Analysis of an Active Traffic Management System Proposed for a Brazilian Highway

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International Journal of Emerging Technology and Advanced Engineering 
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 6, Issue 4, April 2016) 
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Analysis of an Active Traffic Management System Proposed for 
A Brazilian Highway 
Felipe Caleffi
1
, Yann Moisan
2
, Helena B. B. Cybis
3
 
1,2,3
Laboratory of transport systems, of the Federal University of Rio Grande do Sul (UFRGS), Porto Alegre – RS, 90035-180, 
Brazil 
Abstract— This paper presents an evaluation of active 
traffic management (ATM) strategies for a Brazilian freeway. 
The ATM objectives are to improve freeway traffic and safety 
conditions. In order to evaluate the impacts of a future 
implementation, a micro-simulation model was developed 
through the software VISSIM. The paper presents the model 
calibration effort to match the Brazilians drivers’ behaviours 
particularities. The ATM strategies analysed in this paper are 
speed harmonization and a temporary use of the hard 
shoulder. Three simulation scenarios were developed for this 
study: a first scenario without any control strategy - which 
represents the current freeway conditions; a scenario with the 
speed harmonization strategy; and a third scenario with the 
combination of speed harmonization plus temporary use of 
hard shoulder. The paper analyses the traffic performance 
under these control strategies and discusses particular 
consequences of these implementations to a Brazilian road. 
The simulation brings positives results with both strategies. 
Keywords— Active traffic management, Brazilian freeway, 
Hard shoulder running, Micro-simulation, Variable speed 
limits. 
I. INTRODUCTION 
Active traffic management (ATM) strategies rapidly 
increased in several countries in the latest years. ATM 
main strategies include speed harmonization and temporary 
use of the hard shoulder. Speed harmonization involves the 
use of variable speed limits (VSL) to reduce the speed’s 
limits on congested roads to maintain a stable flow regime 
and to reduce accident hazards. Temporary use of hard 
shoulder adds an extra lane, increasing capacity during 
congestion or during incident occurrences, when the road is 
overloaded [1]. Hard shoulder is generally implemented in 
association with speed harmonization to reduce speeds 
before opening the hard shoulder [2]. 
The country’s economic and cultural context where 
ATM has been implemented may vary, however the main 
objectives of these strategies usually involve: reducing 
congestion on highways; improvements on travel time 
reliability; capacity increase; safety improvement; 
reduction of accidents number and seriousness [3] and [4]. 
 
Additional benefits reported from ATM implementations 
frequently include faster response time in case of accident, 
increase on users’ satisfaction due to improvements in 
drivers’ information and stress reduction [5]. Literature 
presents several reports [6], [7], [8], [9], [10], [11] and [12] 
of ATM impact assessments. 
Although ATM schemes that combined strategies have 
not been implemented yet in Brazil, the traffic behaviour 
particularities of the Brazilian freeways provide an 
adequate environment for the implementation of these 
strategies. In addition to frequent congestion, traffic is very 
heterogeneous. Usually each freeway lane presents 
different average speeds, flow rates and traffic 
composition. Overtaking is frequent and, under these 
conditions, very dangerous [13]. ATM can offer the benefit 
of a speed harmonization scheme helping to postpone flow 
breakdown, to decrease number of overtaking and collision 
hazards. Temporary use of the hard shoulders can increase 
capacity in times of congestion, postponing the need for 
high investments. Due to the expansion of the road 
concession program in Brazil, ATM schemes became a 
serious consideration. 
This paper presents an ATM schemes evaluation applied 
to the Brazilian freeway BR-290/RS, located in the state of 
Rio Grande do Sul. The evaluation was based on a 
simulation model, developed with the micro-simulation 
model VISSIM [14]. 
This article is organized in six sections. The second 
section presents the studied freeway segment and its 
operational conditions. The third section describes the 
model calibration. Simulation scenarios are described in the 
fourth section. Results from simulations are presented and 
discussed in the fifth section. Finally, conclusions and 
future steps are presented in the sixth section. 
II. FREEWAY SEGMENT CHARACTERIZATION 
BR-290/RS is a freeway connecting the state capital, 
Porto Alegre, to coastal cities and the north of the country. 
It receives high flows during summers and major holidays, 
increasing speed variability and flow variability. During 
these periods, congestion and flow breakdowns are 
frequent. 
 
