Driver models are essential for virtual safety assessments of automated vehicles. This study evaluates the realism of the i4Driving model, a behavioural driver model designed to emulate human-like driving, using an interactive driving Turing test combined with a think-aloud protocol in connected driving simulators. Thirty participants interacted with either a human-controlled vehicle or the i4Driving model across three motorway scenarios and rated realism, predictability, safety, and aggressiveness. Results showed that the i4Driving model was perceived as less realistic and predictable than human drivers (p < 0.001), yet participants could not reliably distinguish between the two (classification accuracy = 0.673). Think-aloud analysis revealed specific short-comings, such as unrealistic merging and speed adaptation, alongside instances of naturalistic behaviour. These findings highlight the need for improvements in tactical decision-making and demonstrate the value of combining subjective ratings with qualitative insights for refining driver models.

Automated driving systems, which can take over certain dynamic driving tasks from the driver, are becoming increasingly available in commercial vehicles. One of these automated driving systems widely introduced in commercial vehicles is Adaptive Cruise Control (ACC). This system is designed to maintain certain desired driving speeds and time headways as chosen by drivers and based on the settings available within the system. The properties and actual performance of these systems will affect the traffic flow and its stability. However, the specific properties and their workings are rarely publicly available. Therefore, the main aim of this paper is to test the actual performance of a commercial ACC system under different desired speed and distance gap settings, as well as driving modes in a car-following situation. For this purpose, a pilot field test was conducted in the Netherlands in which two identical commercial vehicles equipped with ACC systems were driven simultaneously. The first vehicle was used to create a prespecified speed profile by adapting the ACC system settings manually, while the second vehicle followed the lead vehicle when the ACC system was engaged to test its actual performance. The main findings indicate that the different system settings affect the car-following indicators, and system response times were found to be comparable to human response times. The eco mode was found to affect some of the car-following indicators, and it does not deteriorate safety below the safety level of driving with short headway setting in drive mode.

Truck platooning is an application of cooperative adaptive cruise control (CACC) which relies on vehicle-to-vehicle communications facilitated by vehicle ad-hoc networks. Communication uncertainties can affect the performance of a CACC controller. Previous research has not considered the full spectrum of possible car-following scenarios needed to understand how the longitudinal behaviour of truck platoons would be affected by changes in the communication network. In this paper, we investigate the impact of radio channel parameters on the string stability and collision avoidance capabilities of a CACC controller governing the longitudinal behaviour of truck platoons in a majority of critical car-following situations. We develop and use a novel, sophisticated and open-source VANET simulator OTS-Artery, which brings microscopic traffic simulation, network simulation, and psychological concepts in a single environment, for our investigations. Our results indicate that string stability and safety of truck platoons are mostly affected in car-following situations where truck platoons accelerate from the standstill to the maximum speed and decelerate from the maximum speed down to the standstill. The findings suggest that string stability can be improved by increasing transmission power and lowering receiver sensitivity. However, the safety of truck platoons seems to be sensitive to the choice of the path loos model.

In current motorway design practice, microscopic simulation is used to assess the traffic safety and capacity implications of design variants. Many different simulation packages are available, and many researchers have invested effort in improving and calibrating models that describe driving behavior. This paper examines traffic simulation models for merging situations in high traffic volume. Based on a multicriteria analysis, two simulation software packages (VISSIM version 10 and MOTUS) were selected and calibrated using a recently collected set of rich empirical trajectory data from an on-ramp, an off-ramp, and two weaving segments in the Netherlands. The results show that both packages are as yet unable to simulate turbulent traffic around motorway ramps realistically, in terms of lane change locations and headway distribution. This is a drawback when assessing traffic operations for specific designs. The simulated gap acceptance distributions were found to have comparable shapes as they were derived from empirical data. This is an advantage when assessing the traffic safety of a specific design.

