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Impact of Automated Vehicles on Capacity of the German Freeway Network


Abstract and Figures

This paper shows the results of ex-ante impact assessment of automated vehicles on capacity of freeways using microscopic traffic flow simulation. The simulation was conducted for different penetration rates of automated vehicles in Germany´s national vehicle fleet, which were predicted using a newly developed vehicle cohort stock model. For this aim, the standard segments of German freeway infrastructure including basic, merge, diverge, and weaving segments were simulated. The resulting capacity increments were assigned to a country-wide traffic flow model of Germany. In the next step, an economic appraisal was conducted based on the methodology for the cost-benefit analysis used in the current German Federal Transport Infrastructure Plan (BVWP). The results reveal that the conservative driving behavior of automated vehicles, as foreseen by the current legislation, has a negative impact on the capacity of freeways. On the contrary, automated technologies that allow shorter headways between the vehicles, have the potential to increase the capacity of the freeway network by 30 % and reduce traffic delays significantly. However, small market penetration rates of automated vehicles do not lead to discernible capacity benefits and the potential benefits are likely to be realized at higher penetration into the traffic mix.
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ITS World Congress 2017 Montreal, October 29 November 2
Paper ID SP0792
Impact of Automated Vehicles on Capacity of the German Freeway Network
Martin Hartmann1, Nassim Motamedidehkordi2*, Sabine Krause2, Silja Hoffmann2, Peter Vortisch1,
Fritz Busch2
1. Karlsruhe Institute of Technology, Institute for Transport Studies, Germany
2. Technical University of Munich, Chair of Traffic Engineering and Control,
This paper shows the results of ex-ante impact assessment of automated vehicles on capacity of freeways
using microscopic traffic flow simulation. The simulation was conducted for different penetration rates of
automated vehicles in Germany´s national vehicle fleet, which were predicted using a newly developed
vehicle cohort stock model. For this aim, the standard segments of German freeway infrastructure including
basic, merge, diverge, and weaving segments were simulated. The resulting capacity increments were
assigned to a country-wide traffic flow model of Germany. In the next step, an economic appraisal was
conducted based on the methodology for the cost-benefit analysis used in the current German Federal
Transport Infrastructure Plan (BVWP). The results reveal that the conservative driving behavior of
automated vehicles, as foreseen by the current legislation, has a negative impact on the capacity of
freeways. On the contrary, automated technologies that allow shorter headways between the vehicles, have
the potential to increase the capacity of the freeway network by 30 % and reduce traffic delays significantly.
However, small market penetration rates of automated vehicles do not lead to discernible capacity benefits
and the potential benefits are likely to be realized at higher penetration into the traffic mix.
Automated vehicles, Capacity, Freeway
1. Introduction
Automated Vehicles (AV) are becoming a reality with a lot of pilot projects demonstrating and testing the
technology abilities as public authorities are opening public roads to test AVs in real traffic. Proponents of
vehicle automation argue that reduced accident rates, increased road capacity and increased social
benefits due to better in-vehicle time utilization may occur. However, in short term these vehicles operate
along the conventional vehicles and their impact on the overall traffic network is unclear. While due to
conservative-assumed driving behavior of AVs in the introductory phase the initial impact on capacity might
be negative, it is broadly expected that major diffusion of AVs within the vehicle fleet will show a
harmonizing, and thus positive, impact on capacity of the transportation infrastructure. From the network
operator perspective, it is important to understand the potential capacity increase in order to address
network planning accordingly. In this paper, the ex-ante impacts of partially-, highly- and connected highly
automated vehicles on the freeway capacity in Germany were investigated.
To be able to estimate the impacts of an unknown amount of AVs on the current infrastructure, the
penetration of AVs within the German vehicle stock was predicted for the next decades and considered
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within the capacity analysis. This was done by examining the impacts of AVs on individual types of basic,
merge, diverge, and weaving segments of the freeway network with respect to the predicted penetration
rates. Therefore, a microscopic traffic flow simulation was deployed to model the altered driving behavior
of AVs on the segments typically represented in the German freeway network. Finally, the local impacts
were extrapolated and assigned to the country-wide traffic flow model of Germany (PTV Validate) to draw
economic conclusions.
The paper is structured as follows: First, the literature investigating the impacts of automation on the
freeway capacity is reviewed. Next, the general modelling framework of the capacity analysis and the
assumptions made within the investigation are described in section 3. The simulation scenarios, which are
developed to investigate the impacts on freeway capacity, and the simulation results are illustrated in
sections 4 and 5, respectively. In section 6, the results of the simulation are extrapolated to the entire
freeway network in Germany. Finally, a simplified economic appraisal based on the current method for cost-
benefit analysis used in the German Federal Transport Infrastructure Plan (BVWP) is presented.
2. Literature Review
Much research has been dedicated to impact assessment of AVs on various indicators of traffic flow
efficiency, especially capacity [1,2,3,4,5]; however, these studies focused mainly on longitudinal automation
of vehicles. According to [6,7] automated platooning of vehicles can contribute to substantial influence on
freeway capacity. Vander Werf et al. [8] showed that the longitudinal automation of the driving task can lead
to an increase in lane flow from 2,100 veh/h/ln on a freeway with 100 % conventional vehicles to 2,900
veh/h/ln when the fleet consists of a mix of 20 % conventional vehicles, 20 % Adaptive Cruise Control (ACC)
and 60 % Cooperative Adaptive Cruise Control (CACC) equipped vehicles. Shladover et al. [9] examined
the implications of ACC/CACC technology on freeway capacity through microsimulation with varying
penetration rates of each technology and concluded that CACC can potentially double the capacity of a
freeway lane at a high CACC market penetration rate. However, van Arem et al. [10] illustrated that the
penetration rates lower than 40% do not have an effect on capacity.
