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.     

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.     

Hardware-in-the-loop testbed for evaluating connected vehicle applications

Connected vehicle environment provides the groundwork of future road transportation. Researches in this area are gaining a lot of attention to improve not only traffic mobility and safety, but also vehicles’ fuel consumption and emissions. Energy optimization methods that combine traffic information are proposed, but actual testing in the field proves to be rather challenging largely due to safety and technical issues. In light of this, a Hardware-in-the-Loop-System (HiLS) testbed to evaluate the performance of connected vehicle applications is proposed. A laboratory powertrain research platform, which consists of a real engine, an engine-loading device (hydrostatic dynamometer) and a virtual powertrain model to represent a vehicle, is connected remotely to a microscopic traffic simulator (VISSIM). Vehicle dynamics and road conditions of a target vehicle in the VISSIM simulation are transmitted to the powertrain research platform through the internet, where the power demand can then be calculated. The engine then operates through an engine optimization procedure to minimize fuel consumption, while the dynamometer tracks the desired engine load based on the target vehicle information. Test results show fast data transfer at every 200 ms and good tracking of the optimized engine operating points and the desired vehicle speed. Actual fuel and emissions measurements, which otherwise could not be calculated precisely by fuel and emission maps in simulations, are achieved by the testbed. In addition, VISSIM simulation can be implemented remotely while connected to the powertrain research platform through the internet, allowing easy access to the laboratory setup.     

An eco-routing model considering microscopic vehicle operating conditions

The eco-routing problem concerned in this paper addresses the optimal route choice of eco-drivers who aim to meet an emission standard imposed by regulators, while trying to find the path with the minimum total operating cost, which consists of both travel time and fuel costs. The paper first develops fuel consumption and greenhouse gas emissions estimation models that link emission rates to a vehicle’s physical and operational properties. Unlike most studies in the literature, the emission model developed in this paper retains as many microscopic characteristics as feasible in the context of route planning. Specifically, it is able to approximate the impacts of major acceleration events associated with link changes and intersection idling, and yet does not require detailed acceleration data as inputs. The proposed eco-routing model also explicitly captures delays at intersections and the emissions associated with them. Using a simple probabilistic model, the impacts of different turning movements on eco-routing are incorporated. The proposed model is formulated as a constrained shortest path problem and solved by off-the-shelf solvers. Numerical experiments confirm that vehicle characteristics, especially weight and engine displacement, may influence eco-routing. The results also suggest that ignoring the effects of turning movements and acceleration may lead to sub-optimal routes for eco-drivers.     

Assessing methods for comparing emissions from gasoline and diesel light-duty vehicles based on microscale measurements

This paper assess whether a real-world second-by-second methodology that integrates vehicle activity and emissions rates for light-duty gasoline vehicles can be extended to diesel vehicles. Secondly it compares fuel use and emission rates between gasoline and diesel light-duty vehicles. To evaluate the methodology, real-world field data from two light-duty diesel vehicles are used. Vehicle specific power, a function of vehicle speed, acceleration, and road grade, is evaluated with respect to ability to explain variation in emissions rates. Vehicle specific power has been used previously to define activity-based modes and to quantify variation in fuel use and emission rates of gasoline vehicles taking into account idle, acceleration, cruise, and deceleration. The fuel use and emission rates for light-duty diesel vehicles can also be explained using vehicle specific power -based modes. Thus, the methodology enables direct comparisons for different vehicle fuels and technologies. Furthermore, the method can be used to estimate average fuel use and emission rates for a wide variety of driving cycles.     

Does traffic-related calibration of car-following models provide accurate estimations of vehicle emissions?

Fuel consumption or pollutant emissions can be assessed by coupling a microscopic traffic flow model with an instantaneous emission model. Traffic models are usually calibrated using goodness of fit indicators related to the traffic behavior. Thus, this paper investigates how such a calibration influences the accuracy of fuel consumption and NOx and PM estimations. Two traffic models are investigated: Newell and Gipps. It appears that the Gipps model provides the closest simulated trajectories when compared to real ones. Interestingly, a reverse ranking is observed for fuel consumption, NOx and PM emissions. For both models, the emissions of single vehicles are very sensitive to the calibration. This is confirmed by a global sensitivity analysis of the Gipps model that shows that non-optimal parameters significantly increase the variance of the outputs. Fortunately, this is no longer the case when emissions are calculated for a group of many vehicles. Indeed, the mean errors for platoons are close to 10% for the Gipps model and always lower than 4% for the Newell model. Another interesting property is that optimal parameters for each vehicle can be replaced by the mean values with no discrepancy for the Newell model and low discrepancies for the Gipps model when calculating the different emission outputs. Finally, this study presents preliminary results that show that multi-objective calibration methods are certainly the best direction for future works on the Gipps model. Indeed, the accuracy of vehicle emissions can be highly improved with negligible counterparts on the traffic model accuracy.     