The inclusion of human factors (HF) in mathematical models is proving crucial to allow complex driving behaviour and interactions to be explicitly considered to capture driving phenomena. An important area where such integration is required is for the role of anticipation by drivers to compensate for critical traffic situations. In this paper, we introduce the concept of Anticipation Reliance (AR), which acts as a demand lowering compensative effect for the driving task by relying more on anticipation. We implement AR in a generic multi-scale microscopic traffic modelling and simulation framework to explore and explain the effects of HF on traffic operations and safety in critical traffic situations. This concept addresses a disparity in the description of driver workload in relation to the execution of driving tasks in regard to the confidence that drivers place on tasks that are sub-consciously catered for. The crossover from HF to a mathematical description of this role of AR introduces a ground-breaking concept that explains and models the mechanisms that allow drivers to compensate and avoid accidents in many circumstances, even when driving errors or sub-optimal driving performance occurs. By and large, the HF effects can be subdivided in effects on perception and anticipation; effects on sensitivity and response to stimuli; and effects on personal attributes and characteristics. A key aspect of the framework are two intertwined driver-specific mental state variables—total workload and awareness—that bridge between classic collision-free idealized models for lane changing and car following, and HF models that explain under which conditions the performance of drivers deteriorates in terms of reaction time, sensitivity to stimuli or other parameters. In this paper, we focus on the awareness construct, as described by AR, and explore the effects. We prove the effectiveness of the approach with a case example that demonstrates the ability of the model to dissect a complex traffic situation with both longitudinal and lateral driving tasks, while endogenously considering human factors and that produces accident avoidance and occurrence within the same order of magnitude compared to real traffic accident statistics.

Realistic microscopic traffic simulation is essential for prospective evaluation of the potential impacts of new traffic control strategies. Freeway corridors with interacting bottlenecks and dedicated lanes generate complex traffic flow phenomena and congestion patterns, which are difficult to reproduce with existing microscopic simulation models. This paper discusses two alternative driving behavior models that are capable of modeling freeways with multiple bottlenecks and dedicated lanes over an extended period with varying demand levels. The models have been calibrated using archived data from a complicated 13-mile long section of the northbound SR99 freeway near Sacramento, California, for an 8-hour time period in which the traffic fluctuated from free-flow to congested conditions. The corridor includes multiple bottlenecks, multiple entry and exit ramps, and an HOV lane. Calibration results show extremely good agreement between field data and model predictions. The models have been cross-validated and produced similar macroscopic traffic performance. The main behavior that should be captured for successful modeling of such a complex corridor includes the anticipative and cooperative driver behavior near merges, lane preference in presence of dedicated lanes, and variations in desired headway along the corridor.

Sags are roadway sections along which the gradient increases gradually in the direction of traffic. Sags are generally bottlenecks in freeway networks. Previous research suggests that traffic management measures using advanced Adaptive Cruise Control (ACC) systems could reduce congestion on freeways, but little is known about their potential effectiveness at sags. This article evaluates the effectiveness of a basic ACC system (B-ACC) and two advanced ACC systems – Traffic State-Adaptive ACC (TSA-ACC) and Cooperative ACC (C-ACC) – in mitigating congestion at sags. TSA-ACC adapts the ACC parameters to the macroscopic traffic state estimated by the vehicle itself. C-ACC uses information of other vehicles in the surroundings to adjust its accelerations. Results are obtained using microscopic traffic simulations with different penetration rates. They show that, under high-demand conditions, congestion decreases with increasing percentage of vehicles equipped with B-ACC. With high penetration rates (75% and above), traffic no longer becomes congested at the sag. Moreover, the results show that TSA-ACC and C-ACC reduce congestion more than B-ACC, mainly because they increase the queue discharge capacity of the sag. The two advanced ACC systems prevent the formation of congestion at the sag at lower penetration rates than B-ACC. TSA-ACC is the most effective system. C-ACC is only more effective than B-ACC in scenarios with 20% penetration rate or higher; below that, connectivity between equipped vehicles is too low. Our findings show the potential of using advanced ACC systems to mitigate congestion at sags and indicate some challenges of this traffic management approach.

