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This paper reports new insights into how the selection of route based characteristic parameters can influence the predicted energy consumption for next generation battery electric buses. 24 characteristic parameters have been studied to understand their relative importance on vehicle energy consumption and to develop new data-driven prediction models. The parameters are grouped into two scenarios, representative of the varying levels of route information available to a typical bus operator. A combination of feature selection methods was used to determine which characteristic parameters had the greatest influence on energy consumption. Regression based prediction models were developed, and models were then validated using standard and real vehicle drive cycles. The prediction models had a mean absolute percentage difference of 2.10–10.67%. This paper presents a novel methodology to estimate energy consumption of operating zero emission vehicles, which will support public transport operators, policy makers and energy suppliers in the decarbonisation of public transport. [ABSTRACT FROM AUTHOR]

Urban transportation is a significant contributor to energy consumption and environmental degradation, while solar electric bikes (SE-bikes) represent a viable solution for alleviating both problems. In this paper, the charging and discharging properties of the SE-bike are investigated. An SE-bike is constructed for short and long term experiments. Experimental results indicate that the SE-bike can increase endurance by 20.5 km on a sunny day, and can meet daily cycling needs of 10 km without charging in both summer and autumn in Hefei. To assess the annual performance of the SE-bike, a theoretical model is developed, which includes the effects of environmental factors, vehicle shapes, daily electricity usage, etc. Simulation results reveal that the SE-bike only needs to be charged 5 times per year to meet daily demands in Hefei, which is a relative reduction of 93.0 % compared to 71 times per year for the regular bike without solar power supply. [ABSTRACT FROM AUTHOR]

• Application of solar energy according to land use/land cover map. • Electrification of transportation sectors based on local renewable energies. • Comparison of conversion and storage efficiencies of different energy forms in transportation. • Reducing transportation and urban environmental impacts. • Multi-criteria decision-making of sustainable transportation based on sustainable development pillars. The main aim of this paper is the decarbonization of the transportation sector in Tabriz City regarding sustainable development aims and beyond-2-degrees-scenario by focusing on electric vehicles, as well as on appropriate infrastructures. Quantitative and simulation research methods are used to analysis of total energy, exergy, and losses analysis according to different resources and vehicles. Next, the solar potential of the city is simulated using the Grasshopper plug-in-Rhino simulator and PVsyst software. A LULC analysis has been done using ArcMap 10.8. With renewable electrification, energy consumption will plunge from 15.5 TWh input-energy content of different carbon-based resources to 3.7 TWh with 0.17 MtonCO 2 /year instead of 2.13 MtonCO 2 /year emissions. In this paper, the overall system efficiency of electric vehicles is around 55% which is significantly higher than internal combustion engines. Fully electrification of the transportation systems based on renewable resources mitigates CO 2 emissions of internal combustion engines 15 times, besides reducing the emissionsa variety of harmful pollutants. In terms of multi-criteria decision-making, the technique for order of preference by similarity to the ideal solution was used to rank 50 popular vehicles according to interconnected sustainable development criteria. This paper highlights that conventional internal combustion engines can be replaced by electric vehicles by applying combination of appropriate taxes, tariffs, and incentives. [ABSTRACT FROM AUTHOR]

Heavy-Duty Vehicles (HDVs) pose a significant challenge in meeting emissions reduction targets. However, studies on estimating emissions from Irish HDV fleet are rare. This paper estimates fuel consumption and CO 2 emissions from tractor-trailers that occupy nearly half of Ireland's HDV fleet using the European Commission recommended VECTO tool. To improve the estimation accuracy, driving cycles specific to Irish road conditions were developed utilising real naturalistic driving data from trucks. The Irish driving cycles significantly differ from the VECTO inbuilt driving cycles in percentage time spent in different gears, fuel consumption, and CO 2 emissions. CO 2 emissions estimated with Irish driving cycles range from 733 to 1961 g/km, while with VECTO driving cycles, the values ranged from 728 to 1865 g/km. Further, the study evaluated the sensitivity towards payload, road gradient, and idling percentages. The study insights would provide an essential evidence base for policymakers in Ireland. [Display omitted] • Developed Irish urban, regional, and freeway driving cycles. • Employed the VECTO tool to assess CO 2 emissions from Irish heavy-duty vehicle fleet. • Focused on tractor-trailers with a major share in the Irish fleet. • Evaluated the effect of idling, gradient, and payload on CO 2 emissions. • Fuel consumption is underestimated by up to 7.1% when the road gradient is ignored. [ABSTRACT FROM AUTHOR]

