arXiv subjects

James Spall

Publications and source records attributed to James Spall.

4 recordsLinked to original sources

arXiv2025

Real-time Bus Travel Time Prediction and Reliability Quantification: A Hybrid Markov Model

Accurate and reliable bus travel time prediction in real-time is essential for improving the operational efficiency of public transportation systems. However, this remains a challenging task due to the limitations of existing models and data sources. This study proposed a hybrid Markovian framework for real-time bus travel time prediction, incorporating uncertainty quantification. Firstly, the bus link travel time distributions were modeled by integrating various influential factors while explicitly accounting for heteroscedasticity. Particularly, the parameters of the distributions were estimated using Maximum Likelihood Estimation, and the Fisher Information Matrix was then employed to calculate the 95\% uncertainty bounds for the estimated parameters, ensuring a robust and reliable quantification of prediction uncertainty of bus link travel times. Secondly, a Markovian framework with transition probabilities based on previously predicted bus link travel times was developed to predict travel times and their uncertainties from a current location to any future stop along the route. The framework was evaluated using the General Transit Feed Specification (GTFS) Static and Realtime data collected in 2023 from Gainesville, Florida. The results showed that the proposed model consistently achieved better prediction performance compared to the selected baseline approaches (including historical mean, statistical and AI-based models) while providing narrower uncertainty bounds. The model also demonstrated high interpretability, as the estimated coefficients provided insights into how different factors influencing bus travel times across links with varying characteristics. These findings suggest that the model could serve as a valuable tool for transit system performance evaluation and real-time trip planning.

stat.AP↗

arXiv2023

Training neural networks with end-to-end optical backpropagation

Optics is an exciting route for the next generation of computing hardware for machine learning, promising several orders of magnitude enhancement in both computational speed and energy efficiency. However, to reach the full capacity of an optical neural network it is necessary that the computing not only for the inference, but also for the training be implemented optically. The primary algorithm for training a neural network is backpropagation, in which the calculation is performed in the order opposite to the information flow for inference. While straightforward in a digital computer, optical implementation of backpropagation has so far remained elusive, particularly because of the conflicting requirements for the optical element that implements the nonlinear activation function. In this work, we address this challenge for the first time with a surprisingly simple and generic scheme. Saturable absorbers are employed for the role of the activation units, and the required properties are achieved through a pump-probe process, in which the forward propagating signal acts as the pump and backward as the probe. Our approach is adaptable to various analog platforms, materials, and network structures, and it demonstrates the possibility of constructing neural networks entirely reliant on analog optical processes for both training and inference tasks.

physics.optics↗

arXiv2022

Hybrid training of optical neural networks

Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation. Today's optical neural networks are mainly developed to perform optical inference after in silico training on digital simulators. However, various physical imperfections that cannot be accurately modelled may lead to the notorious reality gap between the digital simulator and the physical system. To address this challenge, we demonstrate hybrid training of optical neural networks where the weight matrix is trained with neuron activation functions computed optically via forward propagation through the network. We examine the efficacy of hybrid training with three different networks: an optical linear classifier, a hybrid opto-electronic network, and a complex-valued optical network. We perform a comparative study to in silico training, and our results show that hybrid training is robust against different kinds of static noise. Our platform-agnostic hybrid training scheme can be applied to a wide variety of optical neural networks, and this work paves the way towards advanced all-optical training in machine intelligence.

cs.LG↗

arXiv2020

Fully reconfigurable coherent optical vector-matrix multiplication

Optics is a promising platform in which to help realise the next generation of fast, parallel and energy-efficient computation. We demonstrate a reconfigurable free-space optical multiplier that is capable of over 3000 computations in parallel, using spatial light modulators with a pixel resolution of only 340x340. This enables vector-matrix multiplication and parallel vector-vector multiplication with vector size of up to 56. Our design is the first to simultaneously support optical implementation of reconfigurable, large-size and real-valued linear algebraic operations. Such an optical multiplier can serve as a building block of special-purpose optical processors such as optical neural networks and optical Ising machines.

physics.optics↗