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 transition will occur from manual driving to automated driving, in which many 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.
Road vehicles have gradually become technologically more advanced throughout the past decades with a focus on advancing vehicle safety and comfort. Although vehicle automation has been on the horizon for just as long [
Different definitions for the levels of vehicle automation exist. Arguably, the most used definition is that of the Society of Automotive Engineers (SAE) [
There is no lack of predictions stating that automated vehicles will solve many of the current problems experienced on roads today, such as congestion, traffic accidents, and lost time [
There is a lot of evidence that suggests that automated systems will be able to improve traffic flow in the future with sufficient penetration [
While many in science and industry focus on the utopic future of vehicle automation, there will be many years to decades in which there is a real possibility that vehicle automation may have a negative effect on traffic flow [
To aid this discussion, this contribution lays out the main foreseen effects of vehicle automation of traffic flow based on a state-of-the-art review of literature and gradually growing understanding of many of the aspects that are expected to influence traffic flow. Further quantification of a number of the effects is estimated in simulation experiments that apply empirically derived characteristics and behaviour of specific types of low-level vehicle automation systems. Application of empirically derived data allows a more realistic estimate, compared to widely applied assumptions. For other effects that cannot yet be realistically modelled due to a lack of behavioural and technological ground truths, further estimations are made of their potential effects based on current advances and best guess estimates.
In the next section, the current state of the art on the effects of traffic flow in mixed traffic is given, especially focusing on vehicle dynamics, traffic flow phenomena, and some additional behavioural aspects. In Section
While the theoretical opportunities of vehicle automation to revolutionise traffic flow are undeniable, they are not expected to be achievable for a number of decades. In the meantime, traffic has already started to undergo a slow transition from human-controlled vehicles to (partially) automated vehicles. Therefore, this section begins with an analysis of the possible time frame in which certain levels of automation may be expected. This is followed by a concise review of literature on the potential effects from changing vehicle-driver dynamics and the effects on traffic phenomena. The wider effects of driving behaviour are not explicitly considered in this research, as the quantitative effects on traffic throughput have not been clearly shown yet from research. There are also potential behavioural effects for drivers of conventional vehicles reacting to automated vehicles, which are again insufficiently researched at this time.
Giving an accurate estimate of the uptake of automated technology is extremely difficult and is heavily dependent on many factors, such as technological development, regulatory incentives or barriers, and economic development [
Estimated automated vehicle share on roads.
Based on the cited literature, the vehicle fleet share of automated vehicles will start to significantly increase from 2020 onwards but may still be less than 25% of all vehicles in 2030. This is not a conservative estimate and shows that the transitional phase from conventional vehicles to automated vehicles is going to be long, lasting many decades [
In the following subsection, aspects of this transitional phase are discussed, aided by the current state of the art on these subjects. The main focus in the discussion relates to automation systems and levels of automation which already exist or are currently close to deployment and for which tangible information is available about their effects in mixed conventional-automated traffic and are expected to be present in this phase of deployment. To that extent, the main focus is on driver assistance systems (SAE level 1) and partial automation (SAE level 2), while not disregarding higher levels of automation.