This paper introduces the gtfs2emis model, a bottom-up method available as an R package to estimate emissions of public transport systems. The method uses General Transit Feed Specification (GTFS) data, a standard format for public transport data widely adopted worldwide, which makes the method easily applicable to cities with limited data. The model requires a GTFS feed of a given transport system and a table with general characteristics of the vehicle fleet profile. The package can estimate over 16 pollutants and energy consumption based on emission factor models from Europe, the United States, and Brazil. It also includes functions to help users examine how emissions are distributed across space, at different times of the day, and by types of vehicles. This paper presents a reproducible example of the city of São Paulo (Brazil) to demonstrate the gtfs2emis package and to discuss the potential applications and limitations of the proposed model. [ABSTRACT FROM AUTHOR]

• Influence of driving behavior on fuel efficiency is analyzed. • Volatility used as a surrogate for identifying driving behavior. • K-means, K-medoids and hierarchical clustering used to classify driving behavior. • Powertrain drive cycle simulations used to assess fuel efficiency and emissions. • Aggressive driving observed to create 23% increase in fuel consumption. Consumption of fossil fuel-based energy for vehicle propulsion and associated emissions are a global concern. One pathway to energy reduction is to examine situations where high-energy consumption occurs on roadways, e.g., speed volatility at work zones, or on sharp curves, which has been understudied. Harnessing second-by-second data from the naturalistic driving study and using the concept of driving volatility, this paper explores driving styles in work-zones and curves using machine learning approaches (k-medoids, hierarchical clustering) and drive-cycle simulations from Autonomie®. Results show that aggressive driving account for 12.2 % and 15.4 % of events that occurred in work zones and on curves and leads to 23 % increase in fuel consumption as opposed to normal driving. These results have implications for transportation agencies to improve work-zone configurations and provide signage or technology on curves to reduce fuel consumption and emissions. Moreover, automated and connected vehicles can smooth out traffic flow with advanced advisories and warnings. [ABSTRACT FROM AUTHOR]

With the increasing popularity of electric buses (EBs), accurate estimation of the trip-level energy consumption of EBs has become increasingly essential. In this paper, an effective energy consumption estimation approach was proposed based on real-world operation data of EBs. Operation data of EBs from 3 different bus routes were collected and pre-processed to extract energy consumption-related features from various aspects such as traffic condition, environment, vehicle status and driving behaviour. The analyses of feature distribution, feature interaction and feature importance were then carried out. And the contributions of features to energy consumption were thoroughly analysed by Shapley value. Finally, different machine learning models were built and compared. The optimal results achieved an MAPE of 4.404% under 10-fold cross-validation, with an improvement of at least 29% over existing studies on the subject. [ABSTRACT FROM AUTHOR]

Understanding the driving behavior of multi-car households is crucial to evaluate the effectiveness of policies aimed at reducing fuel consumption in the passenger car fleet. In this paper, we investigate whether households adjust their vehicle miles traveled (VMT) among their cars in response to changes in fuel prices. We use data from the 2017 National Household Travel Survey (NHTS) on two-car households, where both cars are powered by gasoline, and estimate econometric models that distinguish cars based on their fuel economy. Our results show that an increase in per-mile driving cost significantly reduces the demand for driving the same car and increases demand for driving the other car, with little to no evidence of overall substitution at the daily or annual level. However, further investigation using artificial monthly travel diary data revealed a pronounced substitution towards the most fuel-efficient car only when there is a large and sustained increase in gas price. [ABSTRACT FROM AUTHOR]

This paper inspects the environmental Kuznets curve (EKC) in various transport sectors in 13 European Union (EU) countries. The study uses the augmented mean group estimator (AMG) estimator to examine the relationships between carbon (CO 2) emissions, income, and energy consumption of four transport modes, such as road, maritime, rail, and air, between 1995 and 2019. The outcomes illustrate that the EKC hypothesis is valid for six countries in air transport, four in maritime transport, three in rail transport, and six in road transport. In addition, the findings indicate that fossil fuels significantly increase CO 2 emissions from transportation modes. In light of the study's findings, EU countries should improve energy efficiency in the transportation sector to become carbon neutral by 2050. In order to ensure an environmental balance between transport modes, policymakers should support innovative activities to increase renewable fuel consumption. [ABSTRACT FROM AUTHOR]

Energy consumption management is a referential approach to alleviate range anxiety. In this paper, the data-driven modeling and analysis of energy consumption based on low-frequency real-world electric bus data is implemented. Specifically, two quantitative metrics of energy consumption are identified and calculated regarding to the characteristics of low-frequency real-world data. Also, the Road-Driver-Vehicle-Ambient factors system is proposed, forming a comprehensive digital description of the driving process. Supervised learning models with different levels of complexity are then established to predict the energy consumption, with the mean absolute percentage error of 9.2%, 7.5% and 7.7% respectively. Last but not least, the relationship between each factor and energy consumption is explained through quantitative, qualitative and statistical analysis, which intuitively shows the influence of Temperature, Driver/Vehicle-related and Road-related factors on energy consumption. [ABSTRACT FROM AUTHOR]

