Content uploaded by Konstantinos Katsaros
Author content
All content in this area was uploaded by Konstantinos Katsaros
Content may be subject to copyright.
1
Performance study of a Green Light Optimized
Speed Advisory (GLOSA ) Application Using an
Integrated Cooperative ITS Simulation Platform
Konstantinos Katsaros∗, Ralf Kernchen∗, Mehrdad Dianati∗, David Rieck†
∗Centre for Communications Systems Research (CCSR)
Department of Electronic Engineering, University of Surrey
Guildford, GU2 7XH, United Kingdom
E-mail: {K.Katsaros, R.Kernchen, M.Dianati}@surrey.ac.uk
†Fraunhofer Institute for Open Communication Systems
Kaiserin-Augusta-Allee 31, 10589 Berlin, Germany
E-mail: david.rieck@fokus.fraunhofer.de
Abstract
This paper proposes a Green Light Optimized Speed Advisory (GLOSA) application implementation in a typical
reference area, and presents the results of its performance analysis using an integrated cooperative ITS simulation
platform. Our interest was to monitor the impacts of GLOSA on fuel and traffic efficiency by introducing metrics
for average fuel consumption and average stop time behind a traffic light, respectively. For gathering the results
we implemented a traffic scenario defining a single route through an urban area including two traffic lights. The
simulations are varied for different penetration rates of GLOSA-equipped vehicles and traffic density. Our results
indicate that GLOSA systems could improve fuel consumption and reduce traffic congestion in junctions.
Index Terms
vehicular communications, traffic light advisory, fuel consumption, traffic congestion.
I. INTRODUCTION
Advances in wireless communications and in particular vehicular communications have led to the advent of
cooperative Intelligent Transportation Systems (ITS) [1], [2]. These systems employ vehicular communication
technologies such as 802.11p [3] to enable deployment of applications that could potentially improve road safety,
traffic efficiency, and introduce new entertainment and business applications [4]. Exploitation of ITS for traffic
congestion control in urban areas as well as fuel consumption reduction are among the most promising applications
according to transportation authorities [5], [6]. This can be achieved by vehicle-to-vehicle (V2V) or infrastructure-
to-vehicle (I2V) communications, intelligently advising individual drivers about traffic events, such as the traffic
light phases and beyond.
The potential of V2V communication for improving fuel efficiency has been demonstrated in [7], showing that
vehicular communication can assist to reduce average fuel consumption especially under high traffic density and
long traffic light cycles. Other projects have investigated the impacts on fuel efficiency when using wireless com-
munications between vehicles (V2V) or vehicle-to-infrastructure (V2I) employing different algorithms to smoothly
slow down at a red traffic light or to reach it at the next green phase. Depending on the used consumption models,
different results can be observed. One important aspect, as introduced in [8], is the dependency of the results on
different penetration rates of the communication enabled vehicles. In the same work as well as in [9], the effect
of traffic density is studied. In [8] it is also suggested to either cut the fuel delivery or stop the engine in order
to achieve less fuel consumption. In [10], the IDM car following model [11] is used to control a platoon of 10
vehicles, where only the leading one is equipped with communication capabilities, achieving 30% fuel savings. In
[12] when the algorithm is used only with one vehicle and one traffic light, fuel savings reached 20%. Although
these results seem trustworthy, the simulations are conducted with small number of vehicles which does not consider
the dynamics of the vehicular environment. When the model in [12] is scaled up including 15 traffic light junctions,
2
the results are reduced to 8% reduction in fuel consumption. The optimal activation distance for the algorithm is
also investigated in this work and is found that a distance of 500m achieves the best results in their simulations.
In [10] and [9] traffic efficiency is examined using different performance metrics. In [10], the increase of average
speed is taken as indicator of traffic efficiency where in [9], the flow and the ratio of motionless vehicles are
considered. We examine the average stop time behind a traffic light as a metric of traffic efficiency. This is because
we want to see not only how many vehicles are stopped like [9] but also how long they wait.
In the aforementioned works, different communication technologies are adopted. The authors in [7], [9], [12], [13]
do not discuss in depth the communication mechanisms of their simulations. They assume successful dissemination
of the messages. Others like [10] and [14] use general purpose wireless communication technologies, such as
wireless sensor networks and IEEE802.11 [15]. Lately the IEEE 802.11 standard is preferred due to operational
cost. The highly dynamic network topology of vehicular communications has led to the introduction of IEEE
802.11p [3] that is more suitable for such applications and is the one we used in our simulations.
