Optimizing CAV Driving Behaviour to Reduce Traffic Congestion

and GHG Emissions

Saad Roustom

and Hajo Ribberink

Natural Resources Canada, CanmetEnergy, 1 Haanel Dr, Ottawa, ON, Canada

Keywords: Connected and Automated Vehicles, Driving Behaviour, Traffic Performance, GHG Emissions.

Abstract: This study was conducted to identify an optimal driving behaviour of connected and automated vehicles

(CAV) that can reduce traffic congestion and GHG emissions under different traffic demand levels. The study

employed traffic simulations at the meso scale for the City of Ottawa, Canada, to assess traffic performance

and used correlation models to estimate GHG emissions. Aggressive CAVs showed the greatest potential to

enhance traffic performance and reduce GHG emissions under all traffic demand levels. The results show that

Aggressive CAVs can increase highway capacity and lower vehicle travel time in comparison to Driver

Operated Vehicles (DOVs) or CAVs with a less aggressive driving style. The findings of the study indicate

that CAVs with aggressive driving behavior can play a crucial role in enhancing traffic performance and in

helping to mitigate the adverse impact of transportation on the environment. The results of this study aim to

encourage regulatory bodies to adopt effective CAV-related policies that can enhance traffic performance and

reduce GHG emissions.

1 INTRODUCTION

1.1 Background

Automated Vehicles (AVs) use a combination of

sensors and control algorithms to perform driving

functions normally carried out by the driver. Some

modern vehicles are equipped with low-level

automated features such as adaptive cruise control

(ACC), lane-keep assist (LKA), and automatic

emergency braking (AEB). Connected vehicles

(CVs) are vehicles with on-board radios capable of

exchanging information through wireless

communications with other vehicles, infrastructure,

and internet-based devices. Information generally

exchanged includes position, direction, speed, and

intent which gets presented to other road users as

either safety or mobility advisories. The combination

of these technologies, a connected and automated

vehicle (CAV), has the potential of significantly

reducing the number of collisions, reducing

congestion, reducing greenhouse gas (GHG)

emissions, and improving mobility. However, the

shift towards new transportation behaviours due to

https://orcid.org/0000-0001-7648-5436

automation may also increase greenhouse gas

emissions due to potentially higher vehicle-

kilometers travelled (VKT) and a shift towards new

models of transportation, such as Mobility-as-a-

Service (MAAS), and self-parking capabilities.

Due to the novel and cooperative nature of CAV

technologies, it becomes difficult to assess its impact

in real-world scenario, as some CAV applications

work most effectively at high penetration rates and

are affected by a multitude of environmental

variables. Assessing the impacts of CAVs in a

simulated environment presents itself as a viable

precursor to real-world testing, and results may

provide insight to help guide policy development.

1.2 Objectives

This objective of this study was to assess whether the

specific driving behaviour of CAVs can actively be

utilized to reduce congestion and GHG emissions

from transportation. This was done by investigating

the impact of homogenous CAV vehicle fleets on the

traffic performance and associated GHG emissions

for the City of Ottawa, Canada, through a

198

Roustom, S. and Ribberink, H.

Optimizing CAV Driving Behaviour to Reduce Trafﬁc Congestion and GHG Emissions.

DOI: 10.5220/0011792500003479

In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2023), pages 198-205

ISBN: 978-989-758-652-1; ISSN: 2184-495X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

combination of traffic simulations at the meso scale

and an emissions calculation method based on

emissions correlations developed from micro

simulations results. The study evaluated scenarios

characterized by different CAV driving behaviours

and different traffic demand levels. The results of this

study aim to inform regulatory bodies to support the

development of effective CAV- related policies to

promote the environmentally efficient

implementation of CAV technologies.

2 LITERATURE REVIEW

CAVs have a great potential to improve traffic

performance due to their ability to travel with small

headways and smoother traffic flow. Most studies

confirm that such technology can enhance traffic

characteristics and reduce congestion. However,

there is some uncertainty on optimal driving

behaviour parameters that can increase its efficiency.