The paper investigates the features of emissions and fuel consumption (EFC) in a car-following (CF) platoon based on two experimental datasets. Four classical EFC models are employed and a universal concave growth pattern of the EFC along a platoon has been demonstrated. A general framework of coupling EFC and CF models is tested by calibrating and simulating three classical CF models. This work first demonstrates that, at vehicle-pair level, all models perform well on EFC prediction. The intelligent driver model outperforms the other CF models on calibration accuracy, but this is not true on EFC prediction. Second, at platoon level, the predicted EFC is nearly constant along the platoon which qualitatively differs from the experimental observation. The investigation highlights that accurate estimations at vehicle level may be insufficient for analysis at platoon level due to the significant role of oscillation growth and evolution in EFC estimation. [ABSTRACT FROM AUTHOR]

Benchmarking the energy efficiency of ships is not a straightforward task, mainly due to the diversity of operations. Although driving cycles have been used for decades in evaluating the performance of road vehicles, these do not exist in formal policy-making for maritime transport. This work builds on a previously proposed methodology. It uses noon reports of 327 vessels for 2019 to construct operational cycles for seven size classes of container ships using the main engine power as the main parameter. Concerning the main engine emissions, the resulting cycles reduce variation in the carbon intensity indicator values by more than 30% while maintaining an average accuracy of 97.7% in absolute emissions. These figures show that the concept can improve operational carbon intensity indicators in terms of robustness and their technical counterparts in optimizing ship design. The paper also proposes further work required for benchmarking applications in policy-making. [ABSTRACT FROM AUTHOR]

Policy-makers are investigating ways to reduce greenhouse gas emissions of the transportation sector. Many government agencies have studied the effects of pavement type and characteristics on fuel consumption. Such models, valid for a particular set of simulation conditions, suggest that rigid pavements perform better than flexible pavements. However, it is unclear how this type of model can be applied at the scale of a realistic road network. This paper proposes a statistical approach to assess the relative importance of pavement type and characteristics on fuel consumption of heavy vehicle traffic by considering the specificities of meteorological conditions, heavy vehicles, and driver behaviours. A case study in Canada showed that the potential advantage of using rigid pavements was offset by cold climate effects and the consideration of a realistic statistical payload distribution. Road roughness accounted on average for 1.1% of total fuel consumption, with an increased value of 1.9% in January. [ABSTRACT FROM AUTHOR]

[Display omitted] • Conducted life-cycle assessment of solar pavement. • PV system is the largest contributor to the environmental impacts of solar pavement. • Without considering power generation benefits, solar pavement has high emissions. • In long-term, the development of solar pavement is environmentally beneficial. The sustainable and green development of roads is lately receiving significant attention. As a multi-functional road capable of both accommodating traffic loads and generating clean energy, solar pavements exhibit a strategic opportunity for the future path of development in the road engineering. Although solar pavements have significant environmental benefits during their operation, they can lead to increased carbon emissions during the process of material preparation, construction, maintenance, etc. Therefore, it is essential to implement the framework of Life Cycle Assessment (LCA) to investigate their potential environmental impacts and/or benefits. Hence, this paper systematically analyzes the energy consumption and emissions of solar pavements based on LCA. The results show that the total energy consumption and gas emissions of the solar pavement are far greater than those of the traditional asphalt pavement. Nevertheless, in long-term the power generation function of the solar pavement has significant energy and environmental benefits. [ABSTRACT FROM AUTHOR]

This paper explores a cost-efficient resource assignment for urban battery-electric-buses (BEBs) considering decision robustness based on the optimized charging plan. A network flow model was proposed to minimize the operation cost with mixed fleet, nonlinear charging and the constraint of charging station capacity. A sensitivity analysis was implemented using samples generated from clustered real-world data to simulate traffic-dependent bus travel time and energy consumption. The result shows that charging at night or with short duration in the daytime significantly increases the operation cost. Sufficient charging resources improve the robustness of the charging plan, while economic benefits merely increase when the charging station capacity reaches a certain threshold. Bus operators can reduce the investment in chargers to ensure both economic benefits and operation stability. When bus routes are longer with higher energy consumption, buses with medium or high battery capacity become more demanding, while the requirement on charging resources remains stable. • An electric bus scheduling method considering resources limitation is proposed. • Charging strategy, resource allocation and fleet composition are optimized. • Trip consumption uncertainty is simulated by random samples from real-world data. • Buses with small or medium battery sizes can serve the network optimally. • Economic benefits do not increase when charging resources are sufficient. [ABSTRACT FROM AUTHOR]

