Study: conventional traffic pollution modeling may underestimate emissions in congested areas by up to 60%

15 December 2014

Traditional methods of modeling traffic pollution could be under-estimating emissions by as much as 60%, particularly in areas where congestion occurs for a large part of the day, according to a study by a team at Newcastle University (UK).

Previously, traffic emissions models have used standard factors taking into account details of vehicle fleet composition, average speeds and road type; they took the average speed of traffic as a whole and assumed traffic was traveling at the same speed at the same time, ignoring the stop-start related vehicle emissions often associated with congestion.

<!——>

The Newcastle team devised an improved way to model traffic pollution. The new technique, called PITHEM (Platform for Integrated Traffic, Health and Environmental Modelling), looks at congestion emissions based on individual vehicle type, its speed and acceleration and, takes into account meteorology and local terrain, such as hills and nearby buildings—both of which can constrain the dispersal of pollution in an urban environment.

Whereas previous models looked at steady state traffic conditions, in reality, during peak hours congestion vehicles often decelerate and accelerate and move at different speeds, especially when the road goes up or down hills. Our new model has shown that by looking at congestion emissions rather than average speed emissions, we can gather more accurate information about emissions and air quality. This could help traffic planners understand the impact of a proposed scheme before the money is committed.

By gaining a better understanding of how road networks are influencing emissions, councils can make more effective decisions about how to deal with congestion in our city centers and help reduce the 50,000 premature deaths in the UK each year that are associated with traffic emissions.

Working with Durham County Council, the team used this new method to assess potential traffic scenarios in Durham City center, including proposed schemes to introduce signals at Gilesgate and Leazes Bowl roundabouts to improve traffic flow and increase the reliability of public transport.

The findings, published in the International Journal of Environment and Pollution, showed that these schemes produced a slight reduction in overall emissions of nitrogen dioxide (NO₂). The research concluded the proposed schemes in isolation would not significantly improve air quality, due to the critical location of where congestion was occurring. Instead, the research team helped the council to confirm that additional measures would need to be considered to reduce the volume of traffic in the city center.

The improved modelling platform is now being used by Newcastle, Gateshead, and Leeds local authorities in testing the effectiveness of some of their traffic management schemes.

Resources

• James O’Brien; Anil Namdeo; Margaret Bell; Paul Goodman (2014) “A congestion sensitive approach to modelling road networks for air quality management” International Journal of Environment and Pollution Vol.54, No.2/3/4, pp. 213 – 221 doi: 10.1504/IJEP.2014.065122

rFpro simulator software for ADAS/autonomous vehicle development features low-latency, high-quality graphics for improved testing

15 December 2014

rFpro, a provider of Driver-In-The-Loop simulators for vehicle dynamics engineering, is offering driving simulator software, originally developed for Formula 1, that will enable vehicle manufacturers to test ADAS (advanced driver assistance systems) technologies more accurately than before. The company says that many of the challenges faced by ADAS developers can be reduced by validating the control system response more comprehensively with a driver-in-the-loop (DIL) prior to installation on the vehicle.

<!——>

The upsurge of interest in autonomous driving and ADAS systems has brought a corresponding increase in the demand for testing in order to confirm behavior and validate their response to the unpredictable conditions encountered on public roads. The factors limiting virtual testing in conventional simulation environments have been relatively low graphics quality and poor latency, resulting in a system too slow to use in an emergency maneuver, rFpro says.

While most OEMs and Tier 1 suppliers have successfully adopted a model-based engineering process for the development of their ADAS control systems, conventional graphical simulation environments cannot respond fast enough to cope with the highly dynamic maneuvers experienced when testing safety systems, rFpro says. rFpro’s solution was developed from the ground up to deliver driving simulation for vehicle dynamics applications and allows OEMs to re-introduce professional human test drivers into the model-based development process.

Autonomous systems must make split-second decisions, just like the drivers they protect, so they can only be tested effectively with graphics software that refreshes quickly enough. Our system delivers the graphics up to 100 milliseconds faster than typical 3D engineering graphics, which is equivalent to a car length travelled at highway speeds; this can make the difference between reacting before or after an impact.

Apart from the improved speed of response, rFpro’s software provides much higher quality graphics: essential when testing an autonomous system’s ability to distinguish between features with a similar appearance. Real-world lighting conditions create complex shadows that can confuse ADAS camera systems, Hoyle explains.

