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Sustainability assessment (SA) is a complex appraisal method. It is conducted for supporting decision-making and policy in a broad environmental, economic and social context, and transcends a purely technical/scientific evaluation. This paper focusses on the systematisation of knowledge on technical/scientific sustainability evaluation, by addressing critical decision-making elements focussed on by domain experts. We make a distinction between integrated assessment and SA. Our systemic approach outlines how to move from integrated assessment to SA. The fundamental differences involved concern three levels: ontological, methodological and epistemological. We present a novel methodological framework for SA, based on a literature meta-review of multi-scale and multi-purpose appraisal methodologies, models and indicators. SA is essentially a structured procedure encompassing different field-specific analytical methods and models, for specific applications and decision contexts. External inputs to the methodology are “values” considered in the analysis and boundaries defined, including the relevant sustainability framework. Internal methodological elements comprise approach to be adopted (e.g. “what-if” vs. “what-to”), scenario design and analytical models and measurable indicators for an operational analysis. Methods to quantify uncertainty are key ingredients of the assessment framework. The paper highlights the relevance of and policy challenges for SA development, with due attention for applicability in real-world decision contexts. [ABSTRACT FROM AUTHOR]

With the increasing level of detail of traffic simulation models, the need for a consistent understanding of simulators’ performance and the adequate calibration and validation procedures to control uncertainty is crucial, particularly in applications focusing on complex driving behaviour and detailed outputs, such as road safety analysis. In this work the calibration of traffic microscopic simulation models for safety analysis is analyzed considering four different key uncertainty sources: the input data, the calibration methodology, the model structure and its parameters, and the output data. The use of a multi-step sensitivity analysis (SA) framework is proposed and applied to the simulation of an urban motorway scenario, using a complex traffic simulation model with more than one hundred parameters. A three-level analysis is presented: (1) different advanced SA and calibration methods are described, compared and integrated in a multi-step global SA framework; (2) the proposed method is tested using both vehicle trajectory and aggregated traffic data to assess the impact of model parameters uncertainty and different types of input data on relevant outputs; and (3) accident and non-accident scenario-specific calibrations are performed to test the capacity of the simulator in replicating changes in detailed traffic and safety related measurements. Different techniques are adopted in each phase of the global SA and calibration method, attending to the problem complexity, the dimensionality of the experiment, and minimizing the necessary number of model evaluations. The proposed method successfully identified the role played by all parameters and by the model stochasticity on different safety outputs. The final model calibration, carried out by explicitly considering the presence of uncertainty at different levels, confirmed the potential of advanced microscopic traffic models to adequately replicate detailed traffic and safety measurements, shedding light on different aspects of the interaction between road safety and traffic dynamics. [ABSTRACT FROM AUTHOR]

Sensitivity analysis (SA) is able to identify the most influential parameters of a given model. Application of SA is usually critical for reducing the complexity in the subsequent model calibration and use. Unfortunately it is hardly applied, especially when the model is in the form of a computationally expensive black-box computer program. A possible solution concerns applying SA to the metamodel (i.e., an approximation of the computationally expensive model) instead. Among the other options, the use of Gaussian process metamodels (also known as Kriging metamodels) has been recently proposed for the SA of computationally expensive traffic simulation models. However, the main limitation of this approach is its dependence on the model dimensionality. When the model is high-dimensional, the estimation of the Kriging metamodel may still be problematic due to its high computational cost. In order to overcome this problem, in the present paper, the Kriging-based approach has been combined with the quasi-optimized trajectory based elementary effects (quasi-OTEE) approach for the SA of high-dimensional models. The quasi-OTEE SA is used first to screen the influential and non-influential parameters of a high-dimensional model; then the Kriging-based SA is used to calculate the variance-based sensitivity indices, and to rank the most influential parameters in a more accurate way. The application of the proposed sequential SA is illustrated with several numerical experiments. Results show that the method can properly identify the most influential parameters and their ranks, while the number of model evaluations is considerably less than the variance-based SA (e.g., in one of the tests the sequential SA requires over 50 times less model evaluations than the variance-based SA). [ABSTRACT FROM AUTHOR]

Abstract: Maritime transport plays a central role in the transport sector’s sustainability debate. Its contribution to air pollution and greenhouse gases is significant. An effective policy strategy to regulate air emissions requires their robust estimation in terms of quantification and location. This paper provides a critical analysis of the ship emission modelling approaches and data sources available, identifying their limits and constraints. It classifies the main methodologies on the basis of the approach followed (bottom-up or top-down) for the evaluation and geographic characterisation of emissions. The analysis highlights the uncertainty of results from the different methods. This is mainly due to the level of uncertainty connected with the sources of information that are used as inputs to the different studies. This paper describes the sources of the information required for these analyses, paying particular attention to AIS data and to the possible problems associated with their use. One way of reducing the overall uncertainty in the results could be the simultaneous use of different sources of information. This paper presents an alternative methodology based on this approach. As a final remark, it can be expected that new approaches to the problem together with more reliable data sources over the coming years could give more impetus to the debate on the global impact of maritime traffic on the environment that, currently, has only reached agreement via the “consensus” estimates provided by . [Copyright &y& Elsevier]

