Prediction of vehicle CO2 emission and its application to eco-routing navigation

Transportation sector accounts for a large proportion of global greenhouse gas and toxic pollutant emissions. Even though alternative fuel vehicles such as all-electric vehicles will be the best solution in the future, mitigating emissions by existing gasoline vehicles is an alternative countermeasure in the near term. The aim of this study is to predict the vehicle CO₂ emission per kilometer and determine an eco-friendly path that results in minimum CO₂ emissions while satisfying travel time budget. The vehicle CO₂ emission model is derived based on the theory of vehicle dynamics. Particularly, the difficult-to-measure variables are substituted by parameters to be estimated. The model parameters can be estimated by using the current probe vehicle systems. An eco-routing approach combining the weighting method and k-shortest path algorithm is developed to find the optimal path along the Pareto frontier. The vehicle CO₂ emission model and eco-routing approach are validated in a large-scale transportation network in Toyota city, Japan. The relative importance analysis indicates that the average speed has the largest impact on vehicle CO₂ emission. Specifically, the benefit trade-off between CO₂ emission reduction and the travel time buffer is discussed by carrying out sensitivity analysis in a network-wide scale. It is found that the average reduction in CO₂ emissions achieved by the eco-friendly path reaches a maximum of around 11% when the travel time buffer is set to around 10%.     

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.     

Eco-routing problem considering fuel consumption and probabilistic travel time budget

Road transportation is one of the major sources of greenhouse gas emissions. To reduce energy consumption and alleviate this environmental problem, this study aims to develop an eco-routing algorithm for navigation systems. Considering that both fuel consumption and travel time are important factors when planning a trip, the proposed routing algorithm finds a path that consumes the minimum amount of gasoline while ensuring that the travel time satisfies a specified travel time budget and an on-time arrival probability. We first develop link-based fuel consumption models based on vehicle dynamics, and then the Lagrangian-relaxation-based heuristic approach is proposed to efficiently solve this NP-hard problem. The performance of the proposed eco-routing strategy is verified in a large-scale network with real travel time and fuel consumption data. Specifically, a sensitivity analysis of fuel consumption reduction for travel demand and travel time buffer is discussed in our simulation study.     

Modeling the relationships among urban passenger travel carbon dioxide emissions,transportation demand and supply,population density,and proxy policy variables

To support the development of policies that reduce greenhouse gas (GHG) emissions by encouraging reduced travel and increased use of efficient transportation modes, it is necessary to better understand the explanatory effects that transportation, population density, and policy variables have on passenger travel related CO₂ emissions. This study presents the development of a model of CO₂ emissions per capita as a function of various explanatory variables using data on 146 urbanized areas in the United States. The model takes into account selectivity bias resulting from the fact that adopting policies aimed at reducing emissions in an urbanized area may be partly driven by the presence of environmental concerns in that area. The results indicate that population density, transit share, freeway lane-miles per capita, private vehicle occupancy, and average travel time have a statistically significant explanatory effect on passenger travel related CO₂ emissions. In addition, the presence of automobile emissions inspection programs, which serves as a proxy indicator of other policies addressing environmental concerns and which could influence travelers in making environmentally favorable travel choices, markedly changes the manner in which transportation variables explain CO₂ emission levels.     

Estimating changes in transport CO2 emissions due to changes in weather and climate in Sweden

There is a considerable body of studies on the relationship between daily transport activities and CO₂ emissions. However, how these emissions vary in different weather conditions within and between the seasons of the year is largely unknown. Because individual activity–travel patterns are not static but vary in different weather conditions, it is immensely important to understand how CO₂ emissions vary due to the change of weather. Using Swedish National Travel Survey data, with emission factors calculated through the European emission factor model ARTEMIS, this study is a first attempt to derive the amount of CO₂ emission changes subject to the change of weather conditions. A series of econometric models was used to model travel behaviour variables that are crucial for influencing individual CO₂ emissions. The marginal effects of weather variables on travel behaviour variables were derived. The results show an increase of individual CO₂ emissions in a warmer climate and in more extreme temperature conditions, whereas increasing precipitation amounts and snow depths show limited effects on individual CO₂ emissions. It is worth noting that the change in CO₂ emissions in the scenario of a warmer climate and a more extreme temperature tends to be greater than the sum of changes in CO₂ emissions in each individual scenario. Given that a warmer climate and more extreme weather could co-occur more frequently in the future, this result suggests even greater individual CO₂ emissions than expected in such a future climate.     

