Conducting the study

The simulation environment ITS Modeller was used to assess the impacts of the Congestion Assistant on the traffic flow. This microscopic traffic flow model was found to be a suitable tool for this. It is able to represent the behaviour of vehicles equipped with the Congestion Assistant and their interactions with other (non-)equipped vehicles on a congested motorway section. As with any model, its value largely depends on the resemblance with the real-world. In this study, the reference situation in the ITS Modeller was calibrated and validated using 1-minute speed and flow data measured on the Dutch A12 motorway. The results showed a satisfactory resemblance between the empirical and the simulated traffic flow data, particularly with respect to the onset of congestion. Nonetheless, the further development of the ITS Modeller could benefit from more validation. For example, on the level of individual

drivers the variance in driving behaviour could be better incorporated. This does not only concern the parameter settings, but also the modelling assumptions. For example, the car- following model could be enhanced by distinguishing different driving behaviour in congested and non-congested situations. This would probably also improve the simulated behaviour at the recovery from the jam.

A diversity of measures was used to study the impacts of the Congestion Assistant on traffic efficiency and traffic safety. These measures gave insight into the system’s performance in a traffic flow where equipped and non-equipped vehicles interact with each other. However, other measures are necessary to gain more detailed information about the working of the functions of the Congestion Assistant in the traffic flow. For example, the standard deviation of deceleration can provide more knowledge of the effects of the Active pedal on anticipating the jam. Also ‘zooming in’ on one or more vehicles during the simulation run and collecting data of these vehicles would enhance the understanding of the traffic flow effects. This ‘zoom in’ possibility does not yet exist in the ITS Modeller and it is recommended to include such feature.

Different variants of the Congestion Assistant were studied. Especially, the Stop & Go variants positively influenced the throughput of the traffic flow. In this study, two different time headway settings were examined. It would be interesting to also examine other characteristics of a Stop & Go, such as the acceleration range and the contribution of vehicle- vehicle communication. The Stop & Go in this research supports the driver without deceleration and acceleration limitations. However, for the moment, it is expected that such systems will be restricted to a certain acceleration range (e.g. between -3 m/s2 and +1.5 m/s2), so that sometimes the driver needs to intervene. Another acceleration algorithm might also lead to safer braking and following behaviour, recalling the rather high percentages of hard braking and small TTCs with the current algorithm. Furthermore, in the longer run, it is expected that vehicles will be able to communicate with each other. Through the exchange of information with predecessors, a Stop & Go could further increase the traffic performance by more efficient following behaviour. In the Integrated full-Range Speed Assistant (IRSA) project, for example, a clear added value of vehicle-vehicle communication was found, especially when vehicles equipped with IRSA approached a traffic jam (Van Arem et al., 2007).

Compared to the Stop & Go variants, the Active pedal variants showed smaller positive effects on traffic efficiency, although these variants increased traffic safety due to a smoother approach to the traffic jam. One reason for the small throughput effects might be the deceleration capacity of the Active pedal, which was restricted to -1 m/s2. Some preliminary simulations with an Active pedal that can decelerate with a maximum of -5 m/s2 showed to reduce the amount of congestion much more than the current implementation. So it seems that versions of the Active pedal with a less conservative deceleration capacity (and acting like an Active brake pedal rather than an Active gas pedal) could enhance traffic efficiency better. Furthermore, the Active pedal in this study assumes communication between all vehicles for locating a traffic jam and compliance of the driver with a proposed deceleration. It would be interesting to examine the impacts of only communication between equipped vehicles and less compliance of the driver (as seen in the driving simulator experiment). However, the impacts of the current implementation of the Active pedal on the traffic flow are already small, so it is expected that these adaptations will lead to even smaller impacts.

The Congestion Assistant was tested in specific congested traffic situations due to a left lane drop from 4 to 3 lanes. There are more motorway bottlenecks that can cause traffic breakdown, such as on-ramps and weaving sections. Congestion might develop differently per bottleneck. It would be interesting to investigate to what extent the observed effects of the Congestion Assistant prevail in other congested motorway situations.

