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Found 26 papers, 13 papers with code
Learning and Generalizing Polynomials in Simulation Metamodeling
The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are described by polynomials.
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.
"Is not the truth the truth?": Analyzing the Impact of User Validations for Bus In/Out Detection in Smartphone-based Surveys
Passenger flow allows the study of users' behavior through the public network and assists in designing new facilities and services.
Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction and Multisensory Unsupervised Cause-effect Learning
To close the gap and enhance smartphones towards MaaS, we developed a proprietary smartphone-sensing platform collecting contemporary Bluetooth Low Energy (BLE) signals from BLE devices installed on buses and Global Positioning System (GPS) locations of both buses and smartphones.
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.
Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance
The novelty of our work lies in enforcing interpretability to the embedding vectors by formally associating each of their dimensions to a choice alternative.
Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
To meet this requirement, accurate forecasting of the charging demand is vital.
Improving the Accuracy and Efficiency of Online Calibration for Simulation-based Dynamic Traffic Assignment
Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control.
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.
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.
Population synthesis for urban resident modeling using deep generative models
The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level.
On the Quality Requirements of Demand Prediction for Dynamic Public Transport
Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors.
Estimating Causal Effects with the Neural Autoregressive Density Estimator
Estimation of causal effects is fundamental in situations were the underlying system will be subject to active interventions.
QTIP: Quick simulation-based adaptation of Traffic model per Incident Parameters
In a nutshell, QTIP performs real-time simulations of the affected road for multiple scenarios, analyzes the results, and suggests a change to an ordinary prediction model accordingly.
Uncovering life-course patterns with causal discovery and survival analysis
In the lower level, for the pairs of life events, time-to-event modelling through survival analysis is applied to model time-dependent transition probabilities.
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.
Mining User Behaviour from Smartphone data: a literature review
To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys.
Prediction of rare feature combinations in population synthesis: Application of deep generative modelling
In this paper, two machine learning algorithms, from the family of deep generative models, are proposed for the problem of population synthesis and with particular attention to the problem of sampling zeros.
Rethinking travel behavior modeling representations through embeddings
This paper introduces the concept of travel behavior embeddings, a method for re-representing discrete variables that are typically used in travel demand modeling, such as mode, trip purpose, education level, family type or occupation.
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements.
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Traffic speed data imputation is a fundamental challenge for data-driven transport analysis.
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.
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.
Scalable Population Synthesis with Deep Generative Modeling
It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models.
