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.     

Evaluation of eco-driving training for vehicle fuel use and emission reduction: A case study in Australia

This study evaluates effectiveness of driver education teaching greater fuel efficiency (Eco-Driving) in a real world setting in Australia. The driving behaviour, measured in fuel use (litres per 100 km of travel) of a sample of 1056 private drivers was monitored over seven months. 853 drivers received education in eco-driving techniques and 203 were monitored as a control group. A simple experimental design was applied comparing the pre and post training fuel use of the treated sample compared to a control sample. This study found the driver education led to a statistically significant reduction in fuel use of 4.6% or 0.51 litres per 100 km compared to the control group.     

Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case

This research identifies key variables that influence fuel consumption that might be improved through eco-driving training programs under three circumstances that have been scarcely studied before: (a) heavy- and medium-duty truck fleets, (b) long-distance freight transport, and (c) the Latin American region. Based on statistical analyses that include multivariate regression of operational variables on fuel consumption, the impacts of an eco-driving training campaign were measured by comparing ex ante and ex post data. Operational variables are grouped into driving errors, trip conditions, driver behavior, driver profile, and vehicle attributes.The methodology is applied in a freight fleet with nationwide transport operations located in Colombia, where the steepness of its roads plays an important role in fuel consumption. The fleet, composed of 18 trucks, is equipped with state-of-the-art real-time data logger systems. During four months, 517 trips traveling a total distance of 292,512 km and carrying a total of 10,034 tons were analyzed.The results show a baseline average fuel consumption (FC) of 1.716 liters per ton-100 km. A different logistics performance indicator, which measures FC in liters per ton transported each 100 km, shows an average of 3.115. After the eco-driving campaign, reductions of 6.8% and 5.5% were obtained. Drivers’ experience, driving errors, average speed, and weight-capacity ratio, among others, were found to be highly relevant to FC. In particular, driving errors such as acceleration, braking and speed excesses are the most sensitive to eco-driving training, showing reductions of up to 96% on the average number of events per trip.     

Dynamic travel time prediction using data clustering and genetic programming

The current state-of-practice for predicting travel times assumes that the speeds along the various roadway segments remain constant over the duration of the trip. This approach produces large prediction errors, especially when the segment speeds vary temporally. In this paper, we develop a data clustering and genetic programming approach for modeling and predicting the expected, lower, and upper bounds of dynamic travel times along freeways. The models obtained from the genetic programming approach are algebraic expressions that provide insights into the spatiotemporal interactions. The use of an algebraic equation also means that the approach is computationally efficient and suitable for real-time applications. Our algorithm is tested on a 37-mile freeway section encompassing several bottlenecks. The prediction error is demonstrated to be significantly lower than that produced by the instantaneous algorithm and the historical average averaged over seven weekdays (p-value <0.0001). Specifically, the proposed algorithm achieves more than a 25% and 76% reduction in the prediction error over the instantaneous and historical average, respectively on congested days. When bagging is used in addition to the genetic programming, the results show that the mean width of the travel time interval is less than 5 min for the 60–80 min trip.     

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.     

Regional climate impact of aerosols emitted by transportation modes and potential effects of policies on demand and emissions

The transportation system is one of the main sectors with significant climate impact. In the U.S. it is the second main emitter of carbon dioxide. Its impact in terms of emission of carbon dioxide is well recognized. But a number of aerosol species have a non-negligible impact. The radiative forcing due to these species needs to be quantified. A radiative transfer code is used. Remote sensing data is retrieved to characterize different regions. The radiative forcing efficiency for black carbon are 396 ± 200 W/m²/AOD for the ground mode and 531 ± 190 W/m²/AOD for the air transportation, under clear sky conditions. The radiative forcing due to contrail is 0.14 ± 0.06 W/m² per percent coverage. Based on the forcing from the different species emitted by each mode of transportation, policies may be envisioned. These policies may affect demand and emissions of different modes of transportation. Demand and fleet models are used to quantify these interdependencies. Depending on the fuel price of each mode, mode shifts and overall demand reduction occur, and more fuel efficient vehicles are introduced in the fleet at a faster rate. With the introduction of more fuel efficient vehicles, the effect of fuel price on demand is attenuated. An increase in fuel price of 50 cents per gallon, scaled based on the radiative forcing of each mode, results in up to 5% reduction in emissions and 6% reduction in radiative forcing. With technologies, significant reduction in climate impact may be achieved.     