It is possible that at initial stages of CAV deployment,

CAVs will have a more cautious driving behaviour to

take into consideration user preference, safety and

comfort (Atkins Ltd, 2016). Cautious CAVs may

potentially decrease effective capacity and deteriorate

traffic performance especially in a high demand and

high-speed traffic environment according to a micro

simulation study (Atkins Ltd, 2016). A micro

simulation study evaluated the impact of AVs on

network capacity with different AV driving

parameters. The study found that capacity increases

quasi-linearly with higher AV penetration rates and

that at full AV penetration rate, road capacity

increases by 16% (Lu et al., 2020). Another micro

simulation study reports that AVs at full penetration

rates and under high volumes can improve traffic

performance, but also decrease it at lower traffic

volumes (Bohm & Häger, 2015). AVs can also result

in a better level of service on single-lane roads by

reducing speed deviations and delays according to a

micro simulation study (Wang & Wang, 2017). The

study concludes that benefits of CAVs are achieved

under high levels of connectivity and automation (i.e.,

an aggressive drive style). Another micro-simulation

based study simulated AVs on a network of 13

intersections and total distance of 4.5 km. The study

found out that at increased AV penetration rates, the

traffic flow is improved with a reduction of average

delay up to 31%. An agent-based approach study

found that a full AV traffic environment can increase

the capacity of a multi-lane highway by 250%

compared to regular driver-operated vehicles

(Abdulsattar et al., 2020).

Studies also show that CAVs employment could

potentially reduce GHG emissions or increase it. A

modelling study utilized the SUMO microsimulation

package to examine the impact of different degrees of

vehicle automation on emissions in an urban network.

The results show that acceleration is high highly

correlated with emissions. Although automated

vehicles can achieve higher acceleration values

causing higher emissions, this was compensated by

giving them lower rates of acceleration for higher

automation levels. The results of this study concluded

that automated vehicles could reduce carbon

monoxide (CO) emissions by 38.5%, carbon dioxide

(CO

) emissions by 17.0% and hydrocarbons (HC)

emissions by 36.3% for the best scenario in which the

penetration rate of automated vehicles is 100%

(Biramo & Mekonnen, 2022). A similar study

explored the environmental impacts of AVs along an

urban freeway corridor using Vissim and EPA’s

MOVES. The study considered AV penetration rates

of 10%, 20% and 30%. The study found out that only

a 5% decrease in emissions can be expected with AV

technology at a 30% penetration rate. However, this

comes at cost of penalizing travel time by up to 13%

for both AVs and conventional vehicles when

compared to existing conditions (Tomás et al., 2020).

A micro simulation-based study found that

Aggressive AVs can reduce GHG emissions by 26%

in an uninterrupted flow network and by 3% in an

interrupted flow network under high traffic demand

levels (Stogios et al., 2019). A similar study

concluded that in a congested urban network, a

maximum reduction of CO

emissions is achieved by

4% at a full AV penetration rate (Conlon & Lin,

2019). Other studies show that the reduction in

emissions due to enhanced traffic performance can be

overturned by an increase in traffic demand causing

more GHG emissions (Brown et al., 2014; Wadud et

al., 2016).

While many studies have been conducted to

investigate the impact of CAV driving behaviours on

traffic performance and GHG emissions using micro

simulations, there is a lack of understanding on how

CAVs will impact congestion and emissions in a

larger road network such as a city-wide model. The

study presented in this article assesses different CAV

driving behaviours at the scale of a city with over one

million inhabitants using meso simulation of

scenarios with different CAV driving styles and under

different traffic demand levels. The evaluation of

CAV driving behaviour at the scale of a large city is

critical to creating a deeper understanding of the

potential of CAVs to improve traffic performance and

reduce emissions, because the larger road network in

Optimizing CAV Driving Behaviour to Reduce Trafﬁc Congestion and GHG Emissions

199

the city will allow CAVs to choose different routes,

for instance to avoid congestion, and their impact on

traffic performance will differ from results obtained

through micro simulation studies.

