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Effects of Connected Highly Automated Vehicles on the Propagation of Congested Patterns on Freeways

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Abstract and Figures

Highly automated vehicles (HAV) use sensing technologies to take over the task of driving, while connected vehicles obtain and share information that can allow the driver/vehicle to make better driving decisions. Connected HAVs promise to offer significant improvements in traffic performance, emergence and propagation of congestion due to their capabilities of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, in short-term these vehicles operate along the manually driven vehicles. We employed the microscopic traffic simulation tool Vissim to model HAVs, with communication capabilities, and manually driven vehicles by implementing different behavioral models for car-following and to analyze if and to what extent these vehicles can influence the propagation of congestions along the freeway by means of changing the propagation speed and the throughput of the network.
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EFFECTS OF CONNECTED HIGHLY AUTOMATED VEHICLES ON THE
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PROPAGATION OF CONGESTED PATTERNS ON FREEWAYS
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Nassim Motamedidehkordi
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Chair of Traffic Engineering and Control
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Technische Universitaet Muenchen
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Arcisstrasse 21, 80333 Munich, Germany
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Phone: +49-89-28922665; Fax: +49-89-28922333; Email: nassim.motamedidehkordi@tum.de
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Martin Margreiter
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Chair of Traffic Engineering and Control
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Technische Universitaet Muenchen
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Arcisstrasse 21, 80333 Munich, Germany
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Phone: +49-89-28928586 Fax: +49-89-28922333; Email: martin.margreiter@tum.de
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Thomas Benz
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PTV Group
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Haid-und-Neu-Strasse 15, 76131 Karlsruhe, Germany
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Phone: +49-721-19651336; Fax: +49-721-9651299; Email: thomas.benz@ptvgroup.com
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Word count: 4,528 words text + 8 tables/figures x 250 words (each) = 6,528 words
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Submission Date: July 31, 2015
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Revision Date: November 11, 2015
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Motamedidehkordi, Margreiter, Benz 2
1
ABSTRACT
2
Highly automated vehicles (HAV) use sensing technologies to take over the task of driving, while
3
connected vehicles obtain and share information that can allow the driver/vehicle to make better
4
driving decisions. Connected HAVs promise to offer significant improvements in traffic
5
performance, emergence and propagation of congestion due to their capabilities of vehicle-to-
6
vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, in short-term these
7
vehicles operate along the manually driven vehicles. We employed the microscopic traffic
8
simulation tool Vissim to model HAVs, with communication capabilities, and manually driven
9
vehicles by implementing different behavioral models for car-following and to analyze if and to
10
what extent these vehicles can influence the propagation of congestions along the freeway by
11
means of changing the propagation speed and the throughput of the network.
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Keywords: highly automated vehicles, V2V communication, congestion propagation, traffic jam
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ahead warning, adaptive smoothing method
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Motamedidehkordi, Margreiter, Benz 3
INTRODUCTION
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Nowadays people tend to travel more and more and traffic congestion has become a major
2
challenge for people across the world. In most countries, the construction of new transport
3
infrastructure is not an appropriate option any more, leading to the need of a more efficient way
4
of using the existing road capacities. A lot of research is conducted on how to reduce traffic
5
congestion and many studies propose the possibilities to reach this goal; however, many of them
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only relocate the bottleneck from one location to another instead of targeting the cause of the
7
problem and optimizing the traffic flow.
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Interest has increased in recent decades in the automation of the road transport in order to
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overcome the enormous costs of congestion, pollution and traffic accidents of road transport (1),
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(2). Automated vehicles could increase the segment capacity through lower time headways and
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therefore reduce the temporal and spatial size of recurring congestions (3). Connected vehicles
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could similarly reduce the duration and size of non-recurring congestions resulting from a
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temporary bottleneck like an incident by reducing the frequency of disruptions (4). Connected
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automated driving is expected to reduce the congestion because of the capability to predict the
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near future downstream traffic state. The potential benefit of automation functions like adaptive
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cruise control (ACC) and cooperative adaptive cruise control (CACC) has been shown in other
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studies (5), (6), (7). Other than CACC, inter-vehicle communication of automated vehicles
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provides the possibility of automated merging of vehicles, platooning of cars or trucks and traffic
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jam ahead warning (TJAW) application.
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TJAW application is assumed to avoid rear-end collisions and may reduce the traffic
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congestion problem by taking advantage of communication capabilities of the vehicles as
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wireless communication provides real-time information of proceeding vehicles in case of a traffic
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breakdown. Thus, the vehicles can start reacting to the congestion faster and smoother than
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conventional vehicles and beyond the line of the sight.
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This paper focuses on simulating mixed traffic consisting of connected highly automated
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and manually driven vehicles with the microscopic traffic simulation tool Vissim. This is done in
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order to understand better their impact on freeways in case of traffic congestion because large-
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scale field experiments are scarcely possible. In section 1, we examine existing literature on the
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simulation of HAVs and the assessment of their impact. Thereafter, the simulation parameters
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used in this research to simulate the connected HAVs are explained in detail. The results of the
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simulation of different penetration rates and the method called “Adaptive Smoothing Method
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(ASM) which was used for the reconstruction of spatio-temporal data and generating speed
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contour plots are explained in section 4. In section 5 we draw some preliminary conclusions and
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present the potential further research topics.
