On the public citation graph

Utilising Post-Hoc Methods for the Interpretation of Neural Networks in Choice Analysis

0 signed · 35 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-981-15-0802-8_189openalex-extracted

A Possibility of AI Application on Mode-choice Prediction of Transport Users in Hanoi

inferredcites →https://doi.org/10.1016/j.eswa.2017.01.057openalex-extracted

A comparative study of machine learning classifiers for modeling travel mode choice

inferredcites →https://doi.org/10.1016/j.future.2021.07.012openalex-extracted

STGNN-TTE: Travel time estimation via spatial–temporal graph neural network

inferredcites →https://doi.org/10.1016/j.habitatint.2012.06.002openalex-extracted

Cross-nested logit model for the joint choice of residential location, travel mode, and departure time

inferredcites →https://doi.org/10.1016/j.inffus.2019.12.012openalex-extracted

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

inferredcites →https://doi.org/10.1016/j.ins.2012.10.039openalex-extracted

Using sensitivity analysis and visualization techniques to open black box data mining models

inferredcites →https://doi.org/10.1016/j.jairtraman.2016.04.020openalex-extracted

A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route

inferredcites →https://doi.org/10.1016/j.tbs.2017.09.003openalex-extracted

Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model

inferredcites →https://doi.org/10.1016/j.tbs.2022.07.003openalex-extracted

Predicting the travel mode choice with interpretable machine learning techniques: A comparative study

inferredcites →https://doi.org/10.1016/j.tbs.2022.08.011openalex-extracted

A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions

inferredcites →https://doi.org/10.1016/j.tbs.2023.100625openalex-extracted

Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach

inferredcites →https://doi.org/10.1016/j.trb.2020.08.006openalex-extracted

Enhancing discrete choice models with representation learning

inferredcites →https://doi.org/10.1016/j.trb.2022.05.007openalex-extracted

Spatially correlated nested logit model for spatial location choice

inferredcites →https://doi.org/10.1016/j.trb.2022.07.001openalex-extracted

A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability

inferredcites →https://doi.org/10.1016/j.trc.2018.11.014openalex-extracted

An artificial neural network based approach to investigate travellers’ decision rules

inferredcites →https://doi.org/10.1016/j.trc.2020.01.012openalex-extracted

Deep neural networks for choice analysis: Architecture design with alternative-specific utility functions

inferredcites →https://doi.org/10.1016/j.trc.2020.102701openalex-extracted

Deep neural networks for choice analysis: Extracting complete economic information for interpretation

inferredcites →https://doi.org/10.1016/j.trc.2021.103050openalex-extracted

ResLogit: A residual neural network logit model for data-driven choice modelling

inferredcites →https://doi.org/10.1016/j.trc.2021.103143openalex-extracted

Why did you predict that? Towards explainable artificial neural networks for travel demand analysis

inferredcites →https://doi.org/10.1016/j.trc.2023.104318openalex-extracted

A prediction and behavioural analysis of machine learning methods for modelling travel mode choice

inferredcites →https://doi.org/10.1016/j.trpro.2022.02.047openalex-extracted

A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling

inferredcites →https://doi.org/10.1080/13658816.2019.1652303openalex-extracted

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

inferredcites →https://doi.org/10.1080/23311916.2021.2010510openalex-extracted

Traffic flow prediction models – A review of deep learning techniques

inferredcites →https://doi.org/10.1145/2939672.2939778openalex-extracted

"Why Should I Trust You?"

inferredcites →https://doi.org/10.1155/2020/5364252openalex-extracted

A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information

inferredcites →https://doi.org/10.1155/2021/9219176openalex-extracted

Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks

inferredcites →https://doi.org/10.23919/irs54158.2022.9904989openalex-extracted

Feature Relevance Evaluation using Grad-CAM, LIME and SHAP for Deep Learning SAR Data Classification

inferredcites →https://doi.org/10.3929/ethz-a-004238511openalex-extracted

Acceptance of modal innovation: the case of the SwissMetro

inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-extracted

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-extracted

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1703.01365openalex-extracted

Axiomatic Attribution for Deep Networks

inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-extracted

A Unified Approach to Interpreting Model Predictions

inferredcites →https://doi.org/10.48550/arxiv.2202.11912openalex-extracted

A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions

inferredcites →https://doi.org/10.1145/3233231openalex-snapshot

The Mythos of Model Interpretability

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.5101184openalex-snapshot

H-Rum-Nn: A Hybrid Neural Network Model Compatible with the Random Utility Maximisation

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.