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The segment under study is 10 km long, and contains 
three lanes and an entry ramp. Speed limits are 110 km/h 
for light vehicles and 90 km/h for heavy vehicles. 
An analysis of the road current operating conditions was 
based on data from inductive loops. These data analysis 
indicated that traffic behaviour is significantly different on 
each lane. The left lane (Lane 1) features the highest speeds 
and flow rates, while the right lane (Lane 3) presents the 
lowest speeds and flow rates. Speed-flow relationship for 
data collected on the segment under study, in January 2013 
is shown in Fig. 1. 
Lane 3 receives mostly trucks and buses, which lead to 
reduced flow rates and speed, since speed limits are 
different for light and heavy vehicles. Light vehicles 
traveling at lower speeds than the regulated maximum are 
also prevailing in this lane. The data is grouped into 5 
minute intervals. 
 
Fig. 1. Speed-flow relationship. 
Speed variability observed for all classes of vehicles on 
all three lanes at free flow conditions was useful to define 
drivers’ desired speeds in the simulation model. 
III. CALIBRATION MODEL 
The simulation model calibration involves several 
aspects. Vehicles speeds distribution and vehicles engine 
power distribution were modified to adjust the model to 
fleet and drivers’ characteristics. Parameters impacting on 
drivers’ behaviour have also been changed after a two steps 
process: a sensibility analysis, to define which parameters 
needed to be altered; and minimization of an error function 
called fitness function. 
A. Calibration parameters sensibility analysis 
A sensibility analysis was conducted for VISSIM´s 
driving behaviour parameters [14], in order to determine 
which ones have most significant impact on the road 
segment under study. 
The analysis involved combinations for lane changing 
and car following parameters reported in literature as the 
most important for freeways environments representation. 
256 possible combinations were determined with this 
analysis. The best set of values to be assigned to each of the 
calibration parameters was determined as result of this 
analysis. 
1) Lane Changes 
Calibration for lane changing parameters was conducted 
to improve the Brazilians drivers’ behaviour representation, 
which are very aggressive regarding changing lanes. The 
parameter "safe distance reduction factor" was modified in 
order to find the value that best represents drivers’ 
aggressiveness during lane changes. This parameter is 
crucial because the drivers’ aggressiveness in this case 
study leads to a significantly different performance than the 
resultant from the VISSIM default values. 
2) Car following 
Through the sensitivity analysis, the parameters 
"Headway time (CC1)", "Following variation (CC2)", 
"Threshold for entering following (CC3)" and "Followingpositive and negative threshold (CC4 and CC5)" were 
determined to be the most influencing parameters on the 
model’s performance. These adjusted parameters allowed 
the model to better represent drivers’ behaviour that travel 
more aggressively and maintain shorter distances while 
following another vehicle, with higher acceleration and 
decelerations rates. 
B. Model calibration and validation 
The calibration and validation process was performed by 
comparing data from inductive loops with simulation 
outputs. The comparison was based on a fitness function 
which determined the relative error between performance 
measures obtained on the field, and outputs from the 
simulation. For the average speeds calibration, outputs 
from the model were obtained from virtual loops defined in 
the same locations as freeway’s loops. Equation 1 presents 
the fitness function. 
 (
| |
 
) (1) 
Where: F(Fitness) is the average speeds relative error; 
A.V.Collected is the collected average speed; and A.V.Modelled 
is the modeled average speed. 
Video analyses from cameras positioned along several 
road locations were carried out to verify the adequacy of 
lane changing behaviour. The visual validation of lane 
changing behaviour took into account three vehicles classes 
in the simulation: cars, trucks and buses. 
 