Estimation of nitrogen oxides emissions from petrol and diesel passenger cars by means of on-board monitoring: Effect of vehicle speed,vehicle technology,engine type on emission rates

NO_X emission rates of 13 petrol and 3 diesel passenger cars as a function of average speed from 10 to 120 km/h, emission class (pre-Euro 1 – Euro 5), engine type were investigated by on-board monitoring on roads and highways of St. Petersburg using a portative Testo XXL 300 gas analyzer. The highest level of NO_X emission 0.5–2.5 g/km was inherent to old pre-Euro 1 petrol cars without a catalytic converter. NO_X emissions rates of Euro 1 and Euro 2 petrol cars changed within 0.15–0.9 g/km, Euro 3 – 0.015–0.27 g/km, Euro 4 – 0.013–0.1 g/km, Euro 5 – 0.002–0.043 g/km. Euro 3 – Euro 4 petrol cars generally satisfied corresponding NO_X Emission Standards (ES), except cold-start period, Euro 5 petrol cars did not exceed ES. Warmed, stabilized engines of Euro 3 – Euro 5 petrol cars showed 5–10 times lower NO_X emission rates than corresponding ES in the range of speed from 20 to 90 km/h. NO_X emission rates of diesel Euro 3 and Euro 4 cars varied from 0.45 to 1.1 g/km and from 0.31 to 1.1 g/km, respectively. Two examined diesel Euro 3 and one Euro 4 passenger vehicles did not satisfy NO_X ES at real use. Euro 3 diesel cars showed 28.9 times higher NO_X emissions than Euro 3 petrol cars and Euro 4 diesel car demonstrated 17.6 times higher NO_X emissions than Euro 4 petrol cars at warmed and stabilized engine at a cruise speed ranging from 30 to 60 km/h.     

Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: A case-study of Tampa Bay departures

Taxi-out delay is a significant portion of the block time of a flight. Uncertainty in taxi-out times reduces predictability of arrival times at the destination. This in turn results in inefficient use of airline resources such as aircraft, crew, and ground personnel. Taxi-out time prediction is also a first step in enabling schedule modifications that would help mitigate congestion and reduce emissions. The dynamically changing operation at the airport makes it difficult to accurately predict taxi-out time. In this paper we investigate the accuracy of taxi out time prediction using a nonparametric reinforcement learning (RL) based method, set in the probabilistic framework of stochastic dynamic programming. A case-study of Tampa International Airport (TPA) shows that on an average, with 93.7% probability, on any given day, our predicted mean taxi-out time for any given quarter, matches the actual mean taxi-out time for the same quarter with a standard error of 1.5 min. Also, for individual flights, the taxi-out time of 81% of them were predicted accurately within a standard error of 2 min. The predictions were done 15 min before gate departure. Gate OUT, wheels OFF, wheels ON, and gate IN (OOOI) data available in the Aviation System Performance Metric (ASPM) database maintained by the Federal Aviation Administration (FAA) was used to model and analyze the problem. The prediction accuracy is high even without the use of detailed track data.     

Ultrafine particle infiltration into passenger vehicles. Part II: Model analysis

Experimental studies showed that infiltration and passive ventilation are important air exchange mechanisms inside vehicles but previous mathematical models did not consider either one. In this study, we incorporated infiltration and passive ventilation to advance the existing mathematical models and evaluated how different transport mechanisms affect passenger exposures at increasing speeds. Infiltration was formulated using Bernoulli’s equation and passive ventilation was derived empirically. The new model describes ultrafine particle (UFP) and carbon dioxide (CO₂) transport for a wide range of driving speed under any ventilation conditions. Unlike statistical models, this mathematical model can also provide vehicle-specific and transport mechanism-specific information. The model predictions were in a good agreement with data collected from 10 different vehicle models with an average discrepancy of less than 16% for UFPs and less than 3% for CO₂. Under outdoor air (OA) mode, when the fan is off, the model simulation showed that the infiltration and passive ventilation can substantially increase the UFP I/O (in-cabin/on-road concentrations) ratio from 0.15 at 0 km/h to 0.57 at 130 km/h. At medium fan setting, mechanical ventilation dominates and UFP I/O stays at 0.58 regardless of driving speed. Under recirculation (RC) mode, infiltration increases and the RC-mode filtration only removed 44% and 69% of the infiltrated particles at the lowest and medium fan settings, respectively. Model simulations under OA mode show that infiltration starts to occur above 115 km/h with the lowest fan setting; whereas, medium and higher fan settings prevent infiltration up to 145 km/h.     

Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses

The variance in fuel consumption caused by driving style (DS) difference exceeds 10% and reaches a maximum of 20% under different road conditions, even for experienced bus drivers. To study the influence of DS on fuel consumption, a method for summarizing DS characteristic parameters on the basis of vehicle-engine combined model is proposed. With this method, the author proposes 26 DS characteristic parameters related to fuel consumption in the accelerating, normal running, and decelerating processes of vehicles. The influence of DS characteristic parameters on fuel consumption under different road conditions and vehicle masses is quantitatively analyzed on the basis of real driving data over 100,000 km. Analysis results show that the influence of DS characteristic parameters on fuel consumption changes with road condition and vehicle mass, with road condition serving a more important function. However, the DS characteristics in the accelerating process of vehicles are decisive for fuel consumption under different conditions. This study also calculates the minimum sample size necessary for analyzing the effect of DS characteristics on fuel consumption. The statistical analysis based on the real driving data over 2500 km can determine the influence of DS on fuel consumption under a given power-train configuration and road condition. The analysis results can be employed to evaluate the fuel consumption of drivers, as well as to guide the design of Driver Advisory System for Eco-driving directly.     

Sensitive analysis of emission rates in MOVES for developing site-specific emission database

With the rapid social and economic development, vehicle emissions have been identified as a major source of urban air pollutions in China. Therefore, the study on vehicle emission estimations is necessary. In light of the limitation of emission data for establishing a local comprehensive emission database, this paper analyzes the characteristics of emission rates in MOVES released by U.S. Environmental Protection Agency (EPA) to investigate the feasibility and to design a method for developing site-specific emission database. Sensitive analysis for model year, age group and speed interval is performed, and the regularity is concluded. The result shows that emission rates are almost constant with model year, but increase with age groups because of emission deterioration. With respect to speed intervals, the emission rates in 25–50 mph are higher than both those <25 mph and >50 mph in the same VSP (vehicle specific power) bins. In addition, an analysis of local emission data in Beijing is conducted to build the relationship between vehicle activities and emission rates. After that, a comparison of emission rates by VSP bins in MOVES and in local database is conducted in this study. The result shows that the absolute values are different because of regulatory emission standards, fuel quality, and other factors. However, the relative changes described by normalizing emission rates have a good consistency. Hence, the regularities of emission rates by VSP bins in MOVES can be used to develop local emission database in Beijing.     

Fueling for contingencies: The hidden cost of unpredictability in the air transportation system

The aviation community is increasing its attention on the concept of predictability when conducting aviation service quality assessments. Reduced fuel consumption and the related cost is one of the various benefits that could be achieved through improved flight predictability. A lack of predictability may cause airline dispatchers to load more fuel onto aircraft before they depart; the flights would then in turn consume extra fuel just to carry excess fuel loaded. In this study, we employ a large dataset with flight-level fuel loading and consumption information from a major US airline. With these data, we estimate the relationship between the amount of loaded fuel and flight predictability performance using a statistical model. The impact of loaded fuel is translated into fuel consumption and, ultimately, fuel cost and environmental impact for US domestic operations. We find that a one-minute increase in the standard deviation of airborne time leads to a 0.88 min increase in loaded contingency fuel and 1.66 min in loaded contingency and alternate fuel. If there were no unpredictability in the aviation system, captured in our model by eliminating standard deviation in flight time, the reduction in the loaded fuel would between 6.12 and 11.28 min per flight. Given a range of fuel prices, this ultimately would translate into cost savings for US domestic airlines on the order of $120–$452 million per year.     

