New applications of data science for intelligent transportation systems

Streets and motorways are the basic blocks in the core of our transportation networks. In recent years, increases in available sensory and computing power have allowed us to start massive gatherings of data related to their use and performance, and to obtain insightful information via data science. This, in turn, has increased our ability to create systems that estimate the state of the transportation networks and provide us with control capabilities over it, giving rise to the concept of Intelligent Transportation Systems. These systems aim to provide deeper levels of observability to our transportation networks so that their capacity can be increased without the need of further heavy investment to develop traffic infrastructure, especially in terms of laying new roads and streets. In this thesis we aim to contribute to this process in both urban and interurban settings. Here we propose two different algorithms to estimate and forecast expected travel time in motorways over the long term, ranging from hours to a week. The first of them is centred around the identification of the different traffic regimes and leveraging their specific characteristics to improve estimation and forecasting. The second of them looks further into the differentiation between recurrent and non recurrent congestion from the point of view of statistical analysis in the frequency space, using the natural frequencies of the traffic system to tell them apart and exert prediction. We also delve into how Intelligent Transportation Systems can affect our cities, looking at how reinforcement learning can create independent agents capable of controlling traffic lights at intersections. We do this by first looking at the most effective agent architectures in different junctions of increasing complexity. Then we dive into the difference in performance for agents in charge of vehicular intersections, provided by an array of reward functions that use different measures obtained from the traffic flow. Finally, we expand these systems to also take pedestrians into account, investigating the rewards that produce the lowest waiting times when serving different modes of transportation with opposing needs

Assessment of reward functions in reinforcement learning for multi-modal urban traffic control under real-world limitations

Reinforcement Learning is proving a successful tool that can manage urban intersections with a fraction of the effort required to curate traditional traffic controllers. However, literature on the simultaneous introduction and control of pedestrians to such intersections is very scarce. Furthermore, it is unclear what traffic state variables should be used as reward to minimise waiting times. This paper robustly evaluates 30 different Reinforcement Learning reward functions for controlling a real-world intersection serving pedestrians and vehicles covering the main traffic state variables available via modern vision-based sensors. Some rewards proposed in previous literature solely for vehicular traffic are extended to pedestrians while new ones are proposed. We use a calibrated model in terms of demand, sensors, green times and other operational constraints of a real intersection in Greater Manchester, UK, to which the agent has been since deployed. The assessed rewards can be classified in 5 groups depending on the quantities used: queues, waiting time, delay, average speed and throughput in the junction. The performance of different agents, in terms of waiting time, is compared across different demand levels, from normal operation to saturation of traditional adaptive controllers. We find that those rewards maximising the speed of the network obtain the lowest waiting time for vehicles and pedestrians simultaneously, closely followed by queue minimisation, demonstrating better performance than other previously proposed methods

Reinforcement learning for traffic signal control : comparison with commercial systems

Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems. Within Traffic Signal Control (TSC), this has allowed the emergence of Machine Learning (ML) based systems. Among this group, Reinforcement Learning (RL) approaches have performed particularly well. Given the lack of industry standards in ML for TSC, literature exploring RL often lacks comparison against commercially available systems and straightforward formulations of how the agents operate. Here we attempt to bridge that gap. We propose three different architectures for TSC RL agents and compare them against the currently used commercial systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code for them. The agents use variations of Deep Q-Learning and Actor Critic, using states and rewards based on queue lengths. Their performance is compared in across different map scenarios with variable demand, assessing them in terms of the global delay and average queue length. We find that the RL-based systems can significantly and consistently achieve lower delays when compared with existing commercial systems

How to Data in Datathons

The rise of datathons, also known as data or data science hackathons, has provided a platform to collaborate, learn, and innovate in a short timeframe. Despite their significant potential benefits, organizations often struggle to effectively work with data due to a lack of clear guidelines and best practices for potential issues that might arise. Drawing on our own experiences and insights from organizing >80 datathon challenges with >60 partnership organizations since 2016, we provide guidelines and recommendations that serve as a resource for organizers to navigate the data-related complexities of datathons. We apply our proposed framework to 10 case studies.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmar

Design choices for productive, secure, data-intensive research at scale in the cloud

We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk profiles into five sensitivity tiers, and, at each tier, specifying recommended policies for data classification, data ingress, software ingress, data egress, user access, user device control, and analysis environments. By presenting design patterns for security choices for each tier, and using software defined infrastructure so that a different, independent, secure research environment can be instantiated for each project appropriate to its classification, we hope to maximise researcher productivity and minimise risk, allowing research organisations to operate with confidence

Design choices for productive, secure, data-intensive research at scale in the cloud

We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk profiles into five sensitivity tiers, and, at each tier, specifying recommended policies for data classification, data ingress, software ingress, data egress, user access, user device control, and analysis environments. By presenting design patterns for security choices for each tier, and using software defined infrastructure so that a different, independent, secure research environment can be instantiated for each project appropriate to its classification, we hope to maximise researcher productivity and minimise risk, allowing research organisations to operate with confidence