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As the study objectives is to model freeway behaviour 
and to calibrate the simulation model for periods preceding 
congestion, the validation was particularly concerned with 
analysing the model performance for flow rates above 4000 
veh/h. 
Table I 
Average Speeds And Fitness Values 
Average 
Speed 
Collected 
Ideal Combination 
VISSIM Default 
Parameters Values 
Average 
Speed 
Modelled 
Fitness 
Value 
Average 
Speed 
Modelled 
Fitness 
Value 
Location 1 
88,75 
km/h 
86,40 km/h 2,64% 82,29 km/h 7,27% 
Location 2 
80,12 
km/h 
79,81 km/h 0,38% 72,40 km/h 9,63% 
High fitness values for default parameters shows that the 
calibration was a necessary step in the modelling process, 
and low values for the ―ideal combination‖ prove the 
calibration is satisfactory. Locations 1 and 2 are located in 
the G1 and G4 marks respectively, in Fig. 2. A qualitative 
evaluation of the flow-speed clouds of the data collected 
and simulated was also performed. 
When flow rates reach values up to 4000 veh/h, the 
average speed variability increases significantly and 
congestion starts to build up. Under these circumstances car 
following and lane change parameters have their most 
important influence on model results. 
IV. THE MODEL STRUCTURE 
ATM strategies were implemented with the assistance of 
vehicle actuated programing tool (VAP) provided by 
VISSIM. VAP is a programming tool that enables the 
simulation of traffic control strategies in response to traffic 
conditions in real time [14]. 
In order to evaluate ATM strategies potential benefits in 
the studied segment, three simulation scenarios were 
constructed (A, B and C). Scenario A simulated the road 
segment without any ATM strategy, to represent the current 
freeway traffic conditions. Scenario B modelled only speed 
harmonization strategy, using variable speed limits (VSL). 
Scenario C simulated the road segment controlled by a 
combination of speed harmonization and temporary use of 
hard shoulder. The input demand modelled represents 8 
hours of simulation, related to the period from 14:00 to 
22:00, and flow pattern typical of summer peak period, in 
which the highway BR-290/RS receives the highest flow 
rates. 
A. Model layout 
The modelled road segment has 10 km long and an 
access ramp. Eleven gantries were created in the model to 
simulate speed harmonization and temporary use of hard 
shoulder. These gantries are responsible for indicating a 
possible change in speed limits, and indicating when the 
hard shoulder is available for use. Fig. 2 presents the 
modelled road segment, with the eleven gantries. Detectors 
responsible for gathering information about speeds, flows 
and occupancy for each lane are positioned in the same 
location of each gantry. 
 
Fig. 2. Part under study. 
Gantries are spaced 800 meters, which is indicated by 
[15] as the average spacing on highways where ATM is 
implemented. These gantries are called G1 to G11, 
respectively. 
The segment from gantry 5 to the end was selected to 
receive the temporary hard shoulder strategy, since 
additional traffic from the access ramp and the reduction on 
the number of lanes from four to three lanes in gantry 5 
characterizes a bottleneck. 
 