3 METHODOLOGIES

The approach followed to investigate whether the

level of congestion in a city can be reduced by

actively adjusting the driving behaviour of CAVs was

to simulate a comprehensive set of scenarios covering

a wide range of driving behaviours and traffic

volumes. For each of these scenarios, the evaluation

of the impact of CAVs on traffic performance and

GHG emissions involved meso simulation of the

traffic using the Ottawa Regional Model (MMM

Group Limited, 2014) using the Dynameq traffic

simulation software (INRO, n.d.-a), and applying

emission correlation models to the simulation results

in a post-processing step. The study took into

consideration the regular driver-operated vehicle

behaviour along with three CAV driving behaviours:

Cautious, Normal and Aggressive. The emission

correlations were developed in a different project by

Carleton University (Roustom, 2022), and were based

on micro simulation studies for four main routes in

the City of Ottawa utilizing the same driving

behaviours as used in this study. Carleton University

developed emission regression models that can be

used to estimate the emissions of CAVs for the total

City of Ottawa. The meso simulation tool Dynameq

does not have the capability to calculate emissions for

vehicles in its simulations. Therefore, the models

provided by Carleton University were used in a post-

processing step for GHG emission estimation.

A combination of micro and meso simulation was

needed for this research. The micro simulation

conducted earlier by Carleton University was

required for the detailed evaluation of the driving

behaviour of CAVs. CAVs behave differently on the

road than Driver Operated Vehicles (DOVs), for

instance because their connectivity allows them to

drive closer together. Meso simulation was necessary

to understand how the different driving behaviour of

CAVs would impact the overall traffic flow and

congestion in a large city.

3.1 Meso Simulations

The Ottawa Regional Traffic model (MMM Group

Limited, 2014) was obtained from the City of Ottawa

together with a forecast of expected travel volumes

for the year 2031. The model covers the total road

network of the Ottawa-Gatineau region, consisting of

over 700 traffic zones, over 5,000 intersections and

close to 25,000 road segments. The Ottawa Regional

Traffic Model is implemented in the macro

simulation tool Emme (INRO, n.d.-b) and was

calibrated using data from Ottawa’s Origin-

Destination Survey (MMM Group Limited, 2014).

After receiving the calibrated Ottawa Regional

Traffic Model from the City of Ottawa, it was

exported from Emme into Dynameq and adjusted for

functionality in the meso simulation tool. This

involved adjusting the number of lanes and the layout

of intersections and roads on all major through routes

where needed. Traffic lights were added to all major

intersections, and intersections in the downtown area

were given appropriate stop/yield characteristics.

Signal controls were generated using Dynameq’s

signal control optimizer routine and may not

necessarily reflect the actual signal controls in

Ottawa.

A total of 12 scenarios were evaluated to find the

optimal driving characteristics of CAVs under

different traffic conditions. DOV, Cautious, Normal

and Aggressive driving behaviours were simulated

with homogenous CAV fleets and traffic flows of

80%, 100% and 120% of the forecasted morning peak

hour volumes for the year 2031. Simulations were

optimized to result in minimum overall travel time for

the total fleet. The parameters of each driving

behaviour are shown in Table 1. The values were

taken from the Carleton University study (Roustom,

2022). The simulation runs for the City of Ottawa

were conducted for the period of 6:30 AM to 11:00

AM. Vehicles were added to the network during the

first two and a half hours of the simulation (6:30 AM

to 9:00 AM), and the simulations continued for

another two hours to allow all vehicles to reach their

destinations. General traffic characteristics such as

vehicle-hours travelled (VHT) and vehicle-

kilometers travelled (VKT) were aggregated over the

full 4.5-hour simulation period. The meso-simulation

outputs included road volumes, average speeds,

density, and other road characteristics such as the

number of lanes and link length.

A diverse fleet of passenger and commercial

vehicles was used in the simulations, reflecting

differences in vehicle size experienced in normal

traffic. In all simulations, the vehicle fleet consisted

of either 100% DOVs or 100% CAVs of a certain

type. The rates of deployment and adoption of CAV

technology are dependent on several factors such as

technological advancement, consumer acceptance

and policy regulations. This study assesses the

scenarios

using 100% CAV fleets to investigate the

VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems

200

Table 1: Traffic behaviour parameters.