Modeling of national vehicle fleet is of research interest due to its suitability for testing various fiscal policies
concerning electromobility, road pricing, pollution zones or most recently autonomous vehicles. In the most
recent works, researchers developed stock-flow cohort models suitable for the simulation of AVs market
penetration. The Norwegian stock-flow cohort model [11] forecasts the Norwegian automobile fleet onto the
2050 horizon in a bottom-up manner to provide a tool for short- and long-term fiscal policy analysis. In
Germany, a vehicle technology diffusion model for Germany and USA was developed to study the impacts
of automated driving on the mobility behavior [12]. The results of the same model served as an input into a
travel demand model to quantify different impacts of AVs introduction [13].
3. Framework and assumptions
3.1. AV characteristics
It is recognized that various definitions are available regarding connected and automated vehicles. In this
research the functions, which correspond to the level 1, 2 and 3 of automation (based on the definition of
VDA [14]), are considered. This study examines three different types of AVs:
In a partially automated vehicle (PAV), only longitudinal control of the vehicle is handed over to the system
and the lateral movement, specifically lane changing, is controlled by the driver. PAV functions such as
ACC are already available in the market. A highly automated vehicle (HAV) is the one in which both
longitudinal and lateral movements are controlled by the system. A connected automated vehicle (CAV)
employs Vehicle-to-Vehicle (V2V) technology by which it can adopt different driving behavior (such as
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utilizing shorter headways when following another CAV and more cooperative lane changing) based upon
the surrounding vehicles.
In order to simulate different automation functions, generic models for PAV, HAV and CAV were developed.
The lateral and longitudinal behavior was implemented by changing the parameters of a car-following model
(represented by Wiedemann 99 model) and a lane-change model within the microscopic traffic flow
simulation. An overview of the resulting settings of each AV type is given in Table 1:
Table 1 - Overview of different vehicle types
Vehicle type
Headway [s]
Conventional Vehicle
1.1 (with variation)
Partially Automated Vehicle (PAV)
Highly Automated Vehicle (HAV)
Connected Automated Vehicle (CAV)
0.9 (when following
another CAV)
Extreme Automated Vehicle (CAV*)
The assumptions for the longitudinal behavior of vehicles are as follows:
AVs obey the recommended maximum speed for passenger cars (PC) which is 130 km/h, maximum
allowed speed for heavy vehicles (HV) which is 80 km/h, and minimum headway recommended by German
Road Traffic Act [15]. As shown in Figure 1, it was assumed that the AVs, unlike conventional vehicles,
have the capability of following their predecessor with a constant headway. Adding the V2V communication
component to HAV facilitates the exchange of information and a better cooperation between the CAVs, by
which lower headways are allowed. Regarding the operation of CAVs, they have the desired time gap of
0.9 s and 1.8 s, if it is following another CAV and a non-CAV vehicle, respectively. In order to investigate
the potential impact of AVs on freeway capacity, an extreme headway setting for CAV (CAV*) was simulated
in which the headways are 0.5 s.
Figure 1 - Headway distribution among the different vehicle types
The assumptions for lateral movement of vehicles are as follows:
First, since the lateral movement of PAVs is monitored and controlled by the driver, the identical model as
for conventional vehicles was used. For HAVs and CAVs, the system takes over the control of lane
changing. For HAVs and CAVs, when compared to conventional vehicles, lower deceleration rates of the
preceding vehicle on the destination lane and bigger safety gaps are allowed during lane changing. Here,
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it is assumed that these vehicles follow the German “keep right” rule and return to the right lane as soon as
possible. Besides, both HAVs and CAVs have the cooperative lane change behavior, meaning that in case
the vehicle observes a preceding vehicle on an adjacent lane which intends to change lane, the vehicle
changes its lane in order to facilitate the lane change maneuver execution for the other vehicle.
3.2. Market scenarios and simulated network
In this paper, besides the base scenario (2015, 100 % conventional vehicles), four instances of a trend
scenario (2030, 2040, 2050 and 2050+) and one extreme scenarios 2050+ were covered. The three
instances between 2030 and 2050 correspond to a transition period where the vehicle fleet is represented
by a mixture of conventional and AVs (see Section 3.3. for the specific market shares). Next, the instance
of the trend scenario beyond 2050 corresponds to a saturated AV market. Finally, to explore the maximum
potential benefits of the AVs, the extreme scenario simulates extremely close-headways between the AVs
representing thus as an upper bound within the market scenarios.
Since a microscopic traffic flow simulation of the Germany´s freeway network is impracticable, the network
was split in a number of individual freeway component segments as an input for the simulation. The
definition of these component segments including their capacities is based on the 2015 edition of the
German Highway Capacity Manual (HBS) [16] and includes standard types of basic, merge, diverge, and
weaving segments. To meet the capacities given by the HBS, the component segments shown in Table 1,
modelled and calibrated following guidance from [17], were used for the simulation of the base scenario. In
order to investigate the impact of the AVs on the entire freeway network, the results of individual ´
simulations of freeway component segments are further extrapolated based on the distribution of the
component segments within the freeway network. The location and distribution of the component segments,
determined by using GIS tools, together with the altered capacity values, were transferred to the commercial
network model Validate developed by PTV Group for the extrapolation and economic appraisal described
in Sections 6 and 7, respectively.