Thermodynamic,environmental and economic effects of diesel and biodiesel fuels on exhaust emissions and nano-particles of a diesel engine

In this study, diesel (JIS#2) and various biodiesel fuels (BDF20, BDF50, BDF100) are used to operate the diesel engine at 100 Nm, 200 Nm and full load; while the engine speed is 1800 rpm. The system is experimentally studied, and the energy, exergy, sustainability, thermoeconomic and exergoeconomic analyses are performed to the system. The Engine Exhaust Particle Sizer is used to measure the size distribution of engine exhaust particle emissions. Also, the data of the exhaust emissions, soot, particle numbers, fuel consumptions, etc. are measured. It is found that (i) most of the exhaust emissions (except NO_x) are directly proportional to the engine load, (ii) maximum CO₂ and NO_x emissions rates are generally determined for the BDF100 biodiesel fuel; while the minimum ones are calculated for the JIS#2 diesel fuel. On the other hand, the maximum CO and HC emissions rates are generally computed for the JIS#2 diesel fuel; while the minimum ones are found for the BDF100 biodiesel fuel, (iii) fuel consumptions from maximum to minimum are BDF100 > BDF50 > BDF20 > JIS#2 at all of the engine loads, (iv) particle concentration of the JIS#2 diesel fuel is higher than the biodiesel fuels, (v) soot concentrations of the JIS#2, BDF20 and BDF50 fuels are directly proportional to the engine load; while the BDF100 is inversely proportional, (vi) system has better energy and exergy efficiency when the engine is operated with the biodiesel fuels (vii) sustainability of the fuels are BDF100 > BDF50 > BDF20 > JIS#2, (viii) thermoeconomic and exergoeconomic parameters rates from maximum to minimum are JIS#2 > BDF20 > BDF50 > BDF100.     

A new offline optimization approach for designing a fuel cell hybrid bus

In this study a hydrogen powered fuel cell hybrid bus is optimized in terms of the powertrain components and in terms of the energy management strategy. Firstly the vehicle is optimized aiming to minimize the cost of its powertrain components, in an official driving cycle. The optimization variables in powertrain component design are different models and sizes of fuel cells, of electric motors and controllers, and batteries. After the component design, an energy management strategy (EMS) optimization is performed in the official driving cycle and in two real measured driving cycles, aiming to minimize the fuel consumption. The EMS optimization is based on the control of the battery’s state-of-charge. The real driving cycles are representative of bus driving in urban routes within Lisbon and Oporto Portuguese cities. A real-coded genetic algorithm is developed to perform the optimization, and linked with the vehicle simulation software ADVISOR. The trade-off between cost increase and fuel consumption reduction is discussed in the lifetime of the designed bus and compared to a conventional diesel bus. Although the cost of the optimized hybrid powertrain (62,230 €) achieves 9 times the cost of a conventional diesel bus, the improved efficiency of such powertrain achieved 36% and 34% of lower energy consumption for the real driving cycles, OportoDC and LisbonDC, which can originate savings of around 0.43 €/km and 0.37 €/km respectively. The optimization methodology presented in this work, aside being an offline method, demonstrated great improvements in performance and energy consumption in real driving cycles, and can be a great advantage in the design of a hybrid vehicle.     

Vehicle attribute trade-offs to meet the 2025 CAFE fuel economy target

The literature analyzes changes in vehicle attributes that can improve fuel economy to meet Corporate Average Fuel Economy (CAFE) standards. However, these analyses exclude either vehicle price, size, acceleration or technology advancement. A more comprehensive examination of the trade-offs among these attributes is needed, this case study focuses on technically feasible modifications to a reference 2012 vehicle to meet the 2025 fuel economy target. Scenarios developed to examine uncertainty in technology advancement indicate that expected technology cost reductions over time will be insufficient to offset the costs of additional fuel efficiency technologies that could be used to meet the 2025 fuel economy target while maintaining other vehicle attributes. The mid-price scenario results show the targeted 66% increase in fuel economy from 2012 to 2025 can be achieved with (i) a 10% ($2070) vehicle price increase (lightweight hybrid electric vehicle), (ii) a 31% (2.9 second) increase in the 0–97 km/h (60 mph) acceleration time (smaller engine), or (iii) a 17% (700 L) decrease in interior volume (smaller body) while maintaining other vehicle attributes. These results are consistent with those obtained using methods that generalize the US light-duty vehicle fleet, but are not a forecast of future vehicle attributes because combinations of less perceptible changes to vehicle price, acceleration and size would also be feasible. This study shows there are numerous ways that 2025 fuel economy targets can be met; therefore, the trade-offs quantified provide important insights on the implications of future CAFE standards.     

