A comparative study of the emissions by road maintenance works and the disrupted traffic using life cycle assessment and micro-simulation

Life cycle assessment is being accepted by the road industry to measure such key environmental impacts as the energy consumption and carbon footprint of its materials and laying processes. Previous life cycle studies have indicated that the traffic vehicles account for the majority of fuel consumption and emissions from a road. Contractors and road agencies are looking for road maintenance works that have the least overall environmental impact considering both the roadwork itself and the disrupted traffic. We review life cycle assessment studies and describe the development of a model for pavement construction and maintenance, detailing the methodology and data sources. The model is applied to an asphalt pavement rehabilitation project in the UK, and the micro-simulation program VISSIM is used to model the traffic on that road section. The simulation results are fed into a traffic emissions model and emissions from the roadwork and the traffic are compared. The additional fuel consumption and emissions by the traffic during the roadwork are significant. This indicates that traffic management at road maintenance projects should be included in the life cycle assessment analysis of such work.     

香港典型山区道路汽车行驶工况污染物排放研究

通过安装车载测试系统收集香港港岛山区路段正常行驶工况下尾气中的CO、NOx、HC等污染物和油耗并辅助计算机软件进行分析。研究得出,山区道路设计、地形地貌和驾驶习惯对车辆油耗以及CO、NOx和HC排放有直接关系。可以通过坡道加宽、坡道延长、减少坡道红绿灯等措施减少车辆在山区道路行驶过程中速度变化频率,从而减少油耗以及CO、NOx和HC排放。     

Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model

Τhis study demonstrates the combination of a microscopic traffic simulator (AIMSUN) with an instantaneous emissions model (AVL CRUISE) to investigate the impact of traffic congestion on fuel consumption on an urban arterial road. The micro traffic model was enhanced by an improved car-following law according to Morello et al. (2014) and was calibrated to replicate measured driving patterns over an urban corridor in Turin, Italy, operating under adaptive urban traffic control (UTC). The method was implemented to study the impact of congestion on fuel consumption for the category of Euro 5 diesel <1.4 l passenger cars. Free flow and congested conditions led to respective consumption differences of −25.8% and 20.9% over normal traffic. COPERT 5 rather well predicted the impact of congestion but resulted to a much lower relative reduction in free flow conditions. Start and stop system was estimated to reduce consumption by 6% and 11.9% under normal and congested conditions, respectively. Using the same modelling approach, UTC was found to have a positive impact on CO₂ emissions of 8.1% and 4.5% for normal and congested conditions, respectively, considering the Turin vehicle fleet mix for the year 2013. Overall, the study demonstrates that the combination of detailed and validated micro traffic and emissions models offers a powerful combination to study traffic and powertrain impacts on greenhouse gas and fuel consumption of on road vehicles over a city network.     

Fuel consumption and emission modelling by power demand and a comparison with other models

Part 1 describes a fuel consumption model based upon the instantaneous power demand experienced by a vehicle, which has been developed from chassis dynamometer experiments on 177 in-use Australian vehicles. When applied to an individual vehicle, the model provides aggregate fuel consumption estimates for on-road driving which are within 2% of the actual measured fuel usage. Emission rate models for hydrocarbons and nitrogen oxides which are of the same form as the fuel consumption model are also presented. The vehicle model can be applied in any traffic situation provided on-road power demand is known. On-road instantaneous power demand is derived from the vehicle's mass, drag, velocity acceleration and road gradient. In the first part 1929 km and 2778 links of traffic driving pattern data for both urban and non-urban trips are presented. Correlations between the link power and traffic parameters are presented and it is shown that vehicle link fuel consumption and emissions can be accurately calculated from vehicle mass, engine capacity, link average velocity, link average positive inertial power, link altitude change and link trip time. In the non-urban case, link power, and hence fuel consumption and emissions, are not dependent upon positive inertial power. In Part 2 the instantaneous vehicle power demand model is used to develop fuel usage input information to evaluate a simple average velocity model and an elemental model. The performance of these two models is compared with that of the on-road power method by “driving” all three models over 2281 links and 956 km of recorded on-road velocity, acceleration and gradient data. It is shown that all three models can be made to perform well for long trips. The elemental model, however, suffers from an inability to adequately describe the fuel usage of different stop-start manoeuvres and requires some calibration in order to account for cruise speed fluctuations. For short trips, 3.5 km in length or less, the on-road power demand method is superior. Under these conditions, both the simple V̄ and elemental models are unable to adequately describe the fuel usage relating to inertial power demands. It is shown that for short trips, inertial power demand does not correlate with average velocity and may range from near zero to up to twice the total trip averaged power.     