Table 2 - Selected HBS freeway segments for the simulation
Component Segment
E 1-2
A 4-2
E 1-3
VR 1-1
E 1-4
S 2
A 1-2
S 3
A 2-3
3.3. Vehicle stock model
In order to investigate the impact of various market shares of AVs on the freeway capacity, a cohort vehicle
stock model was developed to forecast the vehicle automation technology diffusion in Germany in the
upcoming years. As illustrated in Figure 2, the vehicle stock model consists of four component models: (1)
model of PC and (2) HV stock, (3) model of vehicle technology market introduction and penetration and
finally (4) a model of vehicle utilization. Merging these four component models provides a model of a vehicle
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technology diffusion in Germany with respect to the trends in car ownership, car utilization and predicted
technology adoption rates.
Figure 2 National vehicle stock model
The passenger car stock is forecasted based on the statistics collected about the passenger car stock in
Germany in the last 40 years [18]. The key indicators represented by average vehicle age, average vehicle
longevity and forecasted vehicle stock are constrained to match other available forecasts in Germany [19],
besides others under the assumption of an upcoming car peak [20]. Since the share of foreign-registered
HVs on German freeway reaches 40 %, neglecting these vehicles would underestimate the effects of the
automation on the freeway capacity in Germany. The annual road toll statistics published by the Federal
Authority for Goods Transport [21] is therefore a suitable database for the determination of the key
indicators of the HV fleet on German freeways.
Next, a literature analysis provided the estimated years of the introduction of vehicle assistant systems on
the market [22, 23]. In this paper, it is assumed that the German car manufacturers will follow a top-down
approach leading to the introduction of the automated vehicle functions in the premium vehicle segments
first. Further, it is assumed that there will be no regulatory change prescribing a compulsory introduction of
the automation within the manufacturer's vehicle fleet. Through an introduction of the vehicle technology
within the newly registered vehicles in the stock model, the technology diffusion is then a result of the aging
process of the vehicle stock with respect to the annual scrapping and survival rates.
Finally, it is assumed, that the AVs will be overrepresented on the freeways as the frequent car users tend
to spend more money on the assistant systems and generally adopt technology faster. To reflect on this, a
passenger car utilization model was fused to the vehicle stock model. The data for the car utilization model
was collected by the household travel survey German Mobility Panel (MOP) [24] and modelled to obtain
among others the average annual and daily vehicle mileage on the freeway [25]. As a result, Table 3 gives
the aggregated penetration rates of AVs (PC and HV) within the vehicle fleet in Germany.
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Table 3 - Penetration rates of each vehicle type in market scenarios
Base (lower bound)
100 %
74 %
11 %
9 %
6 %
32 %
28 %
22 %
18 %
12 %
35 %
27 %
26 %
Saturated AV market
100 %
Extremely close-headways (upper bound)
100 %
4. Simulation Experiment
In order to quantify the potential impacts of AVs on freeway capacity, a wide range of simulation
scenarios, with help of microscopic traffic flow simulation Vissim 7.00, was covered:
Different freeway segments,
Different types of automated vehicles,
Various penetration rates of AVs for 2030, 2040, 2050 and 2050+,
Different loading ratios: the ratio between merging, diverging and weaving volumes and volume
on the freeway mainline.
The general simulation approach within this study is illustrated in Figure 3.
Figure 3 - Simulation approach within the study
In order to account for the stochastic nature of traffic, in each simulation scenario multiple runs with different
“random seeds” were simulated. For each scenario, the number of necessary simulation runs was
determined based on the statistical method provided by FGSV (The Committees of the Road and
Transportation Research Association) [25].
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The capacity of each road segment was calculated with the help of fundamental diagrams and the method
used in [17]. In this study, in order to estimate the capacity, the average breakdown volume in 5-minute
intervals was determined. A breakdown was defined as a speed drop below a threshold speed. The
threshold speed was individually defined for each analyzed segment based on the gap between the
simulated data points representing free flow and congested traffic conditions in the flow speed diagram and
ranges between 60 and 80 km/h. To determine the capacity of the freeway segment by identifying traffic
breakdowns in the simulation, it was necessary to increase the demand beyond the capacity of the
segment. For this aim, the capacity values from HBS were considered as a reference. In case that due to
the new behavior of AVs, no breakdown occurred in the simulation, another method was employed for
capacity estimation. In this method, if the traffic volume did not increase for three consecutive time intervals,
despite the increasing demand, the data point close to the 90th percentile of the traffic demand value was
considered as capacity.
5. Simulation Results
This section describes the simulation results, with the focus on the capacity difference of each simulated
freeway segment in comparison to the base scenario. Figure 4 represents examples of the capacity
increments within each scenario relative to the base scenario, for different road segments. The figure
depicts, the capacity deteriorates due to a gradual increase in penetration rates of PAVs and HAVs, whose
behavior is conservative compared to conventional vehicles, regardless of the road geometry. With an
increase in penetration rates of AVs, let it be partially, highly or connected highly automated vehicle, the
negative impact becomes more apparent. Despite 26 % of vehicles being CAVs in 2050, their positive
impact on the capacity is negligible which confirms the fact that the benefits of CAVs are not significant in
low penetration rates. The capacity benefit can be realized to a greater extent when automated technology
is combined with connected technologies and when extremely short headways are allowed as in scenario
2050+ upper bound. The automation of vehicles has the potential to increase the capacity by 45 % in basic
road segments. In weaving segments, where a merge and diverge are in close proximity, intensive lane
changing has a major impact on capacity and cooperation between vehicles is of high importance. That
explains the fact that the negative impact of conservative PAVs and HAVs on capacity in weaving segments
is higher when compared to basic road segments as well as merge and diverge segments. Likewise, the
CAV* keeping the extremely short headways does not provide sufficient safety gaps for lane changes,
therefore the capacity improvement for weaving segments in 2050+ and 2050+ upper bound scenarios is
not as much as in abovementioned road segments.
