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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]

• 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]

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]

• 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]

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]

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]