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.     

Assessing additional fuel consumption from cabin thermal comfort and auxiliary needs on the worldwide harmonized light vehicles test cycle

Standards for fuel consumption and carbon dioxide emissions are implemented worldwide in most light-duty vehicle markets. Regulatory drive cycles, defined as specific time-speed patterns, are used to measure levels of fuel consumption and emissions. These measurements should realistically reflect real world driving performance, however there is increasing concern about their adequacy due to the discrepancies observed between certified and real world consumption and emissions values. One of the main reasons for the discrepancy is that current testing protocols do not account for non-mechanical vehicle energy needs, such as passengers’ thermal comfort needs and the use of electric auxiliaries on-board. Cabin heating and cooling can especially lead to considerable increase in vehicle energy consumption. This paper presents a simulation-based assessment framework to account for the additional fuel consumption related to the cabin thermal energy and auxiliary needs under the worldwide-harmonized light vehicles test procedure (WLTP). A vehicle cabin model is developed and the thermal comfort energy needs are derived for cooling and heating, depending on ambient external temperature under cold, moderate and warm climates. A modification to the WLTP is proposed by including the generated power profiles for thermal comfort and auxiliary needs. Dynamic programming is used to compute the fuel consumption on the modified WLTP for a rechargeable series hybrid electric vehicle (SHEV) architecture. Results show consumption increases of 20% to 96% compared to the currently adopted WLTP, depending on the considered climate.     

GHG-emission models for assessing the eco-friendliness of road and rail freight transports

Intermodal rail/road transportation is an instrument of green logistics, which may help reducing transport related greenhouse gas (GHG) emissions. In order to assess the environmental impact of road and rail transports, researchers have formulated very detailed microscopic models, which determine vehicle emissions precisely based on a vast number of parameters. They also developed macroscopic models, which estimate emissions more roughly from few parameters that are considered most influential. One of the goals of this paper is to develop mesoscopic models that combine the preciseness of micro-models while requiring only little more information than macro-models. We propose emission models designed for transport planning purposes which are simple to calibrate by transport managers. Despite their compactness, our models are able to reflect the influence of various traffic conditions on a transport’s total emissions. Furthermore, contrasting most papers considering either the road or the rail mode, we provide models on a common basis for both modes of transportation. We validate our models using popular micro- and macroscopic models and we apply them to artificial and real world transport scenarios to identify under which circumstances intermodal transports actually effect lower emissions. We find that travel speed and country-specific energy emission factors influence the eco-friendliness of intermodal transports most severely. Hence, the particular route chosen for a transnational intermodal transport is an important but so far neglected option for eco-friendly transportation.     

Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies

This paper presents a computationally efficient and theoretically rigorous dynamic traffic assignment (DTA) model and its solution algorithm for a number of emerging emissions and fuel consumption related applications that require both effective microscopic and macroscopic traffic stream representations. The proposed model embeds a consistent cross-resolution traffic state representation based on Newell’s simplified kinematic wave and linear car following models. Tightly coupled with a computationally efficient emission estimation package MOVES Lite, a mesoscopic simulation-based dynamic network loading framework DTALite is adapted to evaluate traffic dynamics and vehicle emission/fuel consumption impact of different traffic management strategies.     

Quantifying the effects of input aggregation and model randomness on regional transportation emission inventories

Accurate road-traffic emission inventories are of great interest to metropolitan planning agencies especially in the appraisal of regional transport policies. Integrated road transport emission models are an effective means of establishing emission estimates, yet their development requires significant investments in data and resources. It is therefore important to investigate which data inputs are the most critical to inventory accuracy. To address this issue, an integrated transport and emissions model is developed using the Montreal metropolitan region as a case-study. Daily regional hydrocarbon (HC) emissions from private individual travel are estimated, including the excess emissions due to engine starts. The sensitivity of emission estimates is then evaluated by testing various levels of input aggregation common in practice and in previous research. The evaluated inputs include the effect of start emissions, ambient weather conditions, traffic speed, path choice, and vehicle registry information. Inherent randomness within the integrated model through vehicle selection and path allocation is also evaluated. The inclusion of start emissions is observed to have the largest impact on emission inventories, contributing approximately 67 % of total on-road HC emissions. Ambient weather conditions (season) and vehicle registry data (types, model years) are also found to be significant. Model randomness had a minimal effect in comparison with the impact of other variables.     

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。     

Evaluating the impacts of urban corridor traffic signal optimization on vehicle emissions and fuel consumption

This study investigates the impacts of traffic signal timing optimization on vehicular fuel consumption and emissions at an urban corridor. The traffic signal optimization approach proposed integrates a TRANSIMS microscopic traffic simulator, the VT-Micro model (a microscopic emission and fuel consumption estimation model), and a genetic algorithm (GA)-based optimizer. An urban corridor consisting of four signalized intersections in Charlottesville, VA, USA, is used for a case study. The result of the case study is then compared with the best traffic signal timing plan generated by Synchro using the TRANSIMS microscopic traffic simulator. The proposed approach achieves much better performance than that of the best Synchro solution in terms of air quality, energy and mobility measures: 20% less network-wide fuel consumption, 8–20% less vehicle emissions, and nearly 27% less vehicle-hours-traveled (VHT).     

