Quantifying Impacts of Connected and Autonomous Vehicles on

Traffic Operation using Micro-simulation in Dubai, UAE

Abdul Razak Alozi

and Khaled Hamad

Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, U.A.E

Keywords: Connected and Autonomous Vehicles, Intelligent Transportation Systems, Traffic Operation,

Micro-simulation.

Abstract: Connected and Autonomous Vehicles (CAVs) will change the transportation system we know with their

substantial impacts on the level of safety, traffic operation, fuel consumption, air emissions among other

aspects. A large segment of the general public and decision makers are still sceptical of CAVs’ benefits and

impacts. This study aims at quantifying the impacts of CAVs on traffic operation using micro-simulation of

a 7-kilometer-freeway segment in Dubai, UAE. The simulation was run for different market penetration rates

(MPRs) ranging from 0% (no CAVs) up to 100% (all CAVs), in 10% increment. Additionally, multiple

scenarios under different traffic volumes were also modelled utilizing PTV VISSIM. To quantify the impacts

of CAVs, three performance measures were collected, namely the average delay, average speed, and total

travel time. The results showed that the highest impact of CAVs occurs in terms of delay, with a decreased

average delay of up to 86%. The other performance measures also show improvement, with 42% speed

increase and 25% travel time reduction. Moreover, CAVs show more significant changes at lower traffic

volume conditions (off-peak hour).

1 INTRODUCTION

As transportation engineering touches all kinds of

people and affects their everyday life, it requires an

extensive amount of research backing up the ever-

continuing development of this field. Both

researchers and engineers working in traffic-related

domains often get the opportunity to experience their

work first-hand and its implications for drivers,

commuters, or even pedestrians. Among the

developments that are coming relatively soon to our

roads are Connected and Autonomous Vehicles

(CAVs). The possible changes and impacts these

vehicles will have on people’s commute experiences

are countless.

Due to their significance and vital role in the lives

of the public, CAV impacts are expected to reach

numerous aspects. Some impacts will be directly

related to CAV technologies and applications like

safety, energy, and fuel consumption, while other

impacts will be a result of the side effects of the new

https://orcid.org/0000-0003-0694-1461

https://orcid.org/0000-0002-8110-1115

technology, like land use, public resilience, and other

social effects.

An essential element to study in the case of

connected vehicles is the transition period that will

span for a significant period of our future. In the

transition period, roads and networks will have to

accommodate both connected and conventional

vehicles with varying market shares. CAVs and

conventional vehicle interactions may lead to some

undesirable effects on their operation, limiting the

benefits of CAVs and even possibly affecting the

planners and decision-makers’ attitude towards

accepting CAVs.

Some commercial cars with autonomous features

already roam our streets and have been doing so since

1977 (Bertini et al., 2016) when first automated cars

followed a track of white striped lines on the road at

speeds of 20 mph. It is widely expected that CAVs

will be available on the mass market by 2022 to 2025

(Ye and Yamamoto, 2017).

The field of Intelligent Transportation Systems

(ITS) has become more popular in recent years. ITS

provide various users with real time information to

528

Alozi, A. and Hamad, K.

Quantifying Impacts of Connected and Autonomous Vehicles on Trafﬁc Operation using Micro-simulation in Dubai, UAE.

DOI: 10.5220/0007753905280535

In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 528-535

ISBN: 978-989-758-374-2

make safer and more intelligent decisions when using

the transport networks. They also implement

innovative services that are used in different modes

of transport management. In general, ITS have the

potential to improve safety, productivity, and

mobility of transportation performance which could

be achieved by traffic planners (Z. Yang and Pun-

Cheng, 2017).

Road vehicles have gradually become

technologically more advanced throughout the past

decades with a focus on advancing traffic operation

conditions vehicle safety and comfort. Although

vehicle automation has been on the horizon for just as

long, it is only since the turn of the century that it has

started to find its way into production vehicles

(Calvert et al., 2017).

