Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model
Nima Golshani, Ramin Shabanpour, Seyed Mehdi Mahmoudifard, et al.
Travel Behaviour and Society (2017) Vol. 10, pp. 21-32
Closed Access | Times Cited: 93

Showing 1-25 of 93 citing articles:

Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis
Amir Bahador Parsa, Ali Movahedi, Homa Taghipour, et al.
Accident Analysis & Prevention (2019) Vol. 136, pp. 105405-105405
Open Access | Times Cited: 704
Applying a random forest method approach to model travel mode choice behavior
Long Cheng, Xuewu Chen, Jonas De Vos, et al.
Travel Behaviour and Society (2018) Vol. 14, pp. 1-10
Closed Access | Times Cited: 353
Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models
Xilei Zhao, Xiang Yan, Alan S.L. Yu, et al.
Travel Behaviour and Society (2020) Vol. 20, pp. 22-35
Closed Access | Times Cited: 279
Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling
Dongwoo Lee, Sybil Derrible, Francisco C. Pereira
Transportation Research Record Journal of the Transportation Research Board (2018) Vol. 2672, Iss. 49, pp. 101-112
Open Access | Times Cited: 136
Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups
Jaehyun Ha, Sugie Lee, Joonho Ko
Transportation Research Part A Policy and Practice (2020) Vol. 141, pp. 147-166
Closed Access | Times Cited: 106
A systematic review of machine learning classification methodologies for modelling passenger mode choice
Tim Hillel, Michel Bierlaire, Mohammed Elshafie, et al.
Journal of Choice Modelling (2020) Vol. 38, pp. 100221-100221
Open Access | Times Cited: 101
Machine learning applications in activity-travel behaviour research: a review
Anil Koushik, M. Manoj, N. Nezamuddin
Transport Reviews (2020) Vol. 40, Iss. 3, pp. 288-311
Closed Access | Times Cited: 99
Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships
Yiming Xu, Xiang Yan, Xinyu Liu, et al.
Transportation Research Part A Policy and Practice (2021) Vol. 144, pp. 170-188
Closed Access | Times Cited: 96
Enhancing discrete choice models with representation learning
Brian Sifringer, Virginie Lurkin, Alexandre Alahi
Transportation Research Part B Methodological (2020) Vol. 140, pp. 236-261
Open Access | Times Cited: 91
The overreliance on statistical goodness-of-fit and under-reliance on model validation in discrete choice models: A review of validation practices in the transportation academic literature
Giancarlos Parady, David T Ory, Joan L. Walker
Journal of Choice Modelling (2020) Vol. 38, pp. 100257-100257
Open Access | Times Cited: 82
Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting
Ammar Mohamed Aamer, Luh Putu Eka Yani, I Made Alan Priyatna
Operations and Supply Chain Management An International Journal (2020), pp. 1-13
Open Access | Times Cited: 75
Evaluation of feature selection methods based on artificial neural network weights
Nattane Luíza da Costa, Márcio Dias de Lima, Rommel Barbosa
Expert Systems with Applications (2020) Vol. 168, pp. 114312-114312
Closed Access | Times Cited: 72
Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning
Manu S. Pillai, Gopal Chaudhary, Manju Khari, et al.
Soft Computing (2021) Vol. 25, Iss. 18, pp. 11929-11940
Closed Access | Times Cited: 69
Spatial-temporal correlation-based analysis of multi-source flood coincidence risks: A case study of the middle and lower Yangtze River basin
Guolin Zhao, Yang Xiao, Xu Chen, et al.
Journal of Hydrology Regional Studies (2025) Vol. 58, pp. 102265-102265
Closed Access | Times Cited: 1
A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service
Taehooie Kim, Shivam Sharda, Xuesong Zhou, et al.
Transportation Research Part C Emerging Technologies (2020) Vol. 120, pp. 102786-102786
Closed Access | Times Cited: 63
Predicting Residential Water Demand with Machine-Based Statistical Learning
Dongwoo Lee, Sybil Derrible
Journal of Water Resources Planning and Management (2019) Vol. 146, Iss. 1
Closed Access | Times Cited: 59
A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability
Yafei Han, Francisco C. Pereira, Moshe Ben‐Akiva, et al.
Transportation Research Part B Methodological (2022) Vol. 163, pp. 166-186
Closed Access | Times Cited: 32
Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
Ningzhe Xu, Qifan Nie, Jun Liu, et al.
Travel Behaviour and Society (2023) Vol. 32, pp. 100584-100584
Open Access | Times Cited: 17
Statistical modelling of travel mode choice of public university students in Freetown, Sierra Leone: the case of three campuses
Simeon Stevenson Turay, Augustus Ababio-Donkor, Charles A. Adams, et al.
Urban Planning and Transport Research (2024) Vol. 12, Iss. 1
Open Access | Times Cited: 7
Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint
Qi Qi, Zhuang Wang
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 17, pp. 25114-25128
Closed Access | Times Cited: 6
Trip distribution modeling with Twitter data
Nastaran Pourebrahim, Selima Sultana, Amirreza Niakanlahiji, et al.
Computers Environment and Urban Systems (2019) Vol. 77, pp. 101354-101354
Closed Access | Times Cited: 49
Attitudes on Autonomous Vehicle Adoption using Interpretable Gradient Boosting Machine
Dongwoo Lee, John Mulrow, Chana Joanne Haboucha, et al.
Transportation Research Record Journal of the Transportation Research Board (2019) Vol. 2673, Iss. 11, pp. 865-878
Closed Access | Times Cited: 49
Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory
Kun Gao, Ying Yang, Tianshu Zhang, et al.
Knowledge-Based Systems (2021) Vol. 218, pp. 106882-106882
Closed Access | Times Cited: 34
Can continued anti-epidemic measures help post-COVID-19 public transport recovery? Evidence from Taiwan
Hsu-Sheng Hsieh, Hao-Ching Hsia
Journal of Transport & Health (2022) Vol. 26, pp. 101392-101392
Open Access | Times Cited: 25
Estimating intercity heavy truck mobility flows using the deep gravity framework
Yitao Yang, Bin Jia, Xiao-Yong Yan, et al.
Transportation Research Part E Logistics and Transportation Review (2023) Vol. 179, pp. 103320-103320
Open Access | Times Cited: 13
Page 1 - Next Page

Scroll to top