Short Headway Research Articles

The Predictive Role of visual attention bias in aggressive driving decisions among violation-involved drivers on attitudes of right-of-way

This study, based on dual-process theory, explores the aggressive driving decision-making characteristics and cognitive processing of violation-involved drivers in right-of-way infringement scenarios. It aims to identify an eye-movement indicator that can predict drivers’ prosocial behaviors. The study recruited 75 drivers aged 19–58 years, who completed a video-based aggressive driving decision-making test and the Driver Attitudes of Right-of-way Questionnaire (DARQ). The results indicate that age moderates the relationship between violation history and visual attentional bias. Younger drivers with a history of violations exhibit an attentional bias towards aggressive words, whereas older drivers do not. After controlling for age, violation-involved drivers demonstrated a higher rate of aggressive decision-making, especially in situations with very short lane-change time headway. Visual attentional bias towards aggressive words can effectively predict positive emotions in attitudes towards the right-of-way. This suggests that early eye movement indicators during the driving decision-making process represent a form of socioemotional characteristic. The more positive the driver’s right-of-way attitude, the stronger their prosocial behavior and the weaker their intuitive impulsiveness during the early cognitive processing stages of driving decision-making. This indicates that the driving decision-making eye-movement assessment paradigm is an objective and effective method for evaluating drivers’ pro-sociality.

Reliability-Based Service III Evaluation for Prestressed Girder Bridges Under Platoon Loads

Platooning may benefit heavy truck transportation through fuel savings, reduced congestion, enhanced safety, and lower emissions. In the future, platoons may be able to act as mobile-WIM stations, and their permit process and allowable load limits may differ from unregulated trucks in a convoy. Previous reliability-based studies have focused on the Strength I limit state and have shown that trucks can operate at weights exceeding standard legal load limits even with short headways at operating-level reliability. However, the Service III limit state often governs prestressed concrete bridges. The AASHTO Manual for Bridge Evaluation does not specify a target reliability index (β) nor reliability-based evaluation guidance for the Service III limit state. The work presented here performed reliability analyses to investigate implicit reliability indices (βImplicit) inferred from bridges designed according to current and past AASHTO criteria, as well as cracking probabilities. Design live loads were used to evaluate the Service III limit state for prestressed concrete NU I-girder bridges, optimally designed using LRFD and LFD/allowable stress design (ASD). Various span lengths, numbers, continuity conditions, prestress loss methods, and allowable tension stress levels were considered. Cracking probabilities ranged between 10% and 67%, which indicates that optimally designed bridges may crack during their service life. Although beyond the scope of the study, the present work suggests a reexamination of service behavior and performance is appropriate, using an alternate mechanistic approach to estimate potential cyclic damage and aid life-cycle assessment. Such assessments could provide a more rational framework for platoon operations while maintaining bridge health and safety.

Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles

Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven – 40% 1st generation AVs– 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.

Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line

Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.

Optimal vehicle capacity and dispatching policy considering crowding in public bus transit services

This study investigates the often overlooked impact of on-board crowding on operational and user costs in public transit systems, specifically within a many-to-many bus transit line with varying demand patterns. While previous research has used mathematical programming for similar problems, this paper employs analytical approaches to offer deeper insights and address fundamental questions. First, we propose an approach to determine optimal bus capacities, factoring in in-vehicle crowding costs, assuming a fixed headway. Second, we explore the optimal dispatching policy for buses with fixed capacities, considering crowding costs. Third, we optimize both headway and vehicle capacity simultaneously. Our findings reveal that optimal vehicle capacity correlates with average passenger trip length and the square root of crowding-discomfort costs, especially when crowding increases linearly with load factor. When both headway and capacity are variable, smaller vehicles with shorter headways are favored, particularly in moderate-demand scenarios, especially with cost-effective staff models like autonomous fleets.