With various levels of autonomous vehicle

technology from driver assist all the way to fully

automated driverless vehicles, the terminology used

to describe the automation applications must be clear.

For this purpose, the Society of Automotive

Engineers (SAE) sets out the taxonomy used when

discussing the levels of automation in their

international standard J3016 (Bradburn et al., 2017).

The findings will provide a comprehensive

understanding of road networks in the near future.

They will also serve as a strong basis for the vital

decisions that will be made to ensure the safest and

most beneficial methods of managing roads with

CAVs. Moreover, this study will cover a wide range

of CAV Market Penetration Rates (MPRs).

2 LITERATURE REVIEW

The literature provides different case studies and

simulation environments to analyze the impacts of

CAVs on traffic operation as well as other factors. As

the topic is relatively interesting to many researchers,

the amount of research into the topic is somewhat

extensive.

2.1 Traffic Operation Impacts

Research that studied the impacts on traffic

operations considered many performance measures.

Among these, Guler et al. (2014) studied the delay as

a performance measure and found that the increase in

MPR from 0% up to 60% has a significant impact on

reducing the average delay. This decrease in low

demand scenarios reaches up to 60%. After an MPR

of 60%, the rate of reduction decreases and the value

of information from connectivity technologies

diminishes (Ilgin Guler et al., 2014).

Shi and Prevedouros (2016) considered the effect

on Level of Service (LOS) and their findings

suggested that on a basic freeway segment the

autonomous vehicles improve LOS from D to C when

the MPR reaches 7%. The same case study shows that

the connected vehicles improve LOS from D to C

when the MPR reaches 3% (Shi and Prevedouros,

2016).

Moreover, the capacity difference was analyzed

by Ye and Yamamoto (2017) who found that the

capacity of the road increases as the CAV market

penetration rate increases in a shared road. However,

this increase is split between two phases, at MPRs

lower than 30%, the road capacity increases slightly.

After 30% MPR, the in-crease in capacity is largely

determined by the level of automation, with higher

levels of automation achieving higher capacity

increase (Ye and Yamamoto, 2017).

2.2 Other CAV Impacts

Different studies considered the impacts on different

aspects including the safety impact which was

considered by Yang et al. (2017) who discovered that

when the MPR reaches 25%, the risk of secondary

crashes can be reduced by up to 33% under high-

volume conditions. Additionally, if the traffic

volumes are high, risk of secondary crashes can be

reduced by about 10% at low MPR levels of around

5%. However, the benefit of CAVs would not be

notable under low-volume conditions (H. Yang et al.,

2017).

When considering the effect on greenhouse gas

emissions, Wadud et al. (2016) suggested that

automation might plausibly reduce road transport

GHG emissions and energy use by nearly half

depending on which effects come to dominate. In

addition, many potentials for energy reduction

benefits may be realized first under partial

automation, while the major energy downside risks

appear more likely at full automation (Wadud et al.,

2016).

The impacts of CAV technologies even reach land

use as Zhang et al. (2015) concluded the possibility to

eliminate 90% of parking demand for clients who

adopt the new systems, at a low MPR of 2%. Also,

different Shared Autonomous Vehicle (SAV)

operation strategies and client’s preferences may lead

to different spatial distribution of urban parking

demand (Zhang et al., 2015)

Quantifying Impacts of Connected and Autonomous Vehicles on Trafﬁc Operation using Micro-simulation in Dubai, UAE

529

Figure 1: Study area.

3 METHODOLOGY

Towards achieving our objective, the following three

main steps were undertaken: (1) build the roadway

geometry and calibrate CAV modelling, (2) prepare

and test simulation scenarios; and (3) analyze traffic

operation parameters.