Much research has been performed on the theoretical benefits of vehicle automation on traffic flow, with the majority of the work carried out using microsimulation [
When considering the longitudinal effect of vehicle automation on operational time headways and time gaps, there are two main things to consider: the actual operational time gap of automated vehicles and their influence on surrounding traffic. The latter is considered later in this subsection in the paragraph on “vehicle dynamics.” Experiments carried out in studies on ACC systems have applied varied desired time gaps [
Although traffic flow capacity is defined by the longitudinal time headways and time gaps, these are also greatly influenced by lateral movement of vehicles, mainly involving lane-change manoeuvres. When a vehicle changes lane, it requires a sufficient gap in the destination lane and will leave a gap in the origin lane, which requires and results in larger average time headways in the two lanes. Therefore, more lane changes will lead to lower capacities purely based on average time headways. Furthermore, lane changes often lead to other vehicles accelerating or decelerating and therefore increase traffic heterogeneity, which is well known to reduce operational capacity. This is relevant as it has been found that the use of ACC systems generally leads to fewer lane-changing manoeuvres [
Traffic flow is an interactive and dynamic process in which vehicle interactions play an important role in determining its efficiency. The longitudinal and lateral movements of vehicles are inherently related to vehicle interaction; however, there are more subtle effects of interaction on a vehicular level which play a role. These relate to various stochastic effects and to the level of homogeneity in traffic flow. Disturbances and heterogeneity are known to reduce traffic flow capacity, as larger time headways can appear between vehicles [
While local capacity and flow conditions are probably the most important aspects to consider for traffic efficiency, other traffic phenomena are also important to consider on a macroscopic scale. Propagation of kinematic waves in traffic is highly relevant for the way local disturbances affect traffic flow along an entire corridor or network. In particular, congestion propagation is of special relevance in this respect. String stability of traffic using a controlled system, such as with ACC, is important and is relevant when considering propagation of kinematic waves in traffic. Most research on string stability is focused on the aggravation or attenuation of disturbances in controlled automated traffic [
Overall, there are many dynamic processes that will be affected by vehicle automation in traffic flow, of which varying estimates and evidence exist. The initial car-following effects are generally expected to be negative for traffic flow in the transitional phase before expansive cooperation is available due to higher desired time gaps. The effects of lane-changing remain understudied and its total effect may be minimal when considering all aspects involved. The greatest advantage in the transitional phase in traffic flow may be expected to come from a greater degree of homogeneity in traffic. However, these effects may require a higher penetration rate than will be readily expected during 2020–2030. Initially, the effect on other traffic phenomena appears to be limited based on literature, mainly due to the relatively large penetration rates required to have a substantial effect. An overview of the main vehicle related effects is given in Table
Traffic flow effects.
Aspect | Potential effect | Literature |
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Headway | Negative (highly dependent on time-headway settings, driver-use, and stability) | [ |
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Lane-changing |
Unclear |
[ |
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Vehicle dynamic stochastics |
Small positive |
[ |
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Congestion and propagation | Negligible | [ |
From the initial analysis, it is clear that there is a potential that there may be negative effects on traffic flow in reality during the transitional phase to vehicle automation. However, there are many uncertainties to this depending on different variables. There are three main areas that can be improved compared to previous (simulation) experiments: the validity of the applied parameter settings, the realism of the applied models, and the inclusion of extensive behavioural aspects of driving. In this experimental case, we will improve on the first two areas, validity and modelling practices, to attempt to answer the hypothesis that “
The traffic simulations are carried out using the Lane-change Model with Relaxation and Synchronization (LMRS) [
The model is configured using values that have been derived from practice and are therefore feasible real-life values for the variables. Three types of vehicles are defined: regular manual vehicles, low-level automated ACC vehicles (referred to as ACC vehicles from now on), and manual trucks. The regular manual vehicles make use of the standard settings of the IDM+ and LMRS models, as far as these are compatible with the experiment and consistent with the validation data. A similar approach is also used for the manual trucks, whose total modal share is limited in any case. The ACC vehicles are configured differently compared to the manual vehicles, mainly with regard to the longitudinal settings, with a few adjustments also in their lateral lane-change desire. The main variables that differ for the ACC vehicles compared to the manual vehicles are the desired time headway, the desired free flow speed, and the desired lane-change speed difference (i.e., the difference in speed that is required to increase the lane-change desire of a vehicle). Other minor differences are also present but have very limited effect on the model outcome. The minor variable settings are given in Table
Other relevant vehicles settings used in the model.
Variable | Manual vehicles | ACC vehicle | Manual truck |
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Maximum speed [km/hr] | 200 | 140 | 85 (Gaussian distribution with 2.5 standard deviation) |
Maximum deceleration [m/s2] | 6.0 | 6.0 | 6.0 |
Maximum acceleration [m/s2] | 1.25 | 2.0 | 0.4 |
Lane-change speed insensitivity [km/h] | 69.6 | 85 | 69.6 |
Minimum lane-change headway [s] | 0.4 | 0.8 | 0.56 |
In this research, the ACC settings are derived from empirical data, for which we use the work of [
Vehicle time gaps found adapted from Gorter [
Vehicle time gaps calibrated in the model.
From Figure
For the experiment, use is made of a simple corridor consisting of a uniform 19-kilometre motorway corridor with three lanes and a nominal speed limit of 100 km/h. After kilometre 16.5, there is an onramp present which acts as a bottleneck, with variable severity depending on the inflow from the bottleneck (see Figure
(a) Road corridor for model experiment with detector locations. (b) Exploded view of traffic flow; (c) detailed view of traffic at the bottleneck.