8.6 Conclusions

This chapter presented the results from a microscopic traffic simulation study into the impacts of the Congestion Assistant on the traffic flow. Several variants of the system with different equipment rates were analysed on a four-lane motorway with a lane drop. The Congestion Assistant became operative after the start of a traffic jam and affected the further development of the jam. The Active pedal of the system slowed down the driver when approaching the jam at too high speed, while the Stop & Go took over the longitudinal driving task in the jam. It appeared that both the Active pedal and the Stop & Go have positive effects on the dissipation of traffic jams, but the effects due to the Stop & Go are much larger. Combining the two functions did not lead to better results on traffic efficiency compared to the results of the single functions. In fact, the Active pedal showed no added value when it is combined with the Stop & Go. All variants of the Congestion Assistant reduced the amount of congestion, hereby also decreasing the speed variation, indicating a more stable, homogeneous and safe traffic flow. The Active pedal further increased traffic safety by less hard braking actions and less unsafe following situations when approaching a jam. The Stop & Go, on the other hand, showed more hard braking actions and more potentially unsafe following situations in the jam due to rather fierce acceleration behaviour. Combining the Stop & Go and the Active pedal decreases the percentages of hard braking and small TTCs compared to only the Stop & Go.

In summary, the Congestion Assistant showed to compensate for the unfavourable human behaviour that (also) causes congestion. The Active pedal smoothed the traffic flow when approaching a traffic jam by inducing better anticipation behaviour of the driver. Vehicles equipped with the Stop & Go followed other vehicles more efficiently when driving in and leaving a jam by maintaining smaller headways and eliminating the reaction time of drivers. Especially the Stop & Go reduced the amount of congestion significantly. At the same time, this function also increased the amount of hard braking. Adapting the acceleration algorithm of the Stop & Go will presumably compensate for this effect.

145

Chapter 9

Conclusions and recommendations

The objective of this research was to gain more insight into the user needs for driver assistance and consequently, into the impacts of the so-called Congestion Assistant on the driver and the traffic flow. This final chapter discusses the outcome of the total research project. It starts with a review of the results from the previous chapters by answering the research questions posed in Chapter 1. Next, some directions for further research are discussed. The chapter ends with general implications and conclusions.

9.1 Overview of results

The first objective of this research was to gain more knowledge of the driver’s point of view towards intelligent vehicles. The second objective was to assess the behavioural responses of the driver to the Congestion Assistant, an in-vehicle system that was developed based on in this research observed user needs for driver assistance. The third objective was to assess the influence of the Congestion Assistant on traffic flow characteristics. This section draws the main conclusions concerning these objectives by answering the three corresponding research questions:

• What are the needs of the driver with respect to driver assistance?

• What are the impacts of the Congestion Assistant on the driver, in terms of driving

behaviour, mental workload and acceptance?

• What are the impacts of the Congestion Assistant on the traffic flow, in terms of traffic efficiency and traffic safety?

9.1.1 User needs for driver assistance

A user needs survey was conducted to investigate the perceived needs for driver assistance. In contrast to earlier surveys that generally concentrated on ‘ready to use’ driver support systems, our survey focused on assistance with several driving tasks and situations, and desired combinations thereof. This enabled a better understanding of when and how car drivers want to be assisted by their cars during driving.

The results of the user needs survey are based on the answers of 1049 Dutch respondents that completed the survey on the Internet. In the first part of the survey, they had to express their needs for a variety of driver support functions. The respondents particularly favoured warnings for downstream traffic conditions, such as congestion and accidents, and warnings for traffic in blind spots, for example when changing lanes or approaching an intersection. This shows that drivers appreciate being well informed when driving. Furthermore, driver assistance on motorways was preferred to assistance on rural roads and urban roads. This has probably to do with the complexity of the traffic process, being least complex on motorways. It is likely that drivers assume to be best assisted by a system that is well capable of detecting and interpreting the driving environment. Automatic actions from the car were generally not appreciated. However, one exception to this was congestion driving. Apparently, drivers do not make a problem of handing over control to the car during such an uncomfortable driving task. In the second part of the survey, the respondents had to formulate their ideal driver support system by choosing a limited number of driving tasks and situations to be supported by this system. The ideal system seems to be personal, since the respondents chose various driving tasks and situations that the system should provide assistance with. The majority of respondents, however, preferred the ideal system to give support with reduced visibility and imminent crash situations. This shows that drivers value help from their cars in potentially dangerous situations.

The observed user needs have implications for the design of driver support systems. To fit these needs, systems should integrate several forms of driver assistance, for example by exchanging information between vehicles and using one user interface. This also applies to the Congestion Assistant, which was developed based on the survey results. The system consists of a mix of informing, assisting and controlling functions to support the driver during congested traffic situations on motorways:

• Warning & Information: the driver receives warnings about a traffic jam ahead and information about the length of the jam when driving in it.

• Active pedal: the driver feels a counterforce of the gas pedal when approaching the jam at

too high speed.

• Stop & Go: the system takes over the longitudinal driving task from the driver when driving in the jam.