Winter sunlight, low in the sky, can generate shadows from roadside objects such as crash barrier uprights, which the system then confuses with lane markings. This type of situation can be investigated and corrected by testing with our simulation software. New solutions from PreScan and IPG allow our graphics to be fed straight to the control systems being tested, improving the quality of information delivered compared to the traditional approach of pointing a camera at a computer generated display.

Another key aspect of ADAS development is interaction with the driver of the vehicle who may panic or behave unpredictably during emergency avoidance, instinctively grabbing the steering wheel or fighting the system. Evaluating this type of interaction with a simulator also requires high graphics quality (to provide convincing realism) and low latency (to provide lag-free feedback to the driver).

Testing autonomous systems without a DIL will inevitably lead to inaccurate results. A computer will respond to an emergency situation consistently linearly, a human does the opposite. So it is essential to test these systems with a driver’s response in a safe environment before testing on public roads.

In an ironic twist, the same software developed to make DIL simulators effective in allowing a human driver to interact with a virtual car, is now helping to develop ADAS technologies that take the driver ‘out’ of the loop in an emergency. The benefits are very similar: the human-machine interface can be explored repeatedly under controlled conditions, including any changes required, in complete safety.

To deliver complete DIL simulators for the engineering development of vehicle dynamics, and the control systems and active safety systems that affect vehicle dynamics, rFpro works in partnership with motion platform providers such as McLaren, Ansible Motion and MOOG. The company’s products can wrap around vehicle models from all the popular modeling environments, including Dymola, SIMPACK, Simulink, AVL-VSM, CarSim, CarMaker, LMS AMESim, VI-Grade and C/C++.

Nissan licenses autonomous driving technology to Hitachi Construction Machinery Company

15 December 2014

Nissan Motor Co., Ltd. has licensed its Around View Monitor and Moving Object Detection (MOD) technology (earlier post), jointly developed with Clarion Co., Ltd., for use by Hitachi Construction Machinery Co., Ltd. These two systems are the building blocks of autonomous driving technology that will operate commercially-viable Nissan Autonomous Drive vehicles by 2020.

<!——>

The Around View Monitor (AVM) is a parking support system that offers the driver a bird’s-eye view of the vehicle’s surroundings in real time using four exterior cameras. MOD is a driving assistance technology that analyzes the images from the AVM cameras to detect moving objects around the vehicle and warn the driver with visual and audio alerts. Since the market launch of AVM in 2007 and MOD in 2010, both firsts for any automaker, Nissan has steadily expanded its safety technology offerings, which have become a cornerstone of autonomous drive technology development.

The licensing agreement enables Hitachi Construction Machinery to provide AVM and MOD technology to its massive haul trucks and hydraulic excavators working at large open-pit mines. When drivers start operating the vehicle, drop cargo, back up to load cargo, or when a hydraulic shovel is used in close proximity to the vehicle, the AVM-MOD technology detects any movement or workers in the area around it in real time, enabling the driver to work with greater situational awareness which enhances safety.

Autonomous Drive is being developed to help lower the element of human error during driving and contribute to a reduction in the number of accidents and injuries related to automobiles. The licensing of this technology is an example of Nissan’s intention to offer the AVM and MOD technology to other industries beyond the automotive sector.

U Alberta team develops hybrid sodium-ion capacitor; intermediate in energy power between ultracaps and batteries

15 December 2014

A team led by researchers from the University of Alberta (Canada)
Scientists has developed a hybrid sodium-ion capacitor (NIC) using active materials in both the anode and the cathode derived entirely from peanut shells—a green and highly economical waste globally generated at more than 6 million tons per year. The hybrid NIC stores charge both electrostatically and electrochemically, and sits between ultracapacitors and batteries in terms of power (ultracaps) and energy (batteries) storage capability.

According to their paper, published in the RSC journal Energy Environmental Science, the electrodes deliver among the most promising sodiation capacity–rate capability–cycling retention combinations reported in the literature. The resultant NIC also offers an advanced cyclically stable combination of energy and power, not only in respect to previously developed sodium-ion capacitors, but also as compared to Li-ion capacitors (LICs).

<!——>

The NIC features an ion-adsorption cathode and an ion-intercalation anode.

• The ion-adsorption cathode, based on Peanut Shell Nanosheet Carbon (PSNC), displays a hierarchically porous architecture; a sheet-like morphology down to 15 nm in thickness; a surface area on par with graphene materials (up to 2396 m2 g⁻¹); and high levels of oxygen doping (up to 13.51 wt%). Scanned from 1.5–4.2 V vs. Na/Na⁺, PSNC delivers a specific capacity of 161 mAh g⁻¹ at 0.1 A g⁻¹ and 73 mAh g⁻¹ at 25.6 A g⁻¹.