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LITERATURE REVIEW
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One of the earliest research actions, which pioneered practical self-driving technology on roads,
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was the PROMETHEUS-Project (1987-1995) in the EUREKA program (8). Another step in
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automation, the platooning, was motivated by intelligent highway systems and road infrastructure
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in 1997 within the PATH project in California (9). Some years later, the DARPA Grand
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Challenge starting in 2004 and 2005 on a 350 km off-road track in the Mojave Desert encouraged
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several producers of automated vehicles to test their developments in a real environment and in
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competition to other teams. Two years later, in 2007, the DARPA Urban Challenge already
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included an urban route and therefore containing interactions and cooperation with other
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surrounding vehicles. This was followed up in 2010 by the automated vehicle ‘Leonie’ in
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Braunschweig, Germany, being one of the first vehicles worldwide to fully autonomously drive in
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a real urban traffic environment (10). Since then a vast number of also long-range test drives of
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Motamedidehkordi, Margreiter, Benz 4
automated vehicles have been carried out, like the VisLab Intercontinental Autonomous
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Challenge, which covers 13,000 km from Italy to China with no human intervention (11).
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Additionally, lots of research focuses until now on simulating and determining the
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impacts of such cooperative systems (supported by the IEEE 802.11p standard) and connected
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automated vehicles in different penetration rates on the overall traffic efficiency. In the German
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research project simTD a field operational test (FOT) as well as a traffic simulation on cooperative
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vehicles with V2V and V2I capabilities were conducted. The project showed, that cooperative
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assistance systems could improve the traffic efficiency and the traffic safety for example on
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motorways (12), (13). Also M. Gouy et al. (14) shows in a simulator study how the use of V2V
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communication increases the capacity of the existing road infrastructure. The effects on the road
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capacity of an increasing penetration rate of CACC equipped vehicles were also discussed by
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J. Van der Werf et al. (7). He concluded that high rates could potentially double the capacity of a
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highway.
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Not just cars, but also cooperative trucks are in the focus, like in the CHAUFFEUR 2
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project (15), where a V2V truck platooning capability was developed and tested using the
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microscopic traffic simulation models Vissim and FARSI. The result was a higher road capacity
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and up to 20 % reduction in fuel consumptions due to more stability in the traffic flow of the
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truck platoons. This effect especially showed up at night or on road sections with low traffic
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volumes.
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In contrast to most of the studies which focus on the impact of connected automated
20
vehicles on the throughput and the changes in the capacity of the network, W. Schakel et al. (16)
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analyzes the effects on the traffic flow stability and the occurrence and characteristics of
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shockwaves. It was found out, that at a 50 % penetration rate of vehicles equipped with CACC
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the damping of shockwaves is more quickly and the shockwave speed decreases from -4.4 m/s in
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the base scenario (using an adapted version of the Intelligent Driver Model) to about -18.5 m/s
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with CAAC.
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Also B. Van Arem et al. (6) concentrates on the effects on shockwaves using the traffic
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flow simulation model MIXIC to simulate a highway merging scenario from four to three lanes.
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The results show an improvement of the traffic flow stability and a slight increase in traffic flow
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efficiency as well as a drastically decrease of the number of shockwaves with higher penetration
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rates of CACC-equipped vehicles. In contradiction to W. Schakel et al. (16) no significant change
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in the shockwave speeds could be observed. A decrease in the number of shockwaves and
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therefore an improvement in the traffic stability could also be found in the CarTALK2000
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project, also using the simulation model MIXIC for cooperative vehicles (17).
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There are several steps towards automated driving focusing on automated highway
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systems (AHS), platooning and cooperative adaptive cruise control (CAAC) which were and still
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are in the focus of research in the past years. What most of the studies have in common is the
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realized reduction in the vehicles time headway (THW) as one of the key parameters of
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automated vehicles, which have an influence on the traffic flow. In literature, several values can
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be found:
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THW of 0.5 s when following another CACC equipped vehicle and 1.4 s when following
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a non-CACC equipped vehicle (6).
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Automated vehicle platoons with short following distance with THW of 0.3 s and large
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following distance with THW of 1.4 s (14).
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THW of 0.5 s for 100 % penetration rate of CACC equipped vehicles (7).
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Truck platooning with THW ranging from 0.3 s to 0.6 s at a speed of 80 km/h (15).
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Motamedidehkordi, Margreiter, Benz 5
CACC with THW of 1.2 s including two different time headway distributions: 1.2±0.15 s
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and 1.2±0.3 s (16).
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SIMULATION FRAMEWORK
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In order to develop the understanding of the effect of HAVs on freeways, we present the result of
5
a simulation study, which models a small part of the freeway network in Germany with traffic
6
consisting of HAVs and conventional vehicles. In this section, we detail on the simulation
7
software used and the behavior models and parameters used in the models.
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The Vissim software uses two car-following models developed by Rainer Wiedemann. In
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the Wiedemann 74 model, the minimum safety distance is calculated based on formula 1 (18).
10
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     (1)
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 Speed of the slower vehicle [m/s].
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15
: Is a value in the range [0,1] which is normally distributed around 0.5 with a standard
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deviation of 0.15.
17
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 Average standstill distance, which defines the average desired distance between two
19
vehicles.
20
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: Additive part of the following distance, which allows adjusting the time requirement
22
values.
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 Multiplicative part of the following distance, which allows adjusting the time
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requirement values.