Connected and automated vehicles (CAVs) promise to significantly improve road traffic. To a certain extent, this situation is similar to the expectations at the end of the last century about the positive effects that the introduction of Adaptive Cruise Control (ACC) systems would have had on motorway traffic. The parallelism is interesting because ACC equipped vehicles represent the first level of vehicle automation and are now widely available on the market. In this light, studying ACC impacts can help to anticipate potential problems related to its widespread application and to avoid that AVs and CAVs will lead to the same problems. Only a few test-campaigns had been carried out studying the ACC impacts under real-world driving conditions in quantitative terms. To bridge this gap, the Joint Research Centre of the European Commission has organized a number of experimental campaigns involving several ACC-equipped vehicles to study different implications of their widespread. In this context, the present paper summarizes the outcomes of a test campaign involving 10 commercially available ACC-equipped vehicles. The test campaign has been executed in two different test-tracks of the ZalaZONE proving ground, in Hungary. The tests have been carried out at low-speeds (30–60 km/h) and have involved platoons of vehicles of different brands and different powertrains, which were tested in a variety of vehicle orders and with different settings of their ACC systems. Test results have been used to derive information about the properties of the different ACC systems, to study their string stability, to study the effect of ACC systems on traffic flow, and to draw inference about the possible implications on energy consumption and traffic safety. Results confirm the previous findings in terms of string instability of the ACC and highlight that in the present form, ACC systems will possibly lead to higher energy consumption and introduce new safety risks when their penetration in the fleet increases. However, they also highlight that the materialization of the above findings for AVs depends on the operational logic that manufacturers will adopt during the implementation phase. Therefore, results suggest that functional requirements to guarantee string stability and in general to not disrupt the normal flow of traffic should be introduced both for ACC and for any automated system that will be placed on the market in the future. [ABSTRACT FROM AUTHOR]

Common approaches to assess the evolution of CO 2 emissions from road vehicles are usually based on (a) estimates of future fleet composition, where most approaches consider vehicles at a rather aggregated level, and (b) emission factors, which are either based on CO 2 certification data or statistically-provided functional relationships obtained from real world test data, or a combination of the two. This approach has certain limitations in capturing the effect of new technologies on CO 2 emission related policy initiatives. The present study proposes a new method for the detailed calculation of the European light duty vehicle fleet CO 2 emissions, which could help to overcome such limitations, achieve better results when making CO 2 emissions projections and better support future policies. Simulation at individual vehicle level is combined with fleet composition data, retrieved from the official European CO 2 emissions monitoring database, and publicly available data regarding individual vehicle characteristics in order to calculate vehicle CO 2 emissions and fuel consumption over different conditions and vehicle configurations. The methodology is applied to analyze and assess the impact of the introduction of the new certification procedure, the Worldwide Light duty vehicle Test Procedure (WLTP), on the European car fleet CO 2 emissions. Results show an average WLTP to NEDC CO 2 emissions ratio of approximately 1.2. The increases in CO 2 emissions are higher for cars exhibiting lower NEDC emission values (additional 29 and 25 gCO 2 /km for vehicles emitting 100 and 119 gCO 2 /km, respectively). At higher emission levels (about 250 CO 2 g/km) WLTP and NEDC present comparable results. Three possible scenarios for the translation of projected NEDC CO 2 emissions to WLTP-based ones are quantified. [ABSTRACT FROM AUTHOR]

Official laboratory-measured monitoring data indicate a progressive decline in the average fuel consumption and CO 2 emissions of the European passenger car fleet. There is increasing evidence to suggest that officially reported CO 2 values do not reflect the actual performance of the vehicles on the road. A reported difference of 30–40% between official values and real-world estimates was found which has been continuously increasing. This paper reviews the influence of different factors that affect fuel consumption and CO 2 emissions on the road and in the laboratory. Factors such as driving behaviour, vehicle configuration and traffic conditions are reconfirmed as highly influential. Neglected factors (e.g. side winds, rain, road grade), which may have significant contributions in fuel consumption in real world driving are identified. The margins of the present certification procedure contribute between 10 and 20% in the gap between the reported values and reality. The latter was estimated to be of the order of 40%, or 47.5 gCO 2 /km for 2015 average fleet emissions, but could range up to 60% or down to 19% depending on prevailing traffic conditions. The introduction of a new test protocol is expected to bridge about half of the present divergence between laboratory and real world. Finally, substantial literature was found on the topic; however, the lack of common test procedures, analysis tools, and coordinated activity across different countries point out the need for additional research in order to support targeted actions for real world CO 2 reduction. Quality checks of the CO 2 certification procedure, and the reported values, combined with in-use consumption monitoring could be used to assess the gap on a continuous basis. [ABSTRACT FROM AUTHOR]