Traffic demand is identical for all scenarios and seeds in the experiment. The experiment is carried out for a 120-minute period with an additional 30-minute run-off period, in which no traffic is added, to ensure that the corridor is clear at the end of the simulation. Traffic is released onto the road corridor at an initial rate of 3300 veh/hr and is slowly increased to 6270 veh/hr between 10 and 60 minutes to allow capacity to gradually be reached on the road. This level of flow is maintained for 10 minutes and is linearly decreased again at simulation time of 70 minutes towards 0 veh/hr at time of 120 minutes to allow congestion to dissipate. The simulation is continued for further 30 minutes to ensure that the corridor is empty when finished to allow a fair comparison of all metrics. During the simulation, the inflow rate on the onramp is kept as a percentage of the inflow onto the main corridor. The exact percentage of inflow on the onramp is dependent on the scenario. Therefore, when traffic demand increases on the main corridor, it also increases at the same rate on the onramp.
The experiment is set up to test the vehicle dynamic aspects of automated vehicles driving in mixed traffic and therefore considers scenarios in which longitudinal driving, lane-changing, and homogeneity are present to derive their effects on traffic flow. The variables for the experiment scenarios are grouped as follows: Share of ACC-LKA vehicles using calibrated gap times Inflow rate from the onramp Percentage of trucks on road Share of ACC-LKA vehicles using higher selected gap times
The scenarios that are considered are given in Table
Overview of simulated scenarios.
Scenario group | Applied scenario settings |
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(1) Share of ACC |
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(2) Onramp |
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(3) Truck |
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(4) ACC with higher gaps |
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Three performance indicators are used to evaluate the effect on traffic flow from the different scenarios. These are the following: The The
The breakdown capacity allows us to determine the maximum potential flow under the scenario conditions, while the discharge capacity allows comparison with the breakdown to determine possible effects on the capacity drop for the different scenarios. The travel time allows us to evaluate the effects on efficiency throughout all traffic states, from freely flowing traffic through capacity conditions to congested traffic. This also captures the efficiency of traffic to transverse resulting congestion shockwaves produced by the bottleneck.
Further qualitative analysis of congestion patterns and onset from speed and flow contour plots allows us to analyse specific traffic flow phenomena and characteristics that may not be as evident from the quantitative analysis.
In total, 72 scenarios are considered. For all scenario groups, 90 runs per scenario are performed, totaling 6480 in all. After presenting the results, each set of scenarios are discussed for each of the applied performance indicators, and finally an overall comparison is made between scenarios. Visualisation of the three applied metrics for all scenarios is performed with the use of boxplot regression, in which each boxplot is the accumulation of all seeded runs per scenario. The boundaries for the boxplots are the 25th and 75th percentiles, with the whiskers set at a maximum of 150% of the boxplot range. The results are shown in Figures
Results of all scenarios for the four scenario groups (vertical) and three performance indicators (horizontal). “+” represents outliers.
To compare the results of the scenario groups with each other for the influence of ACC share, bottleneck severity (measured by onramp%), truck share, and ACC gap time, the trends of the means from each scenario are compared in a single figure per performance indicator. This is shown for travel times in Figure
Travel time trend
Capacity trend
Discharge flow trend
Capacity drop trend
The results are now discussed per scenario group starting with the percentage share of ACC. All results that are discussed are statistically significant at a 99% confidence interval of the sample mean for the 90 runs per scenario. A greater share of ACC vehicles has a limited negative effect on the travel times. The effect on capacity is a small decrease in capacity for a share of 10–80% of ACC vehicles of less than 2% (see Figure
The influence of a higher and lower onramp flow gave an unsurprising reduction and increase in capacity and travel times, respectively, as the bottleneck became more severe. The only real outcome of interest for the onramp flow percentage is that, for a low onramp flow, there is no significant deterioration of traffic flow, as seen from higher onramp flows. Further analysis of the results showed that a greater onramp flow forced manual vehicles to perform more lane changes, while for a lower onramp flow fewer additional lane changes were made, which had a positive effect on traffic.
The trend for different traffic shares of trucks also followed an expected trend without any surprises. The results show that a higher truck share leads to lower capacity and longer travel times, while lower truck shares have the opposite effect. There are no further discrepancies in the data to suggest any specific correlation with the ACC vehicle share.