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The segment between the ramp and gantry 5, which 
hosts the increase and subsequent decrease in number of 
lanes, is the most critical segment. Congestion incidence is 
higher in this segment due to the bottleneck and additional 
traffic from the ramp. Average speeds reduce significantly 
when congestion occurs. 
B. ATM control algorithm 
The control strategies used to determine variable speed 
limits (VSL) in the speed harmonization are essential for 
the system successful operation. These strategies are 
implemented through an algorithm that compares indicators 
and thresholds that trigger the speed or temporary use of 
hard shoulder. Information from detectors allows 
controlling these strategies in real time, optimizing the 
process [16]. 
The control algorithm is designed to select speed limits 
and use of hard shoulder based on average measures of 
flow, speed and occupancy over a two minutes period. 
These limits are selected using information from detectors 
which are positioned in each gantry, on each lane, 
including the hard shoulder. Measures are made on the hard 
shoulder only when it is currently in use. This design 
incorporates the state of practice of the first ATM systems. 
Algorithms examples and their boundaries can be found in 
[17] and [18]. 
Parameters values for the control algorithm modelled 
were selected based on a road characterization study during 
critical periods preceding traffic jam occurrences. This 
study gathers freeway speeds, flow rates and occupancy 
averages. Thresholds of flow (4000 veh/h) and occupancy 
(15%) were selected according to a data historical analysis 
from freeway’s sensors. They represent boundaries values 
preceding significant speed decays, when congestion starts. 
ATM algorithm modelled defines three speed limits: 
freeway speed limit (110 km/h); first speed reduction (90 
km/h); and second speed reduction (70 km/h). 
ATM system is activated when traffic data measured 
(each two minutes) on a gantry detector exceed the control 
algorithm thresholds. Speeds displayed in the upstream 
gantries are determined based on their location on an 
―action zone‖ or a ―transition zone‖, described below: 
• Action zone – Speed harmonisation and/or temporary use 
of hard shoulder are activated in the detection gantry and 
the two closest upstream gantries. 
• Transition zone – If speed limit activated is reduced from 
110 to 70 km/h, the last gantry in the action zone (second 
closest upstream) should display speed limit of 90 km/h, to 
provide a gradual speed reduction for drivers. 
The flowchart of the control algorithm modelled in this 
study is presented in Fig. 3. The algorithm’s first step 
consists in reading detectors at every two minutes. 
 
Fig. 3. Control algorithm flowchart. 
Average detected values, corresponding of two minutes 
data, arecompared with the threshold of flow rate, 
occupancy and speed, to determine whether or not the 
ATM system should be activated. ATM deactivation 
happens when freeway flow rate is smaller than 4000 veh/h 
and occupancy is smaller than 15%. 
Fig. 4. shows the speed-flow relationship of the freeway 
for data collected in January 2013, indicating the data set 
where ATM system would be activated, according to the 
control algorithm thresholds. 
 
Fig. 4. Speed-flow relationship indicating the thresholds and the 
activated and deactivated system. 
Red lines indicate the two speed thresholds that activate 
the system, and green line indicates the flow rate threshold 
that activates the system. Blue dots indicate when ATM 
system would be disabled – operating at speed limit of 110 
km/h. Black dots, brown dots, green dots and grey dots 
indicate the freeway status where system was activated, in 
correspondence with the flowchart colours. 
 
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C. Modelled scenarios 
This study presents three simulation scenarios (A - 
without any control, B - with speed harmonization strategy 
and C - speed harmonization and temporary use of hard 
shoulder strategy), in order to examine separately the 
effects of an isolated strategy, and the effect strategies 
combination. The scenario "A - without any control" 
corresponds to the freeway BR-290/RS current conditions, 
operating without any ATM strategy. 
V. RESULTS AND DISCUSSION 
The analysed performance parameters were: (i) average 
speeds in gantry 5; (ii) travel times and (iii) number of lane 
changes accounted by the simulation model in the whole 
modelled segment. Average speeds, travel time and number 
of lane changes are accounted as the averages of six 
simulation replications. 
A. Averages speeds 
The road bottleneck segment is located on the gantry 5 
section. At this point the speeds fall due to disturbances 
caused by the access ramp and the lanes reduction. 
Therefore, it is in this point that ATM strategies have the 
most significant impact on traffic. 
Results of simulation scenarios A, B and C for gantry 5 
are shown in Fig. 5. The figure presents the comparison 
between average speeds obtained for simulation scenarios 
operating without control, with VSL only, and with VSL 
plus hard shoulder. 
In VSL only scenarios, speed drops due to congestion 
are delayed and smoother. VSL only scenarios also lead to 
shorter collapse periods. Speed harmonization adopted in 
segments upstream the bottleneck lead to decreased 
headways, reduced number of lane changes and 
overtakings. VSL generated smother traffic streams, 
reducing conflicts probability that generate incidents. It 
also reduced sudden drops due to congestion and increased 
flow throughput. 
The temporary opening of hard shoulder during 
congestion provided an additional lane, and eliminated the 
bottleneck in the fifth gantry. Speed harmonization 
upstream gantry 5 is essential to obtain positive effects in 
the bottleneck. It promoted a smoother flow regime, with 
fewer conflicts on the bottleneck approach. ATM strategies 
reduced flows oscillation when the road is operating close 
to capacity. 
 