The European Commission has been actively involved in the development of the World-wide harmonized Light duty Test Cycle (WLTC) and corresponding Test Procedure (WLTP), and is currently working to introduce them in the European type approval (TA) procedure. The present study analyzes and estimates the effects of the introduction of WLTP on the average CO 2 emissions and average vehicle energy demands (VEDs) from different segments of the European vehicle market. Twenty gasoline and eleven diesel vehicles were tested on the current European TA procedure NEDC, and on the evolving WLTP. These WLTP tests were then used as base line to estimate for each vehicle a WLTP best case and a WLTP worst case scenario, in line with the finalized version of the WLTP. In the WLTP worst case scenario (WLTP-H) results showed CO 2 emissions and energy demands to be on average 11% and 44% higher than NEDC, respectively. Best case scenario (WLTP-L) has on average 1% higher CO 2 emissions and 26% higher energy requirements than the NEDC. These values should be considered on top of the 8% average difference between the JRC NEDC test results and the official type approval values. The higher vehicle inertia and road loads (RLs) along with the higher vehicle speeds, are the key parameters of the new procedure that contribute to the increased CO 2 emissions and vehicle energy demand (VED). Results from the present study show that moving from NEDC to WLTP have a stronger impact on diesel than on gasoline vehicles. The highest effect of WLTP introduction is measured for vehicles that comply with EURO 6 emission standard (14% increase in CO 2 emissions from NEDC to the simulated worst case WLTP), with also the largest difference between JRC measured NEDC tests and declared type approval values (11% difference). [ABSTRACT FROM AUTHOR]

A vehicle simulation model, CO 2 MPAS supports the introduction of the new WLTP-based certification system for CO 2 emissions in Europe. This paper investigates the possibility to use the underlying simulation methodology to accurately calculate CO 2 emissions over real-world trips, thus to extend the use of the methodology beyond vehicle certification. As a reference, the analysis used measurement data obtained from four vehicles over two different routes under real-world driving conditions. The CO 2 emissions were measured using portable emissions measurement systems. The formal CO 2 MPAS methodology and two modified versions of it that require a reduced number of input data were assessed about their capacity to predict the measured CO 2 emissions. The analysis focused on the accuracy and uncertainty of the three different methodology configurations. As an additional benchmark, the analysis considered the CO 2 emissions estimates obtained from the EMEP/EEA Guidebook methodology used for emissions inventorying in Europe. Results show that the basic CO 2 MPAS configuration demonstrates good performance in predicting CO 2 emissions over on-road tests, reaching a prediction accuracy over an entire test trip of −0,3% and a standard deviation of 3,1%. The modified versions showed slightly higher biases up to 3% and uncertainties (5–7%), but remaining within reasonable limits considering the reduced number of inputs used in each case. Given its ability to predict CO 2 emissions accurately on a local base, CO 2 MPAS could be used for the prediction of instantaneous CO 2 emissions in traffic micro-simulation exercises. [ABSTRACT FROM AUTHOR]

• We explored the views of 72 experts and non-experts in transport, living in 23 countries. • Focus groups were used to explore participants' expectations and concerns. • Results show that benefits like increased safety and accessibility and improved travel experience for both driver and passengers could support Connected and Automated Vehicles' deployment. • A variety of concerns were raised during the discussions, related to privacy, responsibility in case of an accident and increase in maintenance and repair costs. The successful deployment of connected and automated vehicles (CAVs) will largely depend on public opinion. Expectations and concerns are essential components driving the speed of market uptake and final adoption of these technologies. We have studied user expectations and concerns, as well as their provenance, by conducting a series of Focus Group (FG) discussions with transport area experts and non-expert participants, in Italy, Germany, and Spain. Together with user acceptance, potential advantages and disadvantages that CAVs may bring at a societal level were explored. Results show that benefits like increased safety and accessibility and improved travel experience for both driver and passengers could support CAVs' deployment. Nevertheless, a variety of concerns were raised during the discussions, related to privacy, responsibility in case of an accident, and increase in maintenance and repair costs. These aspects could significantly hamper or delay CAVs acceptability and need to be carefully considered by stakeholders. The reported insights from the FG discussions and the fact that some participants noted positive changes of their initial idea of CAVs due to their participation in the discussion, exemplify the importance of citizen engagement activities to address mobility challenges. [ABSTRACT FROM AUTHOR]

This paper presents the World-wide harmonized Light duty Test Cycle (WLTC), developed under the Working Party on Pollution and Energy (GRPE) and sponsored by the European Union (with Switzerland) and Japan. India, Korea and USA have also actively contributed. The objective was to design the harmonized driving cycle from “real world” driving data in different regions around the world, combined with suitable weighting factors. To this aim, driving data and traffic statistics of light duty vehicles use were collected and analyzed as basic elements to develop the harmonized cycle. The regional driving data and weighting factors were then combined in order to develop a unified database representing the worldwide light duty vehicle driving behavior. From the unified database, short trips were selected and combined to develop a driving cycle as representative as possible of the unified database. Approximately 765,000 km of data were collected, covering a wide range of vehicle categories, road types and driving conditions. The resulting WLTC is an ensemble of three driving cycles adapted to three vehicle categories with different power-to-mass ratio (PMR). It has been designed as a harmonized cycle for the certification of light duty vehicles around the world and, together with the new harmonized test procedures (WLTP), will serve to check the compliance of vehicle pollutant emissions with respect to the applicable emissions limits and to establish the reference vehicle fuel consumption and CO 2 performance. [ABSTRACT FROM AUTHOR]