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28
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FIGURE 1 Car-following model and driving states (19).
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Motamedidehkordi, Margreiter, Benz 6
Some thresholds are calculated differently for the Wiedemann 99 car-following model than they
1
were for the Wiedemann 74 model. Nevertheless, the meaning of each threshold is the same. At a
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certain speed, the minimum safety is calculated based on formula 2.
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  (2)
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 Defines the desired rear bumper to front bumper distance between stopped vehicles. This
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parameter has no variation.
8
9
 Defines the time (in seconds) that the following driver wishes to maintain a certain speed.
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The higher this value, the more conservative the driver drives.
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A tool for the simulation of vehicle behavior and traffic properties, which would allow assessing
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the impact and benefit of HAVs with different penetration rates in the vehicle composition, has
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been implemented. For this purpose, the Vissim COM interface was used, which is versatile in
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collecting vehicles information and modifying vehicles parameters during the simulation period.
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Human drivers and connected HAVs are simulated on an 8.5 km stretch of the freeway A5 in
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Germany. Travel demand, the number of vehicles entering the network, was derived directly from
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the data recorded by detectors on the freeway A5. The simulation was used to generate one hour
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of simulation data. The first 15 minutes of the simulation data, while the system initially
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populates and the network is being filled, is not used. In order to trigger stop-and-go traffic that
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ultimately leads to traffic congestion in the network, a temporary artificial bottleneck was created
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in the network using a reduced speed area with very low speeds.
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For manually driven vehicles, the Wiedemann 74 car-following model was assigned as
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they have variations in their driving behavior. In order to calibrate the model, empirical detector
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data and individual vehicle trajectories of the large-scale field operational trial at the German
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research project simTD (20) were used (21). Vehicle fleet, desired speed, desired acceleration and
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deceleration of the vehicles were derived directly from the empirical data. According to
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calibration of the car-following model based on empirical data,  (standstill distance) was
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determined with 1 m.  (additive part of safety distance) and  (multiplicative part of
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safety distance) value were set to 3 and 5 respectively.
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In contrast to the non-automated vehicles, HAVs have no variation in their behavior.
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Therefore, the Wiedemann 99 model was assigned. Additional data about driving behavior are
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needed to parameterize the model. The car-following behavior can be defined by setting
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parameters of the Wiedemann model considering different stages. Since there is no data about the
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behavior of HAVs in the simulation in hand, some assumptions about the parameters of these
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vehicles have been made. The CC0 (standstill distance) was set to 1 meter. CC1 (desired time
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between lead and following vehicle) was set to 0.5 seconds. CC2 (following variation oscillation)
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was determined with the value of 4 meters. CC3 (time before a vehicle starts to decelerate to
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reach safety distance) was set to -8 seconds. CC4 and CC5 values (following thresholds for
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positive and negative speed differences during following state) were set to -0.1 and 0.1
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respectively. CC6 (influence of distance on speed oscillation) was chosen to be 0. The values for
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CC7 (acceleration during the oscillation process), CC8 (desired acceleration starting from
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standstill) and CC9 (desired acceleration at 80 km/h) were set to 0.25, 3.5 and 1.5 m/s²
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respectively. Besides, the safety reduction factor used for lane changing of HAVs was set to 0.75
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and the maximum deceleration for cooperative braking was set to -3.5 m/s².
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The resulting difference in the following behavior of the Wiedemann models for a HAV
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and a conventional vehicle in exactly the same car-following situation can be seen in figure 2. A
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Motamedidehkordi, Margreiter, Benz 7
conventional vehicle has a higher mean time headway while following its predecessor as well as
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higher standard deviation of the THW.
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FIGURE 2 Comparison of the time headway between a highly automated vehicle and a
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conventional vehicle in the car-following mode.
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The spatial propagation of information along the freeway for a distance of 1 km is simulated in
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this research effort. In case the speed of a HAV dropped abruptly to a value below 40 km/h, the
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TJAW transmission is activated. The communication between HAVs in this research is therefore
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limited to transmitting and receiving the TJAW messages. The desired speed of the HAVs, which
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received the warning, was set to 70 km/h. It has to be mentioned, that the desired speed of the
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HAVs, in contrast to manually driven vehicles, does not have a distribution and has the value
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equal to the speed limit.
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The simulation is performed for mixed flow with 0 %, 5 %, 10 %, 20 % and 50 % of
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HAVs randomly dispersed among manually driven vehicles. Three simulations with different
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starting random seeds were run for each penetration rate and the results of the simulation runs
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were averaged in order to overcome the stochastic nature of simulation.
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SIMULATION RESULTS
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With the help of the simulation tool, the data at the exact positions of the inductive loops on the
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freeway were recorded with an aggregation interval of one minute. In reality, stationary detectors
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provided minute-by-minute data of speed and flow. Usually, the traffic state cannot be directly
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measured everywhere but needs to be estimated from incomplete, noisy and local traffic data.
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Commonly, volumes or average vehicle speeds are measured at certain locations on the freeways,
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for example by double-loop detectors. To estimate the overall traffic state from these point
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measurements, an interpolation between the detectors is necessary. The speed contour plots for
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different penetration rates of HAVs can be observed in the linearly interpolated graphs in figure 3.
