1.3 Dissertation Objectives . . . 6 1.4 Dissertation Scope . . . 8 1.5 Research Methodology . . . 9 1.6 Dissertation Organisation . . . 10
1.1 Dissertation Background and Origin
Highways were originally built to provide virtually unlimited mobility to road users. The ongoing dramatic expansion of car ownership and travel demand have, however, led to the situation where, today, traffic congestion is a significant problem in major metropolitan areas all over the world. The reason for the severe traffic congestion experienced around the world is over- utilisation of the existing road networks which potentially leads to dense, stop-and-go traffic, as may be seen in Figure 1.1. In the United States, for example, travel delays increased by a factor of five from a cumulative 1.1 billion hours in 1982 to 5.5 billion hours in 2011 [144]. According to a report compiled by the Texas A&M Transportation Institute and the traffic information company Inrix [30], it is estimated that the average American citizen spends 42 hours per year stuck in traffic. This number rises to 82 hours in urban centres, which naturally are the more congested areas.
Perhaps the worst traffic jam ever recorded occurred in August 2010 on the National Highway 110 in China, and lasted longer than ten days [12]. The traffic jam was reported to be approximately 100 kilometres in length, with several motorists stuck in traffic for up to five days. Apart from the sheer inconvenience and frustration caused on the part of road users by typical rush hour congestion, it also has significant economic implications. Congestion in the United States resulted in a waste of more than three billion gallons of fuel and an accumulated seven billion hours spent by people stuck in traffic during 2015 at an annual nationwide cost of $160 billion, or $960 per commuter [30].
Traffic congestion is not only a major problem in first-world countries such as the United States, China or Germany, but also in South Africa. According to the TomTom Traffic Index [160], a congestion ranking based on GPS data collected from individual vehicles, Cape Town is the
(a) (b)
(c) (d)
Figure 1.1: Severe traffic congestion on (a) National Highway 110, China, (b) Interstate 45, Texas, (c) Bundesautobahn 4, Germany, and (d) N2, South Africa [36].
48th most congested city in the world, and the most congested city in Africa. In order to place these statistics into perspective, Cape Town has the same congestion ranking as New York City according to the TomTom Traffic Index published at the end of 2016, while the morning and afternoon peak congestion in Cape Town exceeds that experienced by commuters in New York City.
As may be seen in Figure 1.2, the traffic congestion levels in Cape Town have increased steadily since 2011, with a significant increase in congestion levels from 30% in 2015 to 35% in 2016. These percentages imply that a journey would take, on average, 35% longer in 2016 due to congestion than it would if free-flowing traffic conditions were to prevail. For the morning and afternoon peaks, the level of traffic congestion is naturally significantly larger than these average values suggest. During the morning peak, travellers experience a 75% increase in travel time, while during the afternoon peak commuters experience a 67% increase in travel time. The result of this level of traffic congestion is that the average Capetonian will spend an additional 42 minutes stuck in traffic per day, which accumulates to approximately 163 hours stuck in traffic congestion per year [160].
Although traffic congestion in Johannesburg is not quite as severe as it is in Cape Town, as travellers in Johannesburg experience average, morning peak and afternoon peak congestion levels of 30%, 62% and 60%, respectively, motorists in Johannesburg still spend 37 minutes per day, or a cumulative 141 hours stuck in congested traffic per year. As may be seen in Figure 1.2, congestion levels in Johannesburg temporarily decreased from 2009 until 2012. This decrease may be attributed to the Gauteng Freeway Improvement Project [160]. The aim of this project was significant highway capacity expansion through which the highways along major routes
2009 2010 2011 2012 2013 2014 2015 2016 24% 26% 28% 30% Year Congestion lev
Figure 1.2: Variation in traffic congestion levels in two major South African metropolitan areas, namely Cape Town and Johannesburg, during the period 2009–2016 [160].