To quantify the impacts of CAVs, the most widely

used approach is micro-simulation, which provides a

commonly acceptable prediction tool that helps in

understanding the behavior of both CAVs and

conventional vehicles on roadway networks once

they become reality. Micro-simulation also reveals all

the possible outcomes of proposed scenarios and

approaches to implement the new technologies. The

latest version of PTV VISSIM (version 11) was

utilized to develop and run the simulations. This state-

of-the-art software provides the most advanced

virtual test bed to carry out and test the objectives of

this study.

3.1 Study Area

The chosen freeway segment shown in Figure 1 of

Sheikh Mohammad Bin Zayed road (E311) spans

seven kilometers of five lanes in each direction. This

roadway segment connects the two cities of Dubai

and Sharjah and experiences high traffic demand

compared to other freeways. It has three interchanges

of the following types: clover-leaf, fully directional,

and semi directional ramp with loops.

The geometry of the freeway segment was

modeled in PTV VISSIM spanning seven kilometers

shown in Figure 2 The model has reduced speed areas

at ramps and loops of the interchanges to reflect

human driving behaviour.

3.2 Model Development and

Calibration

For the purpose of this study, two levels of calibration

had to be followed: (1) calibrate the driver behaviour

to reflect local freeway conditions. (2) calibrate the

predicted behaviour of CAVs to ensure realistic

forecasting of their impacts.

Three sub-models control the simulation process

of the driver behavior in VISSIM: car-following

model, lane changing model, and lateral behavior

within a lane. The parameters of these model were

modified as well as conflict resolution behaviour.

The simulation results were compared with field

measurements to determine how close the simulation

model emulates field conditions. Two validation

measures of effectiveness were collected, namely the

delay and Level of Service (LOS) measurements and

two input values were finely tuned to reach accurate

performance of the model namely, the standstill

distance (CC0) and the headway (CC1).

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

530

Figure 2: Freeway segment model in PTV VISSIM.

We used additional attributes in the driving

behavior dialog of PTV VISSIM to model the

predicted performance of CAVs. The car following

model in VISSIM is based on the continued research

of Wiedemann. In the 2018 version of PTV VISSIM,

the Wiedemann 99 model is utilized and it consists of

ten calibration parameters that have been modified to

reflect CAV behavior.

In addition, a few other parameters were activated

which do not fall under the car following main

parameters:

• Enforced absolute braking distance was activated

(a.k.a. brick wall distance), meaning vehicles

using this driving behavior will always make sure

that they could brake without a collision, if the

leading vehicle comes to an immediate stop (turns

into a brick wall). This condition applies also to

lane changes (for the vehicle itself on the new lane

and for the trailing vehicle on the new lane) and

to conflict areas (for the following vehicle on the

major road).

• The attribute to use implicit stochastics was

disabled; a vehicle using this driving behavior

does not use any internal stochastic variation that

is meant to model the imperfection of human

drivers.

• For all distributions which cannot be explicitly set

by the user, a median value was used instead of a

random value within a range to reflect consistency

of CAV behaviour as opposed to human drivers.

This affects the safety distance, the desired

acceleration, and uncertainty for braking

decisions.

3.3 Considerations and Assumptions

After calibrating the input values (mentioned in

section 3.2), the following assumptions of speed and

geometry were made:

• 5-lanes per direction for the main freeway

segment with operational speed of 120 km/h and

lane width of 3.6 m;

• 2-lanes per direction for the crossing arterial roads

with operational speed of 100 km/h and lane width

of 3.6 m;

• 2-lanes for the directional and semi-directional

left ramps with operational speed of 60 km/h and

lane width of 3.6m; and

• 1-lane for the loops and right ramps with lane

width of 3.6 m and speed of 40 km/h and 60 km/h,

respectively.

• Vehicle composition consists of 90% light

vehicles and 10% heavy vehicles.

For all other not mentioned parameters, we used PTV

VISSIM’s default values.

3.4 Scenario Formulation

Road users will not switch to CAVs all of a sudden,

instead they will start using them gradually as CAVs

become more desirable and reliable, and as the

standards and regulations become solid worldwide.