Figure 4 Simulation result representing the percentage change in capacity of each freeway
segment relative to the base scenario
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It is noteworthy to mention that in diverge segments, due to large headways of PAVs and HAVs, the vehicles
leaving the freeway have the possibility to reach the off-ramp without causing perturbation in mainstream
traffic, therefore in diverge segments traffic was harmonized and fewer traffic breakdowns were observed
in the simulation.
6. Extrapolation to a country-wide traffic flow model
The objective of this section is to give a rationale for extrapolating the simulation results of individual
component segments to a country-wide traffic flow model. The traffic flow model is represented by the
commercial product PTV Validate, which covers the major roads in Germany and splits the country in over
10,000 zones using over 3.5 Mio links. The calibrated PC and HV demand matrices are used to calculate
the traffic flows and are compared against the defined capacities to obtain volume/capacity ratios. Changing
the default capacity values of component segments to the simulation-based results alters necessarily the
network performance indicators. This section presents the results of the extrapolation and sums up the
impacts of the AVs on the mean capacity, total delay, travel time and volume/capacity ratio.
6.1. Capacity
The mean percentage change of nominal capacity is displayed in Figure 5. The figure further shows the
market shares of respective automation levels as defined in Section 3.1 and the resulting impacts on the
capacity comparing to the base scenario (red line). The percentage change of nominal capacity represents
an aggregate value across all individual component segments on German freeway network. The results of
the extrapolation show, that an increasing share of partially- and highly automated vehicles that drive rather
conservatively and “by the book” will result in a capacity decrease up to 7 %. Only with a significant share
of connected and automated vehicles that maximizes fully the potential of vehicle communication resulting
into close-headways and cooperative maneuvering, there will be a significant increase of road capacity
leading to about 30 % capacity increase. These results seem to be rather conservative, however, are
perceived as plausible and in accord with the adopted assumptions.
Figure 5 - Impacts of AVs on the freeway capacity in Germany
2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070
market share / capacity change
Conventional HAV CAV Capacity
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6.2. Other performance indicators
Adjustments of the default capacity values in the traffic flow model lead inevitably to changes in
performance of the transportation network. Figure 6 depicts results aggregated over all freeway links within
the Validate model. The freeway performance trends clearly mirror the trend in capacity changes: initial
negative impacts of the automation are observed. First by around 2050 significant positive impacts on the
network performance will be reached.
Figure 6 - Impacts of AVs on total delay and travel time within the freeway network
In Figure 7 base scenario and upper bound scenarios are visualized on the example of volume/capacity
ratio: the majority of bottlenecks within the network were removed in the upper bound scenario and the
overall load of the network was decreased. Needless to say, neither traffic demand growth nor rebound
effects such as rerouting or induced traffic demand are incorporated within the methodology of this study.
Figure 7 - Volume/capacity ratios: Base (left) vs. Extreme scenario (right)
2030 2040 2050 2050+ 2050+
Total delay vs. Base
2% 4%
-6% -7%
2030 2040 2050 2050+ 2050+
Travel time vs. Base
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7. Economic appraisal
This section presents an economic appraisal of the impacts of the AVs on the capacity and thus on the
travel time in the German freeway network. The appraisal is based on the methodology developed for the
current Federal Transport Infrastructure Plan 2030 [26] that defines the prioritizing strategy in the transport
infrastructure maintenance, expansion and new construction process. In this paper, we base our appraisal
on the cost-benefit analysis (CBA) as the core component of the BVWP appraisal process. The CBA
captures 12 various benefit components, from which three are selected in this appraisal as they are
dependent on travel time changes: Travel time changes in private transport (NRZ), Transport time changes
in heavy goods transport (NTZ) and Changes in operating costs (NB). The travel time reliability component,
as well as the traffic safety component of the CBA, were omitted in this paper. Due to the extent of the
methodology, we refer to the BVWP 2030 and present only the results of the CBA components.
Table 4 - Aggregate annual economic appraisal based on the German BVWP
[M veh∙h/yr]
VHT change
[M veh∙h/yr]
[M €/yr]
[M €/yr]
[M €/yr]
[M €/yr]
Base (lower bound)
Extreme (upper bound)
Table 4 gives the estimated annual economic benefits of AVs based on their impact on the capacity of the
German freeway network. Importantly, this appraisal is closely tied to the assumptions made and a rather
conservative setting of both PAVs and HAVs, basically responding to the driving behavior allowed by the
current legislation. The economic appraisal follows the trend of capacity increments and yields negative
economic benefits during the introductory phase of the automation (20152050). However, large economic
benefits can be achieved once high penetration rates of closely spaced automated, and particularly
connected automated vehicles, contribute significantly to the traffic flow harmonization.
8. Conclusions
This paper presents a study about the impact assessment of AVs on the capacity of German freeway
network with the help of microscopic traffic flow simulation. Simulation results clearly convey that the impact
of vehicle automation on capacity depends strongly on the driving behavior of AVs, especially the
longitudinal behavior, as well as their penetration rate within the vehicle fleet. The change in freeway
capacity is not profound in low penetration rates and the potential benefits are likely to start kicking in at
higher penetration rates. Moreover, the capacity deteriorates in case the conservative driving behavior of
AVs is introduced. Further analysis showed that exploiting the potential benefits of AVs (up to 30 % increase
in capacity for the entire freeway network) and better use of road infrastructure will be achieved once the
headway of these vehicles falls below the value in current regulations.