• There is a divergence on connected and automated vehicles views of European citizens and media when compared to policymakers. • Negative sentiments are prevalent in the media, with most citizens wary of automated vehicles. • The political narrative mostly carries a positive tone. • All actors should be engaged to be able to achieve the benefits of adopting automated vehicles. • Citizen engagement initiatives are encouraged as a means of involving stakeholders in the policymaking process. Do citizens, media and policymakers share the same view on autonomous cars? In the present paper, we analyse data from media articles, a Eurobarometer survey, and policy documents, to understand the perspective of different stakeholders when it comes to autonomous cars. We find significant differences between the groups, with a predominance of negative sentiments in news articles and a majority of citizens being wary of autonomous cars, while the political narrative mostly carries a positive tone. The findings highlight a dichotomous perspective about this potentially disruptive technology. This may represent a problem as the benefits of adopting autonomous cars will only come to surface if all actors are engaged and see the advantages they can bring to people's daily lives. We conclude by encouraging policymakers to promote initiatives to engage citizens in the transformation of road transport and other stakeholders to be advertised the positive implications of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Abstract: Trajectories drawn in a common reference system by all the vehicles on a road are the ultimate empirical data to investigate traffic dynamics. The vast amount of such data made freely available by the Next Generation SIMulation (NGSIM) program is therefore opening up new horizons in studying traffic flow theory. Yet the quality of trajectory data and its impact on the reliability of related studies was a vastly underestimated problem in the traffic literature even before the availability of NGSIM data. The absence of established methods to assess data accuracy and even of a common understanding of the problem makes it hard to speak of reproducibility of experiments and objective comparison of results, in particular in a research field where the complexity of human behaviour is an intrinsic challenge to the scientific method. Therefore this paper intends to design quantitative methods to inspect trajectory data. To this aim first the structure of the error on point measurements and its propagation on the space travelled are investigated. Analytical evidence of the bias propagated in the vehicle trajectory functions and a related consistency requirement are given. Literature on estimation/filtering techniques is then reviewed in light of this requirement and a number of error statistics suitable to inspect trajectory data are proposed. The designed methodology, involving jerk analysis, consistency analysis and spectral analysis, is then applied to the complete set of NGSIM databases. [Copyright &y& Elsevier]

[Display omitted] • Three sampling approaches were investigated for estimating HDV fleet CO2 emissions. • Mean CO 2 emissions deviate less than 2% in the best cases and less than 5% overall. • The JDE sampling preserves well the distribution characteristics. • MEDP and MSD are suitable to investigate fleet emissions when limited data are available. • The alternative methodologies can be used to predict CO 2 emissions in future fleet scenarios. Certification and monitoring of heavy vehicle CO 2 emissions in several countries are based on individual vehicle simulation. Smaller fleet subsets can be used for accurate fleet-level results while preserving the characteristics of the underlying fleet-emissions distributions. The paper focuses on three approaches to capture fleet CO 2 emissions: a) sampling directly from the fleet-data, b) sampling from data of individual vehicle components and c) using key statistics regarding the fleet composition that are available. The first and second approach deliver marginal divergences of the mean, between 1.1 and 2.1% and below 2.7 respectively, preserving the characteristics of the distribution. The third deviated by up to 5%, but lacked the detailed characteristics of the underlying statistical distribution. All three are useful when setting up fleet-wide monitoring schemes where detailed data are not available and to investigate the potential CO 2 savings of various future fleet compositions, and scenarios regarding the diffusion of different types of technologies. [ABSTRACT FROM AUTHOR]