Paratransit, in particular the minibus taxi, is the mainstay of public transport in sub-Saharan Africa. These vehicles are often second-hand, ageing, fuel inefficient, and expensive to operate - issues that electrification can ameliorate. However, modeling and planning large-scale transitions to electric paratransit require reliable estimates of vehicle energy consumption. This paper provides such estimates by applying a vehicle kinetic model to per-second GPS data gathered on minibus taxis. Data include 62 trips across three routes with different driving conditions near Stellenbosch, South Africa. We find a range of energy consumption from 0.29 to 0.51 kWh/km (mean = 0.39 kWh/km). Past estimates in literature relied on per-minute GPS data, which we show leads to inaccurate energy consumption estimates. We recommend new kWh/km values for modeling vehicle operations and grid impact, and discuss how future work can utilize our analysis to advance the transition to electric paratransit sub-Saharan Africa. [Display omitted] • Highest fidelity kWh/km estimates for paratransit in sub-Saharan Africa to date. • Urban, hilly, and inter-city driving conditions are investigated. • 0.29–0.51 kWh/km (mean = 0.39 kWh/km), depending on driving condition. • Specific kWh/km recommendations for usage in operations planning and grid impact. • Per-minute data fails to capture effect of micro-mobility patterns on kWh/km. [ABSTRACT FROM AUTHOR]

This paper investigates how carriers adapt to the sulphur regulation by choosing between sulphur scrubber (SS) and fuel switching (FS). A cost minimization model is proposed to determine the optimal compliance choices, considering factors such as fuel prices, capital cost difference between SS and FS, and shipping service profile. We find that the optimal choice depends on the tradeoff between the capital cost advantage of FS and the fuel cost advantage of SS. And such a tradeoff is moderated by the vessel's annual fuel consumption. Ignoring the speed differentiation behavior of FS vessels would exaggerate the benefits of SS. But whether it can cause a wrong compliance choice depends on the extent of overestimation, which depends on the presence in emission control areas and fuel prices. When fuel price volatility is factored in, a portfolio of FS and SS might be better than simply choosing either one for all vessels. [ABSTRACT FROM AUTHOR]

• A BEB energy consumption model based on characteristic data is developed. • A simple method to extract characteristic data from unordered data is proposed. • A BEB charging plan schedule optimization model is developed. • The energy estimation error is analyzed in the BEB charging schedule model. With the increasing of Battery Electricity Bus (BEB), the orderly charging of buses becomes more and more important. This paper formulates a battery electric bus energy consumption model based on characteristic data, and proposes a simple method to extract the characteristic data from the unordered data. A simple and practical BEB charging plan schedule optimization model based on BEB fleet's departure plan timetable and trip electricity consumption estimation is developed. Given some realistic assumptions, the schedule model is formulated as a mixed integer linear model. The objective function can accurately calculate the charging cost in a linear manner. Only two kinds of constraints are included in the model, they can ensure each BEB journey is feasible. The real-world numerical tests show that: in the simulation cases, the average relative error of the energy consumption model is 7.5–9.2% and the optimized charging plan could reduce the charging cost by about 16%. [ABSTRACT FROM AUTHOR]

We address the situation where required times of arrival (RTAs) are assigned outside the feasible time window that allows aircraft to fly energy-neutral continuous descent operations (CDOs). Two strategies to achieve these RTAs are compared by means of simulated optimal trajectories and in terms of fuel consumption: powered descents along the published arrival route vs. energy-neutral CDOs along re-negotiated routes. A sensitivity study to different parameters led to results primarily for RTAs later than the estimated time of arrival. With strong-tailwind, stretching the route and flying neutral CDOs improves the efficiency of the operation. Light aircraft also benefit from being assigned longer pre-defined routes, while for heavy aircraft it is more efficient to fly powered descents along the published route. This paper could contribute to enhance ground-based decision support tools for air traffic controllers, aiming to manage arrival traffic more efficiently and reducing the overall environmental impact of arrival operations. [ABSTRACT FROM AUTHOR]

