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Found 15 papers, 4 papers with code
A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections
In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources.
A Deep Generative Model for Feasible and Diverse Population Synthesis
Synthesizing population by directly sampling from HTS ignores the attribute combinations that are unobserved in the HTS samples but exist in the population, called 'sampling zeros'.
DT2I: Dense Text-to-Image Generation from Region Descriptions
Our results demonstrate the capability of our approach to generate plausible images of complex scenes using region captions.
Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets
Conditional on buying a new car, the fuel consumption in both markets is found to be relatively unresponsive to fuel price and income, with magnitudes of elasticity estimates ranging from 0. 12 to 0. 15.
A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles
The existing behavior models not only fail to capture the information propagation within the individual's social network, but also they do not incorporate the impact of such word-of-mouth (WOM) dissemination on the consumer's risk preferences.
Revisiting the empirical fundamental relationship of traffic flow for highways using a causal econometric approach
The fundamental relationship of traffic flow is empirically estimated by fitting a regression curve to a cloud of observations of traffic variables.
Willingness to Pay and Attitudinal Preferences of Indian Consumers for Electric Vehicles
Consumer preference elicitation is critical to devise effective policies for the diffusion of electric vehicles (EVs) in India.
Revisiting McFadden's correction factor for sampling of alternatives in multinomial logit and mixed multinomial logit models
We generalise their result to the case of positive conditioning and show that whilst McFadden (1978)'s correction factor may not minimise the overall expected information divergence, it does minimise the expected information loss with respect to the parameters of interest.
Data Augmentation
Methodology
Applications
Robust discrete choice models with t-distributed kernel errors
In a case study on transport mode choice behaviour, MNR and Gen-MNR outperform MNP by substantial margins in terms of in-sample fit and out-of-sample predictive accuracy.
Biogeography-Based Optimization and Support Vector Regression for Freeway Travel Time Prediction and Feature Selection
Identification of important predictors reduces dimensions of input data, which not only lessens computational load, but also provides better understanding of underlying relationship between important predictors and travel time.
Fast Bayesian Estimation of Spatial Count Data Models
Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments.
A New Spatial Count Data Model with Bayesian Additive Regression Trees for Accident Hot Spot Identification
Typically, these methods assume simple linear link function specifications, which, however, limit the predictive power of a model.
Applications
Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual Heterogeneity
Besides, the simulation study demonstrates that a parallelised implementation of the MSL estimator with analytical gradients is a viable alternative to MCMC in terms of both estimation accuracy and computational efficiency, as the MSL estimator is observed to be between 0. 9 and 2. 1 times faster than MCMC.
Methodology Econometrics
Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models
The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for logit kernel.
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
To address the latter, we conduct an extensive simulation-based evaluation to benchmark the extended VB methods against MCMC and MSLE in terms of estimation times, parameter recovery and predictive accuracy.