With automated and cooperative driving making its breakthrough, and related systems in fast development, their future influence and impact on roads and traffic may be extensive. Truck platooning is such an application that relies on the development of Cooperative Adaptive Cruise Control (CACC) and is said to be practice ready. While the main advantages of truck platooning lie in emission and energy reduction, claims are also being made for the influence on traffic flow. In this paper, we pose hypotheses based on some of the main claims. We also attempt to substantiate and give quantitative proof of the potential effects of truck platooning on traffic flow performance. The simulation model LMRS-IDM+ is extended to encompass the main influencing dynamics related to potential effects of truck platooning, based on empirical findings. The effects of truck platooning were tested for the influence of traffic states, truck gap settings, platoon sizes, and the share of equipped trucks. This resulted in outcomes regarding the total traffic performance, the performance of traffic at ramps, and the ability of a platoon to remain platooning as part of a case experiment performed on a part of the Trans-European ITS Corridor. The results showed that truck platooning may have a small negative effect on the total non-saturated traffic flow, however with a much larger negative effect on saturated traffic flow. However, drivers may be reluctant to platoon in saturated traffic in any case. The ability of inflowing traffic to merge at on-ramps was found to be affected by truck platoons, with platoon disengagements occurring under various conditions. The applied gap settings for platooning trucks did not significantly affect the merge time, while a higher gap did lead to a higher number of disengagements. The ability of trucks to platoon was positively affected by a greater percentage of equipped trucks and by larger platoon sizes. Shorter gap times also slightly improved the ability of trucks to remain in platooning formation. Finally, recommendations are given to improve platoon strategies and for policymakers to only allow truck platooning outside of busy (near-) saturated traffic, even though drivers may be reluctant to use the system in these conditions. Also, recommendations are made to investigate potential differences in the effects between the European and American contexts for truck-platooning.

Cooperative Adaptive Cruise Control (CACC) systems have the potential to increase roadway capacity and mitigate traffic congestion thanks to the short following distance enabled by inter-vehicle communication. However, due to limitations in acceleration and deceleration capabilities of CACC systems, deactivation and switch to ACC or human-driven mode will take place when conditions are outside the operational design domain. Given the lack of elaborate models on this interaction, existing CACC traffic flow models have not yet been able to reproduce realistic CACC vehicle behaviour and pay little attention to the influence of system deactivation on traffic flow at bottlenecks. This study aims to gain insights into the influence of CACC on highway operations at merging bottlenecks by using a realistic CACC model that captures driver-system interactions and string length limits. We conduct systematic traffic simulations for various CACC market penetration rates (MPR) to derive free-flow capacity and queue discharge rate of the merging section and compare these to the capacity of a homogeneous pipeline section. The results show that an increased CACC MPR can indeed increase the roadway capacity. However, the resulting capacity in the merging bottleneck is much lower than the pipeline capacity and capacity drop persists in bottleneck scenarios at all CACC MPR levels. It is also found that CACC increases flow heterogeneity due to the switch among different operation modes. A microscopic investigation of the CACC operational mode and trajectories reveals a close relation between CACC deactivation, traffic congestion and flow heterogeneity.

It is remarkable that drivers (on average) can safely navigate through dense traffic at high speeds—conditions in which the time headways between vehicles are in the same order of magnitude as human reaction times. One explanation for this is the ability of drivers to anticipate on the traffic conditions in their surroundings. In this paper, we study, through simulation, the effects of reaction times, errors in perception and anticipation on the probability of accidents on freeways. To this end we extend an existing model for car following and lane changing with a perception and anticipation model inspired by Ensley’s three levels of situational awareness (perception, understanding and projection). By systematically varying driving behavior with different reaction times over a range of perception errors, and anticipation strategies, we compute efficiency effects (capacity and total time spent) and safety effects (the probability density of accidents happening as a function of these different contributing factors and errors). The results provide some evidence that safe driving is robust with respect to perception errors under simple anticipation strategies and small reaction times. When reaction times grow larger, more advanced anticipation strategies are needed to guarantee safe driving.

With low-level vehicle automation already available, there is a necessity to estimate its effects on traffic flow, especially if these could be negative. A long gradual transitionwill occur frommanual driving to automateddriving, inwhichmany yet unknown traffic flow dynamics will be present. These effects have the potential to increasingly aid or cripple current road networks. In this contribution, we investigate these effects using an empirically calibrated and validated simulation experiment, backed up with findings from literature.We found that low-level automated vehicles in mixed traffic will initially have a small negative effect on traffic flow and road capacities.The experiment further showed that any improvement in traffic flow will only be seen at penetration rates above 70%. Also, the capacity drop appeared to be slightly higher with the presence of low-level automated vehicles. The experiment further investigated the effect of bottleneck severity and truck shares on traffic flow. Improvements to current traffic models are recommended and should include a greater detail and understanding of driver-vehicle interaction, both in conventional and in mixed traffic flow. Further research into behavioural shifts in driving is also recommended due to limited data and knowledge of these dynamics.