within the Johannesburg, Ekurhuleni and Tshwane metropolitan boundaries were expanded to at least four lanes in each direction, while along certain sections these highways were expanded to have six lanes in each direction [138]. The subsequent rise in congestion levels from 2012–2016, visible in the figure, may be attributed to the so-called theory of induced travel demand, in which it is suggested that increases in highway capacity will induce additional travel demand, thus not permanently alleviating congestion as envisioned [107]. The alternative to capacity expansion in order to improve traffic flow on highways is more effective control of the existing infrastructure. This may include dynamic traffic control measures such as ramp metering, variable speed limits, dynamic lane assignment, or the use of variable message signs to convey information on the current traffic situation to motorists.
Autonomous driving has often been hailed the future of human transportation with the promise of a congestion-free future due to perfect traffic flow coordination. Recent advances in the field of autonomous driving have led to the situation where in 2018 one is already able to purchase a vehicle that is essentially able to drive entirely by itself, although humans are required to be in the driver’s seat, able to take over whenever required. Examples of such vehicles are the 2017 Mercedes-Benz E-Class [99] as well as the Tesla Model S [158]. These vehicles use a combination of cameras, ultrasonic sensors and radar to steer themselves on highways, change lanes and adjust their speeds according to traffic conditions [158].
Fully autonomous systems eliminate the driver from the control loop and may take complete control of the vehicle. Examples of commercially available vehicles capable of autonomous driving are the Tesla Model S and the Mercedes-Benz E-Class mentioned above. A possible configuration of the sensors employed in semi-autonomous and autonomous vehicles is shown in Figure 1.3.
Autonomous vehicles present a compelling case for their adoption, considering that already they are superior drivers to their human counterparts. This is due to the fact that a computer is simply better at parsing all the weather, GPS and traffic data that have to be taken into account when driving than an easily distracted human driver will ever be. A computer, for example, does not fall asleep behind the wheel, or remove its focus from driving to reply to
Figure 1.3: The configuration and detection zones of the sensors of a semi-autonomous or autonomous vehicle [28].
an urgent text message or answer a phone call [165]. Research reports have shown that human error is the main cause of motor vehicle accidents. In the United States alone, approximately six million vehicle accidents are reported annually to law enforcement [165]. According to the World Health Organisation [176], there are about 1.25 million traffic fatalities each year. Some 94% of these traffic accidents may be attributed to driver error. Furthermore, road traffic accidents are the leading cause of death among people aged 15–29 years — a statistic that is not too surprising when taking into account that 61% of drivers with smartphones admit to texting while driving [153]. An estimate of the annual costs associated with traffic accidents in the United States of America alone amounts to a staggering $836 billion [165]. Looking ahead at the transition phase from human-driven vehicles to autonomous vehicles, a study conducted by the Eno Center for Transportation suggests that a conversion of only 10% of the current vehicles on roads in the United States of America is expected to reduce the number of accidents each year by 211 000, saving approximately 1 100 lives. Cost savings from this modest change in traffic flow composition have also been estimated at $25.5 billion. If this number were to be increased to 90% over the course of time, the number of avoided traffic accidents may rise to 4.2 million annually, saving 21 700 lives per annum [165].
Various estimates have been made as to when the driverless vehicle transition will start in earnest. Elon Musk, the founder and CEO of Tesla Motors, has predicted the revolution to start around 2023, while industry analysts expect it to be between 2035 and 2050 [165]. From a purely technological point of view these numbers may be realistic. What is certain is that this revolution, once it starts, will bring about a self-compounding effect. As the number of autonomous vehicles on the road increases, new road designs will inevitably become more and more machine-centric. This will, in turn, make it harder for humans to drive their conventional vehicles on these roads, leading to more and more people trading in their keys [165]. This compounding effect will be further strengthened by the fact that, due to fewer accidents and smoother vehicle operation, insurance and running costs are expected to be considerably cheaper for autonomous vehicles, thus providing a further incentive to make the transition to driverless vehicles. Litman [89] has made predictions of autonomous vehicle adoption rates based on previ-
in low adoption rates, with these adoption rates increasing once the autonomous vehicle can compete with human-driven alternatives on cost. The process to complete adoption (i.e. until such time that all vehicles on the roads are fully autonomous) is expected to take approximately five decades. The expected slow initial adoption rate, which should increase with time as the technology matures, is also illustrated graphically in Figure 1.4.