Developing a new integrated advanced driver assistance system in a connected vehicle environment

Advanced driver assistance systems (ADASs) can effectively enhance driving and safety performance. Due to the inherent limitations of in-vehicle technologies concerning information sharing, existing studies mainly focus on demonstrating the effectiveness of onboard sensor-based individual ADAS functions rather than their collaborative effectiveness. Thanks to the emerging connected vehicle (CV) technologies, it is viable to physically realize the collaboration and coordination between different ADAS functions. This study aims to synthesize seven ADAS functions into an integrated advanced driving assistance system (iADAS) in a CV environment. The seven ADAS functions include omni-directional collision warning, lane-change warning, curve speed warning, emergency event notification, car-following guidance, identification of variable speed limits, and information services. The activation indicators and activation conditions of these ADAS functions are derived based on a single local coordinate system. This derivation has considered the real-time motion states of the ego and surrounding vehicles. Different ADAS functions are classified based on their roles in accident reduction, traffic efficiency enhancement, and driver convenience improvement. Afterwards, their priority of releasing information is set by considering the classification and primary functionality. Finally, the effectiveness of the iADAS is validated in field tests. Testing results reveal that the iADAS helps reduce rear-end collisions and prevent rollovers or sideslips on curved roads. Furthermore, younger drivers respond faster with higher driving stability regarding lateral collision warnings. Young, well-educated, and low-risk taking drivers maintain a short but safe time headway to the leading vehicle.

The long road to automated trucking: Insights from driver focus groups

Work towards making automated driving systems a reality is well underway. In this study, we look at what is likely to be one of the first widespread implementations of a form of automated driving on public roads, i.e., truck platooning, where virtually connected trucks drive at short headways to save fuel and associated emissions. With progressing technology, we may see platoons with drivers resting while being in the truck, or even platoons in which not all trucks require drivers. Hence, platooning technology has a significant impact on the jobs of truck drivers especially when driver involvement reduces. Driver acceptance of this emerging technology is therefore an important factor in the implementation of platooning and, consequently, automated driving in general. In this study, we explore the range of perspectives that exist among drivers by conducting focus groups in The Netherlands. These discussions indicate that drivers foresee that platooning will eventually become a reality but believe it will have a negative impact on the quality of their work and their job satisfaction.

Capacity characteristics of long-term work zones on signalized intersection approaches

This study explored the capacity characteristics of five long-term WZ configurations at a major signalized intersection in Toronto. In total, 1,766 cycles and 22,057 inter-vehicle headway observations were recorded using over 140 h of real-time traffic videos. The saturation flow rate ranged from 1318.7 to 1767.3 pc/hr/ln across the study sites. Interestingly, the Highway Capacity Manual WZ model was found to significantly overestimate the traffic capacity at the study sites. The analysis assessed the hypothesis that the saturation headway is further compressed when long queues are present on site. The average saturation headway during oversaturated conditions was found to be shorter by 5 to 11% across the study sites as compared to undersaturated conditions. Other influential factors such as day versus night and late-merge were assessed. The saturation flow under daytime conditions was higher by 3 to 5% as compared to nighttimes. Vehicles merging late from the closed lanes had a shorter headway as compared to vehicles approaching from the open lane by around 8 to 10%. Probability distribution functions of the saturation headway were developed, the best-fit curve for all cases was found to be the three-parameter lognormal distribution. The study also developed multiple regression equations to estimate WZ capacity reduction induced by heavy vehicles for a wide range of heavy vehicle percentages. On average, oversaturated conditions yielded a 5.4% additional flow reduction as compared to undersaturated conditions for the same HV%. Moreover, the impact of different snow conditions on the saturation flow was estimated. The fully slushy, partly slushy, and bare-and-wet snow categories resulted in headway elongation of 35.1 to 42%, 21.2 to 23.4%, and 6.2 to 12.6%, respectively. The study explains how the findings can assist transportation agencies and practitioners in improving the policies, practices, and guidelines related to WZ activities and planning.