Calculation of aircraft fuel consumption and CO2 emissions based on path profile estimation by clustering and registration

In this paper, typical flight paths, fuel burn and carbon dioxide (CO₂) emissions are computed using a rich data set and two estimation approaches: (i) a clustering and landmark registration technique and (ii) a method based on the EUROCONTROL’s Base of Aircraft Data (BADA) performance model. Clustering is employed to extract flight characteristics and organize altitude profiles accordingly. Our flight path and CO₂ emissions analysis focuses on the Climb-Cruise-Descent (CCD) cycle, since different operational conditions during the Landing and Take-off cycle may result in significant deviations in terms of fuel burn and CO₂ emissions and different modeling assumptions and approaches should be adopted. The key features of the CCD cycle are the flight distance, the aircraft type and the flight direction. Path segmentation and landmark registration are employed for path representation and smoothening of discontinuities. The paths estimated by the above method are compared to those obtained by the point mass BADA model. Noticeable deviations in the resulting estimates of the operational characteristics are found. Higher deviations in prediction errors are found in the climb and descent duration and the rate of climb and descent. The typical altitude profiles obtained by the two methods are used to determine fuel burn and CO₂ emissions. The difference in the resulting estimates are less stark; on a fleet-wide level the fuel burn of the relevant typical profiles differ by 7%. Emission maps of the U.S. airspace enabling the identification of critical emission spots including routes, airports, seasons and aircraft type are constructed.     

Exhaust emissions generated under actual operating conditions from a hybrid vehicle and an electric one fitted with a range extender

The paper describes exhaust emission tests performed on a PHEV (Plug-in Hybrid Electric Vehicle) and a BEV (Battery Electric Vehicle), in which the combustion engine was used as a range extender. The measurements of the exhaust emissions were performed for CO₂/fuel consumption, CO, THC and NO_x. The RDE measurements were performed including the engine operating parameters and emissions analysis. This analysis shows that the engines of BEVs and PHEVs operate in a different parameter range when under actual operating conditions, which directly translates into the exhaust emission values. This is particularly the case for the emission of NO_x. The investigations were carried out for two routes differentiated by the length and share of the urban and extra-urban cycles. For both routes, the emission of THC and CO were lower for the PHEV engine – HC by 69% (22 mg/km, route 1) and 6% (15 mg/km, route 2), CO by 69% (0.12 mg/km, route 1) and 80% (0.1 mg/km, route 2). For route 1, characterized by a greater share of the urban cycle, the emission of NO_x was lower by 70% (2 mg/km) for the BEV engine, and (route 2) lower by 60% (8 mg/km) for the PHEV engine. Additionally, the curves of the exhaust emissions in time for individual exhaust components have been presented that indicate that in the motorway cycle the emission of THC and CO from the BEV vehicle increases significantly up to ten times compared to urban cycle.     

Exploring transport carbon futures using population microsimulation and travel diaries: Beijing to 2030

Evaluating transport policy for cities in developing countries is often constrained by data availability that limits the use of conventional appraisal models. Here, we present a new ‘bottom-up’ methodology to estimate transport CO₂ emission from daily urban passenger travel for Beijing, a megacity with relatively sparse data on travel behaviour. A spatial microsimulation, based on an activity diary survey and two sample population censuses, is used to simulate, for Beijing’s urban districts, a realistic synthetic population, and their daily travel and CO₂ emission over 2000–2010. This approach provides greater insight into the spatial variability of transport CO₂ emission than has previously been possible for Beijing, and further, enables an examination of the role of socio-demographics, urban form and transport developments in contributing to emissions over the modelled period.Using the 2000–2010 CO₂ emission estimates as a baseline, CO₂ emissions from passenger travel are then modelled to 2030 under scenarios exploring politically plausible strategies on transport (public transport infrastructure investment, and vehicle constraint), urban development (compaction) and vehicle technology (faster adoption of clean vehicle technology). The results showed that, compared to the trend scenario, employing both transport and urban development policies could reduce total passenger CO₂ emission to 2030 by 24%, and by 43% if all strategies were applied together. The study reveals the potential of microsimulation in emission estimation for large cities in developing countries where data availability may constrain more traditional approaches.     

