An artificial neural network based method to uncover the value-of-travel-time distribution
出版年份 2020 全文链接
标题
An artificial neural network based method to uncover the value-of-travel-time distribution
作者
关键词
-
出版物
TRANSPORTATION
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2020-09-24
DOI
10.1007/s11116-020-10139-3
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Deep neural networks for choice analysis: Architecture design with alternative-specific utility functions
- (2020) Shenhao Wang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Enhancing discrete choice models with representation learning
- (2020) Brian Sifringer et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling
- (2018) Dongwoo Lee et al. TRANSPORTATION RESEARCH RECORD
- An artificial neural network based approach to investigate travellers’ decision rules
- (2018) Sander van Cranenburgh et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Parameter Identifiability in Statistical Machine Learning: A Review
- (2017) Zhi-Yong Ran et al. NEURAL COMPUTATION
- A framework for capturing heterogeneity, heteroskedasticity, non-linearity, reference dependence and design artefacts in value of time research
- (2017) Stephane Hess et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Can we open the black box of AI?
- (2016) Davide Castelvecchi NATURE
- Neural networks: An overview of early research, current frameworks and new challenges
- (2016) Alberto Prieto et al. NEUROCOMPUTING
- Understanding valuation of travel time changes: are preferences different under different stated choice design settings?
- (2016) Manuel Ojeda-Cabral et al. TRANSPORTATION
- Values of travel time in Europe: Review and meta-analysis
- (2016) Mark Wardman et al. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
- The promises of big data and small data for travel behavior (aka human mobility) analysis
- (2016) Cynthia Chen et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- The value of travel time: random utility versus random valuation
- (2016) Manuel Ojeda-Cabral et al. Transportmetrica A-Transport Science
- Commuters’ preferences for fast and reliable travel: A semi-parametric estimation approach
- (2015) Paul R. Koster et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Prediction of Individual Travel Mode with Evidential Neural Network Model
- (2014) Hichem Omrani et al. TRANSPORTATION RESEARCH RECORD
- Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios
- (2013) Francisco C. Pereira et al. Journal of Intelligent Transportation Systems
- Experiences from the Swedish Value of Time study
- (2013) Maria Börjesson et al. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
- Catching the tail: Empirical identification of the distribution of the value of travel time
- (2011) Maria Börjesson et al. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
- Meta-analysis of UK values of travel time: An update
- (2010) Pedro A.L. Abrantes et al. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
- Statistical methods versus neural networks in transportation research: Differences, similarities and some insights
- (2010) M.G. Karlaftis et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Non-trading, lexicographic and inconsistent behaviour in stated choice data
- (2010) Stephane Hess et al. TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
- The information content of a stated choice experiment: A new method and its application to the value of a statistical life
- (2009) Jan Rouwendal et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Discrete choice models with multiplicative error terms
- (2008) M. Fosgerau et al. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
- Neural networks and statistical techniques: A review of applications
- (2007) Mukta Paliwal et al. EXPERT SYSTEMS WITH APPLICATIONS
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