Even though many car manufacturers state their readiness to mass-produce AVs in the next years, large
market penetration of AVs is decades away and the potential impact of the automation cannot be fully
exploited in the near future and with the existing regulations. By then, there might be significant changes in
the current legislature, economic situation (sharing economy) as well as mobility behavior. As a result, our
approach makes a number of assumptions relating to the driving behavior of AVs, structure of the vehicle
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stock and market penetration rates as well as the adoption of the new technology. Therefore, it is necessary
to understand the results of this study are in a very close context to the assumptions made. Since the
abovementioned aspects of AVs are associated with significant uncertainty, additional research is
necessary to better anticipate the impact of AVs.
This research was financially supported by the Research Association of Automotive Technology (FAT),
working group 7 „Optimization of Road Traffic Systems“. Thanks go to PTV Group AG for providing the
Vissim simulation tool license.
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... Since some of the assumptions used for theoretical analysis may be unrealistic, other studies have further validated deduction results using simulation experiments [19][20][21][22][23][24][25][26][27][28][29][30][31]. Specifically, Lu et al. [21] considered two types of vehicles, "no automation" and "full automation". ...
... Based on prior research, we can deduce that AVs generally have a positive impact on traffic flow in most cases. However, existing research on traffic flows in a mixed environment has mostly focused on simple road scenarios such as a highway or freeway [22][23][24][25][26][27][28]31], an urban road [16,30], or a simple network [19][20][21]29], with little attention given to large-scale, real-world road networks. Moreover, the simulation parameters in these studies were mostly set according to an ideal situation, which is far from reality. ...
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With the rapid development of autonomous driving, Autonomous Vehicles (AVs) have started to appear on public roads, which has inevitably affected current traffic conditions and the operations of Manual Vehicles (MVs). Current research on AVs' influence has mainly been conducted at individual level of driving behaviors, while few studies have focused on the overall network level to consider the traffic flow pattern due to mixed traffic. In this work, considering varying signal control schemes and demand loading patterns, we conducted simulation experiments based on a grid network and a real-world network in Beijing using SUMO. Traffic flow with the mixture of MVs, low-level AVs (LAVs), and high-level AVs (HAVs) were emulated so to investigate how the network performs at various levels of mixed traffic. Driving behaviors between the three types of vehicles were calibrated using driving data drawn from OpenACC dataset, and Waymo Open Dataset. The capacity and critical accumulation of the Macroscopic Fundamental Diagram (MFD) were chosen as the key indicators of network performance. We found that AVs positively boost network capacity (up to 19.0% increase) but a negative influence on critical accumulation was also observed (up to 9.0% decrease). However, the positive impact of OpenACC and Waymo's AVs on macroscopic traffic is still far from ideal since they may be too conservative. AVs can boost flow when traffic is in unsaturated or saturated states. However, when traffic flow is oversaturated, AVs can instead cause flow and average speed to drop faster than that in the MV-only scenario.
... Nowadays, game engines are becoming a popular tool for researchers to simulate CCAM [119], from the human perspective [120], the vehicle perspective [121]- [123], and the system perspective [124], [125]. Among these simulators we can distinguish: [128], the impact of autonomous vehicles on freeway capacity was evaluated using microscopic traffic flow simulation for various penetration rates. The results indicate that AVs allow for shorter headways between vehicles, resulting in a 30% increase in freeway network capacity and a significant reduction in traffic delays for higher penetration rates of AVs. ...
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Connected and Automated Shuttles (CAS) are an emerging technology that has been recently introduced into urban intelligent transport systems in order to increase their efficiency. However, most of existing studies are focusing on their design and acceptance from the general public, while their integration to complement more conventional mobility systems is largely ignored. Indeed, the integration of CAS with existing transport systems represents a promising opportunity to improve the overall efficiency and safety of existing mobility services, be it offered to citizens or companies. However, this integration presents significant challenges for mobility service operators, who must consider the potential impact of these new technologies on traffic patterns, infrastructure investments, and travel behavior. This paper reviews the current state of connected and automated shuttle’s technologies, including the different experiments and pilot projects in Europe for the transport of people, goods or both, in addition to the scientific efforts to build new services (e.g., optimisation and AI-based models). We discuss the challenges that mobility service providers face when planning for the long-term integration of CAS, and propose a digital twin approach to help overcome these challenges. The proposed approach is based on advanced simulation and modeling software that can create realistic 3D representations of transportation systems and has the potential to simulate the impact of CAS on traffic patterns and infrastructure investments. This paper serves as a starting point for future investigations on the integration of CAS with the existing mobility systems by identifying research gaps, limitations and challenges, and potential areas of research to overcome these challenges and improve the effectiveness of future mobility services.
... Finally, with the smooth driving of AVs combined with automated platooning, fewer accidents are prone to happen on freeways, greatly reducing commute time. As a result, freeway capacity would increase by 30% [43]. However, as mentioned in the introduction, driver behavior will change, resulting in careless driving from human drivers. ...
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We are at the advent of a fourth industrial revolution, where autonomous vehicles (AVs) are a major pillar brought by this new wave. We can already observe the impacts of AVs, especially in changing behavior in drivers, as highlighted by statistics on attentiveness by Tesla drivers. There are six levels of AVs, ranging from levels 0-5. Level 0 AVs have no autonomous features, while level 5 AVs can navigate without any human intervention. This paper will focus on the implications and impacts that level 4 or 5 AVs would have on different facets of society (e.g., mobility, environment, public health, infrastructure, economy, public behavior, and equity). Information reported in this paper was searched for via a four-step process broken down into finding keywords, searching for papers with such keywords in Google Scholar, filtering said papers and reports based on certain criteria, and finally reporting the found information. The paper includes a literature review that summarizes current predictions or patterns on the implications and impacts of AVs. Additionally, this paper provides suggestions for policies and planning for implementing high-level AVs into our current society, highlighting how to properly optimize the benefits AVs could bring and discussing social norms that could be a barrier to implementation.