The available truck Global Positioning System (GPS) data, typically collected with large time gaps, rely on imputation techniques to obtain second-by-second data that are required in models for estimating truck energy consumption and emissions. However, existing speed imputation techniques either require a large amount of high-resolution data for model training or rely on special movement assumptions. To fill the gap and effectively apply the low-resolution truck GPS datasets, this paper proposes a simple imputation technique that adopts the Brownian bridge structure to impute missing speed data. The proposed technique introduces a feasible imputation region and a combined drift into the imputation procedure to capture vehicle acceleration constraint, travel distance constraint, and speed volatility. The calibrated model is applied to a set of low-resolution truck GPS data. The results demonstrate the robustness of the proposed technique in enhancing estimation accuracy when using low-resolution GPS data to estimate fuel consumption and emissions. [ABSTRACT FROM AUTHOR]

This paper describes the results of an experimental campaign carried out on three urban buses fuelled by natural gas, aimed at measuring pollutant emissions and fuel consumption in real driving conditions. The buses, used for public transport in three Italian cities, were equipped with a Portable Emission Measurement System. Emission tests were performed over the routes on which the buses operate in their service, and cold start emissions were also investigated. The results showed that the differences in CO and NOx emissions can be attributed to the calibration of the fuel supply system. PM emissions were low and similar to those reported in the literature for other CNG buses, while CO 2 and fuel consumption were affected by the driving cycle. Cold start strongly impacted CO and NOx emissions because of the effect of temperature on catalytic converter efficiency, with cold over-emissions strongly dependent on ambient and coolant temperatures at engine start. [ABSTRACT FROM AUTHOR]

It is important to understand real-world performance of fuel-saving technologies for heavy goods vehicles (HGVs). This paper concerns applying data mining to quantify the fuel-saving benefit of a low-rolling-resistance (LRR) tyre using in-service operational data. Two HGVs of the same specification were employed, with one using LRR tyres and the other conventional tyres. A smartphone-based data logger was developed to collect data from the HGVs' operations on publics roads for three months. Data from vehicle Controller Area Network, smartphone sensors, weather and elevation databases were collected. A data mining methodology was developed to mine data segments representing dynamically similar driving conditions between the two HGVs, and compare their fuel consumption rates through fitting data to a theoretical vehicle fuel consumption model. It was found that at a 95% confidence level the LRR tyres exclusively brought about a fuel-saving benefit between 6.89% and 8.37% in typical UK motorway driving conditions. [ABSTRACT FROM AUTHOR]

The EU 'Fit for 55' package proposes the adoption of various legislative tools to eliminate shipping CO 2 emissions, among which are the inclusion of shipping in the EU Emissions Trading System (EU ETS) and the FuelEU Maritime Initiative. This paper analyses the latter by utilising the EU's Monitoring, Reporting, Verification (MRV) database to evaluate the fuel types used, fuel consumption and CO 2 emissions of ships operating within the European Economic Area (EEA) for 2020, the Initiative's baseline. Forecasts of potential future pathways for alternative fuel deployment are then developed that could be used to achieve compliance with the limits set by the FuelEU Maritime Initiative. The results of this study provide significant input to maritime stakeholders and policy-makers on the potential impact of the Initiative for the uptake of clean marine fuels and the contribution of the maritime sector to a climate neutral Europe by 2050. [ABSTRACT FROM AUTHOR]

Emissions from aviation contribute increasingly to both air pollution and climate change. Understanding how these emissions are distributed across regions, seasons, and different altitudes is important to fully ascertain the climate impacts caused by our current aviation sector and set a clear benchmark for our mitigation efforts. The Aviation Transport Emission Assessment Model (AviTEAM) is developed for this purpose. The model estimates various emission species from aviation activities based on flight trajectories, airplane, engine types, and atmospheric conditions. In this paper, we present the AviTEAM framework and apply it to study the emissions from commercial aviation in Norway. We find emissions associated with Norwegian air transport for 2019 of 4.5 Mt CO 2 , 26.2 ktNO x , 3.26 ktHC, and 1.8 ktCO. Furthermore, we exemplify how Automatic Dependent Surveillance-Broadcast data in combination with flight dynamic and emission models can be used to generate high-resolution geospatial emission datasets for research and national reporting purposes. • Provides an updated aviation emission inventory for Norway. • Uses Automatic Dependent Surveillance-Broadcast data for activity modeling. • Assesses each flight with aircraft type and engine-specific modeling. • Shows the distribution of fuel consumption and various emission species. • Concludes that the approach informs the scientific and other audiences. [ABSTRACT FROM AUTHOR]