Platoon Optimization Based on Truck Pairs

Truck platooning technology allows trucks to drive at short headways to save fuel and associated emissions. However, fuel savings from platooning are relatively small, so forming platoons should be convenient and associated with minimum detours and delays. In this paper, we focus on developing optimization technology to form truck platoons. We formulate a mathematical program for the platoon routing problem with time windows (PRP-TW) based on a time–space network. We provide polynomial-time algorithms to solve special cases of PRP-TW with two-truck platoons. Based on these special cases, we build several fast heuristics. An extensive set of numerical experiments shows that our heuristics perform well. Moreover, we show that simple two-truck platoons already capture most of the potential savings of platooning. History: Accepted by Pascal van Hentenryck, Area Editor for Computational Modeling: Methods and Analysis. Funding: This work was supported by the Netherlands Organization for Scientific Research (NWO) as part of the Spatial and Transport Impacts of Automated Driving [Grant 438-15-161] project. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2020.0302 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2020.0302 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for Platoon Optimization Based on Truck Pairs

Truck platooning technology allows trucks to drive at short headways to save fuel and associated emissions. However, fuel savings from platooning are relatively small so forming platoons should be convenient and associated with minimum detours and delays. In this paper, we focus on developing optimization technology to form truck platoons. We formulate a mathematical program for the platoon routing problem with time windows (PRP-TW) based on a time-space network. We provide polynomial time algorithms to solve special cases of PRP-TW with two-truck platoons. Based on these special cases, we build several fast heuristics. An extensive set of numerical experiments show that our heuristics perform well. Moreover, we show that simple two-truck platoons already capture most of the potential savings of platooning. This repository contains the data and results.

Joint design of electric bus transit service and wireless charging facilities

Cities worldwide are deploying electric buses rapidly to enhance eco-friendly public transportation. Most existing electric transit systems rely solely on stationary charging at terminals and have to equip buses with large-sized batteries to maintain operation in networks. Consequently, the stationary-charging electric bus (SE-Bus) systems are subject to high battery costs, limited range per charge, long charging time (layover time), etc. These limitations may be addressed by integrating the new wireless power transfer technology, which provides contactless charging to the operating buses without extra delays. To guide wireless-charging electric bus (WE-Bus) designs in corridors, this paper proposes an optimization model to jointly optimize (i) the electric supply system and (ii) the transit service that are interdependent.Specifically, we construct parsimonious models for system metrics using five design variables/functions, i.e., battery size, the number of wireless power transfer (WPT) pads, stationary charging rate, service headways, and stop densities. Accordingly, we formulate the optimal design problem to minimize the generalized system cost as the sum of the transit agency’s and patrons’ costs. The problem is attacked by first decomposing into two sub-problems regarding (i) electric supply system design and (ii) transit service design and then solving them iteratively. The (i) sub-problem is converted to a linear programming problem by linearization, and the (ii) sub-problem is analyzed with optimal conditions. Numerical tests show the proposed solution approach produces accurate results.Extensive numerical experiments unveil several findings. First, the WE-Bus systems outperform the SE-Bus in various scenarios except when wireless charging facilities are too costly. Second, the optimized WE-Bus designs can reduce costs not only to the agency (with smaller-sized bus battery and reduced fleet cost) but also to patrons (who enjoy improved services with shorter headways). Additional parameter analyses also investigate the impacts of demand levels, wireless charging rates, depths of discharge, etc. Lastly, a case study in Chengdu (China) illustrates the application of the proposed model.

Using pupillometry and gaze-based metrics for understanding drivers’ mental workload during automated driving

This Horizon2020-funded driving simulator-based study on automated driving investigated the effect of different car-following scenarios, and takeover situations, on drivers’ mental workload, as measured by eye tracking-based metrics of pupil diameter and self-reported workload ratings. This study incorporated a mixed design format, with 16 drivers recruited for the SAE Level 2 (L2; SAE International, 2021) automation group, who were asked to monitor the driving and road environment during automation, and 16 drivers in the Level 3 (L3) automation group, who engaged in a non-driving related task (NDRT; Arrows task) during automation. Drivers in each group undertook two experimental drives, lasting about 18 min each. To manipulate perceived workload, difficulty of the driving task was controlled by incorporating a lead vehicle which maintained either a Short (0.5 s) or Long (1.5 s) Time Headway (THW) condition during automated car-following (ACF). Each ACF session was followed by a subsequent request to takeover, which happened either in the presence or absence of a lead vehicle. Results from standard deviation of pupil diameter values indicated that drivers’ mental workload levels fluctuated significantly more when monitoring the drive during L2 ACF, compared to manual car-following (MCF). Additionally, we found that drivers’ mental workload, as indicated by their mean pupil diameter, increased steeply around takeovers, and was further exacerbated by the presence of a lead vehicle during the takeovers, especially in the Short THW condition, for both groups. Pupil diameter was found to be sensitive to subtle variations in mental workload, and closely resembled the trend seen in self-reported workload ratings. Further research is warranted to assess the feasibility of using eye-tracking-based metrics along with other physiological sensors, especially in real-world settings, to understand whether they can be used as real-time indicators of drivers’ mental workload, in future driver state monitoring systems.