Fig. 5. Speeds for simulations A, B and C in gantry 5. Values on the 
graph represent averages for two minute intervals. 
ATM system was activated during a longer period in 
scenario C (VSL plus hard shoulder) than in scenario B 
(VSL only), what seems paradoxical. The extended 
activation period in scenario C is explained by the fact that 
vehicles on the hard shoulder are travelling at same speeds 
of vehicles on the third lane. Considering the significant 
difference observed on vehicles average speeds on each 
lane, the extra lane provided by the hard shoulder would 
contribute to reduce the average speed of the whole section, 
maintaining the system active for a longer period. 
Due to the observed heterogeneity on lane flows, as 
presented in Fig. 1, it was conducted an analysis on the 
VSL implementation impact on lanes’ flows was 
conducted. Flow rates per lane for simulations A and B are 
presented in Fig. 6. Gantry 4 has been selected for this 
particular study due to its position upstream the 
perturbation from the access ramp that captures the shock 
waves propagation. 
When not operating at critical conditions, flow pattern 
on each lane tend to keep its particular distribution: flows 
on the left lane are clearly higher than on the right lane. At 
critical conditions, when speeds present a significant decay, 
flow rates on the three lanes tend to level. On scenario A 
(Without control), lane flows tend to remain levelled for a 
longer period than in scenario B (VSL only). 
 
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Fig. 6. Flow rates per lane for simulations A and B in the gantry 4. 
This lane flows equalization is the reflection of an 
increase in lane changes. In scenario B, the congestion 
period is shorter and so is the time period when lanes’ flow 
are equalized. Flow patterns in scenario B lead to a 
reduction in lane changes and consequently to an expected 
increase in safety. 
Scenario C is not analysed because it did not present 
congestion and lanes’ flow patterns were not altered. 
B. Lane changes 
One of the ATM benefits is the increase in road safety. 
There is natural understanding that lane changing 
manoeuvres may provide potential incidents, and may be 
considered an accident hazard indicator. VSL 
implementation is expected to decrease lane changes 
because vehicles would not be encouraged to overtake due 
to speeds variability reduction. Incidents probability is 
expected to decrease when reducing lane changes [6]. This 
study evaluated the lane changes accounted during the 
simulation period on all scenarios. 
Brazilians drivers present an aggressive driving 
behaviour pattern and lane changes observed on the field is 
higher than naturally represented by the simulation models 
default values. 
 
 
Therefore, the simulation model was calibrated from 
videos analysis in order to represent lane changes 
behaviour observed on the road. 
Number of lane changes accounted in the three 
simulation scenarios, at intervals of two minutes, for the 10 
km modelled is presented in Fig. 7. 
 
Fig. 7. Comparison of lane changes between simulations. 
Lane changes occurrence in scenario B (VSL only) 
decreased 30% when compared with scenario A (no ATM 
strategy). The reduction of lane changes in scenario C 
(VSL plus hard shoulder) compared to scenario A was 
43%. 
In spite of the additional lane available in scenario C, a 
smaller number of lane changes were recorded. The 
additional capacity eliminated speed drops that characterize 
congestion, allowing vehicles to travel at more comfortable 
speeds, reducing the desire for overtaking. 
C. Travel times 
Another benefit of ATM systems, pointed out by [1], [6] 
and [11], is the increase in travel time reliability. As 
underlined by [19], various freeways with ATM schemes 
recorded travel times reduction as well as a reduction in 
travel time variability. 
The modelled vehicles travel times crossing studied the 
10 km segment was measured every two minutes and 
averages are presented in Fig. 8. for the three scenarios. 
There is a significant reduction in travel times when 
ATM strategies are implemented, particularly between 
16:00 and 20:00 hours, period during which the freeway is 
operating with high flow rates. Speed harmonization 
presents significant impacts on travel times when 
significant drops in speed occur. 
 