Finally, when the ACC vehicles are set at a higher gap time, there are significantly different outcomes. The travel time trend follows a similar line to the calibrated gap times, only with a high negative deviation and without any eventual improvement at 100% ACC share. The capacity trend does show a significantly different trend compared to the calibrated gap times; the capacity continues to decrease with increasing ACC share, even for the higher ACC shares. It would seem that the positive effect of more stable traffic with fewer lane changes does not outweigh the larger difference in gap times compared to the manual vehicles. However, the fact that the travel times do improve for the higher ACC shares can be explained when the capacity drop trend is considered. The capacity drop for ACC vehicles with larger time gaps hardly changes for increasing levels of ACC share. For all other scenarios, we see that the capacity drop significantly increases for higher ACC shares in just about equal measure. This increase is not down to a lower discharge flow but rather a higher breakdown capacity with the discharge flow remaining constant. A higher breakdown capacity must be seen as positive; however, a higher capacity drop is bad for travel time reliability. Therefore, the results need to be considered carefully from a policy perspective.
The main findings from the experiments show that, under realistic levels of ACC share, there may be a limited negative effect on both road capacity and traffic flow. For the empirically calibrated gap times, there was a small decrease in capacity of just 1-2%, while for the larger ACC gap times a decrease of 2–7% was found in capacity. A higher mean gap for ACC vehicles at lower penetration rates is the probable cause, while the advantages of smoother traffic flow only start to occur at higher penetration rates. The experiments further show that there are no substantial effects due to bottleneck severity (in this case, onramp flow) or truck share in relation to the ACC share. The experiment further showed that the negative influence on the discharge flow is marginally greater than on the capacity flow, which in turn results in slightly higher capacity drop values for greater ACC shares. However, the higher capacity drops were only significant for unrealistically high shares of ACC vehicles, certainly higher than the 28% ACC share estimated in Section
Overall findings from the experiment.
Aspect | Found effect of low-level vehicle automation (ACC with reduced lane-changing) | Main possible cause |
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Gap time | Small negative |
Higher mean gap for ACC vehicles, especially relevant for low penetration rates |
Very small negative |
Lower discharge flow at high penetration rate due to less aggressive acceleration | |
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Interaction with trucks | No substantial effect | |
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Bottleneck severity | No substantial effect |
To give some indication of the influence of the assumptions made for the static variables in the experiment, an additional sensitivity analysis is performed. This allows us to convey the extent that these variables have on the results and acts as a further face validation of the results. The sensitivity analysis is performed for the following variables:
Sensitivity of max. speed
Insensitivity of lane-change speed
Sensitivity of max. acceleration
Sensitivity of lane-change relaxation
The
The findings from the experiment and from the review of literature lead us to a number of conclusions with regard to the traffic flow effects of automated vehicles in the initial transitional period. The transition from the current fleet of manual vehicles to the future fleet of vehicles in the coming decades ranging from 2015 to 2035 is expected to be gradual, leading to a combined share of low-level automated vehicles (SAE levels 1 and 2) between 25 and 30%. These vehicles can influence traffic flow through their different behaviour and through the extent of their share of all vehicles. The experiments showed that the relatively low share, of less than 30% low-level automated vehicles, has a limited effect compared to higher arbitrary penetration levels. Nevertheless, some effects are found and are summarised: Small negative impact on capacity and flow due to higher gap times Marginal increase of capacity drop Negligible effect on secondary traffic flow phenomena
These findings should be considered by road authorities, as even small decreases in traffic flow on heavily used roads can have a substantial effect on traffic performance. However, on the other hand, the extent to which negative effects are expected are probably sufficiently limited to not require significant expansion of infrastructure to alleviate the effects. Therefore, we can revisit the hypothesis that “
The years following the initial transitional period, probably 2030–2050, are going to be characterised by a strong uptake of vehicle cooperation. Obviously, before 2030, vehicle cooperation may already be present on roads, however at a penetration rate that is too low to make a substantial difference to traffic flow. The positive effects of cooperation have already been well stated, such as increased flow stability and efficiency, and are not explicitly the subject of this paper; however, they do act as a boundary in which the following phase of automation transition in traffic will occur.