Driving

Behaviour

Effective length

(m)

Response

Time

(seconds)

DOV Car length + 2 1

Cautious CAV Car length + 1.5 1.5

Normal CAV Car length + 1.5 0.9

Aggressive

CAV

Car length + 1 0.6

full potential of this technology. Future work can be

done to study transition phases which will include a

mixed fleet vehicle environment.

3.2 GHG Emission Estimation

Using the results of the micro simulations, Carleton

University calculated detailed second-by-second

GHG emissions for four example roads in the City of

Ottawa. The example roads each had different traffic

characteristics, representing typical congested traffic

conditions in the downtown area (Bronson Avenue),

arterial roads with short distances between

intersections (Baseline Avenue), arterial roads with

large distances between intersections, and highway

traffic. Additionally, a regression analysis was

performed on the GHG emission results to develop

correlations between the GHG emission intensity (kg

eq.) and parameters displaying vehicle

movement characteristics (average speed and average

vehicle density) and road information (road segment

length and number of lanes). The correlations were

used in this study in a post-processing step to

calculate overall GHG emissions for the vehicle fleet

in Ottawa based upon traffic information per road

segment from the Dynameq simulations. Since an

emission correlation was developed for each of the

four example routes, in the calculation of the

emissions for the total city the appropriate correlation

was selected per road segment according to the posted

speed limit of the segment:

4 RESULTS

4.1 Traffic Performance

For each scenario, the total amount of vehicle-hours

travelled and vehicle-kilometers travelled were

determined by aggregating the travel times of all

vehicles in the meso simulation. Table 2 and Table 3

present the results for the evaluated scenarios. The

results in Table 2 show that Aggressive CAVs were

able to reduce total vehicle-hours travelled by 14.2%.

CAVs with normal driving behaviour also reduce

travel time compared to DOVs, but the effect is much

smaller than for Aggressive CAVs. CAVs with a

cautious driving style increase travel time for all

traffic flow levels and cause higher levels

of congestion. The small differences in VKT between

Table 2: Vehicle-hours travelled (VHT) results for the

Ottawa Regional Model under different scenarios.

Case

Traffic

Flow

Driving

Behaviour

Vehicle-hour Travelled

80%

DOV 69,668

Compare to

DOV 80%

Cautious 74,951 7.6%

Normal 68,864 -1.2%

Aggressive 67,463 -3.2%

100%

DOV 90,898

Compare to

DOV 100%

Cautious 106,867 17.6%

Normal 89,024 -2.1%

Aggressive 85,134 -6.3%

120%

DOV 124,106

Compare to

DOV 120%

Cautious 163,603 31.8%

Normal 118,597 -4.4%

Aggressive 106,507 -14.2%

Table 3: Vehicle-kilometers travelled (VKT) results for the

Ottawa Regional Model under different scenarios.

Case

Vehicle-kilometers

Travelled

Traffic

Flow

Driving

Behaviour

80%

DOV 4,150,204

Compare

to DOV

80%

Cautious 4,115,115 -0.8%

Normal 4,160,152 1.1%

Aggressive 4,173,698 0.3%

100%

DOV 5,144,241

Compare

DOV100%

Cautious 5,179,543 0.7%

Normal 5,154,417 -0.5%

Aggressive 5,193,811 0.8%

120%

DOV 6,239,120

Compare

to DOV

120%

Cautious 6,434,729 3.1%

Normal 6,221,226 -3.4%

ressive 6,217,404 -0.1%

Optimizing CAV Driving Behaviour to Reduce Trafﬁc Congestion and GHG Emissions

201

CAV and DOV scenarios indicate that differences in

VHT are mostly due to differences in traffic

conditions, i.e., the level of congestion.

It is clear from Table 2 that CAVs with aggressive

driving behaviour can obtain the largest reduction in

average travel time compared to DOVs for all traffic

flow levels. To better demonstrate the impact of CAV

driving behaviour and traffic volume on travel time, the

average travel time per vehicle was calculated for each

scenario. The results are displayed in Figure together

with the average travel time for uncongested

conditions (at 10% traffic flow). The different lines in

the figure illustrate the increase in average travel time

with an increase in traffic volume for a certain driving

behaviour. The DOV scenarios showed a 19% increase

in average travel time when traffic volume increased

from 80% to 120% of the morning commute volume.