• Formalizing heterogeneity of the vehicle-driver system. • Free-flow term of a CF model leads to the formation and propagation of oscillations. • Simulations reproduce periodical oscillations with variable amplitudes and periods. • The concave growth pattern of the standard deviation of the speed is observed. • Validation was performed on three real-world car-following datasets. • The proposed approach provides accurate fuel consumption estimations. Road traffic congestion is the result of various phenomena often of random nature and not directly observable with empirical experiments. This makes it difficult to clearly understand the empirically observed traffic instabilities. The vehicles' acceleration/deceleration patterns are known to trigger instabilities in the traffic flow under congestion. It has been empirically observed that free-flow pockets or voids may arise when there is a difference in the speeds and the spacing between the follower and the leader increases. During these moments, the trajectory is dictated mainly by the characteristics of the vehicle and the behaviour of the driver and not by the interactions with the leader. Voids have been identified as triggers for instabilities in both macro and micro level, which influence traffic externalities such as fuel consumption and emissions. In the literature, such behaviour is usually reproduced by injecting noise to the results of car-following models in order to create fluctuations in the instantaneous vehicles' acceleration. This paper proposes a novel car-following approach that takes as input the driver and the vehicle characteristics and explicitly reproduces the impact of the vehicle dynamics and the driver's behaviour by adopting the Microsimulation Free-flow aCceleration (MFC) model. The congested part of the model corresponds to the Lagrangian discretization of the LWR model and guarantees a full consistency at the macroscopic scale with congested waves propagating accordingly to the first-order traffic flow theory. By introducing naturalistic variation in the driving styles (timid and aggressive drivers) and the vehicle characteristics (specification from different vehicle models), the proposed model can reproduce realistic traffic flow oscillations, similar to those observed empirically. An advantage of the proposed model is that it does not require the injection of any noise in the instantaneous vehicle accelerations. The proposed methodology has been tested by studying a) the traffic flow oscillations produced by the model in a one-lane road uphill simulation scenario, b) the ability of the model to reproduce car-following instabilities observed in three car-following trajectory datasets and c) the ability of the model to produce realistic fuel consumption estimates. The results prove the robustness of the proposed model and the ability to describe traffic flow oscillations as a consequence of the combination of driving style and vehicle's technical specifications. [ABSTRACT FROM AUTHOR]

Abstract CO 2 emissions of light-duty vehicles are certified over standardised, laboratory-based conditions and reported to the consumers. Such tests reflect specific operating conditions that differ from what an individual driver experiences. Vehicle simulation can bridge the gap and help provide customised, vehicle and trip-specific values. This study investigates the potential of using a simulation-based approach for calculating CO 2 emissions over real-world operation, when limited information and test-data are available. The methodology introduced in the European vehicle certification regulation since 2017 is used as a basis. Seven vehicles were tested over multiple on-road trips and in some cases on a chassis dyno. First, the analysis focused on the accuracy of the simulations when only limited information for the vehicle and its components are used. Subsequently, the model was calibrated on test data. The first case presented an error between 1.0% and 4.4% depending on the test, while the standard deviation was 10.0%. When using WLTP for calibration, the average error dropped to −2.9% to −0.2%, and the standard deviation decreased to 2.0%. When calibrating over on-road tests, the average error was 0.7% for the on-road tests and 4.5% for the WLTP. Highlights • A simulation-based method for estimating lab and real world CO 2 of LDVs is investigated. • 3 different approaches were investigated based on generic, real world or lab input data. • The average error remained low (<5%) and was <2% when calibrating on tests-data. • Test-data based calibration significantly reduces the uncertainty from 10% to about 3%. • The approach could serve for extrapolating certification data to real world or vice versa. [ABSTRACT FROM AUTHOR]

Highlights • Relation between consumption and average speed for ICEV is questioned for PEV. • For PEVs higher energy consumption is connected to the free-flow test-cases. • Need to rethink the connection between congestion and energy inefficiency. • Results are based on real and simulated vehicle trajectories. Abstract Decreasing road transport's harmful effects on environment and health and reducing road accidents are major policy priorities. A variety of technologies could drastically improve air quality, reduce energy consumption and CO 2 emissions of road vehicles: in this respect, a prominent trend leverages Electric Vehicles (EVs), supported by improved performance and energy efficiency through connectivity and automation. A noteworthy research question in the transition from Internal Combustion Engine Vehicles (ICEVs) to the alternative technologies, is to understand how Intelligent Transport Systems and other traffic-related measures can contribute to the reduction of fuel consumption and greenhouse gas emissions. In fact, a widely acknowledged tenet assumes that congestion removal or mitigation in presence of ICEVs implies also a reduction of transport-related externalities. This paper explores whether this effect still holds for EVs, by performing an analysis of energy consumption over different vehicle trajectories, under both congested and free-flow conditions. Calculations are carried out using two vehicle simulators: the VT-CPEM (Virginia Tech Comprehensive Power-based Energy consumption model) model for EVs and the CO 2 MPAS (CO 2 model for Passenger and commercial vehicle Simulation) vehicle simulator for the ICEVs, for both electric and conventional cases passengers and freight/commercial powertrains have been analysed. Results are presented on real and simulated data related to four powertrain-vehicle combinations, in terms of general trends of energy/fuel consumption versus speed. Interestingly, results show that, differently from ICEVs, the relationship between congestion and energy consumption underlying EVs can change with higher energy consumption connected to an increased average traffic speed. [ABSTRACT FROM AUTHOR]