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For better illustration, the detector locations are marked with dashed lines. It can be observed that
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Motamedidehkordi, Margreiter, Benz 8
the calculated speeds between the detectors are not realistic and the artifacts of the linear
1
interpolation can be obviously distinguished especially where the detectors are located far from
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each other (more than 1 km).
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In order to avoid the artifacts of this interpolation, which can be observed in the graphs,
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the data was reconstructed using the adaptive smoothing method (ASM).
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FIGURE 3 Speed contour plots for a) base scenario and b) 5 %, c) 10 %, d) 20 % and e)
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50 % penetration rate of connected highly automated vehicles within the vehicle fleet.
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Motamedidehkordi, Margreiter, Benz 9
ADAPTIVE SMOOTHING METHOD
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The role of this two-dimensional spatio-temporal interpolation algorithm is to estimate at each
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location and time the discrete speed   as a result of the continuous function of
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the local speed average  ASM was developed by Treiber and Helbing (22) and yields to a
4
plausible reconstruction of the traffic state. This smoothing procedure is based on a two-
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dimensional interpolation in space and time and takes into account the information about the two
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typical velocities of underlying regimes in free flow and congested traffic (23):
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1) In free flow traffic, perturbations move downstream (i.e., in the direction of traffic flow)
8
(24) with the propagation velocity , which is slightly lower than the local average
9
speed of the vehicles.
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2) In congested traffic, however, perturbations travel upstream (against the movement of the
11
vehicles) due to the reaction of the drivers to their leading vehicle.
12
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Based on these two traffic regimes two smoothed speed fields are considered.
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

   
 (3)
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17


   
 (4)
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: Smoothed speed in free flow traffic.
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22
: Smoothed speed in congested traffic.
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: Normalization of the weighting function in free flow traffic.
25
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: Normalization of the weighting function in congested traffic.
27
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: Kernel that includes all the data points .
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: Perturbations propagation velocity in free flow traffic.
31
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: Perturbations propagation velocity in congested situations.
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The kernels  and of the linear homogeneous filters realize the required
35
smoothing and are particularly transmissible for perturbations propagating with the typical
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velocities and  observed in congested and free flow traffic, respectively.
37
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For the weighting kernel the symmetric exponential function has been used in which and
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are the smoothing widths in the spatial and temporal coordinates. The exponential function
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operates as a low pass filter.
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    

 (5)
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Motamedidehkordi, Margreiter, Benz 10
1
2
FIGURE 4 Smoothing kernels for free flow and congested traffic. The slope of each kernel
3
represents different characteristic velocities cfree and ccong (23).
4
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In the end, due to different smoothing in free flow and congested traffic, the average rate in the
6
formula below will be applied: The speed factor  in the equation depends on the average
7
speeds  and  which takes    for congested traffic and   for free flow traffic.
8
The predictor  leads to a better segregation of congested traffic from free flow traffic. The
9
parameter  is the transition between free flow and congested traffic.
10
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    (6)
12
13

 
  (7)
14
15
  (8)
16
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In table 1 the parameters used for the ASM in this study are illustrated:
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TABLE 1 Parameters of the "Adaptive Smoothing Method" used for this study.
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21
Parameter
Meaning
Range of spatial smoothing in
Range of temporal smoothing in

Propagation velocity of perturbations in free traffic

Propagation velocity for perturbations in congested traffic
Crossover for free to congested traffic

Width of transition region
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Motamedidehkordi, Margreiter, Benz 11
This smoothing method was applied on the simulation result of each scenario. The speed contour
1
plots for different penetration rates of connected HAVs after smoothing are illustrated in figure 5.
2
3
4
5
FIGURE 5 Smoothed speed contour plots for a) base scenario and b) 5 %, c) 10 %, d) 20 %
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and e) 50 % penetration rate of connected HAVs within the vehicle fleet.
7
8
Although the reconstructed traffic state between the detectors looks more realistic than the non-
9
smoothed data, this low pass filter smoothed out perturbations observed in the graphs in figure 2.
10
Therefore, based on the application of the reconstructed data, the plausibility of using this filter
11
should be carefully considered.
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Motamedidehkordi, Margreiter, Benz 12
From the graphs observation, it turns out that with increasing penetration rates of
1
connected HAVs, the congestion area becomes smaller and the queue length decreases. This is
2
primarily due to a reduction in time headways, as the vehicles are able to follow each other at
3
very short gaps. Moreover, stop-and-go instabilities caused by driver response lags and higher
4
acceleration rates of the vehicles are avoided. These parameters provide the possibility to pack
5
more vehicles within the existing infrastructure. On the other hand, as we have more HAVs in the
6
vehicle fleet, a more homogeneous traffic flow and fewer perturbations were observed. The
7
following parameters were carried out from the model to evaluate the network performance:
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Propagation speed of congestion.
9
Network throughput.
10
Congested traffic is characterized by a high traffic density and a low average velocity (23). Since,
11
in contrast to the density, the velocity can be directly measured with stationary detectors, we
12
chose the velocity to determine the congestion. In order to calculate the propagation speed of the
13
congestion, an abrupt change of the speed recorded by detectors to values below 50 km/h (within
14
one minute) has been recognized and a linear regression analysis with fits the best line to these
15
points has been conducted. The slope of this line is considered the propagation speed of
16
congestion along the freeway. In the figure below the propagation speeds for different penetration
17
rates can be found. The negative values confirm that the congested pattern was propagating in the
18
opposite direction of traffic and a backward shockwave was forming.