This paper aims to implement a GLOSA system to reduce traffic congestion by decreasing the average stop
time behind traffic lights while reducing fuel consumption and CO2emissions. The GLOSA application provides
the advantage of timely and accurate information about traffic lights cycles and traffic lights position information
through infrastructure-to-vehicle (I2V) communication, and provides drivers with speed advice guiding them with
a more constant speed and with less stopping time through traffic lights. The main challenges in achieving this
include the modelling of the vehicle traffic, the communications between traffic lights and vehicles and finally the
driver’s behaviour. Individual research has been performed for each one of these areas, but complete simulations
by taking into account the dynamics of all parameters are scarce. Thus, for our implementation of GLOSA, we
used an integrated simulation tool based on the Fraunhofer VSimRTI [16], which enables online two-way coupling
of different simulators for monitoring the influence of GLOSA application on the traffic and fuel consumption.
Our results show up to 7% reduction in average fuel consumption and up to 89% in average stop time. While
conducting the simulations we found that an optimal GLOSA activation distance is around 300m.
The rest of this paper is organised as follows. In section II, we present our integrated simulation approach, and
in section III, we give an overview of the integrated simulation platform. The design of the GLOSA algorithm is
thoroughly discussed in section IV. In section V, the simulation set-up is presented followed by our evaluation
results. Finally in section VI we conclude and provide ideas for future work.
II. SIMULATION APPROACH
We designed and tested GLOSA in an integrated simulation platform. The first step was to define a reference
scenario which is depicted in Fig. 1. In this reference area we placed a traffic light. Vehicles will enter following a
common arrival process such as Poisson distribution. In the scenario the equipped vehicles rate is varied, since we
want to investigate the influence of the penetration rate and which is the minimum percentage of equipped vehicles
for GLOSA to have a positive impact on the traffic efficiency and fuel consumption. Road traffic will be modelled
using the microscopic Stefan Krauss (SK) model [17], a car-following model with two basic rules. First, vehicles
in free motion have a target speed and try to cruise at it. Second, when a vehicle senses the distance to the vehicle
ahead to be less than a certain threshold, it slows down keeping a safe distance. The speed of the vehicles is within
a certain range [Vmin,Vmax ] where Vmin is the minimum speed that vehicles can cruise without causing further
traffic congestion and Vmax is the maximum speed that is forced by the speed limit of the area. Acceleration is also
bounded and asymmetric - higher deceleration than acceleration - for more realistic simulations. The SK model is
integrated in SUMO [18] traffic simulator that we used in our work.
We have set up two simulation scenarios in order to compare the results. In the first scenario (S0), there is
no driver’s assistance information and the traffic is governed by the SK model. In the second scenario (S1) we
provide information through the GLOSA messages and the driver receives speed advisory messages. We assume
that all drivers who get a speed advise, will follow it. We control the percentage of GLOSA equipped vehicles to
monitor the impact of penetration rate. Therefore, we have defined two performance metrics that we check in all
scenarios in order to derive our conclusions. The first metric (P1) is the average stop time of vehicles waiting at the
intersection behind red lights measuring the traffic efficiency of GLOSA application. We assume that there is no
other reason for the vehicles to halt apart from stopping at a traffic light or a queue caused by the traffic light (we
do not simulate brake downs or accidents in our scenarios). The second metric (P2) is the fuel consumption derived
3
Fig. 1. Reference Scenario
from the fuel consumption and emission model in [19] measuring the fuel efficiency of GLOSA application. The
emissions of CO2are estimated from the same model to be proportional to fuel consumption.
III. INT EG RATE D SIMULATION PLATF OR M
Simulating the GLOSA use case poses a challenge in terms of combining and synchronizing different simulation
aspects, e.g. vehicular traffic, network communication and application handling. To address these challenges, the
integrated simulation platform VSimRTI was used to simulate this use case. VSimRTI borrows some concepts
of the High Level Architecture (HLA) [20] to enable the coupling of the most appropriate simulators for a
scenario. VSimRTI is a lightweight framework, which is responsible for tasks such as synchronization of simulators,
interaction between them and lifecycle management of simulators but also applications. VSimRTI supports V2X-
specific elements such as road-side units or application-equipped vehicles from the beginning.
To simulate the GLOSA application, VSimRTI app was used. VSimRTI app is an application simulator developed
for VSimRTI. It provides a couple of simple JAVA interfaces to create V2X applications, while offering access to
all relevant simulation data such as vehicle status or communication modules. Messages sent to specific vehicles
are forwarded to the associated application and application output can be directed to a specific vehicle (e.g. giving
speed advisory to a vehicle).