 
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Fig. 8. Comparison of travel times between simulations. 
Considering that the VSL plus hard shoulder practically 
eliminated congestion for the tested demand, scenario C 
provided the most reliable conditions regarding travel 
times. A comparison between maximum and minimum 
travel time, travel time variability and average travel times 
are presented in Table II, for the period between 16:00 and 
20:00. 
Table II 
Comparison Between Travel Times During Peak Period For The 
Studied 10 Km Segment 
Travel Time 
16:00 to 20:00 
Without 
Control 
[min] 
VSL [min] 
VSL plus Hard 
Shoulder [min] 
Maximum 13:43 11:11 08:35 
Minimum 07:17 07:01 06:51 
Variability 06:26 04:10 01:44 
Average Time 11:51 09:49 07:40 
For the simulated peak demand, average travel times 
reduced 17% and travel time variability decreased 35% 
when comparing scenarios A and B. Average travel time 
reduced 35% and travel time variability decreased 73% 
when comparing scenarios A and C. 
VI. CONCLUSION 
The particularities in Brazilian freeways, such as, 
different average speeds and flow between lanes, along 
with different traffic compositions in each lane makes the 
country a good candidate to receive ATM innovative 
strategies. 
ATM has the potential to provide benefits by 
harmonizing the traffic speeds, delaying the onset of 
congestion, and reducing lane changes and overtakings. 
The study results shown that ATM systems have a 
positive impact on traffic operation. ATM reduced average 
travel times and variability, and reduced the number of lane 
changes, implying a reduced probability of conflicts and 
incidents. 
ATM success mainly depends on users’ understanding 
and their behaviour regarding these strategies. In modelling 
environments, vehicle behaviour is predetermined by the 
traffic simulator, and often cannot represent the behaviours 
variability displayed by users in real traffic conditions. The 
same can be said regarding the strategies deployed in the 
simulator. Thus, an ATM implementation on freeways 
should take into account issues such as users understanding 
before implementing new control strategies. Users 
understanding contribute to improve compliance and 
minimize drivers’ behavioural variability when deploying 
innovative strategies. 
After carried out this research, the concessionary 
running the freeway under study decided to implement 
those strategies, beginning with the temporary use of hard 
shoulder due to financial and constructions issues. Results, 
coming from the practical use of this ATM strategy, 
showed operating conditions improvement when 
congestion occurs and a good level of understanding and 
compliance from users was noticed. 
Acknowledgment 
The authors thank the support from CONCEPA – 
Concessionary running the freeway modelled, and ANTT – 
Brazilian Terrestrial Transport National Agency, enabling 
access to traffic data. 
REFERENCES 
[1] P. V. Sisiopiku, A. Sullivan, and G. Fadel, ―Implementing active 
traffic management strategies in the U.S.,‖ University Transportation 
Center for Alabama, Department of Management and Safety in 
Transportation Systems, Report no. 08206, 2009. 
[2] B. Kuhn, ―Smart Traffic 2008: Active congestion management – 
infrastructure and operations, Session 3: active traffic management,‖ 
5th Conference in The Series, Brisbaine, Australia, 2008. 
[3] M. Aron, R. Seidowsky, and S. Cohen, ―Safety impact of using the 
hard shoulder during congested traffic. The case of a managed lane 
operation on a French urban motorway,‖ Transportation Research 
Part C, vol. 28, pp. 168-180, 2013. 
[4] C. Lee, B. Hellinga, and F. Saccomanno, ―Evaluation of variable 
speed limits to improve traffic safety,‖ Transportation Research Part 
C, vol. 14, no. 3, pp. 213-228, 2006. 
[5] D. Gettman, and L. Head, ―Surrogate Safety Measures From Traffic 
Simulation Models‖. Transportation Research Record: Journal of the 
Transportation Research Board, 1840(1), pp. 104–115, 2003. 
 