[Display omitted] A fuel cut-off injection strategy can be employed during deceleration to increase fuel efficiency, but NOx is immediately emitted after fuel injection recovery. In this paper, the NOx emissions under various driving cycles are investigated. The NOx mass emissions from gasoline vehicles with a fuel cut-off strategy measured are much higher than those without a cut-off. More comprehensive and aggressive tests were carried out to analyse NOx emissions. The total NOx emissions can be classified as associated with warming up, fuel cut-off and other. The proportion of fuel cut-off-caused NOx mass emissions was the highest (61.2%) in real driving emissions 1 (RDE1) owing to its aggressive driving and frequent pedal switching. In comparison, for Worldwide harmonized Light duty vehicle Test Cycle (WLTC) and Random Test Standardized aggressive (RTS-95), warming-up induced NOx dominates, followed by fuel cut-off and other, while for both RDE cycles, fuel cut-off is the primary factor. [ABSTRACT FROM AUTHOR]

This paper examines the impact of geographical location on the energy consumption of double deck battery electric buses operating in four UK cities. By considering seasonal variation in ambient temperature and an assumed battery degradation process, the implications on operational cost and carbon emissions were investigated. Using a MATLAB/Simulink model the total energy demand of a vehicle operating on the UKBC was simulated. Energy consumption was combined with hourly seasonal averages of carbon intensity (gCO 2 /kWh) and wholesale electricity costs (£) to assess charging at different times of the day and year. At the beginning of life, the range could decrease by up to 17.3% due to heating load requirements. From beginning of life to end of life, range was predicted to decrease by up to 26%. Whilst the average operational costs are relatively consistent across the four regions, the average carbon emissions can be five times higher depending on location. [ABSTRACT FROM AUTHOR]

[Display omitted] This paper assessed the impact of five different WMA additives categorized under organic, chemical and foaming-based technologies on the energy requirement and green house gas (GHG) emissions. The maximum bearable cost, corresponding to different additives, was determined to assess the optimal choice of warm mix technologies. Results demonstrated that the addition of WMA additives lowers the heat energy by 3–12% compared to HMA. Although diesel is the most expensive fuel (considered in this study), its use can offset the cost of additives, for some WMA additives, namely: SR0.7, C0.2, C0.35, and R0.4. Calculations indicated that among the various warm mix additives, Sasobit Redux produces least emissions, followed by Aspha-min, Rediset, Sasobit, Cecabase, and viscosity graded binder. It was concluded that the use of WMA technologies can provide possible cost savings, and reduction in GHG emissions. [ABSTRACT FROM AUTHOR]

New technological advances have made possible the recent development of automated and connected vehicles. However, there are still challenges to overcome. For example, maintaining constant values for speed and distance between vehicles when the connection has been interrupted. In this line of research, this paper presents the extension of the 3DCoAutosim simulation platform to integrate platooning by means of a dynamic-flexible model that we validate by creating use cases on traffic efficiency. We measured the effects of dynamic-flexible and consistent platooning on battery energy consumption taking into account real-world trips data, collected in Upper Austria. For this purpose we used three electric vans with an autonomous leader, a first semi-autonomous follower with driver and a last autonomous follower. The results showed that the proposed model is able to demonstrate a decrease of the total battery energy consumption when driving in platoon mode. • Connected semi-autonomous vehicles in platoon decrease battery energy consumption. • Dynamic-flexible platooning spontaneously reforms the platoon after a fragment. • Adaptive algorithms with the surrounding traffic increase the efficiency of transport. • Validated metamodel-based simulations represent real world systems. [ABSTRACT FROM AUTHOR]

This paper develops an infrastructure-enabled eco-approach specifically for transit systems, where a two-stage simulation approach is proposed to include the impact of bus stops, and can further quantify the effects of infrastructure-enabled eco-approach on transit operating and service. The developed system is then embedded into a well-developed simulation environment, i.e., Simulation of Urban Mobility (SUMO), and a series of real-world-based cases are designed to compare the proposed system with traditional method and non-control case at segment-, line-, and network-level service respectively. Results reveal that the proposed method outperforms the traditional eco approach and can significantly improve the energy efficiency and reduce CO 2 emission by up to 54.33%, where the environmental benefits are more clearly seen when more transit services are involved. In particular, it has the capability of marginalizing the speed fluctuations around the bus stop. [ABSTRACT FROM AUTHOR]

Road grade is crucial in vehicle control and emission studies but challenging to obtain in large-scale road networks due to current methods' expensive deployment costs or limited accuracy. This paper proposed a scale-deployable and cost-efficient road grade estimation solution based on the fuel consumption rate (FCR) difference between flat and graded roads. Real-world road grades from design drawings and 261,814 second-by-second vehicle operating data from 680 light-duty vehicles were collected to examine the proposed method's performance. Sensitivity tests for vehicle types and sample sizes were conducted. Results show that (1) the proposed method acquired road grade with an accuracy of 0.12% mean absolute error (MAE), (2) in positive vehicle specific power (VSP) bins, a 1% road grade caused an average 16% FCR change, and (3) larger-scale fuel consumption data contributed to reducing estimation error which converged from 0.25% to 0.12% as the segment passes increased from 50 to 400. [ABSTRACT FROM AUTHOR]

