Candes et al., (2006) for recovering traffic patterns using GPS traces. My
approach can provide accurate recovery when tested using the ground-truth
traffic data from loop detectors, and is among few that have exploited the
effectiveness of Compressed Sensing on traffic pattern processing Lin et al.,
(2019).

In order to further improve the estimation accuracy of traffic conditions for
spatial data interpolation, I proposed an iterative approach, which embeds
map-matching and travel-time estimation as its sub-routines. This process is
further improved by the statistical modeling and learning of traffic
conditions of a road segment. The results of my approach are accurate
estimations of traffic conditions in areas with GPS data coverage—achieving up
to 97% relative improvement in estimation accuracy over previous
techniques—and coarse estimations of traffic conditions in areas without GPS
data coverage (of a city).

For achieving accurate estimations of traffic conditions in data-deficient
areas, using the abovementioned results, I presented a method to dynamically
interpolate spatial missing traffic data. In particular, I have leveraged
traffic simulation to ensure the consistency of traffic flows on the
boundaries of areas with and without GPS data coverage. A metamodel-based
simulation optimization is further developed to save the computational cost of
using traffic simulation in optimization. Compared to the simulation-only
approach, my technique has achieved on average a 7% error rate and up to 90
times speedup. My approach is the first dynamical and efficient method for
interpolating large-scale traffic data while ensuring the flow consistency on
city-scale boundaries.

After fully reconstructing spatial-temporal traffic at a city scale, I
visualized the reconstructed traffic in various forms such as 2D flow map, 2D
animation, and 3D animations. These visual representations can be adopted to
improve many ITS applications including the analysis of traffic patterns at
street level, region level, and the city level, and enrich virtual environment
applications such as virtual tourism and the training of general driving
behaviors or autonomous driving.

Regarding autonomous driving, I presented ADAPS, a framework that consists of
two simulation platforms and a hierarchical control policy. ADAPS can be used
to simulate, analyze various traffic scenarios, especially accidents, and
automatically produce labeled training data. In addition, ADAPS represents a
more efficient online learning mechanism compared to previous techniques,
attributing to the switch from the reset modeling approach to the generative
modeling approach. Using the hierarchical control policy and the efficient
online learning mechanism of ADAPS, robust control policies for autonomous
driving can be learned and applied to obtain normal driving and safe
navigation in dangerous situations including accidents.

###  Future Work

There are many future development and research directions can stem from this
dissertation, at the macroscopic level of traffic (i.e., _city-scale
traffic_), the microscopic level of traffic (i.e., _autonomous driving_), the
connection between the two levels, and beyond. I will discuss a few of them in
each category in the following.

####  Macroscopic Level

On the “city-scale traffic” side, first of all, it would be useful to develop
an interactive simulation platform. Using the platform, policy makers, city
planners, and other users can easily edit a road network and alter a transport
policy in order to test the effectiveness of these changes via observing the
response of simulated traffic flows propagating in a city. Building such a
platform would require several elements: a 3D virtual environment with a user
interface, a road network construction mechanism Wilkie et al., (2012);
Musialski et al., (2013), a road network editing mechanism Chen et al.,
(2008), and a real-time traffic simulation technique Sewall et al., 2011b ;
Wilkie et al., (2013); Garcia-Dorado et al., (2014). The 3D virtual
environment can be built using a game engine such as Unity
(https://unity.com/) or Unreal (https://www.unrealengine.com). The rest of the
elements have been explored to various degrees in existing studies. Unifying
these elements would be an interesting topic.

Secondly, simulating city-scale traffic, depending on the levels of detail,
can be computationally prohibitive. A scalable approach that can combine
modern machine learning techniques and traffic flow models is highly
desirable. Such a approach can especially benefit applications with highly
interactive and real-time demands such as the simulation platform mentioned
above. The metamodel-based simulation optimization presented in Chapter 3 is
an example work in this direction. However, the functional component of the
metamodel is currently chosen to be quadratic polynomial, which offers limited
expressiveness. With the emergence of deep learning LeCun et al., (2015), it
would be interesting to replace the quadratic polynomial with a deep neural
network or an LSTM network for potential improvements on the traffic
reconstruction accuracy.

Thirdly, traffic participants are not limited to cars, mixed traffic involving
cars, pedestrians, cyclists, and other motorists are commonly seen in many
regions across the globe. A simulation model that can encompass all these
different traffic modalities can enrich various real-world and virtual-world
applications mentioned in the previous chapters. In order to achieve this
goal, knowledge from traffic engineering literature can be exploited. Mixture
models, although not necessarily built for simulation, have been explored and
developed Faghri and Egyháziová, (1999); Laxman et al., (2010). Examining the
possibility of extending these models for mixture traffic simulation is a
promising research direction.

