Deep Reinforcement Learning Models for Real-Time Traffic Signal Optimization with Big Traffic Data

One of the most significant changes that the globe has faced in recent years is the changes brought about by the COVID19 pandemic. While this research was started before the pandemic began, the pandemic has exposed the value that data and information can have in modern society. During the pandemic traffic volumes changed substantially, leaving the inefficiencies of existing methods exposed. This research has focussed on exploring two key ideas that will become increasingly relevant as societies adapt to these changes: Big Data and Artificial Intelligence. For many municipalities, traffic signals are still re-timed using traditional approaches and there is still significant reliance on static timing plans designed with data collected from static field studies. This research explored the possibility of using travel-time data obtained from Bluetooth and WiFi sniffing. Bluetooth and WiFi sniffing is an emerging Big Data approach that takes advantage of the ability to track and monitor unique devices as they move from location to location. An approach to re-time signals using an adaptive system was developed, analysed, and tested under varying conditions. The results of this work showed that this data could be used to improve delays by as much as 10\% when compared to traditional approaches. More importantly, this approach demonstrated that it is possible to re-time signals using a readily available and dynamic data source without the need for field volume studies. In addition to Big Data technologies, Artificial Intelligence (AI) is increasingly playing an important role in modern technologies. AI is already being used to make complex decisions, categorise images, and can best humans in complex strategy games. While AI shows promise, applications to Traffic Engineering have been limtied. This research has advanced the state-of-the art by conducting a systematic sensitivity study on an AI technique, Deep Reinforcement Learning. This thesis investigated and identified optimal settings for key parameters such as the discount factor, learning rate, and reward functions. This thesis also developed and tested a complete framework that could potentially be applied to evaluate AI techniques in field settings. This includes applications of AI techniques such as transfer learning to reduce training times. Finally, this thesis also examined framings for multi-intersection control, including comparisons to existing state-of-the art approaches such as SCOOT

Development and Evaluation of A Framework for Linking Traffic Simulation and Emission Estimation Models

The need to understand the effect of policy decisions on environmental indicators is strong. The emergence of new technologies brought about by connected vehicle technologies, which are difficult to evaluate in field settings, means that policies must often be evaluated with software models. In these cases, however, the transportation model and the emissions model are often separate, and multiple different ways to connect these models are possible. Although the estimations provided by each model will vary, each method also differs in terms of the computational time. This research is motivated by the need to understand the consequences of choosing a particular method to link a traffic and emissions model. Within the literature, aggregated approaches that simply use average speeds and volumes are often selected for their convenience and lower data needs. A number of different scenarios were therefore constructed to compare the estimates of these aggregated approaches to other methods that use disaggregated data, such as the use of individual discrete trajectories, the use of a velocity binning scheme that characterises networks based on their velocity profile or the use of a clustering algorithm developed for this study. This research presents a clustering algorithm that can be used to reduce the computational loads of an emissions estimation process without loss of accuracy. The results of the analysis highlight the consequences of choosing each approach. Aggregated approaches produce unreliable estimates as they are backed by assumptions that may not be valid in every case. Using individual trajectories creates high computational loads and may not be feasible in all cases. The wealth of data available from a traffic microsimulation mean that using an aggregated approach neglects to utilise the full potential of the model; however, the hybrid approaches presented in this research (clustering and velocity binning) were found to make excellent use of this data while still minimizing computational demands

Fast imaging of live organisms with sculpted light sheets.

Light-sheet microscopy is an increasingly popular technique in the life sciences due to its fast 3D imaging capability of fluorescent samples with low photo toxicity compared to confocal methods. In this work we present a new, fast, flexible and simple to implement method to optimize the illumination light-sheet to the requirement at hand. A telescope composed of two electrically tuneable lenses enables us to define thickness and position of the light-sheet independently but accurately within milliseconds, and therefore optimize image quality of the features of interest interactively. We demonstrated the practical benefit of this technique by 1) assembling large field of views from tiled single exposure each with individually optimized illumination settings; 2) sculpting the light-sheet to trace complex sample shapes within single exposures. This technique proved compatible with confocal line scanning detection, further improving image contrast and resolution. Finally, we determined the effect of light-sheet optimization in the context of scattering tissue, devising procedures for balancing image quality, field of view and acquisition speed.This work was funded by grants from the Wellcome Trust, the Medical Research Council, the CamBridgeSense network, Carlsberg Foundation, the Alzheimer Research UK Trust and the Biotechnology and Biological Sciences Research Council and the Wolfson Foundation.This is the final version of the article. It first appeared at http://dx.doi.org/10.1038/srep09385

2002-2003 Conservatory All-Stars

A Hank Ellman Memorial Concert, sponsored by Sonny Ellma