Does winter road maintenance help reduce air emissions and fuel consumption?

Winter road maintenance (WRM) has been shown to have significant benefits of improving road safety and reducing traffic delay caused by adverse weather conditions. It has also been suggested that WRM is also beneficial in terms of reducing vehicular air emissions and fuel consumptions because snow and ice on road surface often cause the drivers to reduce their vehicle speeds or to switch to high gears, thus decreasing fuel combustion efficiency. However, there has been very limited information about the underlying relationship, which is important for quantifying this particular benefit of a winter road maintenance program. This research is focused on establishing a quantitative relationship between winter road surface conditions and vehicular air emissions. Speed distribution models are developed for the selected Ontario highways using data from 22 road sites across the province of Ontario, Canada. The vehicular air emissions under different road surface conditions are calculated by coupling the speed models with the engine emission models integrated in the emission estimation model - MOVES. It was found that, on the average, a 10% improvement in road surface conditions could result in approximately 0.6–2% reduction in air emissions. Application of the proposed methodology is demonstrated through a case study to analyse the air emission and energy consumption effects under specific weather events.     

Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles

The quest for more fuel-efficient vehicles is being driven by the increasing price of oil. Hybrid electric powertrains have established a presence in the marketplace primarily based on the promise of fuel savings through the use of an electric motor in place of the internal combustion engine during different stages of driving. However, these fuel savings associated with hybrid vehicle operation come at the tradeoff of a significantly increased initial vehicle cost due to the increased complexity of the powertrain. On the other hand, telematics-enabled vehicles may use a relatively cheap sensor network to develop information about the traffic environment in which they are operating, and subsequently adjust their drive cycle to improve fuel economy based on this information – thereby representing ‘intelligent’ use of existing powertrain technology to reduce fuel consumption. In this paper, hybrid and intelligent technologies using different amounts of traffic flow information are compared in terms of fuel economy over common urban drive cycles. In order to develop a fair comparison between the technologies, an optimal (for urban driving) hybrid vehicle that matches the performance characteristics of the baseline intelligent vehicle is used. The fuel economy of the optimal hybrid is found to have an average of 20% improvement relative to the baseline vehicle across three different urban drive cycles. Feedforward information about traffic flow supplied by telematics capability is then used to develop alternative driving cycles firstly under the assumption there are no constraints on the intelligent vehicle’s path, and then taking into account in the presence of ‘un-intelligent’ vehicles on the road. It is observed that with telematic capability, the fuel economy improvements equal that achievable with a hybrid configuration with as little as 7 s traffic look-ahead capability, and can be as great as 33% improvement relative to the un-intelligent baseline drivetrain. As a final investigation, the two technologies are combined and the potential for using feedforward information from a sensor network with a hybrid drivetrain is discussed.     

An integrated modeling approach for facilitating emission estimations of alternative fueled vehicles

This paper develops an integrated model for reliable estimation of daily vehicle fuel savings and emissions using an integrated traffic emission modeling approach created by incorporating the US Environmental Protection Agency’s vehicle emission model, MOVES, and the PARAMICS microscopic traffic simulation package. A case study is conducted to validate the model using a well-calibrated road network in Greenville, South Carolina. For each transportation fuel considered, both emission and fuel consumption impacts are evaluated based on market shares.     

Mobility and environment improvement of signalized networks through Vehicle-to-Infrastructure (V2I) communications