• The low-surface-area ion-intercalation Peanut Shell Ordered Carbon (PSOC) anode delivers a total capacity of 315 mAh g⁻¹ with a flat plateau of 181 mAh g⁻¹ occurring below 0.1 V (tested at 0.1 A g⁻¹), and is stable at 10,000 cycles (tested at 3.2 A g⁻¹).

The assembled NIC operates within a wide temperature range (0–65 °C), yielding at room temperature (by active mass) 201, 76 and 50 Wh kg⁻¹ at 285, 8,500 and 16,500 W kg⁻¹, respectively. At 1.5–3.5 V, the hybrid device achieved 72% capacity retention after 10,000 cycles tested at 6.4 A g⁻¹, and 88% after 100,000 cycles at 51.2 A g⁻¹.

In a review of the technology in the RSC’s Chemistry World, Professor David Mitlin, who led the research, explained:

In conventional batteries the cathode often limits performance and so what people are starting to do is swap regular cathodes for supercapacitor cathodes. Ions are adsorbed onto the surface of the cathode in an NIC, which avoids the degradation seen in batteries due to ion absorption into the bulk.

In addition to being easy to source and being inexpensive, peanut shells (both inner and outer) offer structural characteristics desirable for anode and cathode materials, respectively.

Resources

• Jia Ding, Huanlei Wang, Zhi Li, Kai Cui, Dimitre Karpuzov, Xuehai Tan, Alireza Kohandehghan and David Mitlin (2015) “Peanut shell hybrid sodium ion capacitor with extreme energy–power rivals lithium ion capacitors” Energy Environ. Sci. doi: 10.1039/C4EE02986K

EIA: price of gasoline tends to have little effect on travel demand; price elasticity has fallen

15 December 2014

The 28% drop in the US average retail price of gasoline since 23 June may not have much effect on automobile travel, and in turn, gasoline consumption, according to an analysis by the US Energy Information Administration (EIA). Gasoline is a relatively inelastic product—i.e., changes in prices have little influence on demand—and has become more so over the past few decades.

<!——>

The US average retail price per gallon of regular motor gasoline has fallen 28% from its 2014 peak of $3.70 per gallon on June 23, to $2.68 per gallon on December 8, with no noticeable uptick in travel. Source: EIA. Click to enlarge.

Price elasticity measures the responsiveness of demand to changes in price. Almost all price elasticities are negative—i.e., an increase in price leads to lower demand, and vice versa. Air travel, especially for vacation, tends to be highly elastic: a 10% increase in the price of air travel leads to an even greater (more than 10%) decrease in the amount of air travel. Price changes have greater effects if the changes persist over time, as opposed to being temporary shocks.

Automobile travel in the United States is much less elastic, and its price elasticity has fallen in recent decades. The price elasticity of motor gasoline is currently estimated to be in the range of -0.02 to -0.04 in the short term—it takes a 25% to 50% decrease in the price of gasoline to raise automobile travel 1%.

In the mid 1990s, the price elasticity for gasoline was higher, around -0.08, meaning it only took a 12% decrease in the price of gasoline to raise automobile travel by 1%.

EIA’s Short-Term Energy Outlook (STEO) uses a price elasticity of -0.02 to estimate and forecast consumption of motor gasoline, while also considering anticipated changes in travel demand and fuel economy. The December STEO expects that gasoline prices in 2015 will be 23% lower than the 2014 average, and consumption in December will be virtually unchanged from year-earlier levels, as increased fuel economy balances out increases in vehicle miles traveled in response to lower prices and other factors.

Price elasticities can be difficult to interpret, as demand can change for reasons beyond changes in fuel price, including changes in other economic factors (e.g., income), demographics, driver behavior, vehicle fuel efficiency, and other structural factors. EIA suggests some possible explanations for the decline in gasoline price elasticity in recent decades include:

• The slowing of per-capita vehicle miles traveled (VMT). After increasing for decades, VMT per capita slowed in the late 1990s and even declined in recent years.

• The retirement of the baby boomer generation, because retirees tend to drive less than the working-age population.

• Population migrations to urban area, as opposed to rural and suburban areas, because urban residents typically drive less.

• Declines in licensing rates for teenagers, as young people delay or avoid getting their drivers’ permits and licenses.

• The reduced share of household income devoted to motor gasoline expenses. As gasoline represents a smaller share of household expenditures, drivers may be less sensitive to fluctuations in price.