IV. GLOSA ALGORITHM
The GLOSA algorithm has been implemented to support the aforementioned simulation approach and is presented
in the Algorithm 1. First, vehicles enter the communication range of a traffic light according to the Poisson
distribution as mentioned before. The RSU (Road Side Unit) attached to a traffic light broadcasts periodically
CAM’s (Co-operative Awareness Messages) including the position, timing information and additional data for the
traffic light. When OBU receives a CAM, the algorithm checks if its source is a traffic light or not. From the position
information within the message and the vehicle’s own position and heading, it calculates whether this traffic light
is relevant (on its route) or not (line 1). The application can then calculate the distance from the traffic light and
with the current speed and acceleration, the time that it would take to reach it (Time-to-Traffic-Light TT L) (line
2). Next, it checks the traffic light phase at that time (TT L) (line 3). If the traffic light is green when the vehicle
reaches it, then the vehicle continues its trip trying to reach the maximum speed limit of the road (lines 4-6). If
it is red, it calculates the speed that it should have in order to reach it in the next green phase (lines 7-9). If it is
yellow, depending on the remaining yellow time and the acceleration capabilities of the vehicle it could advice to
accelerate or decelerate again within the permitted range (lines 10-13). Finally the driver gets an advise with the
speed limited within the permitted range [Vmin,Vmax ](line 15). This algorithm runs every second which makes it
more robust against external interference, such as other vehicles, that do not follow the same advisory speed or are
non-equipped.
4
Algorithm 1 GLOSA Algorithm
1: Find the most relative traffic light
2: Calculate Distance and Time to traffic light TTL
3: Check phase at TT L
4: if GREEN then
5: Continue Trip
6: Target Speed (Ut) = Umax
7: else if RED then
8: Calculate remaining Red Time (Tred)
9: Calculate target speed for Tred +TT L :Ut
10: else if YELLOW then
11: Calculate remaining Yellow Time (Tyellow )
12: Check for possible acceleration
13: Calculate target speed for Tyellow +Tred +TT L :Ut
14: end if
15: Advisory speed = MAX (Ut,Umin) & MIN (Ut,Umax )
The algorithm gets as input the current speed U0, acceleration aof the vehicle and the distance to the traffic
light Dtl. Using basic rules of motion, given by (1)
d=u∗t+ 1/2∗a∗t2,(1)
where dis the distance, uis the initial speed, tis the time and ais the acceleration, the time to reach the traffic
light (TT L) can be calculated as shown in (2).
TT L =
d
uwhen a = 0
−u
a+ru2
a+2d
awhen a 6=0
(2)
The target speed (Ut) for the red and yellow light phase is calculated using (3)
Ut=2∗d
t−U0,(3)
where dis the distance to traffic light (Dtl), tis the time to reach the traffic TT L light plus the remaining time for
the next green phase (Tred or Tyellow +Tred respectively) and U0is the current speed.
V. SIMULATION SET-UP AND EVAL UATION RESULTS
For the evaluation of the GLOSA application a series of simulations were conducted. The configuration of the
environment for the project consists of the SUMO [18] traffic simulator used to produce and cope with the vehicle
traffic, the JiST/SWANS [21] is used for the communications and finally an application simulator which runs the
GLOSA application written in Java. The underlying simulation scenario is a road network section of Guildford town
centre in United Kingdom as depicted in Fig. 2. Vehicles start from point A on York Road according to a Poisson
distribution and travel until point B on Waterden Road on one lane and without overtaking. The number of vehicles
is defined to 100 in order to gather sufficient data in terms of time and number of independent vehicles to get
statistically accurate results. The travel distance is 0.6miles (0.965km). Within this route there are two traffic lights
(T L1and T L2). For these two traffic lights the timing regarding the previous route is: 20-4-6 (Green-Yellow-Red)
and 20-4-36 seconds, respectively. The difference in red time for T L2is because of the London Road’s green phase
duration. The speed limit on the road is set to 15m/s (54km/h) which is near the usual limit in an urban area. The
minimum advisory speed is set to 6m/s (21.6km/h) in order not to travel to slow and cause more congestion. The
simulation runs until every vehicle has travelled the whole distance from point A to point B. The communication
range of the traffic lights is set near 500m and using IEEE 802.11p communication as access mechanism they
broadcast their CAM’s.
5
Fig. 2. Simulation Scenario Map
Fig. 3. Influence of activation distance on GLOSA performance
The simulations can be divided into three categories. First, we tested the influence of the activation distance for
GLOSA on the overall performance. In these simulations all vehicles are equipped with the GLOSA application.