As of July 2017, the emissions type-approval of light-duty vehicles in Europe is based on the Worldwide Harmonized Light-duty vehicles Test Procedure (WLTP), introduced to replace the old and outdated New European Driving Cycle (NEDC) test procedure. Since some elements of the European Legislation are still based on the NEDC (2020 CO 2 emission targets, vehicle labelling, national vehicle taxation policies, etc.) in order to allow sufficient lead time to vehicle manufacturers and national authorities to adapt to the new procedure, a simulation-based approach was chosen to calculate CO 2 emissions and fuel consumption according to the NEDC regime in the period 2017–2020. To achieve this objective without significantly increasing the cost and duration of the certification procedure, existing regulation foresees that vehicles are tested over the WLTP for CO 2 emissions, the test results are used as input in a simulation model that then calculates the corresponding CO 2 according to the NEDC test protocol. A dedicated vehicle simulation model (CO 2 MPAS) was developed for the purpose and is currently used for the type-approval of new vehicles in Europe. The development specifications of CO 2 MPAS were challenging, as it had to be highly accurate, exhibit fast operation, and function with a limited number of input data. This paper presents the development principles and process followed, details of the physical models employed in CO 2 MPAS, and provides information regarding its accuracy, validity and in use operation. CO 2 MPAS achieves low errors in the prediction of the NEDC cycle that in the controlled sample used for its development are of the order of 1% with a standard deviation of 3%, while the respective in-use numbers are of the order of 1.5% and 5%. In parallel, random sampling and testing of a 10% of the type-approved vehicles also occurs in order to guarantee the quality of the CO 2 MPAS results and the validity of the process. It is concluded that CO 2 MPAS can be used to accurately estimate emissions of conventional vehicles within a ±4% accuracy range, even when limited input data are available. In addition, the in-use data analyzed suggest that the use of the tool enables the certification of about 2/3 of the new vehicle models without the need of additional experimental tests. This is an important achievement as it reduces the costs and time necessary to certify light-duty vehicle CO 2 emissions during the transitional period. Finally, it can be concluded that the use of CO 2 MPAS does not affect the declared CO 2 emissions of vehicles over NEDC conditions. [ABSTRACT FROM AUTHOR]

• The safety of future automated vehicles depends on the requirements defined by international regulations. • Safety requirements should be testable, verifiable and robust. • Safety requirements should not dictate how an automated vehicles should operate but set their performance. • Proper models should be used to set performance requirements to reproduce the complexity of a traffic scenario. • Fuzzy logic is an appropriate tool to define the performance requirement of automated vehicles. UN Regulation 157, the first global regulation regarding the type-approval of Automated Driving Systems (ADS), has been adopted in 2021. In it, safety performance requirements are being defined for vehicles of automation Level 3, according to the SAE J3016, with a limited Operational Design Domain (ODD). In particular, for three types of events that are related to motorway driving, two models are provided to distinguish between preventable traffic scenarios, for which the ADS is expected to avoid an accident, and unpreventable traffic scenarios, for which accidents cannot be avoided and the ADS can only mitigate their severity. The models recreate the short-term behavior of a driver who reacts to an emergency. Two possible actions are predicted: either no reaction or full braking when danger is identified. In the present paper the two models are analyzed and compared with two additional models: an industry proposed model, the Responsibility Sensitive Safety framework (RSS), and the Fuzzy Safety Model (FSM) proposed by the authors. As in the case of the two regulation models, also the RSS, although more sophisticated, assumes that the possible reaction by the driver is binary. This approach neglects the ability of a human driver to drive defensively and anticipate possible risks. Defensive drivers, indeed, may use comfortable decelerations in anticipation, to avoid finding themselves in an emergency situation. The FSM uses fuzzy logic to mimic this behavior. Results show that anticipation plays a very important role to reduce the number of unpreventable traffic scenarios. In addition, by validating the classification capabilities of the four models with real traffic data, the FSM proved to be the most suitable of the investigated models. On the basis of these results, the FSM has been included in the proposal for amending UN Regulation 157, thus allowing to set higher safety standards for the first automated vehicles that will be introduced into the market. [ABSTRACT FROM AUTHOR]

Estimation/updating of Origin–Destination (OD) flows and other traffic state parameters is a classical, widely adopted procedure in transport engineering, both in off-line and in on-line contexts. Notwithstanding numerous approaches proposed in the literature, there is still room for considerable improvements, also leveraging the unprecedented opportunity offered by information and communication technologies and big data. A key issue relates to the unobservability of OD flows in real networks – except from closed highway systems – thus leading to inherent difficulties in measuring performance of OD flows estimation/updating methods and algorithms. Starting from these premises, the paper proposes a common evaluation and benchmarking framework, providing a synthetic test bed, which enables implementation and comparison of OD estimation/updating algorithms and methodologies under “standardized” conditions. The framework, implemented in a platform available to interested parties upon request, has been flexibly designed and allows comparing a variety of approaches under various settings and conditions. Specifically, the structure and the key features of the framework are presented, along with a detailed experimental design for the application of different dynamic OD flow estimation algorithms. By way of example, applications to both off-line/planning and on-line algorithms are presented, together with a demonstration of the extensibility of the presented framework to accommodate additional data sources. [ABSTRACT FROM AUTHOR]