Increasing commercial vehicles are equipped with automated driving features. Adaptive cruise control, a critical longitudinal control system of commercial automated vehicles (AVs), may have significant impacts on fuel consumption. To investigate the impacts, this paper collected high-resolution trajectory data of commercial AVs with different operating scenarios, speed ranges, and headway settings on the highway system. The AVs' fuel consumption was calculated by several state-of-the-art or classical vehicle fuel consumption models. From empirical analyses, we found that as the AV headway setting increases, the corresponding fuel consumption decreases. Also, we found that as the speed of AV traffic increases, the impacts of AV headway settings on fuel consumption decrease. Moreover, we compared the fuel consumption of AVs and human-driven vehicles (HVs). We found that for the same settings, the AVs always require less fuel consumption than the HVs. Following these findings, a set of managerial insights were provided into relevant stakeholders for future AV traffic. [ABSTRACT FROM AUTHOR]

• We identify the relationship between bike sharing and public transit (PT). • We estimate the environmental impacts of bikeshare as it also substitutes PT. • 27.9% of bikeshare trips substitute buses and 8.1% substitute subways. • Bikeshare has significant environmental benefits by replacing other modes. • Environmental benefits are overestimated if not considering the substitution for PT. Bike sharing has the potential to benefit the environment with less energy consumption and CO 2 emissions. Most previous studies on this topic assumed that bikeshare trips mainly substitute cars. However, bikeshare also replaces ecologically friendly transportation modes like public transit (PT). Therefore, it is vital to examine bikeshare's modal shift from PT to fully assess its environmental impacts. This paper examines the bikeshare and PT relationships in Chengdu,and environmental effects of bike sharing under two cases based on this relationship. The first case only considers the substitution of bikeshare for car and walking, and 19,964.31 L of fuel and 4125.13 kg of CO 2 can be saved; while the second case also considers its substitution for buses and subways, and 13,198.68 L of fuel and 2564.31 kg of CO 2 can be saved. Results indicate that the environmental benefits of bike sharing may be overestimated if not considering its substitution for PT. [ABSTRACT FROM AUTHOR]

Transportation is a basic social need, but most trips are done by private vehicles, which is not environmentally sustainable with growing urban populations. Micromobility (e.g., shared bikes) represents a significant opportunity to replace short private vehicles trips (0–3 miles) and reduce transportation sector emissions. This paper uses Seattle as a case study and estimates that up to 18% of short car trips could be replaced by micromobility. A static traffic assignment model is developed to simulate and compare the results of peak hour traffic under a base case scenario (2014 traffic conditions) to scenarios where a portion of short car trips are substituted by micromobility. Results indicate that micromobility could reduce congestion on heavily congested corridors and wide-scale bike lane deployment can maximize congestion benefits, but the impacts to energy use and emissions are disproportionately low and other measures (e.g., vehicle electrification) are needed to meet climate change emissions targets. [ABSTRACT FROM AUTHOR]

• The design of descent profiles with 3 deg are created using real flight data. • Thrust level restriction is used while determining the parameters for CDO design. • The cruise phase is also considered while comparing designed and reference scenarios. • Holding maneuvers and procedure constraints affect the vertical flight efficiency. • Fuel and emissions savings increase as the vertical inefficiency increase. This paper aims to evaluate the vertical profile efficiency of arrival traffic at Trabzon Airport in terms of fuel consumption, carbon dioxide (CO 2), hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxide derivatives (NOx). For this purpose, 3-deg descent profiles were created using the parameters obtained from real flight data under the near idle thrust and clear configuration restrictions and these were then compared to the reference real flight profiles. The cruise section was also considered while comparing the designed continuous descent profiles to baseline scenarios. According to the results of our analyses, the altitudes at which landing traffic deviates from the optimum profile is especially around 6000ft and after the top of descent point. It was not possible to note a high level of vertical inefficiency in the vertical profile of arrival traffic, however, holding maneuvers, waypoint restrictions play an important role in overall descent time, fuel consumption, and emissions. [ABSTRACT FROM AUTHOR]