With the increasing number of vehicles equipped with Adaptive Cruise Control (ACC), it becomes important to assess its impact on traffic flow efficiency, in particular with respect to capacity and queue discharge rate. Simulation studies and surveys suggest that ACC has both positive and negative effects on traffic flow, but empirical evidence on this topic is scarce. A naturalistic driving study has been conducted with 8 participants who drove their own ACC-equipped vehicle during their regular trips on freeways for a period of 4 to 5 weeks. We measured spacing, headway, speed, acceleration, lane use, and the number of lane changes, and compared these between ACC On and ACC Off in different traffic states, for a total of 48 hours of driving data. Results show that with ACC On, average spacing and headways were larger, whereas standard deviations were smaller. Larger headways can be assumed to reduce capacity, whereas more constant spacing, headway, speed, and acceleration indicate more stable traffic. With ACC On, drivers performed 36% fewer lane changes in saturated traffic, resulting in increased use of either the faster or the slower lane, depending on the driver. Furthermore we found that headways were smaller with ACC On than ACC Off when only selecting accelerations below-0.5m/s2 and above 0.5m/s2, which is the opposite of the overall finding. The latter result suggests that ACC has an important limitation: a lack of anticipation. On the other hand, the smaller headways with ACC On during acceleration indicate an increased queue discharge rate.

Objective: Adaptive cruise control (ACC), a technology that allows for automated car following, is becoming increasingly prevalent. Previous surveys have shown that drivers generally regard ACC as pleasant but that they have to intervene when the ACC reaches its operational limits. The former research has been mostly concerned with specific car brands and does not fully reflect the diversity of ACC types in traffic today. The objective of the present research was to establish the determinants of pleasure in using ACC. Methods: A 55-item online questionnaire was completed by Dutch users of diverse ACC systems. Results: Respondents (N = 182) rated their ACC highly, with a mean score of 8.0 on a scale from 1 (extraordinarily negative) to 10 (extraordinarily positive) and were most pleased with ACC on high-speed roads and in low-density traffic. Moreover, the findings point to specific operational limits such as associated with cut-in situations. Pleasure was greater for the types of ACC that are able to decelerate to a full stop, according to 48% of our sample. An analysis of the free-response items indicated that respondents who were displeased with ACC mentioned its occasional clumsiness and the dangerous situations it may evoke, whereas those who were pleased with ACC valued the complementarity of human and machine and emphasized the roles of responsibility and experience in using ACC. Conclusion: Pleasure in using ACC is a function of both technological advances and human factors.

CO₂ utilization is gaining attention as a greenhouse gas abatement strategy complementary to CO₂ storage. This study explores the techno-environmental performance of CO₂ utilization trough dry reforming of methane into syngas for the production of dimethyl ether (DME). The CO₂ source is a hydrogen production unit at a refinery, where solvent based CO₂ capture is applied. Aspen+ modelling and hybrid life cycle assessment (LCA) is used to assess the techno-environmental performance of this utilization option compared to a reference case without CO₂ capture and a case with CO₂ capture and storage. Results of the technical assessment show that although 94% of the captured CO₂ can be utilised for DME production, only 9% of CO₂ is avoided in the entire process as a result of direct CO₂ formation during DME synthesis and the combustion of syngas to provide the heat demanded by the dry reforming process. Besides, a substantial amount of electricity is required for syngas compression. Consequently, the LCA results indicate that climate change potential (CCP) is reduced by 8% while it is 37% higher than CCP when CO₂ is stored and DME is produced conventionally. Sensitivity analyses are performed on various process conditions. Overall, this study indicates that this utilization route lowers the CCP although the reduction is limited compared to CCS. While the techno-environmental analysis is a useful tool to gain better insights in the performance of CO₂ utilization options, the complex environmental trade-offs make it difficult to draw robust conclusions on the performance.

Automated driving may lead to much higher road capacities, combined with increased road safety, increased driver comfort, and lower costs. Although this vision may hold ground in the long run, first a transitional period will take place in which increasing percentages of vehicles with many levels of automation will drive on the world’s road networks. This transition poses a fundamental scientific challenge. The models used today to simulate and predict vehicular traffic are not valid to predict emergent properties of traffic flows under increasing amounts of vehicle automation. For example, there is no idea of how drivers of nonautomated vehicles will respond to other drivers reading their morning papers behind the steering wheel or the consequences of such interactions on traffic safety and capacity. In this paper, the authors do not propose a new behavioral theory with which the effects of increasing vehicle automation can be predicted. What the authors propose is an advanced open-source simulation framework, OpenTrafficSim, which makes it possible to extend microscopic models incrementally with explanatory mental models, such that new behavioral theories can be tested and shared within the community. Given the societal importance of predicting the effects on safety and efficiency of vehicle automation, the authors sincerely hope this paper will fuel the discussion on how both open-source and closed-source simulation software can be adapted and made ready for the next generation of traffic simulation models that are needed in the coming decades.