Minimizing CO2e emissions by setting a road toll

The main purpose of this paper is to develop a bi-level pricing model to minimize the CO₂e emissions and the total travel time in a small road network. In the lower level of the model, it is assumed that users of the road network find a dynamic user equilibrium which minimizes the total costs of those in the system. For the higher level of the model, different road toll strategies are applied in order to minimize the CO₂e emissions. The model has been applied to an illustrative example. It shows the effects on traffic flows, revenues, total time and CO₂e emissions for different numbers of servers collecting tolls and different pricing strategies over a morning peak traffic period. The results show that the CO₂e emissions produced can be significantly affected by the number of servers and the type of toll strategy employed. The model is also used to find the best toll strategy when there is a constraint on the revenue that is required to be raised from the toll and how this affects the emissions produced. Further runs compare strategies to minimize the CO₂e emissions with those that minimize total travel time in the road system. In the illustrative example, the results for minimizing CO₂e emissions are shown to be similar to the results obtained from minimizing the total travel time.     

Reducing CO<Subscript>2</Subscript> from cars in the European Union

The European Union (EU) recently adopted CO₂ emissions mandates for new passenger cars, requiring steady reductions to 95 gCO₂/km in 2021. We use a multi-sector computable general equilibrium (CGE) model, which includes a private transportation sector with an empirically-based parameterization of the relationship between income growth and demand for vehicle miles traveled. The model also includes representation of fleet turnover, and opportunities for fuel use and emissions abatement, including representation of electric vehicles. We analyze the impact of the mandates on oil demand, CO₂ emissions, and economic welfare, and compare the results to an emission trading scenario that achieves identical emissions reductions. We find that vehicle emission standards reduce CO₂ emissions from transportation by about 50 MtCO₂ and lower the oil expenditures by about €6 billion, but at a net added cost of €12 billion in 2020. Tightening CO₂ standards further after 2021 would cost the EU economy an additional €24–63 billion in 2025, compared with an emission trading system that achieves the same economy-wide CO₂ reduction. We offer a discussion of the design features for incorporating transport into the emission trading system.     

The significant impacts on traffic and emissions of ferrying children to school in Beijing

After having implemented numerous regulations, e.g., coercive policies on vehicle use and purchase, it is becoming increasingly difficult to find further potential to control vehicle emissions in Beijing, as the air quality is still poor. This research provides a different approach for policy-makers to reduce vehicle emissions by managing demand. We found that parents ferrying their children to and from school is an important but long-neglected contributor to traffic congestion and vehicle emissions. This phenomenon is very common in China because of the social culture. In this research, parallel tests during both the school season and the non-school season were adopted, and emissions in both seasons were calculated based on travel demand and emission models. The results revealed that emissions factors (in g/km) for criteria pollutants and CO₂ increased by over 10% during rush hours during the school season due to traffic condition deterioration compared with non-school season. Daily HC, CO, NO_x, PM and CO₂ emissions from the passenger car fleet were 8.3%, 7.8%, 6.4%, 6.3% and 6.5% higher compared with those during the non-school season, respectively. These differences are greater than the total vehicular emission reduction by other control measures in 2014 in Beijing. For policy makers, providing safe and efficient ways to ferry children would be a useful and harmonious strategy for future vehicle emission control.     

Can rail/sail compete with air travel to Cyprus? A comparison of emissions

Rail and sea voyage journeys to Cyprus from a variety of origins are constructed to derive the travel emissions and travel time per person to compare popular aviation routes. The hypothetical ‘slow travel’ routes are approximately eight to ten times longer than flying. Emissions are lower from certain origins by about 100 kg CO₂ per person per round trip under reasonably high occupancy conditions when compared to current direct air services. Emissions from the sea voyages are derived from a sample of 162 marine vessels using the energy efficiency design index for European ships running at 20 knots.     