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FIGURE 6 Propagation speed of congestion for different penetration rates of HAVs.
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Motamedidehkordi, Margreiter, Benz 13
The graph below shows the throughput (total outflow during one hour) of the network for the
1
different penetration rates of HAVs during the one-hour simulation run. The simulation results
2
showed that the throughput increase is non-linear and most of the change is observed before the
3
penetration rate of 20 %. 50 % of connected HAVs, which are randomly dispersed among the
4
conventional vehicles, will increase the throughput of the network by 5 % in comparison to the
5
base scenario.
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8
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FIGURE 7 Network throughput for different penetration rates of HAVs.
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Motamedidehkordi, Margreiter, Benz 14
1
CONCLUSIONS AND DISCUSSIONS
2
This study focuses on evaluating the potential effect of connected HAVs on the propagation of
3
the congestion caused by a bottleneck on a freeway by means of a microscopic simulation study.
4
HAVs were defined and implemented by selecting appropriate models and parameters describing
5
their driving behavior. For conventional vehicles, the Wiedemann 74 parameters were calibrated
6
based on data from local detectors. Wiedemann 99 was configured to fit the assumptions for the
7
car-following behavior of HAVs (e.g. headway distribution). Besides that, the driving behavior of
8
a HAV is changed in case it receives a TJAW from other HAVs which are experiencing a
9
downstream congestion. The impact of this setup has been evaluated by simulating 5 %, 10 %,
10
20 % and 50 % penetration rates of HAVs.
11
It has been shown that with the increase of the penetration rate of these vehicles within the
12
fleet the congestion length and the congestion area become smaller. The communication between
13
HAVs allows them to react earlier and safer on a congestion tail by receiving a warning whilst
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approaching. On the other hand, keeping smaller time headways between these vehicles increases
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the capacity of the existing road. As a result, having a higher penetration rate of HAVs
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significantly reduces shockwave speeds from the value of 13.9 km/h for 0 % of HAVs down to
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8.7 km/h with a 50 % penetration rate. Therefore, HAVs improve traffic flow and thus decrease
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the traffic congestion whilst retaining driving comfort but their effect is highly dependent on the
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penetration rate.
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In this study, an assumption has been made about the driving behavior of HAV and of the drivers
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of the surrounding manually driven vehicles. Extensive test drives with HAVs would be
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preferable for calibrating their driving behavior and dynamics in the simulation models. The
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identification and integration of the interaction and behavior of road users in the simulation of
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automated vehicles is a future research need.
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... In 2016, for CAVs, Aria [37] considered a CC0 value of 1.5 for basic freeway and 2.5 for both merging and diverging freeway segments based on the values proposed by Leyn and Vortisch [22]. Similarly, Motamedidehkordi et al. [38] assumed 1.0 for simulating CAVs in the freeway network proclaiming the absence of data in this research topic at the time. In 2021, Rao et al. [39] considered 5 feet (1.52 m) for AAVs in freeway segments with various geometric and traffic conditions. ...
... In the same way, Tomás et al. considered a value of 0.50 in their study targeted at the investigation of the environmental impacts of AAVs on urban roads [5]. Besides, Motamedidehkordi et al. used a CC1 value of 0.50 for simulating connected highly AVs in a freeway network after comparing several values in the literature [38]. ...
... e default value is 4.0, which results in a moderately steady following [36]. Motamedidehkordi et al. considered the default value for connected highly AVs in a freeway, emphasizing the absence of related works in the literature [38]. Besides, in 2021, Rao also used the default value for simulating AVs in different freeway geometry [39]. ...
Article
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Recently, in the literature, microscopic simulation is one of the most attractive methods in impact assessment of automated vehicles (AVs) on tra c ow. AVs can be divided into di erent categories, each having di erent driving characteristics. Hence, calibrating microscopic simulators for di erent AV categories could be challenging in AVs' impact assessment. e PTV Vissim microscopic tra c simulation software has been calibrated for simulating diverse types of AVs in a large body of literature. ere are two main streams of studies in literature adapting AVs' driving behaviors in Vissim following either internal (i.e., adjusting the parameters of the Vissim's default driving behavior models) or external (i.e., adapting AVs' behavior through external VISSIM interfaces) modeling approaches. e current paper investigates how the PTV Vissim has been internally calibrated for the simulation of di erent types of AVs and compares the calibrated values in the literature with default values introduced in the recent version of PTV Vissim. In the present paper, the reviewed studies are partitioned into two main categories according to the characteristics of the studied AVs, the studies focused on autonomous automated vehicles (AAVs) and the ones focused on cooperative automated vehicles (CAVs). Our ndings indicate that the literature expects a lower value for parameters including standstill distance (CC0), headway time (CC1), following variation (CC2), the threshold for entering "following" (CC3), negative/positive following thresholds (CC4/CC5), speed dependency of oscillation (CC6), oscillation acceleration (CC7), safety distance reduction factor (SDRF), and minimum headway front/rear (MinHW) for AVs than conventional vehicles (CVs). Besides, the literature expects higher values for parameters including standstill acceleration (CC8), acceleration at 80 km/h (CC9), looking distances, and maximum deceleration for cooperative braking (MaxDCB) for AVs. When cautious AVs are introduced, deterring e ects are expected in the literature (e.g., higher CC0). Moreover, CAVs can have higher looking distance values compared with AAVs.