International Journal of Emerging Technology and Advanced Engineering 
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 6, Issue 4, April 2016) 
17 
 
[6] M. Mirshahi, J. Obenberger, C. Fuhs, C. Howard, R. Krammes , B. 
Kuhn, R. Mayhew, M. Moore, K. Sahebjam, C. Stone, and J. Yung, 
―Active traffic management: the next step in congestion 
management,‖ FHWA-PL-07-012, Federal Highway Administration, 
Washington D.C., USA, 2007. 
[7] R. C. Carlson, I. Papamichail, M. Papageorgiou, and A. Messmer, 
―Optimal motorway traffic flow control involving variable speed 
limits and ramp metering,‖ Transportation Science, vol. 44, no. 2, 
pp. 238-253, 2010. 
[8] J. Geistefeldt, ―Operational experience with temporary hard shoulder 
running in Germany,‖ Transportation Research Record: Journal of 
the Transportation Research Board, vol. 2278, no. 1, pp. 67-73, 
2012. 
[9] A. Hegyi, B. De Schutter, and H. Hellendoorn, ―Model predictive 
control for optimal coordination of ramp metering and variable 
speed limits,‖ Transportation Research Part C, vol. 13, no. 3, pp. 
185-209, 2005. 
[10] R. Vadde, D. Sun, J. O. Sai, M. A. Faruqi, and P. T. Leelani, ―A 
simulation study of using active traffic management strategies on 
congested freeways,‖ Journal of Modern Transportation, vol. 20, no. 
3, pp. 178-184, 2012. 
[11] M. J. Ogawa, A. J. Arlow, R. J. Meekums, S. Self, and P. Unwin, 
―M42 Active Traffic Management monitoring and evaluation: 
Results from Hard Shoulder Running up to 60 mph,‖ Road Transport 
Information and Control Conference and the ITS United Kingdom 
Members' Conference (RTIC 2010) - Better transport through 
technology, IET, London, UK, 2010. 
[12] B. Sultan, R. Meekums, and M. Brown, ―The impact of active traffic 
management on motorway operation,‖ Road Transport Information 
and Control - RTIC 2008 and ITS United Kingdom Members' 
Conference, IET, Manchester, UK, 2008. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
[13] F. Caleffi, Y. Moisan, H. B. B. Cybis, F. D. Michel, and F. Hirsh, 
―Simulação de alternativas operacionais de um segmento especial de 
autoestrada através do software Vissim,‖ XXVI ANPET - Congresso 
de Pesquisa e Ensino em Transportes, vol. 1, pp. 36-47, Brazil, 2012. 
[14] PTV, ―Vissim user manual,‖ V.5.30, Germany, 2010. 
[15] J. C. Jeffrey, M. C. Knopp, K. Fitzpatrick, M. A. Doctor, C. Howard, 
G. M. Laragan, J. A. Rosenow, B. A. Struve, B. A. Thrasher, and E. 
G. Young, ―Freeway geometric design for active traffic management 
in Europe,‖ FHWA-PL-11-004, Federal Highway Administration, 
Washington D.C., USA, 2011. 
[16] N. Nezamuddin, N. Jiang, T. Zhang, S. T. Waller, and D. Sun, 
―Traffic operations and safety benefits of active traffic strategies on 
TxDOT freeways,‖ Federal Highways Administration, FHWA/TX-
12/0-6576-1, Texas, USA, 2011. 
[17] P. Allaby, B. Hellinga, and M. Bullock, ―Variable speed limits: 
safety and operational impacts of a candidate control strategy for 
freeway applications,‖ IEEE Transactions on Intelligent 
Transportation Systems, vol. 8, no. 4, pp. 671-680, 2007. 
[18] M. Papageorgiou, E. Kosmatopoulos, and I. Papamichail, ―Effects of 
Variable Speed Limits on Motorway Traffic Flow,‖ Transportation 
Research Record: Journal of the Transportation Research Board, vol. 
2047, no. 1, pp. 47-48, 2008. 
[19] M. D. Fontaine, and J. S. Miller, ―Planning for Active Traffic 
Management in Virginia: International Best Practices and 
Implementation Strategies,‖ Virginia Center for Transportation 
Inovation and reaserch, Final Report VCTIR 13-R1, Virginia, USA, 
2012.