Alcohol impairs driver attention and prevents compensatory strategies

While the negative effects of alcohol on driving performance are undisputed, it is unclear how driver attention, eye movements and visual information sampling are affected by alcohol consumption. A simulator study with 35 participants was conducted to investigate whether and how a driver’s level of attention is related to self-paced non-driving related task (NDRT)-engagement and tactical aspects of undesirable driver behaviour under increasing levels of breath alcohol concentration (BrAC) up to 1.0 ‰. Increasing BrAC levels lead to more frequent speeding, short time headways and weaving, and higher NDRT engagement. Instantaneous distraction events become more frequent, with more and longer glances to the NDRT, and a general decline in visual attention to the forward roadway. With alcohol, the compensatory behaviour that is typically seen when drivers engage in NDRTs did not appear. These findings support the theory that alcohol reduces the ability to shift attention between multiple tasks. To conclude, the independent reduction in safety margins in combination with impaired attention and an increased willingness to engage in NDRTs is likely the reason behind increased crash risk when driving under the influence of alcohol.

Comparing the Efficiency and Effectiveness of Different Train-Following Control Algorithms for Fleets of Heavy-Haul Freight Trains under Moving Blocks

With the nationwide installation of positive train control (PTC) technology recently completed, freight railroads are searching for ways to leverage the technology to improve their operations beyond increasing safety. One possibility is reducing train headways through developing and installing a PTC-based moving-block system, thus increasing the capacity of railroad mainlines without adding costly track infrastructure. Though operating trains at shorter headways can theoretically increase line capacity, effectively controlling following trains becomes more difficult compared with a fixed-block system, requiring a fast-reacting train crew or control algorithm that effectively minimizes headway and fuel consumption while attenuating all fluctuations in lead-train speed. Otherwise, rapid changes in throttle and brake settings may be required, reducing following-train fuel efficiency, generating in-train forces, and diminishing the expected capacity gains from shortened headways. This research, sponsored by the Federal Railroad Administration, aims to better understand how closely following freight trains respond to different throttle- and brake-control algorithms. Using insights from connected automobile and truck platooning technology, the project team developed several train-following control algorithms, analyzed their stability, and simulated their performance with fleets of freight trains subject to different speed profiles. While moving-block systems do require additional train spacing beyond the minimum safe braking distance to account for train control actions, effective train-following control algorithms can minimize this distance. Also, the developed control laws exhibit a trade-off between minimizing train headway and fuel consumption, potentially allowing railway operators to choose an optimal balance.

A New Driver Model Based on Driver Response

In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of 15 vehicles for 120 s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent.

Safety at first sight? – Manual drivers’ experience and driving behavior at first contact with Level 3 vehicles in mixed traffic on the highway

Soon, manual drivers will interact with conditionally automated vehicles (CAVs; SAE Level3) in a mixed traffic on highways. As of yet, it is largely unclear how manual drivers will perceive and react to this new type of vehicle. In a driving simulator study with N = 51 participants aged 20 to 71 years (22 female), we examined the experience and driving behavior of manual drivers at first contact with Level 3 vehicles in four realistic driving scenarios (highway entry, overtaking, merging, introduction of a speed limit) that Level 3 vehicles may handle alone once their operational domain extends beyond driving in congested traffic. We also investigated the effect of an external marking via a visual external human–machine interface (eHMI), with participants being randomly assigned to one of three experimental groups (none, correct, incorrect marking). Participants experienced each driving scenario four times, twice with a human-driven vehicle (HDV), and twice with a CAV. After each interaction, participants rated perceived driving mode of the target vehicle as well as perceived safety and comfort. Minimum time headways between participants and target vehicles served as an indicator of safety criticality in the interactions. Results showed manual driver can distinguish CAVs from HDVs based on behavioral differences. In all driving scenarios, participants rated interactions with CAVs at least as safe as interactions with HDVs. The driving data analysis showed that manual driver interactions with CAVs were largely uncritical. However, the CAVs’ strict rule-compliance led to short time headways of following manual drivers in some cases. The eHMI used in this study neither affected the subjective ratings of the manual drivers nor their driving behavior in mixed traffic. Thus, the results do not support the use of eHMIs on the highway, at least not for the eHMI design used in this study.