Eco-system optimal time-dependent flow assignment in a congested network

This research addresses the eco-system optimal dynamic traffic assignment (ESODTA) problem which aims to find system optimal eco-routing or green routing flows that minimize total vehicular emission in a congested network. We propose a generic agent-based ESODTA model and a simplified queueing model (SQM) that is able to clearly distinguish vehicles’ speed in free-flow and congested conditions for multi-scale emission analysis, and facilitates analyzing the relationship between link emission and delay. Based on the SQM, an expanded space-time network is constructed to formulate the ESODTA with constant bottleneck discharge capacities. The resulting integer linear model of the ESODTA is solved by a Lagrangian relaxation-based algorithm. For the simulation-based ESODTA, we present the column-generation-based heuristic, which requires link and path marginal emissions in the embedded time-dependent least-cost path algorithm and the gradient-projection-based descent direction method. We derive a formula of marginal emission which encompasses the marginal travel time as a special case, and develop an algorithm for evaluating path marginal emissions in a congested network. Numerical experiments are conducted to demonstrate that the proposed algorithm is able to effectively obtain coordinated route flows that minimize the system-wide vehicular emission for large-scale networks.     

Development of a corrected average speed model for calculating carbon dioxide emissions per link unit on urban roads

The purpose of our study is to develop a “corrected average emission model,” i.e., an improved average speed model that accurately calculates CO₂ emissions on the road. When emissions from the central roads of a city are calculated, the existing average speed model only reflects the driving behavior of a vehicle that accelerates and decelerates due to signals and traffic. Therefore, we verified the accuracy of the average speed model, analyzed the causes of errors based on the instantaneous model utilizing second-by-second data from driving in a city center, and then developed a corrected model that can improve the accuracy. We collected GPS data from probe vehicles, and calculated and analyzed the average emissions and instantaneous emissions per link unit. Our results showed that the average speed model underestimated CO₂ emissions with an increase in acceleration and idle time for a speed range of 20 km/h and below, which is the speed range for traffic congestion. Based on these results, we analyzed the relationship between average emissions and instantaneous emissions according to the average speed per link unit, and we developed a model that performed better with an improved accuracy of calculated CO₂ emissions for 20 km/h and below.     

Energy use and emissions of two stroke-powered tricycles in Metro Manila

CO, CO₂, NO_x and HC emissions of two stroke-powered tricycles in Metro Manila are examined using an instantaneous emissions model. Results show that fuel consumption and HC emissions in middle class residential areas and main roads are similar but lower than levels in low income residential areas. On the average, tricycles in Metro Manila consume 24.41 km/l of fuel and produces 9.5, 9.7, 40.5 and 0.07 g/km of HC, CO, CO₂ and NO_x, respectively. They fail to satisfy HC, CO and NO_x emission limits set by reference standards in the Philippines and other Asian countries. They produce greater HC and CO emissions than gasoline fueled private cars and diesel powered public jeepneys, taxis and buses on a per passenger-km basis but significantly lower NO_x emissions. Tricycles account for 15.4% of the total HC emissions from mobile sources in the metropolis while their contributions to CO, CO₂ and NO_x are minimal.     

The effects of physical activity on greenhouse gas emissions for common transport modes in European countries

This paper applies a life cycle methodology to estimate activity-related contributions of transport modes to GHG emissions. The methodology uses national input–output tables, environmental accounts, household budget data and nutritional data to derive food-sector GHG coefficients of consumption for ten European countries. The food energy requirements for each mode of transport are estimated taking account of the modal activity level and energy requirements. Typical national food energy-related emissions for walking, cycling, and driving ranged from 25.6 to 77.3 gCO₂-eq/pass.km, 10.4–31.4 gCO₂-eq/pass.km and 1.7–5.2 gCO₂-eq/pass.km; passenger transport was found to result in no food-related emissions above those for a resting individual. Emissions vary between countries depending on the emissions intensities of their energy sectors as well as food prices and average body weights. A life cycle assessment of modal emissions in the UK is undertaken using the food-energy emissions intensities estimated and car travel was found to have the highest emissions intensity, followed by bus, cycling and walking.     