Traffic signals, even though crucial for safe operations of busy intersections, are one of the leading causes of travel delays in urban settings, as well as the reason why billions of gallons of fuel are burned, and tons of toxic pollutants released to the atmosphere each year by idling engines. Recent advances in cellular networks and dedicated short-range communications make Vehicle-to-Infrastructure (V2I) communications a reality, as individual cars and traffic signals can now be equipped with communication and computing devices. In this paper, we first presented an integrated simulator with V2I, a car-following model and an emission model to simulate the behavior of vehicles at signalized intersections and calculate travel delays in queues, vehicle emissions, and fuel consumption. We then present a hierarchical green driving strategy based on feedback control to smooth stop-and-go traffic in signalized networks, where signals can disseminate traffic signal information and loop detector data to connected vehicles through V2I communications. In this strategy, the control variable is an individual advisory speed limit for each equipped vehicle, which is calculated from its location, signal settings, and traffic conditions. Finally, we quantify the mobility and environment improvements of the green driving strategy with respect to market penetration rates of equipped vehicles, traffic conditions, communication characteristics, location accuracy, and the car-following model itself, both in isolated and non-isolated intersections. In particular, we demonstrate savings of around 15% in travel delays and around 8% in fuel consumption and greenhouse gas emissions. Different from many existing ecodriving strategies in signalized road networks, where vehicles’ speed profiles are totally controlled, our strategy is hierarchical, since only the speed limit is provided, and vehicles still have to follow their leaders. Such a strategy is crucial for maintaining safety with mixed vehicles.     

Analysis of fuel consumption and pollutant emissions of regulated and alternative driving cycles based on real-world measurements

Discrepancies between real-world use of vehicles and certification cycles are a known issue. This paper presents an analysis of vehicle fuel consumption and pollutant emissions of the European certification cycle (NEDC) and the proposed worldwide harmonized light vehicles test procedure (WLTP) Class 3 cycle using data collected on-road. Sixteen light duty vehicles equipped with different propulsion technologies (spark-ignition engine, compression-ignition engine, parallel hybrid and full hybrid) were monitored using a portable emission measurement system under real-world driving conditions. The on-road data obtained, combined with the Vehicle Specific Power (VSP) methodology, was used to recreate the dynamic conditions of the NEDC and WLTP Class 3 cycle. Individual vehicle certification values of fuel consumption, CO₂, HC and NO_x emissions were compared with test cycle estimates based on road measurements. The fuel consumption calculated from on-road data is, on average, 23.9% and 16.3% higher than certification values for the recreated NEDC and WLTP Class 3 cycle, respectively. Estimated HC emissions are lower in gasoline and hybrid vehicles than certification values. Diesel vehicles present higher estimated NO_x emissions compared to current certification values (322% and 326% higher for NO_x and 244% and 247% higher for HC + NO_x for NEDC and WLTP Class 3 cycle, respectively).     

Instantaneous emissions models set in GIS for the TRANSIMS outputs

A novel methodology that provides more detailed estimates of vehicular polluting emissions is offered, in order to contribute to the improvement and the precision of emission inventories of vehicle sources through the consideration of instantaneous speed changes or acceleration instead of average vehicular speeds. This paper presents the construction and application of an instantaneous emissions model designated hereunder as “Transims’s Snapshots-Based Emissions”, which is set on a Geographic Information System that incorporates instantaneous fuel consumption factors and fuel-based emission factors to attain highest resolution of both, spatial and temporal distribution of vehicular polluting emissions based on traffic simulation through cellular automata with TRANSIMS. This work was applied to the road network of the Mexico City Metropolitan Area as case study. The development of this powerful tool led to obtaining 86,400 maps of the spatial and temporal distribution of vehicular emissions per vehicle circulating on the road network, including the following pollutants: carbon monoxide and carbon dioxide, nitrogen oxides, total hydrocarbons, sulfur oxides, polycyclic aromatic hydrocarbons, black carbon, particles PM₁₀ and PM_2.5. The said maps allowed identification with highest level of detail, of the emissions and Hot-spots of fuel consumption. Also, the model permitted to obtain the emissions’ longitudinal profiles of a given vehicle along its route. This study shows that the integration method of the polynomial regression models represents an opportunity for each city to develop more easily and openly its own regional emissions models without requiring deeper programming knowledge.     

A field evaluation case study of the environmental and energy impacts of traffic calming

This study quantifies the energy and environmental impact of a selection of traffic calming measures using a combination of second-by-second floating-car global positioning system data and microscopic energy and emission models. It finds that traffic calming may result in negative impacts on vehicle fuel consumption and emission rates if drivers exert aggressive acceleration levels to speed up to their journeys. Consequently by eliminating sharp acceleration maneuvers significant savings in vehicle fuel consumption and emission rates are achievable through driver education. The study also demonstrates that high emitting vehicles produce CO emissions that are up to 25 times higher than normal vehicle emission levels while low emitting vehicles produce emissions that are 15–35% of normal vehicles. The relative increases in vehicle fuel consumption and emission levels associated with the sample traffic calming measures are consistent and similar for normal, low, and high emitting vehicles.     

