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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]

• 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]

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]

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]