• Shared micromobility can reduce energy consumption of passenger travel by 2.6%. • Micromobility induced transit trips offer highest energy saving potential. • Micromobility energy impacts are tradeoff between energy intensity & service range. • Sensitivity Analysis shows distance threshold has strong influence on energy impact. Shared micromobility has garnered widespread popularity in recent years, but limited attention has been given to the energy impacts of trips replaced by micromobility. This paper investigates the energy bounds of shared micromobility adoption. Travel demand data at the national and city level were analyzed to identify trips that can be served through micromobility, and scenarios with varying levels of micromobility adoption were evaluated. Results show that peak adoption of shared micromobility can reduce energy consumption from reported passenger travel by 1% at the national level and 2.6% at the city level, with micromobility-induced transit trips identified as the largest contributor for energy reduction. Sensitivity analysis was carried out to show how the energy impacts would change with various levels of key micromobility-related parameters, and results show distance threshold having a stronger influence on the energy impacts, compared to redistribution energy intensity. [ABSTRACT FROM AUTHOR]

• Estimates energy usage and emissions of domestic intercity air travel in the U.S. • Analyzes emissions dynamics of intercity air travel via supply and demand factors. • Emissions per passenger have declined by nearly 40% over the last two decades. • Route frequency, fleet efficiency and deployment are leading contributing factors. Domestic intercity air travel accounts for nearly 80% of all air travel in the U.S. As low-cost airlines and economic growth have increased the affordability of intercity air travel, the benefits to travelers and cities are touted by most. Yet, an accounting of the energy consumption and emissions from domestic intercity air travel is rarely discussed. This paper fills that gap and provides an analysis of the trends in fuel consumption and emissions by intercity air travel as well as an analysis of the contributing factors to these emissions. Using flight tracking and carrier data, emissions per passenger are found to have declined by 40% over the past two decades while total emissions have increased by 10% over the past 8 years. A decomposition analysis of the market factors highlights changes in route frequency, fleet deployment, and fleet efficiency as the leading factors contributing to the decline of intercity emissions. [ABSTRACT FROM AUTHOR]

• A microscopic power-based energy consumption model of electric buses is proposed. • The model leverages on the analysis of e-Bus data from two months of operations. • The simulation of auxiliary systems energy consumption is integrated in the model. • The model was calibrated against field data, yielding an average error of 2.5%. • Model validation was conducted at both the microscopic and macroscopic levels. In this paper a microscopic energy consumption model of electric buses (e-Buses) is developed, leveraging on a vast data collection campaign of e-Bus daily operations. The model is composed of two modules, which estimate the instantaneous power required for traction and the average power required by the auxiliary systems. The model simulates the instantaneous energy consumption as a function of line and vehicle characteristics, and operating conditions including ambient temperature, bus load and encountered traffic. The model was calibrated against trajectory data from 435 trips, showing an error in reproducing the instantaneous battery state-of-charge of 5.5% in 95% of experiments (2.5% on average). A two-level model validation was performed against data from 110 trips. At a microscopic level, results proved that the calibrated model is robust against variability of line operating conditions. At a macroscopic level, the model was validated against the macroscopic speed-consumption function, showing an error of 6%. [ABSTRACT FROM AUTHOR]

• Document the approaches and methods used in the TEMPO model. • All-inclusive passenger and freight transport demand model that covers the entire U.S. • Generate scenarios of technology adoption and future energy use and emissions. • Innovative household-level representation of passenger mobility and choices. • Comparison with the EIA AEO for a business-as-usual scenario. This paper documents the approaches and methods used in the Transportation Energy & Mobility Pathway Options™ (TEMPO) model to evaluate passenger and freight demand for transportation and mobility services, project vehicle ownership and technology adoption decisions, and determine transport mode choices to derive scenarios of future energy use and emissions. TEMPO is an all-inclusive transportation demand model that covers the entire United States, with an implicit spatial resolution and an hourly temporal resolution that allows for generating time-resolved energy use profiles to assess multisectoral integration aspects. Key features of the TEMPO model include the ability to perform endogenous out-of-sample forecasting to extrapolate recent emerging trends and analyze impacts of disruptive technological breakthroughs and behavioral changes. TEMPO employs an innovative representation of passenger mobility demand stemming from household-level decisions that determine vehicle adoption, ownership, and use based on sociodemographics (e.g., income, household composition), technology attributes (e.g., travel cost, time), geography (e.g., urban, suburban, rural) and population-specific multiday mobility and travel requirements. This representation enables a more forward-looking perspective on the use of new mobility options and the adoption of alternative fuel vehicles, as well as a more accurate representation of their energy usage profiles than previous modeling approaches. A comparison with the U.S. Energy Information Administration's Annual Energy Outlook showcases the ability of TEMPO to accurately replicate widely accepted projections by representing the key elements of the entire transportation sector at the appropriate level of resolution. TEMPO is intended to generate future scenarios of technology adoption, energy use, and emissions in the transportation sector to compare alternatives, inform decision makers, and assess integration with energy infrastructure and supply systems at an appropriate spatiotemporal resolution. [ABSTRACT FROM AUTHOR]