... (PTV Group, 2020) In 2016, for CAVs, Aria considered a CC0 value of 1.50 for basic freeway and 2.50 for both merging and diverging freeway segments based on the values proposed in the study (Leyn and Vortisch, 2015) (Aria, 2016). Similarly, in 2016, Motamedidehkordi et al. assumed 1.00 for simulating CAVs in the freeway network proclaiming the absence of data in this research topic at the time (Motamedidehkordi et al., 2016). ...
... In the same way, Tomás et al. considered a value of 0.50 in their study targeted on the investigation of the environmental impacts of AAVs on urban roads (Tomás et al., 2020). Besides, Motamedidehkordi et al. used a CC1 value of 0.50 for simulating connected highly AVs in a freeway network after comparing several values in the literature (Motamedidehkordi et al., 2016). ...
... (PTV Group, 2020). Motamedidehkordi et al. considered the default value for connected highly AVs in a freeway emphasizing the absence of related works in the literature (Motamedidehkordi et al., 2016). In 2016, based on the value proposed in the study (Leyn and Vortisch, 2015), Aria simulated CAVs with a CC2 value of 4.00 in basic freeway and merging segments of a freeway, while considering 5.00 for CAVs in a diverging freeway segment (Aria, 2016). ...
Preprint
Recently, the impact assessment of automated vehicles (AVs) has received considerable interest among the researchers due to the AVs' potential mobility benefits. Microscopic simulation is one of the most attractive methods of investigating the AVs' impacts on road traffic flow in the literature. AVs can be divided into different categories each of which could have different driving behaviors. Hence, calibrating the microscopic simulators for different AVs' categories could be a challenging phase in AVs impact assessment. PTV Vissim microscopic traffic simulation software has been calibrated for the simulation of different types of AVs in a large body of literature. The current paper attempts to investigate how PTV Vissim has been calibrated for simulation of different types of AVs and compare the calibrated values in the literature with the default values introduced in the most recent version of this software, PTV Vissim 2020. In the present paper, the reviewed studies are partitioned into two main categories according to the characteristics of the studied AVs, the studies focused on autonomous automated vehicles (AAVs) and the ones focused on cooperative automated vehicles (CAVs). Our findings indicate that the literature expects a lower value for the parameters including standstill distance (CC0), headway time (CC1), following variation (CC2), threshold for entering 'following' (CC3), negative/positive following thresholds (CC4/CC5), speed dependency of oscillation (CC6), oscillation acceleration (CC7), safety distance reduction factor (SDRF) and minimum headway front/rear (MinHW) for AVs than conventional vehicles (CVs). Besides, the literature expects a higher value for the parameters including standstill acceleration (CC8), acceleration at 80 km/h (CC9), looking distances, and maximum deceleration for cooperative braking (MaxDCB) for AVs. When cautious AVs are introduced, deterring effects are expected in the literature (e.g. higher CC0). Moreover, CAVs can have higher looking distance values compared with AAVs. In certain instances, there is a different looking regarding the values suggested in the literature to the ones in PTV Vissim. For instance, the literature expects SDRF value to be smaller for aggressive AVs than for CVs while PTV Vissim foresees it in the inverse.
... is can, for example, be adjusting reaction time, gap-related parameters, acceleration parameters, and speed limit acceptance. ere are traffic simulation investigations [2][3][4][5][6][7][8][9][10][11][12][13] that utilize this approach to simulate ACC/CACC equipped vehicles, or vehicles assumed to be highly automated in a specific road environment. Some investigations focus on one-lane roads without on/off-ramps and without overtaking possibilities (e.g., [14]), which, due to the fact that there is no lane-changing, is equivalent to simulation of just the ACC part of the automated vehicle functionality. ...
... In some cases, also the speed limit acceptance is considered, as in Calvert et al. [1]. In addition to these AV/ACC investigations, there are several simulation investigations focusing purely on the effects of ACC [9,21,[24][25][26][27][28][29][30][31], as well as investigations on effects of some kind of CACC [11,16,21,[32][33][34]. ...
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Full-text available
The introduction of automated vehicles is expected to affect traffic performance. Microscopic traffic simulation offers good possibilities to investigate the potential effects of the introduction of automated vehicles. However, current microscopic traffic simulation models are designed for modelling human-driven vehicles. Thus, modelling the behaviour of automated vehicles requires further development. There are several possible ways to extend the models, but independent of approach a large problem is that the information available on how automated vehicles will behave is limited to today’s partly automated vehicles. How future generations of automated vehicles will behave will be unknown for some time. There are also large uncertainties related to what automation functions are technically feasible, allowed, and actually activated by the users, for different road environments and at different stages of the transition from 0 to 100% of automated vehicles. This article presents an approach for handling several of these uncertainties by introducing conceptual descriptions of four different types of driving behaviour of automated vehicles (Rail-safe, Cautious, Normal, and All-knowing) and presents how these driving logics can be implemented in a commonly used traffic simulation program. The driving logics are also linked to assumptions on which logic that could operate in which environment at which part of the transition period. Simulation results for four different types of road facilities are also presented to illustrate potential effects on traffic performance of the driving logics. The simulation results show large variations in throughput, from large decreases to large increases, depending on driving logic and penetration rate.