Cautious CAVs experienced a 46% increase in average

travel time, while the increase in average travel time

for CAVs with aggressive driving behaviour was only

5%, which corresponds to less than 30% of the increase

in travel time experienced by DOVs.

The results of Figure 1 indicate that the

introduction of CAVs with aggressive driving

behaviour has the potential to lead to a drastic

reduction in congestion and similarly limit the

corresponding increase in average travel time, thanks

to their ability to travel with shorter inter-vehicle

distances and creating a more harmonized traffic

flow.

The total of all vehicle-hours travelled (VHT) by

the vehicle fleet on different road types and the

corresponding total of all vehicle-kilometres travelled

(VKT) driven on these roads during the morning

commute period was compared for the simulated

scenarios. Table 4 displays the VHT values for all

scenarios and the difference between the VHT for the

CAV scenarios and their respective DOV scenarios.

In a similar way, Table displays the VKT values for

all scenarios and the difference between the VKT for

the CAV scenarios and their corresponding DOV

scenarios. In general, the overall number of

kilometers driven in the city is similar for each of the

driving behaviours, as was shown in Table 3.

Figure 1: Comparison of average travel time for each driving behaviour under different traffic flow conditions.

Table 4: Vehicle-hours travelled (VHT) for different road classifications for all scenarios, and the difference in VHT of CAV

scenarios compared to the corresponding DOV scenario.

0% 20% 40% 60% 80% 100% 120%

Average Travel Time

(minutes)

Traffic Flow %

Cautious DOV Normal Aggressive

Scenario

Highway

Arterial and Other Roads

Traffic

Flow

Driving

Behaviou

0 - 50 km/h 50 - 65 km/h 65 - 100 km/h

80%

DOV 21,210 19,419 8,372 20,667

Cautious 21,357 1% 21,619 11% 9,640 15% 22,335 8%

Normal 21,091 -1% 19,176 -1% 8,200 -2% 20,398 -1%

Aggressive 20,771 -2% 18,680 -4% 7,926 -5% 20,086 -3%

100%

DOV 26,220 26,205 11,690 26,784

Cautious 28,018 7% 32,918 26% 14,163 21% 31,769 19%

Normal 26,088 -1% 25,339 -3% 11,221 -4% 26,376 -2%

Aggressive 25,923 -1% 23,913 -9% 10,165 -13% 25,133 -6%

120%

DOV 33,285

37,783

15,841

37,198

Cautious 38,202 15% 54,423 44% 20,749 31% 50,228 35%

Normal 32,962 -1% 35,292 -7% 15,201 -4% 35,143 -6%

Aggressive 31,201 -6% 30,726 -19% 13,184 -17% 31,396 -16%

VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems

202

Table 5: Vehicle-kilometers travelled (VKT) for different road classifications for all scenarios, and the difference in VKT of

CAV scenarios compared to the corresponding DOV scenario.