• Commercial ACC systems have a string-unstable behavior. • String-stable ACCs rely on the use of communication which is not currently available. • ACC can still be string stable by taking advantage of RADAR sensing characteristics. • Monitoring two vehicles downstream is sufficient to ensure string-stability in a wide range of scenarios. • RADAR based multianticipation can provide a plug-and-play solution to achieve efficient traffic. Adaptive Cruise Control (ACC) systems have been expected to solve many problems of motorway traffic. Now that they are widespread, it is observed that the majority of existing systems are string unstable. Therefore, small perturbations in the speed profile of a vehicle are amplified for the vehicles following upstream, with negative impacts on traffic flow, fuel consumption, and safety. Increased headway settings provide more stable flow but at the same time it deteriorates the capacity. Substantial research has been carried out in the past decade on utilizing connectivity to overcome this trade-off. However, such connectivity solutions have to overcome several obstacles before deployment and there is the concrete risk that motorway traffic flow will considerably deteriorate in the meanwhile. As an alternative solution, the paper explores multianticipation without inter-vehicle communication, taking advantage of the recent advancements in the field of RADAR sensing. An analytical study is carried out, based on the most widely used model and parameter settings used to simulate currently available commercial ACC systems, comparing the transfer functions and step responses for the nominal and the multianticipative formulations. Then, a microsimulation framework is employed to validate our claim on different speed profiles. Analytical results demonstrate that multianticipation enhances stability without impacting traffic flow. On the contrary, the simulation study indicates that the multianticipative-ACC can produce higher road capacity even in the presence of external disturbances and for a wide range of calibrated parameters. Finally, optimality conditions for the tuning of the headway policy are derived from a Pareto optimization. [ABSTRACT FROM AUTHOR]

The study proposes a methodology for quantifying the impact of real-world heterogeneous driving behavior on vehicle energy consumption, linking instantaneous acceleration heterogeneity and CO 2 emissions. Data recorded from 20 different drivers under real driving are benchmarked against the Worldwide Harmonized Light Vehicle Test Cycle (WLTC), first by correlating the speed cycle with individual driver behavior and then by quantifying the CO 2 emissions and consumption. The vehicle-Independent Driving Style metric (IDS) is used to quantify acceleration dynamicity, introducing driving style stochasticity by means of probability distribution functions. Results show that the WLTC cycle assumes a relatively smooth acceleration style compared to the observed ones. The method successfully associates acceleration dynamicity to CO 2 emissions. We observe a 5% difference in the CO 2 emissions between the most favourable and the least favourable case. The intra-driver variance reached 3%, while the inter-driver variance is below 2%. The approach can be used for quantifying the driving style induced emissions divergence. [ABSTRACT FROM AUTHOR]

• Accuracy of ninety CF models and ACC algorithms is compared. • New controllers with spacing policies from the IDM and Gipps' models are studied. • The impact on accuracy of increasingly complex lower-level dynamics is evaluated. • The linear controller with Gipps' spacing policy is the most accurate model. • The IDM is the most robust model, also showing zero crashes in cross-validation. This paper investigates the accuracy and robustness of car-following (CF) and adaptive cruise control (ACC) models in reproducing measured trajectories of commercial ACCs. To this aim, a general modelling framework is proposed, in which ACC and CF models have been incrementally augmented with physics-based extensions: namely, perception delay, linear or nonlinear vehicle dynamics, and acceleration constraints. This framework has been applied to the Intelligent Driver Model (IDM), Gipps' model, and to three basic ACC algorithms. These are linear controllers which are coupled with a constant time-headway spacing policy, and with two other policies derived from the traffic flow theory: the IDM desired distance function, and Gipps' equilibrium distance-speed function. The ninety models resulting from the combination of the five base models with the aforementioned extensions, have been assessed and compared through a vast calibration and validation experiment against measured trajectory data of vehicles driven by ACC systems. Overall, the study has shown that physics-based extensions provide limited improvements to the accuracy of existing models. In addition, if an investigation against measured data is not carried out, it is not possible to argue which extension is the most suited for a specific model. The linear controller with Gipps' spacing policy has resulted the most accurate model, while the IDM the most robust to different input trajectories. Eventually, all models have failed to capture the behaviour of some car brands – just as models fail with some human drivers. Therefore, the choice of the "best" model is independent of the car brand to simulate. [ABSTRACT FROM AUTHOR]