... The study found that the average density improved by 8.09%, average travel speed enhanced relatively by 8.48% and average travel time improved by 9.00% in the 100% AV scenario. Motamedidehkordi [29] simulated a stretch of freeway and showed that with an increase in MPR of CAVs, congestion length and congestion area became smaller. In addition, the speed of the shockwave reduced. ...
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Full-text available
Autonomous Vehicles (AVs) with their immaculate sensing and navigating capabilities are expected to revolutionize urban mobility. Despite the expected benefits, this emerging technology has certain implications pertaining to their deployment in mixed traffic streams, owing to different driving logics than Human-driven Vehicles (HVs). Many researchers have been working to devise a sustainable urban transport system by considering the operational and safety aspects of mixed traffic during the transition phase. However, limited scholarly attention has been devoted to mapping an overview of this research area. This paper attempts to map the state of the art of scientific production about autonomous vehicles in mixed traffic conditions, using a bibliometric analysis of 374 documents extracted from the Scopus database from 1999 to 2021. The VOSviewer 1.1.18 and Biblioshiny 3.1 software were used to demonstrate the progress status of the publications concerned. The analysis revealed that the number of publications has continuously increased during the last five years. The text analysis showed that the author keywords “autonomous vehicles” and “mixed traffic” dominated the other author keywords because of their frequent occurrence. From thematic analysis, three research stages associated with AVs were identified; pre-development (1999–2017), development (2017–2020) and deployment (2021). The study highlighted the potential research areas, such as involvement of autonomous vehicles in transportation planning, interaction between autonomous vehicles and human driven vehicles, traffic and energy efficiencies associated with automated driving, penetration rates for autonomous vehicles in mixed traffic scenarios, and safe and efficient operation of autonomous vehicles in mixed traffic environment. Additionally, discussion on the three key aspects was conducted, including the impacts of AVs, their driving characteristics and strategies for their successful deployment in context of mixed traffic. This paper provides ample future directions to the people willing to work in this area of autonomous vehicles in mixed traffic conditions. The study also revealed current trends as well as potential future hotspots in the area of autonomous vehicles in mixed traffic.
... Furthermore, their acceleration when changing lanes was equal to HVs, except the vehicle was not able to break fast enough; also, they have used a higher minimum distance when changing lanes (1 m instead of 0.5), and kept a constant velocity when driving idle (HVs with varying speed). Motamedidehkordi et al. (14) have used a standstill distance of 1 m. ...
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Full-text available
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. 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.
... Connected highly automated vehicles promise significant improvements in traffic performance, congestion generation and propagation due to their vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication capabilities. The authors of [41], [42], and [43] have undertaken a study, which simulates a shock wave damping on freeways by these connected and highly automated vehicles (V2I and V2V). In the studies, a microscopic traffic simulation is used to determine whether and to what extent the driving behavior parameters of the model used affect the shock waves on freeways. ...
Conference Paper
Full-text available
This paper presents an approach that increases the resilience of a freeway network while differentiating patterns of freeway congestion events and investigating hot spots of each pattern both spatially and temporally. Based on an automated pattern recognition, an emerging congestion event can be identified and classified into one of four predefined congestion patterns. Determining the spatial and temporal extensions of several congestion events, hot spots of each pattern can be localized. Additionally, possible traffic management and control measures are compiled and evaluated by expert statements to mitigate and dissolve the found congestion hot spots. This approach provides a helpful toolbox for freeway operators to classify occurring congestion into predefined categories and to select appropriate countermeasures based on the hot spot analysis to increase the resilience of the overall system. By applying the presented methodology, optimized traffic information is provided to the operator in time-critical situations, which enables an improved decisionmaking process in traffic management. The data base is three large-scale data sets from stationary detectors, vehicle re-identification sensors, and floating car data collected on a German freeway in 2019.
... Furthermore, reacting automatically and almost instantly would allow the trucks forming a same platoon to travel more closely together 1 . This is expected to increase the roads' capacity (Young et al., 2019 ;Aki et al., 2012 ;Dávila et al., 2010), and to enhance the traffic flow (Van Arem et al., 2006 ;Kunze et al., 2009 ;Alam et al., 2015 ;Motamedidehkordi et al., 2016). Moreover, the greater spatial closeness within the platoons would decrease the air drag experienced by the trucks, resulting in lower fuel consumption and less GHG emissions. ...
... • The issue of stochasticity in the driving pattern has been accounted for by calibrating the spread of speed and acceleration distributions, as is suggested by the literature [28]; • The adaptation of CAVs into traffic fleets has been further carried out by changing the parameters of Wiedemann 74 car-following model that is available in VISSIM [29]; • To deal with the communication capability of CAVs, we define and enable a Boolean attribute to discriminate between CAVs and conventional vehicles in terms of the ability to send and receive information. 2. Simulation of the PRRP-framework: We employ a signal control operating upon an internal script 8 developed based on the PRRP-framework. ...