Scenario

Highway

Arterial and Other Roads

Traffic

Flow

Driving

Behaviou

0 - 50 km/h 50 - 65 km/h 65 - 100 km/h

80%

DOV 1,772,901 726,219

311,835

1,339,249

Cautious 1,605,509 -9% 776,531 7% 346,243 11% 1,386,832 4%

Normal 1,801,163 2% 720,948 -1% 307,091 -2% 1,330,950

Aggressive 1,841,164 4% 710,527 -2% 299,875 -4% 1,322,132

100%

DOV 2,080,292 948,865 418,597 1,696,487

Cautious 1,865,808 -10% 1,065,292 12% 459,008 10% 1,789,435 5%

Normal 2,135,973 3% 929,953 -2% 405,665 -3% 1,682,826

Aggressive 2,272,802 9% 897,972 -5% 381,455 -9% 1,641,582

120%

DOV 2,344,502 1,239,439 531,695

2,123,484

Cautious 2,073,624 -12% 1,468,212 18% 569,485 7% 2,323,408 9%

Normal 2,400,831 2% 1,200,553 -3% 522,348 -2% 2,097,494

Aggressive 2,614,169 12% 1,115,977 -10% 475,643 -11% 2,011,615

However, this does not mean that each scenario had

the same traffic characteristics. This can be seen from

analyzing the VKT results aggregated for different

road classifications according to their type and posted

speed limit, as presented in Table. These more

detailed results show different aspects of vehicle

movements under the various scenarios. Compared to

DOVs, Aggressive CAVs drive fewer kilometers on

arterial roads and on other roads but have higher VKT

values for highways in combination with lower VHT

values for this road type. This indicates that

Aggressive driving behaviour increases highway

capacity allowing for more vehicle-kilometers

travelled with lower VHT. Cautious CAVs on the

other hand, have significantly increased VHT values

and higher VKT values on arterial and other roads in

comparison to DOV. This reflects the situation that

vehicles are avoiding the more congested highways,

which is also demonstrated by the relatively higher

VHT values but lower VKT values for highways

under this scenario.

4.2 GHG Emissions

The GHG emissions of the total vehicle fleet in

Ottawa were calculated and expressed as total

equivalent CO

emissions produced throughout the

total morning commute simulation period. The GHG

emission results are shown in Table 6.

Table 6: GHG emissions for the total vehicle fleet from the

Ottawa Regional Model under different scenarios.

Case

Traffic

Flow

Driving

Behaviour

CO2 eq kg

80%

DOV 809,147

Compare to

DOV 80%

Cautious 887,518 9.7%

Normal 769,484 -4.9%

Aggressive 763,048 -5.7%

100%

DOV

1,036,577

Compare to

DOV 100%

Cautious 1,158,066 11.7%

Normal 984,645 -5.0%

ressive 975,155 -5.9%

120%

DOV 1,302,093

Compare to

DOV 120%

Cautious 1,493,366 14.7%

Normal 1,230,897 -5.5%

ressive 1,195,355 -8.2%

The trends in total GHG emissions for the different

scenarios are consistent with the results for the travel

time as shown

. CAVs with CAVs with an aggressive

driving behaviour having the largest reduction in

GHG emissions in comparison to DOVs.

The relative reduction in GHG emissions

increases at higher traffic volumes, indicating a larger

benefit of an aggressive driving style for higher levels

of congestion as shown in figure 2 which displays the

Optimizing CAV Driving Behaviour to Reduce Trafﬁc Congestion and GHG Emissions

203

average amount of GHG emissions per vehicle for

each scenario under different traffic demand

conditions. GHG emission results for CAVs with a

normal driving behaviour follow similar emissions

trends as for the aggressive driving style, but with

lower reduction percentages. Cautious CAVs cause

an increase in GHG emissions for all traffic flow

levels.

5 CONCLUSIONS

This study employed meso simulations and emission

estimation to investigate whether equipping CAVs

with a specific driving style could result in a reduction

in congestion, in average travel time and in GHG

emissions. This was done by assessing the impact of

CAV vehicle fleets on traffic performance and GHG

emissions for the City of Ottawa in Canada. The study

evaluated four different driving behaviours: DOVs,

Cautious CAVs, Normal CAVs, and Aggressive

CAVs. The city-wide model was simulated with

traffic volumes equal to 80%, 100% and 120% of

peak-hour traffic forecasted for the year 2031.The

results of the simulations show that an aggressive

driving style is the most optimal driving behaviour for

CAVs in all scenarios, and that equipping CAVs with

this driving style can lead to significant reductions in

travel time and in GHG emissions compared to

DOVs, especially at higher traffic volumes. The

detailed results of the meso simulations showed that

equipping CAVs with an aggressive driving style will

increase road capacity, allowing more vehicles to use

the highway and effectively reduce congestion on

arterial and other roads.

The results of the study presented in this article

clearly indicate that the driving behaviour of CAVs

can be utilized to reduce travel time and congestion

in an urban environment when employing a

homogenous fleet of 100% CAVs. However, it is

unclear what level of penetration of Aggressive

CAVs in a mixed CAV/DOV fleet will be necessary

to start seeing the benefits of enhanced traffic

movement. Similarly, it is unknown how fleets of

CAVs with different driving behaviours (Cautious,

Normal, and Aggressive) will impact congestion and

transportation emissions. These subjects will be part

of ongoing research on this topic.