Also, in order to check the integrity of the algorithm we excluded the first traffic light and run only with one
(T L2). The timing for this was also altered in order to have equal red and green phases. The results of the two
performance metrics can be seen in Fig. 3. An optimal point of activation is found at a distance near 350m. At
shorter activation distances, the reaction time (time required for the driver to slow down to the advised velocity)
is not enough to have benefits. The fuel consumption is also slightly increased due to the fact that the average
trip time is increased (vehicles are advised to travel at lower speeds). At longer activation distances, the benefits
regarding fuel consumption are slightly decreased but remain near the optimal levels. The results for two traffic
lights with a distance near 400m between them (Fig. 2), shift this optimal point to a shorter distance of 250m which
will be the value used to produce the next set of results. This is due to the fact that vehicles do not have enough
time to accelerate and reach a higher velocity after the effect the first traffic light has on their velocity before they
run the GLOSA algorithm once again for the second traffic light. Hence, further simulations have to be made for
larger scale scenario and more traffic lights to conclude which activation distance to use. Having a shorter activation
distance means that we can reduce the transmission power of the RSU and thus having better resource allocation
and less collisions in the communications. Compared with the work in [12], where the minimum activation distance
is found near 500m, our work shows better characteristics in this aspect.
Second, we measured the influence that GLOSA penetration rate has on the two performance metrics and how the
non-equipped vehicles are affected. The simulations were conducted in a high traffic density environment (Poisson
expected value λ= 0.2). From [8], we learn that an increase in penetration rates of equipped vehicles allows for a
better reduction of fuel consumption in the overall traffic scenarios. As it can be seen from Fig. 4 and Fig. 5 this is
verified not only for fuel efficiency, but also for traffic efficiency. The most interesting outcome from these figures
is that even the non-equipped vehicles are getting affected in a beneficial way from the GLOSA equipped vehicles
and this is due to the SK model. They follow the leading vehicle which - if equipped with GLOSA - forces them
to adjust their speeds accordingly since we assume that there are no overtaking in our simulations. The second
notice is that the average stop time is reduced even when the penetration rate is small, but in order to see positive
results in fuel efficiency we need at least 50% equipped vehicles. The observed average maximum reduction in
fuel consumption is 7% which is slightly higher than the average maximum fuel savings in [12] for their scaled
6
Fig. 4. Influence of GLOSA penetration rate on average stop time
Fig. 5. Influence of GLOSA penetration rate on average fuel consumption
Fig. 6. Influence of vehicle traffic density on the GLOSA performance
up scenario.
Finally, we simulated different traffic densities (high, medium and low) in order to capture the influence they
have on the overall performance of the GLOSA application. The results shown in Fig. 6 suggest that the higher
the traffic density (moving from left to right in the plot), the more benefits we have regarding in fuel efficiency
reaching a maximum of 7% fuel reduction. On the other hand, the benefits we get regarding traffic efficiency are
decreased, which was also reported in [9]. This is because the vehicles are more scarcely distributed therefore they
do not influence each other, they all follow precisely the advisory speed and there are smaller queues at the traffic
lights making the GLOSA algorithm work better.
7
VI. CONCLUSIONS AND FUTURE WORK
The results suggest that the GLOSA application has a positive effect on both performance metrics. The higher
the GLOSA penetration rate is, the more benefits we have with a maximum of 80% reduction in stop time and up
to 7% reduction in fuel consumption in a high traffic density scenario. There is a critical point of 50% of equipped
vehicles where the effect of GLOSA starts to be more visible on fuel consumption. As the density decreases, the
benefits for fuel efficiency are reduced, but there is still improvement compared to non-equipped vehicles. The
traffic efficiency on the other hand is increased with the decrease in traffic density reaching 89%. There is also an
optimal activation distance where the GLOSA application should advise the driver and this is near 300m from the
traffic lights but it depends slightly on the road network. Closer to this distance, the time to react is limited and
further away there are no more benefits. If the complexity of the algorithm is to be increased, the distance could
be also increased. The work presented by this paper is an example of what can be achieved in terms of fuel and
traffic efficiency when vehicles are enabled to communicate with traffic lights and how we can exploit an integrated
simulation platform to achieve this.
There are various ways in which this application could be extended to achieve more accurate results. First of
all, we assumed that there are no vehicles waiting at the traffic light which is not always the case. Therefore, the
distance to traffic light could be replaced by the distance to the end of the queue instead to achieve more reasonable
results. The simulation network should also be extended to a larger scale scenario using real data for vehicle input.
Finally having results from field tests would provide data to compare field tests and simulations to evaluate the
estimations made by the simulations.
ACKNOWLEDGMENT
The work was done within the joint research project PREDRIVE C2X, which is funded by DG Infso of the
European Commission within the 7th Framework Program.