Environmental benefits from improving transportation efficiency in wood procurement systems

Forest operations use fossil fuels, which should be considered when environmental impact in the wood procurement is of concern. Road freight transportation is the most common operation in timber transportation, and thus is an important source of greenhouse gas emissions. This study assesses the impact of the new larger and heavier vehicles (LHV) on environmental emissions using the synchronized calculation method. The maximum (theoretical) and operational effects of 76 t LHV with calculations made for three weight limits (60, 64 and 68 t) are compared in Finland. Based on Enterprise Resource Planning (ERP) data, environmental energy efficiency (measured in relation to the trip) increased 9.2%. The reduction in fuel consumption was 12.5%, though this is likely to under-estimate the long-term effects that will be achieved when forest operations are fully adjusted to the maximum weight limit. A comparison with the European countries and a preliminary sensitivity analysis of the system demonstrate that the technological development to improve the transporting efficiency is essential for realizing 76 t LHV utilization in Finland.     

The impact of road grade on carbon dioxide (CO2) emission of a passenger vehicle in real-world driving

To accurately estimate real-world vehicle emission at 1 Hz the road grade for each second of data must be quantified. Failure to incorporate road grade can result in over or underestimation of a vehicle’s power output and hence cause inaccuracy in the instantaneous emission estimate. This study proposes a simple LiDAR (Light Detection And Ranging) – GIS (Geographic Information System) road grade estimation methodology, using GIS software to interpolate the elevation for each second of data from a Digital Terrain Map (DTM). On-road carbon dioxide (CO₂) emissions from a passenger car were recorded by Portable Emission Measurement System (PEMS) over 48 test laps through an urban-traffic network. The test lap was divided into 8 sections for micro-scale analysis. The PHEM instantaneous emission model (Hausberger, 2003) was employed to estimate the total CO₂ emission through each lap and section. The addition of the LiDAR-GIS road grade to the PHEM modelling improved the accuracy of the CO₂ emission predictions. The average PHEM estimate (with road grade) of the PEMS measured section total CO₂ emission (n = 288) was 93%, with 90% of the PHEM estimates between 80% and 110% of the PEMS recorded value. The research suggests that instantaneous emission modelling with LiDAR-GIS calculated road grade is a viable method for generating accurate real-world micro-scale CO₂ emission estimates. The sensitivity of the CO₂ emission predictions to road grade was also tested by lessening and exaggerating the gradient profiles, and demonstrates that assuming a flat profile could cause considerable error in real-world CO₂ emission estimation.     

Effects of e-bikes on bicycle use and mode share

In Norway, as in many countries, a political goal is to increase bicycle use, and the e-bike is promising in this respect. However, concerns have been raised about mode-share effects. It has been argued that if the e-bike’s only function is in cycling becoming cycling with electric assistance, there would be no benefit to either the environment or public health. Little is yet known about the use of the e-bike, or of its potential in reducing motorized travel. In the current study, 66 randomly selected participants were given an e-bike to use for a limited period of time and the results compared with those of a control group (N = 160). E-bike cycling trips increased from 0.9 to 1.4 per day, distance from 4.8 km to 10.3 km and, as a share of all transport, from 28% to 48%, whereas with the control group there was no increase in cycling. The effect of the e-bike increased with time, indicating a learning effect among users, and was greater for female than for male cyclists. There were no differences with age. Overall, the results suggest that the e-bike is indeed practical for everyday travel.     

Capitalization of BRT network expansions effects into prices of non-expansion areas

A before and after hedonic model is used to determine the property value impacts on properties already served by the transit system caused by extensions to Bogotá’s bus rapid transit system. Asking prices of residential properties belonging to an intervention area (N = 1407 before, 1570 after) or a control area (N = 267 before, 732 after) and offered for sale between 2001 and 2006 are used to determine capitalization of the enhanced regional access provided by the extension. Properties offered during the year the extension was inaugurated and in subsequent years have asking prices that are between 13% and 14% higher than prices for properties in the control area, after adjusting for structural, neighborhood and regional accessibility characteristics of each property. Furthermore, the appreciation is similar for properties within 500 m and properties between 500 m and 1 km of the BRT.