less energy and results in less emission of pollutants and greenhouse gases (by 20% to 60%). Furthermore, the analysis of some urban cities shows that the implementation of this high solar reflectance concrete could increase the city albedo by 0.02 to 0.07. This amount of albedo modification has the potential to benefit economics and the environment in many ways, ranging from decreasing energy demand to improving air quality.