REFERENCES
[1] L. Figueiredo, I. Jesus, J. Machado, J. Ferreira, and J. Martins de Carvalho, “Towards the development of intelligent transportation
systems,” 2001, pp. 1206 –1211.
[2] H. Hartenstein and K. P. Laberteaux, “A tutorial survey on vehicular ad hoc networks,” IEEE COMMUNICATIONS MAGAZINE, vol. 46,
no. 6, pp. 164–171, JUN 2008, aCM MobiCom/MobiHoc 2007, Montreal, CANADA, 2007.
[3] “Draft standard for information technology–telecommunications and information exchange between systems–local and metropolitan area
networks –specific requirements part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications amendment
7: Wireless access in vehicular environments,” IEEE Unapproved Draft Std P802.11p/D9.0, July 2009, 2009.
[4] “Car 2 car communication consortium manifesto,” Online, CAR 2 CAR Communication Consortium, www.car-2- car.org.
[5] “National traffic signal report card,” Online, National Transportation Operations Coalition, 2007, http://www.ite.org/REPORTCARD.
[6] “Traffic congestion and urban mobility,” Online, Texas Transportation Institute, 2010, http://tti.tamu.edu/infofor/media/topics/congestion
mobility.htm.
[7] A. Widodo, T. Hasegawa, and S. Tsugawa, “Vehicle fuel consumption and emission estimation in environment-adaptive driving with
or without inter-vehicle communications,” in PROCEEDINGS OF THE IEEE INTELLIGENT VEHICLES SYMPOSIUM 2000, 2000,
Proceedings Paper, pp. 382–386.
[8] A. Wegener, H. Hellbruck, C. Wewetzer, and A. Lubke, “Vanet simulation environment with feedback loop and its application to traffic
light assistance,” nov. 2008, pp. 1 –7.
[9] M. Chao-Qun, H. Hai-Jun, and T. Tie-Qia, “Improving urban traffic by velocity guidance,” in INTERNATIONAL CONFERENCE
ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL 2, PROCEEDINGS, 2008, Proceedings Paper, pp.
383–387.
[10] M. Sanchez, J.-C. Cano, and D. Kim, “Predicting traffic lights to improve urban traffic fuel consumption,” in 2006 6th International
Conference on ITS Telecommunications Proceedings. IEEE, 2006, Proceedings Paper, pp. 331–336.
[11] M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” PHYSICAL
REVIEW E, vol. 62, no. 2, Part A, pp. 1805–1824, AUG 2000.
[12] T. Tielert, K. M., H. Hartenstein, R. Luz, H. S., and T. Benz, “The impct of traffic-light-to-vehicle communication on fuel consumption
and emmisions,” in Internet of Things 2010 - Second International Conference for Academia and Industry, 2010.
[13] B. Asadi and A. Vahidi, “Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and
reducing trip time,” Control Systems Technology, IEEE Transactions on, vol. PP, no. 99, pp. 1–9, 2010.
[14] I. Iglesias, L. Isasi, M. Larburu, V. Martinez, and B. Molinete, “I2v communication driving assistance system: On-board traffic light
assistant,” sep. 2008, pp. 1 –5.
[15] “Supplement to ieee standard for information technology - telecommunications and information exchange between systems - local
and metropolitan area networks - specific requirements. part 11: Wireless lan medium access control (mac) and physical layer (phy)
specifications: High-speed physical layer in the 5 ghz band,” IEEE Std 802.11a-1999, p. i, 1999.
8
[16] T. Queck, B. Sch¨
unemann, I. Radusch, and C. Meinel, “Realistic simulation of v2x communication scenarios,” in APSCC ’08:
Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference. Washington, DC, USA: IEEE Computer Society, 2008,
pp. 1623–1627.
[17] S. Krauss, “Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics,” Ph.D. dissertation, 1998.
[18] D. Krajzewicz, G. Hertkorn, C. Rssel, and P. Wagner, “Sumo (simulation of urban mobility) - an open-source traffic simulation,” sep.
2002, pp. 183–187.
[19] R. D.C.Biggs, “An energy-related model of instantaneous fuel consumption,” Traffic engineering & control, vol. 27, pp. 320–325, 1986.
[20] Institute of Electrical and Electronics Engineers, IEEE standard for modeling and simulation (M&S) High Level Architecture (HLA)–
federate interface specification. IEEE Standard 1516.1. New York, NY, USA: IEEE, 2000.
[21] R. Barr, Z. J. Haas, and R. van Renesse, “Jist: an efficient approach to simulation using virtual machines:research articles,” Softw.
Pract. Exper., vol. 35, no. 6, pp. 539–576, 2005.