... The effects increased with a higher percentage of the respective vehicles. Similarly, Hartmann et al.(9) have conducted a highway traffic simulation with different AV penetration rates and have concluded that longer HWT (1.8 s instead of 1.1 s) has a negative impact on capacity while a shorter one (0.5 s and 0.9 s, respectively) increases the road capacity by up to 30%. Also, Papamichail et al.(10) have shown that the HWT (0.8 -2 s) has an impact on road capacity on a single lane road. ...
Traffic congestion might be partly solved by using autonomously driving vehicles which are expected to enter the market at a significant rate within the next years (Kaltenhäuser et al. in Transp Res Part A: Policy Pract 132:882–910, 2020, [1]; Bansal and Kockelman KM in Transp Res Part A: Policy Pract 95:49–63, 2017, [2]; Nieuwenhuijsen et al. in Transp Res Part C: Emerg Techno 86:300–327, 2018, [3]). Several studies have been undertaken to examine the impact of autonomous vehicles (AVs) on road traffic. Also, autonomous vehicles and connected autonomous vehicles (CAVs) have been simulated in the literature with different operational parameters, leading to different results. Hence, in our study we examine how different parameters for the operation of AVs and CAVs influence urban traffic in the case of Munich, Germany. Furthermore, the impact of different percentages of AVs and CAVs on urban traffic is studied. For this, the traffic will be studied for the whole city, as well as for certain travel routes, e.g. in the main travel direction (into the city in the morning), in opposite direction or along the highway surrounding Munich. Last but not least, future scenarios with an enhanced travel behaviour will be studied. The results show that the headway and reaction times of the vehicles have the largest impact on urban traffic. Here, vehicles with large reaction times have a negative impact on urban traffic while short reaction times have a positive one. The results can be used to configure future AVs such that they reduce congestions and optimize urban traffic flow.
... Previous studies have provided insights into the various impacts of CAVs. Hartmann et al. (2017) found that CAVs can provide a 45% increase in capacity on basic freeway segments. Wang et al. (2017) pointed out the increment in capacity with the growth of CAV MPR. ...
... Likewise, the studies illustrate that when more safety and comfort issues are taken into account, cautious driving behavior in AVs might have a detrimental effect on traffic performance [10][11][12]. Another finding in Germany describes that aggressive driving behavior can lessen delay considerably and improve the capacity of freeway infrastructure by 30%, whereas the cautious driving behavior of AVs has a deleterious effect on capacity [13,14]. Regarding the potential impacts of AVs on emissions, literature reports diverse findings. ...
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Different types of automated vehicles (AVs) have emerged promptly in recent years, each of which might have different potential impacts on traffic flow and emissions. In this paper, the impacts of autonomous automated vehicles (AAVs) and cooperative automated vehicles (CAVs) on capacity, average traffic speed, average travel time per vehicle, and average delay per vehicle, as well as traffic emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matter (PM10) have been investigated through a microsimulation study in VISSIM. Moreover, the moderating effects of different AV market penetration, and different freeway segments on AV’s impacts have been studied. The simulation results show that CAVs have a higher impact on capacity improvement regardless of the type of freeway segment. Compared to other scenarios, CAVs at 100% market penetration in basic freeway segments have a greater capacity improvement than AAVs. Furthermore, merging, diverging, and weaving segments showed a moderating effect on capacity improvements, particularly on CAVs’ impact, with merging and weaving having the highest moderating effect on CAVs’ capacity improvement potential. Taking average delay per vehicle, average traffic speed, and average travel time per vehicle into account, simulation results were diverse across the investigated scenarios. The emission estimation results show that 100% AAV scenarios had the best performance in emission reductions in basic freeway and merging sections, while other scenarios increased emissions in diverging and weaving sections.
... Some expectations are the capability of automated vehicles to perfectly handle the DDTs, the capability of shorter reaction times and always react in the same way to every event, showing little variations resulting into more deterministic models. Other examples in which automated driving was modeled by changing parameters in behavioral models for human driven vehicles include [40], [41], [79]. ...
The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations.
... Reducing the number of crashes will not only increase the safety levels but also decrease the congestion phenomena in the network as it has been revealed that 25% of congestion is provoked from incident occurrence (FHWA, 2005). Therefore, automation is expected to significantly affect traffic efficiency and this effect has been quantified through the estimation of road capacity (Tientrakool et al., 2011), delays and travel time under various penetration and time headway scenarios or by investigating the dedicated lane scenario in different road networks (Tientrakool et al., 2011, Shladover, 2018, Hartmann et al., 2017 as well as total kilometres travelled . Average time, road and intersection capacity has also been used as indicators for assessing the impact of different types of automation on efficiency (Tientrakool et al., 2011, Shladover, 2012. ...