Equipping power plants with carbon capture technology can affect cooling demand and water use. This study has explored the potential impact of large scale deployment of power plants with carbon capture technologies on future regional water stress in Europe. A database including 458 of European largest power plants with data on location, technology, age, fuel type, amount of electricity generation and cooling method has been developed. This data has been combined with literature data on water use rates and developed scenarios to calculate corresponding water use of these European power plants for 2030 and 2050 under different conditions, such as the penetration level of carbon capture technologies and installed technologies. Water stress methodology based on water withdrawal has been used to explore the impact of carbon capture and storage on future water stress levels. Our findings indicate that by 2030, no considerable increase in water stress is expected due to the instalment of carbon capture technologies. However, when assuming a high penetration level of carbon capture technologies, water stress in 2050 might substantially increase in many regions in Europe. The extent of the increase in water stress strongly depends on penetration level of carbon capture, installed power plant and cooling technologies and applied water stress methodology. When using water consumption to estimate water stress, the results do not indicate significant changes in water stress for the scenarios with carbon capture. Nevertheless, as water stress based on water withdrawal is currently the common method, the results of this study provide reasons for concern regarding the potential impact of carbon capture on future European water stress levels and indicate the need for future research to monitor and possibly prevent potential water stress increases from the instalment of carbon capture technologies.

Combining co-firing biomass and carbon capture and storage (CCS) in power plants offers attractive potential for net removal of carbon dioxide (CO₂) from the atmosphere. In this study, the impact of co-firing biomass (wood pellets and straw pellets) on the emission profile of power plants with carbon capture and storage has been assessed for two types of coal-fired power plants: a supercritical pulverised coal power plant (SCPC) and an integrated gasification combined cycle plant (IGCC). Besides, comparative life cycle assessments have been performed to examine the environmental impacts of the combination of co-firing biomass and CCS. Detailed calculations on mass balances of the inputs and outputs of the power plants illustrate the effect of the different content of pollutants in biomass on the capture unit. Life cycle assessment results reveal that 30% co-firing biomass and applying CCS net negative CO₂ emissions in the order of 67-85g/kWh are obtained. The impact in all other environmental categories is increased by 20-200%. However, aggregation into endpoint levels shows that the decrease in CO₂ emissions more than offsets the increase in the other categories. Sensitivity analyses illustrate that results are most sensitive to parameters that affect the amount of fuel required, such as the efficiency of the power plant and assumptions regarding the supply chains of coal and biomass. Especially, assumptions regarding land use allocation and carbon debt of biomass significantly influence the environmental performance of BioCCS.

This paper shows the results of a sustainability assessment of deploying carbon capture and storage in European coal power plants in 2030, versus a scenario without CCS. The assessment examines potential impacts in five categories: impacts on human health, impacts on the natural environment, impacts on exhaustive resources, impacts on prosperity and impacts on social well-being. The analysis is undertaken in a prospective framework for the year 2030 under the economic scenarios which are based on the Blue map scenario of the IEA Energy Technology Perspectives.

We present a structure for driver models regarding different driving tasks in microscopic simulation. This structure is part of an open and extendable simulation framework which facilitates development and research into ITS applications and driver behavior. The structure deals with typical difficulties of providing a high level of flexibility while allowing a high level of overview and user-friendliness. For driver behavior this is not a trivial task as different driving tasks depend on each other and as one requires a consistent driver, e.g. an aggressive driver is usually aggressive regarding all aspects of driving. This results in a high degree of interaction and dependency of driver models where we would like to segregate as much as possible in order to make them individually exchangeable. Our model structure allows a number of ways in which models may interact with varying degrees of generality. Inevitably this leads to concessions where we favor flexibility over user-friendliness. Specifically, the structure does not force appropriate use of model interactions. Appropriate use is thus up to the user.