Green approaches at sea – The benefits of adjusting speed instead of anchoring

In this paper, the concept ‘green approaches’ already used in aviation is applied to cargo transportation at sea. Instead of anchoring outside a port waiting for berth, ships can adjust their speed to arrive just in time for berthing. With improved incentives for reducing speed and shared information about berthing times, green approaches instead of anchoring can be a way to reduce fuel consumption and emissions without increasing the transit times of goods. The present study estimates the benefits to society as a whole for the EU ports in the Baltic Sea with Automatic Identification System data applying a new method using data collected in real time. Data consists of all anchored ships awaiting berth on 40 different occasions in 2015 and are subsequently extrapolated to a year. Fuel consumption by the individual ships, emissions and values are calculated from the detailed data with established models and estimates of unit values. The potential benefits are estimated at 27 million euros per year in the scenario where the near 15,000 anchorings by ships annually awaiting berth may instead start a green approach 12 h prior to arrival and may reduce speed by 25%, using the middle unit values for fuel and emissions. The methodology used in the paper can be applied to estimate the benefits of green approaches in other areas with anchored vessels.     

Eco-driving: An economic or ecologic driving style?

In this work the trade-off between economic, therefore fuel saving, and ecologic, pollutant emission reducing, driving is discussed. The term eco-driving is often used to refer to a vehicle operation that minimizes energy consumption. However, for eco-driving to be environmentally friendly not only fuel consumption but also pollutant emissions should be considered. In contrast to previous studies, this paper will discuss the advantages of eco-driving with respect to improvements in fuel consumption as well as pollutant gas emissions. Simulating a conventional passenger vehicle and applying numerical trajectory optimization methods best vehicle operation for a given trip is identified. With hardware-in-the-loop testing on an engine test bench the fuel and emissions are measured. An approach to integrate pollutant emission and dynamically choose the ecologically optimal gear is proposed.     

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.     

Exhaust emissions and fuel consumption of a triple-fuel spark-ignition engine powered passenger car

This paper examines the influence of compressed natural gas, liquefied petroleum gas and gasoline fuel on the exhaust emissions and the fuel consumption of a spark-ignition engine powered passenger car. The vehicle was driven according to the urban driving cycle and extra urban driving cycle speed profiles with the warmed-up engine. Cause and effect based analysis reveals potential for using different fuels to reduce vehicle emission and deficiencies associated with particular fuels. The highest tank to wheel efficiency and the lowest CO₂ emission are observed with the natural gas fuelled vehicle, that also featured the highest total hydrocarbon emissions and high NO_x emissions because of fast three way catalytic converter aging due the use of the compressed natural gas. Retrofitted liquefied petroleum gas fuel supply systems feature the greatest air-fuel ratio variations that result in the lowest TtW efficiency and in the highest NO_x emissions of the liquefied gas fuelled vehicle.     

Impact of license plate restriction policy on emission reduction in Hangzhou using a bottom-up approach

Many emission models have been developed for estimating the impact of transport policies on vehicle emissions. Macroscopic models, such as MOBILE and COPERT, are used for area analysis, while microscopic models, such as CMEM, are applied for corridor analysis. It is well known that driving dynamics are critical for estimating vehicle emissions. MOVES can be used for both macroscopic and microscopic emission analysis, and its advantage lies in the consideration of driving dynamics. Using a bottom-up approach, we study the impact of license plate restriction policy on vehicle emission reduction by localizing the emission rates in MOVES according to the vehicle emission standards in China. We implement the approach to evaluate the impact on the total vehicle emissions in Hangzhou, China before and after the implementation of license plate restriction policy. In the restricted region, the reductions of total Vehicle Kilometer Traveled (VKT) and total emissions are 9.6% and 6.9%, respectively. The result shows that the license plate restriction policy is effective in achieving the targeted emission reduction.     

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.     

Preparing highway emissions inventories for urban scale modeling: A case study in Philadelphia

Highway emissions represent a major source of many pollutants. Use of local data to model these emissions can have a large impact on the magnitude and distribution of emissions predicted and can significantly improve the accuracy of local scale air quality modeling assessments. This paper provides a comparison of top–down and bottom–up approaches for developing emission inventories for modeling in one urban area, Philadelphia, in calendar year 1999. A bottom–up approach relies on combining motor vehicle emission factors and vehicle activity data from a travel demand model estimated at the road link level to generate hourly emissions data. This approach can result in better estimates of levels and spatial distribution of on-road motor vehicle emissions than a top–down approach that relies on more aggregated information and default modeling inputs.