The advent of autonomous vehicles brings major changes in the transportation systems influencing the infrastructure design, the network performance, as well as driving functions and habits. The penetration rate of this new technology highly depends on the acceptance of the automated driving services and functions, as well as on their impacts on various traffic, user oriented and environmental aspects. This research aims to present a methodological framework aiming to facilitate the modelling of the behaviour of new AV driving systems and their impacts on traffic, safety and environment. This framework introduces a stepwise approach, which will be leveraged by stakeholders in order to evaluate the new technology and its components at the design or implementation phase in order to increase acceptance and favor the adoption of the new technology. The proposed framework consists of four sequential steps: i. conceptual design, ii. data collection, processing and mining, iii. modelling and iv. autonomous vehicles impact assessment. The connection between these steps is illustrated and various Key Performance Indicators are specified for each impact area. The paper ends with highlighting some conceptual and modeling challenges that may critically affect the study of acceptance of autonomous vehicles in future mobility scenarios.
In the literature, automated vehicle (AV) modeling studies tend to depict positive impacts of AV technologies on traffic. However, recent field experiments of production AVs (production vehicles with automated driving features) showed negative impacts on traffic flow stability and capacity. These inconsistencies may hinder the development and deployment of AV technologies. To identify major causes of the discrepancy, a breakout session was held at the 2022 Transportation Research Board (TRB) Automated Road Transportation Symposium (ARTS). Leading researchers from academia, industry, and government agencies were invited to present their thoughts on the issue. This book chapter summarizes the essence of the presentations and discussions at the breakout session. It provides insights into the modeling and simulation of AVs, AV technology development, and traffic management in the era of AVs.Keywordsautomated vehiclestraffic flow impactstraffic simulationfield experimentsadaptive cruise control
As one of the innovative technologies of intelligent transportation systems (ITS), Connected and Autonomous Vehicles (CAVs) have been deployed gradually. Given that there will be a long transition period before reaching a fully CAVs environment, it is crucial to assess the potential impacts of CAVs on mixed traffic flow. Considering platoon formation process, this study develops a platoon cooperation strategy based on “catch-up” mechanism, and then analyzes the impact on fundamental diagram, traffic oscillation, and traffic safety within mixed traffic. Simulation results show that with an increasing market penetration rate (MPR) of CAVs, road capacity shows an increasing trend. Compared with base scenario, a clear increase in road capacity is also observed under platoon scenario. With an increasing MPR, traffic oscillation is shown to reduce largely. Furthermore, the proposed platoon strategy could dampen frequent shockwaves and shorten the propagation range of waves. Regarding traffic safety, multiple surrogate safety measures (SSMs) are used to evaluate the traffic risk: including Criticality Index Function (CIF), Potential Index for Collision with Urgent Deceleration (PICUD), and Deceleration Rate to Avoid a Crash (DRAC). With increasing MPR, collision risk identified by CIF and DRAC shows an increase tendency, while that identified by PICUD has no apparent trend. Furthermore, the platoon strategy is shown to increase the severity of traffic conflicts significantly. Overall, this study provides novel insights into CAVs deployment through the analysis of platoon strategy.
Technical Report
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To analyse and quantify the possible impact of autonomous driving on mobility behaviour, we used an approach that combined both qualitative and quantitative methods. In the first phase of the project, we analysed the status quo in our selected countries and the expected developments in autonomous driving, and identified potential user segments and influencing areas related to autonomous driving which affect mobility behaviour. The insights were drawn from expert workshops, and from focus groups with potential users. In the second part of the study, using the results derived from the first phase of the project, we developed three scenarios to be examined with respect to the selected countries, which differed in the projected share of AVs within the vehicle fleet, as well as the way in which they are used as shared vehicles. Using a travel demand model, the impact of autonomous driving on mobility behaviour has been quantified.
Conference Paper
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Vehicle automation technology advances at rapid pace and the market entry of automated vehicles (AV) can be expected within the next years. Vehicle automation technology transitions gradually through different levels of automation (level 1 through level 4). However, substantial impact on travel choices only seem likely once drivers do not need to attend to the driving task anymore for most of a trip; i.e. drivers can take their “brain off” and engage in other activities such as work or entertainment. This is likely to impact on travel choices such as destination and mode choice because drivers might be willing to spend more time in the car or because the car is more attractive relative to other modes. At the moment, the future outlook in terms of AV regulation does not include the prospect of AVs being allowed to move without a driver; i.e. there must be a driver on board able to take over the driving task. This prospect rules out autonomous shared vehicle systems (autonomous car sharing, autonomous ride sharing) to a large degree, making privately owned AVs a likely scenario. The paper presents results from modelling travel behavior impacts of introducing AVs into the private car fleet. In order to model such a 2035 scenario, we combined a vehicle technology diffusion model and an aspatial travel demand model and applied this to Germany and the USA. Differentiating by passenger car segment, we introduce AVs among the newly registered vehicles from 2021 onward assuming an s-shaped market-take-up until 2035. By then, 50% of the new vehicles and 25% of the passenger car fleet are projected to be AVs. Again differentiating by segment and age, the AVs can be found among specific driver groups. In addition we assume that AVs are owned by mobility impaired travelers who did not have the option to drive previously. Subsequently, we use a travel demand model consisting of trip generation, distance choice and mode choice to forecast travel by different traveler groups and by car availability (no car, conventional car, AV). For modelling the impact of AVs compared to conventional cars, we reduced access/egress times due to quicker parking / valet parking and we reduced values of car travel time savings for travelers with AVs. While the model results overall conform to expectation the impact of AVs on travel behavior are not large: There is a ~5% increase in VMT for both Germany and the USA, resulting from somewhat longer trips combined with slight modal shifts from other modes towards the car. These results have important implications: If the regulatory framework for AVs is such that a private AV scenario is the most likely development, AVs are not likely to revolutionize travel. AVs will change travel behavior – but their impact might be marginal compared to other external factors.
