Acharath Mohanakrishnan, A., & Sattler, M. L., & Chen, V. (2025, March), Development and application of TeEMs (Telecommuting Expectation Models): Predicting post-pandemic Telecommuting Choice and Frequency using Machine learning models  Paper presented at 2025 ASEE -GSW Annual Conference, Arlington, TX, Texas. 10.18260/1-2--55041

\bibitem{asee_peer_55041}
  Acharath Mohanakrishnan, A., \& Sattler, M. L., \& Chen, V. (2025, March), \emph{Development and application of TeEMs (Telecommuting Expectation Models): Predicting post-pandemic Telecommuting Choice and Frequency using Machine learning models}
  Paper presented at 2025 ASEE -GSW Annual Conference, Arlington, TX, Texas.
  10.18260/1-2--55041

Aiswarya Acharath Mohanakrishnan, Melanie L. Sattler P.E., and Victoria Chen.  "Development and application of TeEMs (Telecommuting Expectation Models): Predicting post-pandemic Telecommuting Choice and Frequency using Machine learning models".  2025 ASEE -GSW Annual Conference, Arlington, TX, Texas, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/55041 Internet.  20 Jul, 2025

\bibitem{asee_peer_55041}
  Aiswarya Acharath Mohanakrishnan, Melanie L. Sattler P.E., and Victoria Chen.
  "Development and application of TeEMs (Telecommuting Expectation Models): Predicting post-pandemic Telecommuting Choice and Frequency using Machine learning models".
  \emph{2025 ASEE -GSW Annual Conference, Arlington, TX, Texas, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/55041 Internet.  20 Jul, 2025

@INPROCEEDINGS{asee_peer_55041
author = "Aiswarya Acharath Mohanakrishnan, Melanie L. Sattler P.E., and Victoria Chen"
title = "Development and application of TeEMs (Telecommuting Expectation Models): Predicting post-pandemic Telecommuting Choice and Frequency using Machine learning models"
booktitle = "2025 ASEE -GSW Annual Conference"
year = "2025"
month = "March"
address = "Arlington, TX, Texas"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/55041}
number = {10.18260/1-2--55041}
}

TY  - CPAPER
AB  - Telecommuting, increasingly prevalent in today's workforce, has altered the travel patterns of workers, necessitating adjustments in travel demand modeling to optimize transportation planning and service provision. This study uses supervised machine learning algorithms to predict the choice and frequency of an individual adopting telecommuting, based on post pandemic data from “COVID-19 and the Future Survey” (a joint project of Arizona State University and the University of Illinois Chicago with support from the National Science Foundation). The models will offer valuable insights for traffic management agencies, enabling them to make informed decisions regarding traffic flow and congestion management. Choice Model, a binary classification model, classifies a worker into commuter or telecommuter, and Frequency Model, which is a multiclass classification model, classifies the telecommuters into 5 categories (1 day, 2 days, 3 days, 4 days and 5 days) based on frequency of telecommute adoption in a week. To make predictions, both models use socio-demographic attributes that are nationally available like age, gender, education, household income, household size, No. of vehicles, children and employed adults in household, employment category, commute time and commute distance. Machine Learning algorithms such as Decision trees, Random Forests, Support Vector Machines (SVM), Logistic Regression, Gradient Boosting, AdaBoost and XGBoost algorithms were trained to find the best model. The AdaBoost model showed the best performance among the algorithms used to predict an individual’s choice of telecommute adoption.
The TeEMs Choice Model was demonstrated by a case study in the Dallas Fort Worth Metroplex using the 2017 National Household Travel Survey (NHTS) data. The findings indicate a notable rise in telecommute adoption within the Metroplex, increasing from 13.5% during pre-pandemic time to 34% in the post pandemic. The scenario analysis predicted a 20.5% change in home-based work trips due to telecommuting in the Metroplex. A Transportation Impact Scenario Analysis was also conducted to examine telecommuting's effects on transportation, particularly changes in vehicle miles traveled (VMT) and Vehicle Hours Travelled (VHT), accounting for rebound effects pre- and post-pandemic. Scenario 1 evaluated a 15% reduction in HBWT and 9.3% rebound trips, resulting in reduction of 6 million VMT/day (2.65%). Scenario 2, 3, 4 and 5 evaluated 20% trip reduction due to telecommuting and various rebounds ranging from 10.9 to 13.4%. On average, a reduction of 8.3 million VMT per day (3.6%) was observed in these scenarios. The final scenario evaluated 35% telecommute adoption, which was very close to 34% which was the telecommute rate in DFW according to the TeEMs demo. Even with 20.5% rebound trips, VMT decreased by 15.9 million per day (6.9%). The Vehicle Hour Travelled (VHT) also showed a trend similar to VMT. Scenario 1 showed a reduction of 0.38 million VHT per day (5.65%) from pre-covid s. Scenarios 2, 3, 4, and 5 exhibited an average reduction of 7.5%, equivalent to approximately 0.5 million VHT per day. Meanwhile, Scenario 6 showed a total reduction of 0.9 million VHT per day (13.4%). The findings suggest that telecommuting has the potential to reduce VMT and VHT. The demonstration of TeEMs Choice Model for DFW suggests that telecommuters have more than doubled post-pandemic, and with the growing population influx to the Metroplex, promoting telecommuting could serve as a valuable strategy to alleviate congestion.

AU  - Aiswarya Acharath Mohanakrishnan
AU  - Melanie L. Sattler P.E.
AU  - Victoria Chen
CY  - Arlington, TX, Texas
DA  - 2025/03/09
PB  - ASEE Conferences
TI  - Development and application of TeEMs (Telecommuting Expectation Models): Predicting post-pandemic Telecommuting Choice and Frequency using Machine learning models
UR  - https://peer.asee.org/55041
DO  - 10.18260/1-2--55041
ER  -