Article
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The growth of vehicle ownership has necessitated the adoption of new approaches to cope with the arising problems. In this regard, while technological advancement in connected and automated vehicles (CAVs) leads to a new source of information upon which effective intersection control mechanisms can be built, it also necessitates considering the issue of mixed traffic, on the one hand, and enhancing current infrastructures, on the other. This paper first proposes a framework, which enhances pre-timed signals to incorporate CAVs and wireless communications. In this framework, Round-Robin has been selected as the main algorithm treating vehicles' platoons as the units. Dealing with mixed traffic conditions and embedding an algorithm for prioritization of special vehicles are also discussed in the context of this framework. The result is a proposal of the Platoon-based Round-Robin algorithm with Priorities (the PRRP-framework). This framework is further integrated into a speed advisory system to mutually augment each other's functions to provide for as much continuous movement of mixed traffic as possible. Performance indices obtained from the corresponding simulations show that while the PRRP-framework is promising even with a low proportion of the CAVs, it is possible to get out considerably more benefits in the case of the proposed integration. © 2021 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
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This paper proposes a novel driving strategy for Connected and Automated Vehicles (CAVs) in a lane-free traffic environment. To this end, a combination of artificial forces and a reinforcement learning approach are used. To ensure the safe driving behavior of vehicles, an artificial ellipsoid border is assumed around each vehicle by which the lateral and longitudinal forces are obtained and applied. Furthermore, a longitudinal repulsive force based on a Deep Deterministic Policy Gradient (DDPG) network is exerted on the vehicles to avoid longitudinal collisions. Using this approach, the reaction of vehicles is improved, and vehicles may experience closer longitudinal space gaps allowing higher network throughput. The proposed lane-free driving methodology is implemented in the SUMO traffic simulator to showcase its benefits. Additionally, by implementing typical lane-based scenarios in SUMO with the same road condition and traffic demand as lane-free scenarios, a comparison in terms of average speed and time delay has been drawn between the proposed innovative approach and its conventional counterpart, proving the developed approach's functionality.
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This work describes an integrated approach to determining the effects of cooperative Intelligent Transportation Systems (ITS) on traffic efficiency and road safety by combining different test environments: a Field Operational Test (FOT) in real traffic, its interactions with a traffic simulation environment and the usage of data from other sources like a driving simulator. Since each of the test environments has its own advantages and limitations, the authors present a solution for combining them in terms of scenario design and evaluation planning. Such an integrated test and analysis concept offers the possibility of a holistic evaluation for traffic impacts of cooperative ITS. It is the basic design principle of the German research project simTD, which shows its feasibility in practical use.
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Book
This edited volume presents the proceedings of the AMAA 2015 conference, Berlin, Germany. The topical focus of the 2015 conference lies on smart systems for green and automated driving. The automobile of the future has to respond to two major trends, the electrification of the drivetrain, and the automation of the transportation system. These trends will not only lead to greener and safer driving but re-define the concept of the car completely, particularly if they interact with each other in a synergetic way as for autonomous parking and charging, self-driving shuttles or mobile robots. Key functionalities like environment perception are enabled by electronic components and systems, sensors and actuators, communication nodes, cognitive systems and smart systems integration. The book will be a valuable read for research experts and professionals in the automotive industry but the book may also be beneficial for graduate students.
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Shockwaves are a boundary that shows discontinuity in flow-density domain. The physical realization of a shockwave is point in time and space at which vehicles change their speed abruptly. The formation and dissolving of congestion are the phenomena that are important for the traveler information and congestion management perspectives. Shockwave analysis is the method to identify congested areas and estimate the rate of formation and dissipation of the congestion. The microscopic traffic simulation tool Vissim was used to address the main objective of this study, namely to determine if and to what extent the driving behavior parameters of the model used influence the shockwaves on motorways. After precise calibration of the car following behavior based on the detected shockwaves from data of the German research project simTD, the possible influences on driver behavior through communication between the vehicles and highly automated vehicles was sketched in order to figure out whether these applications can change the shockwave propagation speed on motorways, lead to suppression of shockwaves and improve the network performance as well as increase the traffic safety.
Chapter
Shockwaves are a boundary that shows discontinuity in a flow-density domain. The physical realization of a shockwave is a point in time and space at which vehicles change their speed abruptly. The formation and dissolving of congestion are phenomena that are important for the traveler information and congestion management perspectives. Shockwave analysis is the method to identify congested areas and estimate the rate of formation and dissipation of the congestion. The microscopic traffic simulation tool Vissim was used to address the main objective of this study, namely to determine if and to what extent the driving behavior parameters of the model used influence the shockwaves on motorways. After precise calibration of the car following behavior based on the detected shockwaves from data of the German research project sim TD , the possible influences on driver behavior through highly automated vehicles was sketched in order to figure out whether these applications can change the shockwave propagation speed on motorways, lead to suppression of shockwaves and improve the network performance as well as increase the traffic safety.
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Automation may be assumed to have a beneficial impact on traffic flow efficiency. However, the relationship between automation and traffic flow efficiency is complex because behavior of road users influences this efficiency as well. This paper reviews what is known about the influence of automation on traffic flow efficiency and behavior of road users, formulates a theoretical framework, and identifies future research needs. It is concluded that automation can be assumed to have an influence on traffic flow efficiency and on the behavior of road users. The research has shortcomings, and in this context directions are formulated for future scientific research on automation in relation to traffic flow efficiency and human behavior.
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Simulating any nontrivial traffic situation requires describing not only acceleration and braking but also lane changes. When modeling traffic flow on entire road networks, additional discrete-choice situations arise such as deciding if it is safe to enter a priority road, or if cruising or stopping is the appropriate driver’s reaction when approaching a traffic light which is about to change to red. This chapter presents a unified utility-based modeling framework for such decisions at the most basic operative level.
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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.