Greenhouse gas emission benefits of vehicle lightweighting: Monte Carlo probabalistic analysis of the multi material lightweight vehicle glider

Vehicle lightweighting reduces fuel cycle greenhouse gas (GHG) emissions but may increase vehicle cycle (production) GHG emissions because of the GHG intensity of lightweight material production. Life cycle GHG emissions are estimated and sensitivity and Monte Carlo analyses conducted to systematically examine the variables that affect the impact of lightweighting on life cycle GHG emissions. The study uses two real world gliders (vehicles without powertrain or battery) to provide a realistic basis for the analysis. The conventional and lightweight gliders are based on the Ford Fusion and Multi Material Lightweight Vehicle, respectively. These gliders were modelled with internal combustion engine vehicle (ICEV), hybrid electric vehicle (HEV), and battery electric vehicle (BEV) powertrains. The probability that using the lightweight glider in place of the conventional (steel-intensive) glider reduces life cycle GHG emissions are: ICEV, 100%; HEV, 100%, and BEV, 74%.The extent to which life cycle GHG emissions are reduced depends on the powertrain, which affects fuel cycle GHG emissions. Lightweighting an ICEV results in greater base case GHG emissions mitigation (10 t CO₂eq.) than lightweighting a more efficient HEV (6 t CO₂eq.). BEV lightweighting can result in higher or lower GHG mitigation than gasoline vehicles, depending largely on the source of electricity.     

Energy factors for flexible fuel engines and vehicles operating with gasoline-ethanol blends

This work investigates the energy factors for fuel conversion from the analysis of brake specific fuel consumption (BSFC) maps of a sample of 15 engines, representative of 75% of current models available in the Brazilian market. The method also employs the engine driving patterns of power output versus crankshaft speed obtained from bench dynamometer tests. The energy factors obtained from the engine analysis was validated against experiments carried out with two production vehicles in laboratory tests following the 1975 US Federal Test Procedure (FTP-75) procedure and road tests following 16 different urban and highway routes. The fuels used in the tests were hydrous ethanol (E100, 6 v/v % water) and a blend of 22 v/v % anhydrous ethanol and 78 v/v % gasoline (E22). The energy factors found from the 3D engine BSFC map analysis were higher than those obtained from the Willans line, currently adopted as a standard, by 52% for E22 and 57% for E100. The results from the 3D engine BFSC maps and the first vehicle following the FTP-75 cycle and 15 road routes were similar, also close to the results from the second vehicle, qualifying them to be representative of modern flexible fuel spark ignition engines and vehicles.     

Connectivity-based optimization of vehicle route and speed for improved fuel economy

Traditionally, vehicle route planning problem focuses on route optimization based on traffic data and surrounding environment. This paper proposes a novel extended vehicle route planning problem, called vehicle macroscopic motion planning (VMMP) problem, to optimize vehicle route and speed simultaneously using both traffic data and vehicle characteristics to improve fuel economy for a given expected trip time. The required traffic data and neighbouring vehicle dynamic parameters can be collected through the vehicle connectivity (e.g. vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-cloud, etc.) developed rapidly in recent years. A genetic algorithm based co-optimization method, along with an adaptive real-time optimization strategy, is proposed to solve the proposed VMMP problem. It is able to provide the fuel economic route and reference speed for drivers or automated vehicles to improve the vehicle fuel economy. A co-simulation model, combining a traffic model based on SUMO (Simulation of Urban MObility) with a Simulink powertrain model, is developed to validate the proposed VMMP method. Four simulation studies, based on a real traffic network, are conducted for validating the proposed VMMP: (1) ideal traffic environment without traffic light and jam for studying the fuel economy improvement, (2) traffic environment with traffic light for validating the proposed traffic light penalty model, (3) traffic environment with traffic light and jam for validating the proposed adaptive real-time optimization strategy, and (4) investigating the effect of different powertrain platforms to fuel economy using two different vehicle platforms. Simulation results show that the proposed VMMP method is able to improve vehicle fuel economy significantly. For instance, comparing with the fastest route, the fuel economy using the proposed VMMP method is improved by up to 15%.     

Assessing the importance of transportation activity data for urban emission inventories

The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NO_x, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NO_x and −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.     