ACKNOWLEDGEMENTS

Funding for this study was received from Transport

Canada under the ecoTECHNOLOGY for Vehicles

program. The authors express great thanks to the City

of Ottawa and the TRANS committee for sharing the

Ottawa Regional Traffic Model for use in this study.

REFERENCES

Abdulsattar, H., Siam, M. R. K., & Wang, H. (2020).

Characterisation of the impacts of autonomous driving

on highway capacity in a mixed traffic environment: an

agent-based approach. IET Intelligent Transport

Systems, 14, 1132 –1141. https://doi.org/10.1049/iet-

its.2019.0285

Atkins Ltd. (2016). Research on the Impacts of Connected

and Autonomous Vehicles (CAVs) on Traffic Flow

Summary Report Department for Transport.

https://trid.trb.org/view/1448450

Biramo, Z. B., & Mekonnen, A. A. (2022). Modeling the

potential impacts of automated vehicles on pollutant

emissions under different scenarios of a test track.

Environmental Systems Research, 11(1), 28.

https://doi.org/10.1186/s40068-022-00276-2

Bohm, F., & Häger, K. (2015). Introduction of Autonomous

Vehicles in the Swedish Traffic System : Effects and

Changes Due to the New Self-Driving Car Technology.

https://www.semanticscholar.org/paper/Introduction-

of-Autonomous-Vehicles-in-the-Swedish-Bohm-

H%C3%A4ger/ea2be6805b2adaba043df516e132f128

9ce103cb#citing-papers

Brown, A., Gonder, J., & Repac, B. (2014). An Analysis of

Possible Energy Impacts of Automated Vehicles. In G.

Meyer & S. Beiker (Eds.), Road Vehicle Automation

(pp. 137–153). Springer International Publishing.

https://doi.org/10.1007/978-3-319-05990-7_13

Conlon, J., & Lin, J. (2019). Greenhouse Gas Emission

Impact of Autonomous Vehicle Introduction in an

Urban Network. Transportation Research Record,

2673(5), 142–152. https://doi.org/10.1177/03611

98119839970

INRO. (n.d.-a). Dynameq.

INRO. (n.d.-b). Emme.

Lu, Q., Tettamanti, T., Hörcher, D., & Varga, I. (2020). The

impact of autonomous vehicles on urban traffic network

capacity: an experimental analysis by microscopic

traffic simulation. Transportation Letters, 12(8), 540–

549. https://doi.org/10.1080/19427867.2019.1662561

MMM Group Limited. (2014). TRANS Model – Evolution

of the TRANS Regional Travel Forecasting Model.

Roustom, S. G. (2022). Environmental Impacts of

Connected and Automated Vehicles Considering

Traffic Flow and Road Characteristics. Thesis

(M.App.Sc.) - Carleton University, 2022.

Tomás, R. F., Fernandes, P., Macedo, E., Bandeira, J. M.,

& Coelho, M. C. (2020). Assessing the emission

VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems

204

impacts of autonomous vehicles on metropolitan

freeways. Transportation Research Procedia, 47, 617–

624. https://doi.org/https://doi.org/10.1016/j.trpro.2020

.03.139

Stogios, C., Kasraian, D., Roorda, M. J., & Hatzopoulou,

M. (2019). Simulating impacts of automated driving

behavior and traffic conditions on vehicle emissions.

Transportation Research Part D: Transport and

Environment, 76, 176–192. https://doi.org/10.1016/

j.trd.2019.09.020

Wadud, Z., MacKenzie, D., & Leiby, P. (2016). Help or

hindrance? The travel, energy and carbon impacts of

highly automated vehicles. Transportation Research

Part A: Policy and Practice, 86, 1–18.

https://doi.org/10.1016/j.tra.2015.12.001

Wang, Y., & Wang, L. (2017). Autonomous vehicles’

performance on single lane road: A simulation under

VISSIM environment. 2017 10th International

Congress on Image and Signal Processing, BioMedical

Engineering and Informatics (CISP-BMEI), 1–5.

https://doi.org/10.1109/CISP-BMEI.2017.8302162

Optimizing CAV Driving Behaviour to Reduce Trafﬁc Congestion and GHG Emissions

205