####  Microscopic Level

On the “autonomous driving” side, the system presented in Chapter 4 is an end-
to-end system, which means a single model is trained to map the sensor input
directly to the control command output. Such an approach is straightforward
and usually results in a more compact model as it does not contain
intermediate steps. However, an end-to-end system based on deep learning can
be hard to interpret. Also, a large number of training examples are often
needed to train an end-to-end model.

In contrast, the traditional engineering pipeline, which consists of several
modules, can be adopted for autonomous driving. In this approach, the sensor
input will get processed and passed to its subsequent modules such as
detection, tracking, and planning, before the final control command is
produced. This conventional approach has both advantages and disadvantages.
Advantages include, since there are multiple modules in the pipeline, less
training examples are needed to learn a model; the prior knowledge can be
incorporated into the problem; the explainability is improved as the final
control command is produced by a planning algorithm rather than directly from
the raw sensor input. Disadvantages include, the uncertainty and errors of
each module are difficult to propagate backwards to its preceding modules,
thus causing the system to suffer potential compounding errors; the
computation is not shared between modules: each module is trained
independently for a different objective; human experts are usually needed to
tailor each module so that the system can achieve maximum performance.

While both end-to-end and traditional engineering approaches have their own
characteristics, given that the safety is of the leading concerns these days
regarding autonomous driving, the traditional engineering approach is likely
to prevail in the near future due to its superior explainability and
controllability. Hence, it would be interesting to develop the “traditional
engineering” version of ADAPS. The only element needs to be modified is the
hierarchical control policy, which currently is represented by a single model
with three neural networks. The other elements such as the simulation
platforms and online learning mechanism remain applicable.

Another aspect can be improved in ADAPS is the generation of accident data.
Currently, the accident simulation is simple and arbitrary. However, in
traffic engineering, there exists rich literature on accident analysis and
prevention. By exploring which, a systematically way of simulating accidents
can be developed, which can bring further justifications to ADAPS on
autonomous driving training and testing. One imminent research direction is to
incorporate the pre-crash scenarios published by the National Highway Traffic
Safety Administration of the United States Najm et al., (2013) into our
simulation platform, and then develop a sampling mechanism to produce accident
data for learning a control policy.

Beyond the abovemetioned immediate research topics that can be built on top of
ADAPS, there are many other interesting research directions. In general, the
safety, control, and coordination aspects of autonomous driving all need
further exploration and development. One future research direction can be
exploring the possibility of using simulations to assist sample-efficient
learning for a control policy. Another direction, inspired by the observation
that the training of autonomous driving is largely context-dependent, is to
develop theory and practice in transferring the learned behaviors of an
autonomous vehicle from one environment to other environments. This
generalization ability, observed in humans, is largely missing in autonomous
driving at the moment.

####  Connection Between The Two Levels

Although this dissertation has been addressing the macroscopic level and the
microscopic level of traffic as two separate topics, the two aspects have
tight connection, where many applications and developments can be drawn. From
the macro-to-micro perspective, the estimated city-scale traffic conditions
can be immediately adopted for better routing and planning of AVs. The
reconstructed traffic can be incorporated into virtual environments to provide
rich traffic semantics for training the navigation and decision-making of
autonomous driving. From the micro-to-macro perspective, AVs can be treated as
probe vehicles to gather traffic information in a city so that traffic
reconstruction can be achieved with higher accuracy. The AV can also be
dispatched to multiple road users as a sharing transportation tool. This way
not only the number of vehicles on the road is reduced, which can assist in
alleviating traffic jams, but also less space is needed to physically
accommodate the large number of vehicles, which implies additional socio-
economic benefits. Back to the macro-to-micro perspective, an efficient
traffic reconstruction technique can contribute to the design of the
dispatching algorithm for AVs to maximize their sharing functionality.

In conclusion, now and into the near future, AVs will be operating not only in
traffic but also along with human-driven vehicles. This assembly brings many
challenges as well as research opportunities. It would be interesting to
develop simulation models for the mixture of human-driven and autonomous
vehicles, with the flexibility to choose the percentage of each type of the
vehicle. As AVs can be considered part of the overall cyber-physical system,
they can serve as additional “degrees of freedom” to the traffic system, which
can be potentially “tuned” to regulate traffic flows Wu et al., (2018). The
applications range from alleviating traffic congestion to assisting flow
distribution in social gatherings or evacuation situations. Lastly, it would
be imperative to consider human factors in addition to technology development,
given essentially autonomous and intelligent systems are designed and built to
improve people’s life. As technology advances, developing collaborative rather
than competitive relationships between autonomous systems and humans is the
challenge that scientists and engineers will be facing. My future efforts will
be centered around this challenge, with the focus on building an cooperative
mixed traffic system.

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