Analysis of rear-end crash potential and driver contributing factors based on car-following driving simulation

Objective The purpose of the study was to investigate the factors associating the car-following behavior, which could lead to rear-end crash potential. The safe distance in this study was when the traffic flow was in an asymptotic stability state. Method Traffic flow observation was conducted to observe how vehicle characteristics associating car-following behavior. However, it was not possible to determine the driver characteristics and their driving habits using the traffic flow observation alone. Therefore, a driving simulation and questionnaires were used in combination to investigate how driving characteristics were associate with car-following behavior. Result From the two observation methods, several factors were found to significantly associate the rear-end crash potential including vehicle types, traveling lanes, the purposes of vehicle usage, age, income, the number of driving hours per week, stress, and anger or aggressive behavior. Conclusion The traffic flow observation indicated that among the different types of vehicles, sedan/hatchback cars were more likely to be involved in rear-end crashes than others type of vehicles. Vehicles using the high-speed lane typically drove closer to each other and had a higher chance of rear-end collisions. The observation of driver characteristics showed that younger and high-income drivers were more likely to drive with less gap, resulting in higher possibilities of a rear-accident. Despite being in a normal emotional state, drivers with high frequency of stress, anger or aggression while driving had shorter headway distances and were more likely to cause rear-end collisions.

Car-Following Properties of a Commercial Adaptive Cruise Control System: A Pilot Field Test

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 pre-specified speed profile by adapting the ACC system settings manually, whereas 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.

The Aachen Rail Shuttle ARS - Autonomous and energy self-sufficient feeder transport

This article is about a research and development project that deals with the rail bus of the 3rd generation. The vehicle concept is aimed to be energyself-sufficient and locally emission free. It has the dimensions of a standard city road bus and is designed to operate autonomously on sight in rural areas. The Aachen Rail Bus (ARS) is intended to provide a significantly better service by shorter headways in order to attract more passengers from the countryside to the rail. The research challenges are to prove that the ARS can operate at line of sight, that the energy storage system is suitable for the proposed operation concept and that the passive safety system protects the light and relatively weak passenger compartment, which shall be easily exchangeable by an adequate interface to the chassis that includes all technical equipment.

Flow-aware platoon formation of Connected Automated Vehicles in a mixed traffic with human-driven vehicles

Connected Automated Vehicles (CAVs) bring promise of increasing traffic capacity and energy efficiency by forming platoons with short headways on the road. However at low CAV penetration, the capacity gain will be small because the CAVs that randomly enter the road will be sparsely distributed, diminishing the probability of forming long platoons. Many researchers propose to solve this issue by platoon organization strategies, where the CAVs search for other CAVs on the road and change lanes if necessary to form longer platoons. However, the current literature does not analyze a potential risk of platoon organization in disrupting the flow and reducing the capacity by inducing more lane changes. In this research, we use driving model of Cooperative Adaptive Cruise Control (CACC) vehicles and human-driven vehicles that are validated with field experiments and find that platoon organization can indeed drop the capacity with more lane changes. But when the traffic demand is well below capacity, platoon organization forms longer CAV platoons without reducing the flow. Based on this finding, we develop the Flow-Aware platoon organization strategy, where the CAVs perform platoon organization conditionally on the local traffic state, i.e., a low flow and a high speed. We simulate the Flow-Aware platoon organization on a realistic freeway network and show that the CAVs successfully form longer platoons, while ensuring a maximal traffic flow.