The period between first introduction of CAVs and

the time they become the main mode of transportation

is called the transition period in which traffic will be

a combination of conventional vehicles and CAVs,

passing through different market shares of CAVs of

market penetration rates (MPRs). For that reason, we

need to study the behavior and traffic flow during

Quantifying Impacts of Connected and Autonomous Vehicles on Trafﬁc Operation using Micro-simulation in Dubai, UAE

531

each MPR. Some studies analyze low MPRs (0-25%)

because they investigate the early adoption conditions

(H. Yang et al., 2017), others use a wider range (0-

75%) (Z. Chen et al., 2016). The rest of the literature

in the field of CAV simulation use the full range of

MPR (0-100%) but with different increments

depending on their focus (Y. Chen et al., 2017;

Talebpour & Mahmassani, 2016; K. Yang et al.,

2016; Ye & Yamamoto, 2017).

In this study, we chose to cover the full range of

MPR from 0% to 100% CAVs with 10% increment.

This range was used to analyze traffic operation at

every stage. After calibrating the model, the eleven

scenarios of MPR were formulated.

Table 1: Traffic volume combinations.

Scenario ID

Major Road

LOS

Minor Road

LOS

1. BB LOS B LOS B

2. BD LOS B LOS D

3. BE LOS B LOS E

4. DB LOS D LOS B

5. DD LOS D LOS D

6. DE LOS D LOS E

7. EB LOS E LOS B

8. ED LOS E LOS D

9. EE LOS E LOS E

Furthermore, for each one of the MPR scenarios

different traffic volume conditions were studied. The

traffic volume effect was analyzed using traffic

condition combinations between the major freeway

and the minor intersecting roads. Based on three LOS

conditions: B, D, and E, the volume per lane was

determined, and then multiplied by the number of

lanes for each road. The following nine combinations

of LOS listed in Table 1 were used.

For each scenario multiple runs were used with

different seed numbers to eliminate randomness of

the model.

The total number of scenarios and number of

required runs for each one was determined as follows:

Scenarios = 11MPRs × 9volumecombinations

Scenarios = 99scenario

Runs = 99scenarios × 5runsperscenario

Runs = 495simulationruns

To extract the results, the average of the middle

three runs was considered for each scenario.

4 RESULTS AND DISCUSSION

The results of our analysis are summarized in three

performance measures: average delay, average speed,

and total travel time. The results for each measure

under the nine scenarios (from Table 1) are

summarized in a single chart which shows the MPR

on the x-axis and the percent change (increase or

reduction) in the performance measure computed

relative to the 0% MPR level.

4.1 Average Delay Results

Figure 3 shows the results of the average delay for all

scenarios. It shows that the reduction in average delay

is substantial reaching up to 86% in the best case with

LOS B on both major and minor roads and 100%

MPR. In all traffic volume combinations, the highest

reductions in delay happen during initial stages of

Figure 3: Average delay results.

-100%

-90%

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

10%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

% Reduction in Average Delay

CAV Market Penetration Rate

VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems

532

Figure 4: Average speed results.

Figure 5: Total travel time results.

CAV utilization (0% to 20% CAVs) and right before

full automation (70% to 80% CAVs). The bulk of the

transition period between 20% and 70% has lower

effects on average delays with a total of 17%

reduction in the best-case scenario.

It’s

also clear that the benefits of CAVs are mag-

nified in low traffic flow conditions (off-peak hour);

this is apparent with the biggest reductions in delay

happening at LOS B on the minor road and especially

in the case of highest LOS on both major and minor

roads.

4.2 Average Speed Results

Figure 4 shows the results of the average speed which

follow a clear trend of increase ranging from 25% to

40%. The highest impact reaches over 40% increase

in average speed with 100% MPR and LOS E on

major road and B on minor road. The overall trend is

clear, showing a steady rise in speed with the

automation takeover. The results of different traffic

volumes are somewhat similar; meaning that speed

increases in all conditions (peak hour and off-peak

hour).