There are also other effects on traffic flow as a consequence of low-level automation which are not considered in this research but may also be relevant. Examples are the following: Effect on user awareness and alertness Effect on safety due to quicker reactions times Effect on safety due to performing secondary tasks User acceptance, particularly in critical situations such as merging
The effects of driver interaction with the system are an intriguing area of research that has yet to lead to conclusive generic effects for traffic flow. Also, safety is indirectly of importance to traffic flow. An estimated 25% of delays in Netherlands were attributed to incidents on motorways, with similar values found in countries like UK [
Simulation of low-level automated vehicles with current simulation models is relatively achievable in many cases through use of current model settings. In fact, one could argue that prevailing traffic simulation models essentially model automated vehicles instead of their human counterparts. This is particularly true for car-following, since in the longitudinal direction drivers are largely constrained by forward system dynamics. The ideal driver model (as well as IDM+ used in this paper) describes an elegant control law that can reproduce most of the longitudinal phenomena we observe in (macroscopic) data, such as the capacity drop, flow instability, and wide moving jams.
The term
In terms of lateral movement, the discrepancies between what models predict and how human drivers really behave may be even larger. The applied LMRS model in the experiment in this research has a wide range of lane-change and lane-choice parameters and settings that allow for plausible lateral movements at least in terms of the phenomena they reproduce (lane distributions and speed synchronization). But results that seem plausible may not necessarily reflect actual underlying behaviour. Recent empirical research [
The conclusion must be that simulating longitudinal driving behaviour of automated vehicles may be straightforward; modelling the response of human drivers to these automated vehicles certainly is not. For lateral behaviour, the picture is blurrier, because we lack sufficient empirical evidence to model real human behaviour. The paradox is that most traffic simulation models mimic “ideal” (collision-free) human behaviour. Now that vehicle automation becomes a reality, there is a stronger need than ever to incorporate more sophisticated human factors in the underlying models for car-following and lane-changing behaviour.
This also means that conclusions drawn from simulations, including the ones in this paper, must be viewed in the light of the (many) assumptions made. This is especially the case for the influence of lateral movement, both in decision-making and in action. Nevertheless, the macroscopic behaviour and calibration of the vehicle behaviour which primarily focused on longitudinal behaviour in this paper still stand and constitute a clear advancement in estimation of the effects of automated driving.
The gradual introduction of automation technology in vehicles will influence traffic flow in the future; however, there are too many uncertainties to be able to clearly state to what extent. We have summarised many of the common effects that may be expected in the transitional period from manual driving to low-level automated driving and added to the knowledge base with additional findings. This transitional period will be elongated and will probably last well into the 2030s before any significant penetration rate of higher automated vehicles or cooperative vehicles will be present on roads.
The summary, aided with the help of an additional experiment case, gives us reason to believe that low-level automated vehicles in mixed traffic will have a small negative effect on traffic flow and road capacities. The main reason behind the reduction is higher gap times maintained by automated vehicles, while the influence of decreased lane changes does not show significant effects and can only be claimed in theory. The experiment further showed that any improvement in traffic flow will only be seen at penetration rates above 70%, which is far higher than will be the case in reality. The capacity drop also appears to be slightly higher with the presence of automated vehicles; however, large negative effects only occur at the higher unrealistic penetration rates. The experiment also investigated the effect of bottleneck severity and the influence of slower truck shares on traffic flow. Neither of these variables was significantly influenced by the presence of the low-level automated vehicles.
The main focus of the experiment was not on macroscopic traffic flow effects, such as on networks or during an elongated period; however, from literature, it can be argued that there may be a reduction of accidents, which could result in fewer delays. Furthermore, there is also much deliberation on the behavioural effects on drivers and their ability to be able to drive appropriately when faced with a different workload and mental tasks. This is the case for both the drivers of automated vehicles and manual vehicles drivers encountering automated vehicles. For these effects, too little is known to be able to properly quantify the traffic flow effects and this requires many additional researches.
Furthermore, we argue that current knowledge and model development still lack with regard to appropriately capturing much of real driving behaviour, especially in a lateral sense. The applied LMRS-IDM+ model combination offers an improvement in model development but still lacks full implementation of real driver behaviour due to a lack of empirical ground truths and theoretical constructs. A greater effort is required to acquire these behavioural aspects, as well as the previously mentioned cognitive aspects, if traffic simulation models are to be sufficiently enhanced to allow a full comprehensive evaluation of future traffic systems, which involve yet nondeployed vehicle types.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This research is sponsored by the strategic research support programme of Amsterdam Institute of Advanced Metropolitan Solutions (AMS;