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Found 35 papers, 16 papers with code
Bayesian Active Learning for Censored Regression
To address this, we derive the entropy and the mutual information for censored distributions and derive the BALD objective for active learning in censored regression ($\mathcal{C}$-BALD).
Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities
Finally, we identify dwell time prediction as the key determinant in overall AT prediction accuracy when autonomous shuttles are deployed in low-traffic areas or under regulatory speed limits.
Deep Evidential Learning for Bayesian Quantile Regression
The proposed method is based on evidential learning, which allows the model to capture aleatoric and epistemic uncertainty with a single deterministic forward-pass model.
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems.
Railway Network Delay Evolution: A Heterogeneous Graph Neural Network Approach
This paper aims to develop a heterogeneous graph neural network (HetGNN) model, which can address different types of nodes (i. e., heterogeneous nodes), to investigate the train delay evolution on railway networks.
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests.
Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand
As a result, machine learning models that rely on these observed records for forecasting charging demand may be limited in their application in future infrastructure expansion and supply management, as they do not estimate the true demand for charging.
Context-aware Bayesian Mixed Multinomial Logit Model
This paper proposes an effective approach to model context-dependent intra-respondent heterogeneity, thereby introducing the concept of the Context-aware Bayesian mixed multinomial logit model, where a neural network maps contextual information to interpretable shifts in the preference parameters of each individual in each choice occasion.
Representation learning of rare temporal conditions for travel time prediction
We present a vector-space model for encoding rare temporal conditions, that allows coherent representation learning across different temporal conditions.
On the importance of stationarity, strong baselines and benchmarks in transport prediction problems
Over the last years, the transportation community has witnessed a tremendous amount of research contributions on new deep learning approaches for spatio-temporal forecasting.
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs.
Unboxing the graph: Neural Relational Inference for Mobility Prediction
Our approach has several advantages: 1) a Variational Auto Encoder structure allows for the graph to be dynamically determined by the data, potentially changing through time; 2) the encoder structure allows the use of external data in the generation of the graph; 3) it is possible to place Bayesian priors on the generated graphs to encode domain knowledge.
Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management
Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility.
Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
To meet this requirement, accurate forecasting of the charging demand is vital.
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles.
Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling
This paper presents two novel approaches for uncertainty estimation adapted and extended for the multi-link bus travel time problem.
Modeling Censored Mobility Demand through Quantile Regression Neural Networks
We address this gap by extending current Censored Quantile Regression models to learn multiple quantiles at once and apply these to synthetic baseline datasets and datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark.
Gaussian Process Latent Class Choice Models
We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs).
Generalized Multi-Output Gaussian Process Censored Regression
When modelling censored observations, a typical approach in current regression methods is to use a censored-Gaussian (i. e. Tobit) model to describe the conditional output distribution.
Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification with the aim of comparing the two approaches on various measures including prediction accuracy and representation of heterogeneity in the choice process.
Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility
Mobility-on-demand (MoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of vehicles.
Scaling Bayesian inference of mixed multinomial logit models to very large datasets
Variational inference methods have been shown to lead to significant improvements in the computational efficiency of approximate Bayesian inference in mixed multinomial logit models when compared to standard Markov-chain Monte Carlo (MCMC) methods without compromising accuracy.
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited.
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices.
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion.
Multi-output Bus Travel Time Prediction with Convolutional LSTM Neural Network
Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas.
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data
Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems.
A Bayesian Additive Model for Understanding Public Transport Usage in Special Events
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise.
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Traffic speed data imputation is a fundamental challenge for data-driven transport analysis.
Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems
Spatio-temporal problems are ubiquitous and of vital importance in many research fields.
Learning Supervised Topic Models for Classification and Regression from Crowds
The growing need to analyze large collections of documents has led to great developments in topic modeling.
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach
Accurate time-series forecasting is vital for numerous areas of application such as transportation, energy, finance, economics, etc.
Deep learning from crowds
Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the state-of-the-art in various domains.
Can Topic Modelling benefit from Word Sense Information?
This paper proposes a new topic model that exploits word sense information in order to discover less redundant and more informative topics.