The difference between the results of the two

performance measures: average delay and average

speed show that the delay is highly affected by the

traffic volume variation. Average delay results show

a huge decrease at higher MPRs under low traffic

volume conditions. This observation is not apparent

-5%

10%

15%

20%

25%

30%

35%

40%

45%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

% Increase in Average Speed

CAV Market Penetration Rate

-25%

-20%

-15%

-10%

-5%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

% Reduction in Total Travel Time

CAV Market Penetration Rate

Quantifying Impacts of Connected and Autonomous Vehicles on Trafﬁc Operation using Micro-simulation in Dubai, UAE

533

in average speed results, where the difference is not

affected by different volume scenarios (except for a

slight variation).

4.3 Total Travel Time Results

Figure 5 shows the results of the total travel time. The

overall trend is a clear reduction in travel time with

the increase of CAV shares ranging from 10% to

24%. Moreover, the graph shows big variations

between different traffic flow conditions in terms of

total travel time. The benefit gets to almost 24%

reduction in total travel time for LOS B on both major

and minor roads at 100% MPR, this enforces the

outcome that CAV benefits become more significant

at low traffic volumes.

At high traffic volumes (LOS E on major road and

LOS E on minor road), the reduction in travel time

becomes turbulent. Although the overall trend is a

reduction of 10% in travel time, the results at lower

MPRs show sometimes a slight increase; this could

be a result of oversaturation of the network which

causes long queues filling the whole segment.

5 CONCLUSION

In this paper, the impacts of Connected and

Autonomous Vehicles (CAVs) were studied and

evaluated using micro-simulation. The driver

behavior, car following, and lane changing models

were modified to reflect the behavior of CAVs in the

widely-used simulation tool, i.e. PTV VISSIM. Three

performance measures were collected, namely the

average delay, average speed, and total travel time.

These measures were used to compare the traffic

operation under nine different traffic scenarios. Each

of these scenarios was evaluated for eleven market

penetration rates, ranging from 0% (no CAVs) to

100% (all CAVs). The results can be summarized as

follows:

• The highest benefit of CAVs is reduced average

delays reaching up to 86% reduction during off-

peak hour conditions.

• Average speed reduction is not affected by traffic

volume; as all volume conditions experience

somewhat the same change.

• Total travel time reflects the same effect of traffic

volume as the average delay (more benefit at

lower volumes). However, this effect is lower;

reaching up to 23% reduction.

• The highest benefits of CAVs are observed in two

stages; first stage at 0% to 20% MPR, and second

stage at 70% to 80% MPR.

• The benefits of CAVs are not realized at the same

rate as their market share increases. This is

probably due to the interactions between regular

vehicles and CAVs. These interactions cause

driver confusion in regular vehicles as well as

sudden and unnecessary breaking by CAVs in

some situations, like unexpected lane changes,

aggressive driving, or weaving movement.

• Overall, CAV technologies and their utilization

show more significant changes at lower traffic

volume conditions (off-peak hour). This point

matches the findings of similar studies in other

regions of the world.

These findings could help decision-makers to

understand the expected impacts of CAVs from a

traffic operation perspective and could help plan for

the adoption of these new vehicles in a way that

ensures the highest benefit and lowest risk before they

are introduced to the mass market.

The authors recommend further studies to expand

on the topic by including more performance measures

to evaluate the impacts of CAVs. Another possibility

is to investigate the effect of different strategies like

dedicated CAV lanes for example.

Change is coming to us whether we like it or not,

CAV technologies will affect all levels of society in

their daily commutes. Therefore, it is crucial to grasp

the limitations and different impacts of the vehicles

of the future.

ACKNOWLEDGEMENT

The authors would like to acknowledge the support of

the Emirates Islamic Bank in this research study.

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