Analysis of real driving gaseous emissions from light-duty diesel vehicles

Increasingly strict emissions standards are providing a major impetus to vehicle manufactures for developing advanced powertrain and after-treatment systems that can significantly reduce real driving emissions. The knowledge of the gaseous emissions from diesel engines under steady-state operation and under transient operation provides substantial information to analyze real driving emissions of diesel vehicles. While there are noteworthy advances in the assessment of road vehicle emissions from real driving and laboratory measurements, detailed information on real driving gaseous emissions are required in order to predict effectively the real-time gaseous emissions from a diesel vehicle under realistic driving conditions. In this work, experiments were performed to characterize the behavior of NOx, unburned HC, CO, and CO₂ emitted from light-duty diesel vehicles that comply with Euro 6 emissions standards. The driving route fully reflected various real-world driving conditions such as urban, rural, and highway. The real-time emission measurements were conducted with a Portable Emissions Measurement System (PEMS) including a Global Positioning System (GPS). To investigate the gaseous emission characteristics, authors determined the road load coefficients of vehicle specific power (VSP) and regression coefficient between fuel use rate and VSP. Furthermore, this work revealed the correlation between the rates of average fuel use and each gaseous emission.     

Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale

Coupling a traffic microsimulation with an emission model is a means of assessing fuel consumptions and pollutant emissions at the urban scale. Dealing with congested states requires the efficient capture of traffic dynamics and their conditioning for the emission model. Two emission models are investigated here: COPERT IV and PHEM v11. Emission calculations were performed at road segments over 6 min periods for an area of Paris covering 3 km². The resulting network fuel consumption (FC) and nitrogen oxide (NOx) emissions are then compared. This article investigates: (i) the sensitivity of COPERT to the mean speed definition, and (ii) how COPERT emission functions can be adapted to cope with vehicle dynamics related to congestion. In addition, emissions are evaluated using detailed traffic output (vehicle trajectories) paired with the instantaneous emission model, PHEM.COPERT emissions are very sensitive to mean speed definition. Using a degraded speed definition leads to an underestimation ranging from −13% to −25% for fuel consumption during congested periods (from −17% to −36% respectively for NOx emissions). Including speed distribution with COPERT leads to higher emissions, especially under congested conditions (+13% for FC and +16% for NOx). Finally, both these implementations are compared to the instantaneous modeling chain results. Performance indicators are introduced to quantify the sensitivity of the coupling to traffic dynamics. Using speed distributions, performance indicators are more or less doubled compared to traditional implementation, but remain lower than when relying on trajectories paired with the PHEM emission model.     

Experimental testing and simulations of speed variations impact on fuel consumption of conventional gasoline passenger cars

Speed variations are considered as an alternative for reducing fuel consumption during the use phase of passenger cars. It explores vehicle engine operating zones with lower fuel consumption, thus making possible a reduction in fuel consumption when compared to constant speed operation. In this paper, we present an evaluation of two conditions of speed variations: 50–70 km/h and 90–110 km/h using numerical simulations and controlled tests. The controlled tests performed on a test track by a professional pilot show that a reduction in fuel consumption is achievable with a conventional gasoline passenger car, with no adaptations for realizing speed variations. Numerical simulations based on a backward quasi-static powertrain model are used to evaluate the potential of speed variations for reducing fuel consumption in other speed variation conditions. When deceleration is performed with gear in neutral position, simulations show that speed variations are always correlated to a lower fuel consumption. This was suspected through previous numerical tests or evaluation on test bench but not in controlled tests conditions.     

Fuel saving and other benefits of dynamic advisory speeds on a multi-lane arterial road

Current traffic control systems which customarily give no direct information to the driver could benefit by the addition of dynamic advisory speed signs. Such signs, first introduced in the 1950s, can enable drivers to pass through successive green signals and give reduced fuel consumption, stops, travel time, emissions, noise and accidents. To attempt to quantify these reductions, a discrete vehicle simulation using program MULTSIM has been run on a 6 km idealized road and also on 2.3 km of the multi-lane arterial, Military Road, Sydney. Typically, with all drivers complying, fuel savings of the order of 10% and a halving of stops have been predicted. Travel time reductions and fuel savings depend on the coordination speed. Higher coordination speeds favour travel time savings over fuel consumption reductions, whilst lower coordination speeds favour fuel savings. Operation near the minimum fuel consumption speed is desirable. Traffic management could benefit from the change in shape of platoons. This should allow more stringent signal timing in adaptively controlled networks, giving greater flexibility for operational designs.