The potential mitigation of CO2 emissions via modal substitution of high-speed rail for short-haul air travel from a life cycle perspective – An Australian case study

The objective of this study is to provide a strategic evaluation of the mitigation of CO₂ emissions via modal substitution of high-speed rail for short-haul air travel on the Sydney–Melbourne, Australia city-pair from a life cycle perspective. It has been demonstrated that when considering CO₂ emissions from vehicle operations, the modal shift from air to high-speed rail on this city-pair has the potential to provide a means of CO₂ mitigation. However, uncertainty exists with regard to the level of mitigation potential when considering the whole-of-life performance of the systems. Given the significant difference in the infrastructure requirements between the air mode and the high-speed rail mode, this study quantifies the life cycle CO₂ load attributable to each system and examines the effect on CO₂ mitigation potential. The study concluded that while the inclusion of the linehaul infrastructure did increase the CO₂ load associated with high-speed rail mode, it did not equate to or exceed the CO₂ load per trip as experienced by the air mode. The avoided annual life cycle CO₂ emission in the target year 2056 was 0.37 Mt representing an 18% reduction when compared to the air mode only on the city pair. In fact, the scenario comparison indicated that the substitution of high-speed rail for short-haul air travel on the city pair resulted in CO₂ emissions avoidance throughout the longitudinal period.     

VISSIM/MOVES integration to investigate the effect of major key parameters on CO2 emissions

This paper looks at CO₂ emissions on limited access highways in a microscopic and stochastic environment using an optimal design approach. Estimating vehicle emissions based on second-by-second vehicle operation allows the integration of a microscopic traffic simulation model with the latest US Environmental Protection Agency’s mobile source emissions model to improve accuracy. A factorial experiment on a test bed prototype of the I-4 urban limited access highway corridor located in Orlando, Florida was conducted to identify the optimal settings for CO₂ emissions reduction and to develop a microscopic transportation emission prediction model. An exponentially decaying function towards a limiting value expressed in the freeway capacity is found to correlate with CO₂ emission rates. Moreover, speeds between 55 and 60 mph show emission rate reduction effect while maintaining up to 90% of the freeway’s capacity. The results show that speed has a significant impact on CO₂ emissions when detailed and microscopic analysis of vehicle operations of acceleration and deceleration are considered.     

In-use measurement of the activity, fuel use, and emissions of eight cement mixer trucks operated on each of petroleum diesel and soy-based B20 biodiesel

In-use micro-scale fuel use and emission rates were measured for eight cement mixer trucks using a portable emission measurement system. Each vehicle was tested on petroleum diesel and B20 biodiesel. Average fuel use and emission rates increase monotonically versus engine manifold absolute pressure. A typical duty cycle includes loading at a cement plant, transit while loaded from the cement plant to work site, creeping in a queue of vehicles at the worksite, unloading, and transit without load from the site to the plant. For B20 versus petroleum diesel, there is no significant change in the rate of fuel use, CO₂ emissions, and NO emissions, and significant decreases in emissions for CO, hydrocarbons, and particulate matter. For loaded versus unloaded onroad travel, fuel use and CO₂ emissions rates are approximately 60% higher and the rates for other pollutants are approximately 30–50% higher. A substantial portion of cycle emissions occurred at the work site. Inter-vehicle and intra-cycle variability are also quantified using the micro-scale methodology.     

Examining the effects of the built environment and residential self-selection on commuting trips and the related CO2 emissions: An empirical study in Guangzhou,China

Numerous studies have established the link between the built environment and travel behavior. However, fewer studies have focused on environmental costs of travel (such as CO₂ emissions) with respect to residential self-selection. Combined with the application of TIQS (Travel Intelligent Query System), this study develops a structural equations model (SEM) to examine the effects of the built environment and residential self-selection on commuting trips and their related CO₂ emissions using data from 2015 in Guangzhou, China. The results demonstrate that the effect of residential self-selection also exists in Chinese cities, influencing residents’ choice of living environments and ultimately affecting their commute trip CO₂ emissions. After controlling for the effect of residential self-selection, built environment variables still have significant effects on CO₂ emissions from commuting although some are indirect effects that work through mediating variables (car ownership and commuting trip distance). Specifically, CO₂ emissions are negatively affected by land-use mix, residential density, metro station density and road network density. Conversely, bus stop density, distance to city centers and parking availability near the workplace have positive effects on CO₂ emissions. To promote low carbon travel, intervention on the built environment would be effective and necessary.