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PurposeVarious regulatory and fiscal policy instruments are in force to reduce the amount of greenhouse gases and local pollutants emitted by private cars. The incentives operate primarily—or exclusively—on the newest generation of cars. But how fast will technological developments affecting new vehicle models penetrate into the car fleet? The speed at which the adverse effects of private car use will be mitigated through the normal vehicle renewal process, or through an accelerated one, carries considerable interest. Suitable modelling tools are needed. This paper aims to demonstrate the usefulness and flexibility of a bottom-up stock-flow modelling approach to private car fleet forecasting and policy analysis. Methods In the BIG model of the Norwegian automobile fleet, the annual stocks and flows characterising the car fleet are specified as matrices of 682 mutually exclusive and exhaustive cells, formed by cross-tabulations between 22 vehicle segments and 31 age classes. New car registrations follow from a disaggregate generic discrete choice model based on two decades of complete sales data for individual passenger car models. ResultsExample projections are presented onto the 2050 horizon under a low carbon fiscal policy scenario as well as a business-as-usual scenario. The fiscal policy is seen to make a large difference in terms of long term fuel consumption and CO2 emissions. Conclusions Stock-flow cohort modelling of the automobile fleet is a powerful and handy tool for policy analysis. Even quite simple and straightforward accounting relations may provide important insights into the dynamics of fleet development. It is possible to incorporate, into the stock-flow modelling framework, interesting and useful behavioural relations, explaining aggregate automobile ownership and travel demand, scrapping and survival rates, or consumer choice in the market for new cars.
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nalytical methods for the assessment of traffic flow quality on freeways are valid for standard types of basic, merge, diverge, and weaving segments. For more complex freeway facilities, microscopic traffic flow simulation is widely used. In this case, the definition and the computation of the level of service must be compatible with the analytical methods. For the forthcoming edition of the German Highway Capacity Manual, standards and user guidance for the application of microscopic traffic flow simulation tools for quality of service assessment on freeways were developed. For typical freeway segments, standard parameter sets for the simulation tools VISSIM, AIMSUN, PARAMICS, BABSIM, and Simulation of Urban Mobility were developed. This study applied these simulation tools for a diverge with a two-lane exit ramp. The results show that the simulation results represent the manual's design capacities well.
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Self-organizing traffic jams are known to occur in medium-to-high density traffic flows, and it is suspected that adaptive cruise control (ACC) may affect their onset in mixed human-ACC traffic. Unfortunately, closed-form solutions that predict the occurrence of these jams in mixed human-ACC traffic do not exist. In this paper, both human and ACC driving behaviors are modeled using the General Motors fourth car-following model and are distinguished by using different model parameter values. A closed-form solution that explains the impact of ACC on congestion due to the formation of self-organized traffic jams (or “phantom” jams) is presented. The solution approach utilizes the master equation for modeling the self-organizing behavior of traffic flow at a mesoscopic scale and the General Motors fourth car-following model for describing the driver behavior at the microscopic scale. It is found that, although the introduction of ACC-enabled vehicles into the traffic stream may produce higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.
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With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following model that also serves as the basis of an ACC implementation in real cars. The model is based on the intelligent driver model (IDM) and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the IDM in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e. 1 per cent more ACC vehicles will lead to an increase in the capacities by about 0.3 per cent. This sensitivity multiplies when considering travel times at actual breakdowns.
Automated Highway System (AHS) is an example of a large-scale, multi-agent, hybrid dynamical system. In this paper, the use of computer aided simulation tool for design and evaluation of control laws, for an AHS based on platooning, is outlined. The hierarchical control architecture for AHS is described along with the details of the simulation tool SmartPath. The role of SmartPalh in design and evaluation of AHS control laws is also depicted in the paper.
This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of "autonomous driving".
The use of private cars in Germany has not yet been analyzed from a longitudinal perspective: most travel surveys consider only a single day. Daily car usage is not identical over a given period because car owners use their vehicles for daily routines (e.g., commuting) as well as for infrequent events, such as holiday trips. Another problem of short-period surveys is that they underestimate the share of cars used for long-distance travel. The current work may help to improve the reliability and realism of statements about the extent to which German cars could be replaced by electric vehicles. The authors developed a hybrid modeling approach that aims to obtain car mileage per day for a full year. This approach is based on empirical data with different granularities. Input data are derived from the annually conducted German Mobility Panel, including a survey of fuel consumption and odometer readings, and the long-distance travel survey INVERMO. The study showed that 13.1% of the modeled German private car fleet never exceeded 100 km/day during a full year. Furthermore, cars were driven more than 100 km on 13.3 days/year on average. Mainly used cars (first cars) of a household were used for longer distances rather than second cars. A comparison of average mobility figures from the model approach with the Mobility in Germany national travel survey showed the model results as reliable and realistic.
The effects on traffic flow of increasing proportions of both autonomous and cooperative adaptive cruise control (ACC) vehicles relative to manually driven vehicles were studied. Such effects are difficult to estimate from field tests on highways because of the low market penetration of ACC systems. The research approach used Monte Carlo simulations based on detailed models presented in the literature to estimate the quantitative effects of varying the proportions of vehicle control types on lane capacity. The results of this study can help to provide realistic estimates of the effects of the introduction of ACC to the vehicle fleet. Transportation system managers can recognize that the autonomous ACC systems now entering the market are unlikely to have significant positive or negative effects on traffic flow. An additional value of studying ACC systems in this way is that these scenarios can represent the first steps in a deployment sequence that will lead to an automated highway system. Benefits gained at the early stages in this sequence, particularly through the introduction of cooperative ACC with priority access to designated (although not necessarily dedicated) lanes, can help support further investment in and development of automated highway systems.