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

• Adaptive Cruise Control (ACC) is an increasingly widespread technology in modern vehicles. • ACC can be considered a precursor of automated vehicles behavior on motorways. • There is still limited understanding of commercial ACC control logic. • Actual impact of ACC on motorway traffic dynamics is still widely unknown. • openACC aims to allow the scientific community to better understand ACC and its implications. Adaptive Cruise Control (ACC) systems are becoming increasingly available as a standard equipment in modern commercial vehicles. Their penetration rate in the fleet is constantly increasing, as well as their use, especially under freeway conditions. At the same time, limited information is openly available on how these systems actually operate and their differences depending on the vehicle manufacturer or model. This represents an important gap because as the number of ACC vehicles on the road increases, traffic dynamics on freeways may change accordingly, and new collective phenomena, which are only marginally known at present, could emerge. Yet, as ACC systems are introduced as comfort options and their operation is entirely under the responsibility of the driver, vehicle manufacturers do not have explicit requirements to fulfill nor they have to provide any evidence about their performances. As a result, any safety implication connected to their interactions with other road users escapes any monitoring and opportunity of improvement. This work presents a set of experimental car-following campaigns, providing an overview of the behavior of commercial ACC systems under different driving conditions. Furthermore, the suggestion of a unified data structure across the different tests facilitates comparison between the different campaigns, vehicles, systems and specifications. The complete data is published as an open-access database (OpenACC), available to the research community. As more test campaigns will be carried out, OpenACC will evolve accordingly. The activity is performed in the framework of the openData policy of the European Commission Joint Research Centre with the objective to engage the whole scientific community towards a better understanding of the properties of ACC vehicles in view of anticipating their possible impacts on traffic flow and prevent possible problems connected to their widespread introduction. In this light, OpenACC, over time, also aims at becoming a reference point to study if and how the parameters of such systems need to be regulated, how homogeneously they behave, how new ACC car-following models should be designed for traffic microsimulation purposes and what are the key differences between ACC systems and human drivers. [ABSTRACT FROM AUTHOR]

• Two Fuzzy Surrogate Safety Metrics (FSSMs) for rear-end collision are presented. • Unsafe situations are distinguished between critically and proactively unsafe. • The new metrics are assessed based on test track experiments. • FSSMs outperform traditional metrics based on crisps thresholds. • FSSMs are suitable for supporting driving assistance and automation systems. The present paper discusses two fuzzy Surrogate Safety Metrics (SSMs) for rear-end collision, the Proactive Fuzzy SSM (PFS) and Critical Fuzzy SSM (CFS). The objective is to investigate their applicability for evaluating the real-time rear-end risk of collision of vehicles to support the operations of advanced driver assistance and automated vehicle functionalities (from driving assistance systems to fully automated vehicles). The proposed Fuzzy SSMs are evaluated and compared to other traditional metrics on the basis of empirical observations. To achieve this goal, an experimental campaign was organized in the AstaZero proving ground in Sweden. The campaign consisted of two main parts: a car-following experiment with five vehicles solely driven by Adaptive Cruise Control (ACC) systems and a safety critical experiment, testing the response of the Autonomous Emergency Braking (AEB) system to avoid collisions on a static target. The proposed PFS is compared with the safe distance defined by the well-known Responsibility Sensitive Safety (RSS) model, showing that it can produce meaningful results in assessing safety conditions also without the use of crisp safety thresholds (like in the case of RSS). The CFS outperformed the well-known Time-To-Collision (TTC) SSM in the a-priori identification of the cases, where the tested vehicles were not able to avoid the collision with the static target. Moreover, results show that CFS at the time of the first deceleration is correlated with the velocity of the vehicle at the time of collisions with the target. [ABSTRACT FROM AUTHOR]

The interest on the impact of vehicle automation and connectivity in the future road transport networks is very high, both from a research and a policy perspective. Results in the literature show that many of the anticipated advantages of connected and automated vehicles or automated vehicles without connectivity (CAVs and AVs respectively) on congestion and energy consumption are questionable. Some studies provide quantitative answers to the above questions through microsimulation but they systematically ignore the realistic simulation of vehicle dynamics, driver behaviour or instantaneous emissions estimates, mostly due to the overall increased complexity of the transport systems and the need for low computational demand on large-scale simulations. However, recent studies question the capability of common car-following models to produce realistic vehicle dynamics or driving behaviour, which directly impacts emissions estimations as well. This work presents a microsimulation study that contributes on the topic, using a scenario-based approach to give insights regarding the impact of CAVs and AVs on the evolution of emissions over a highway network. The motivation here is to answer whether the different driving behaviours produce significant differences in emissions during rush hours, and how significant is the impact of detailed vehicle dynamics simulation and instantaneous emissions in the outcome. The status of the network is assessed in terms of flow and speed. Furthermore, emissions are estimated using both the average-speed EMEP/EEA fuel consumption factors and a generic version of the European Commission's CO 2 MPAS model that provides instantaneous fuel consumption estimates. The simulation results of this work show that AVs can deteriorate the status of the network, and that connectivity is the key for improved traffic flow. Emissions-wise, the AVs have the highest fuel consumption per km travelled among other types, while CAVs only marginally lower the overall consumption of human-driven vehicles. For the same traffic demand, the total emissions for different vehicle types remain at comparable levels. • Traditional car-following models provide overestimated instantaneous acceleration values. • AVs do not facilitate Traffic Flow, and generate the highest emissions values per kilometer. • CAVs increase the capacity of the network and therefore during peak hours, they generate more emissions in absolute values. • In total, the differences in emissions per kilometre driven between CVs, AVs, CAVs do not exceed 6%. • Results show that the use of vehicle dynamics-based modelling in microsimulation (i.e. MFC free-flow model) is in cases, essential. [ABSTRACT FROM AUTHOR]