Table 1.1: Autonomous vehicle implementation prediciton rates [89].
Stage Decade Vehicle Sales Vehicle Fleet Vehicle Travel
Available with large premium 2020s 2–5% 1–2% 1–4%
Available with moderate premium 2030s 20–40% 10–20% 10–30%
Available with minimal premium 2040s 40–60% 20–40% 30–50%
Standard feature on most vehicles 2050s 80–100% 40–60% 50–80%
Saturation 2060s ? ? ?
Mandatory on all vehicles ? 100% 100% 100%
From the predictions in Table 1.1 it is clear that there will be a significant period of time during which mixed traffic flow of autonomous and human driven vehicles on the roads will prevail. The duration of the transition period seems long compared to the turnaround times of new innovations in the mobile telephone or personal computer technologies. One reason for this phenomenon is that motor vehicles typically cost fifty times as much and last ten times longer than mobile telephones or personal computers [89]. Since this transition phase is expected to take such a long time, it is important to implement traffic control measures which are not only able to take into account the mixed traffic flow of both human-driven vehicles and autonomous vehicles, but already start to exploit the expected benefits achievable through the efficient external control of autonomous vehicles, integrating these with human-driven vehicles in such a manner that every user of the system is able to experience the benefits.
Figure 1.4: Expected autonomous vehicle sales, fleet composition and travel distance projections, given as percentages of total vehicle compositions, for the years 2020–2070 [89].
Recent advances in the field of Artificial Intelligence (AI) have shown great promise in terms of effective pattern recognition and successful strategy identification, even in situations where the range of alternatives is very large. Board games have proven to be a major testing ground for AI, by setting benchmarks for assessing the progress of AI, since an intelligent playing strategy is typically required in order to win these games. The game of Go has long held the reputation as the most challenging of classic games for AI due to its enormous search space and the difficulty of evaluating board positions and moves [149]. The Google-owned company DeepMind, however, mastered the formidable challenge posed by Go in March 2016, when its program, AlphaGo, beat the best Go player in the world, Lee Sedol, 4–1 in a five-match series [44]. A combination of AI techniques are employed in the program, so as to learn effective strategies for playing the game, without evaluating the entire range of possible moves at each stage of the game [149]. This remarkable feat has demonstrated the ability of AI algorithms to learn new strategies successfully within a complex, dynamic, uncertain environment.
It is, however, not only within the paradigm of board games that intricate AI systems have been applied with great success. Another remarkable application of AI is the so-called MogIA system. This system, developed by the Indian start-up company Genic.ai, took 20 million data points from public platforms such as Google, Facebook and Twitter, and, based on these data, correctly predicted Donald Trump as the winner of the 2016 United States presidential election, a result which was generally unexpected [19]. Furthermore, AI techniques have been applied to a wide variety of medical problems with great success. Esteva et al. [31] report on a deep convolutional neural network, which has been trained to identify melanoma (skin cancer) based on image classification. After training the neural network on 127 463 images, it was able to correctly classify the skin condition displayed in an image as benign or malignant in nature at a 72.1± 0.9% overall accuracy. Two dermatologists, on the other hand, achieved accuracies of 65.56% and 66.0%, respectively, when they were presented a subset of the validation set presented to the neural network.
The success of AI in respect of this wide variety of problems raises the question whether it would be possible to implement suitable AI algorithms to find effective highway traffic control measures in an online manner, allowing a computer to learn which control strategies work well in a dynamic traffic control environment.