Lead Presenter Biography

Sijia is a modeler with WSP’s Travel Demand Modeling Group. She has 7 years of work experience in software engineering, activity-based travel model development and implementation, network creation and modeling, and travel forecasting. Sijia has implemented ABMs for many large metropolitan regions.

Co-Authors

Presentation Description

A key shortcoming of telecommuting representations in implemented activity-based travel models (ABMs) is that the formulations do not simulate workers working at home. Both ActivitySim and the initial CT-RAMP2 platforms include a telecommute frequency model that then informs the daily activity pattern model (i.e., mandatory, non-mandatory, or stay-at-home pattern). Workers with a higher telecommute frequency or working from home exclusively are less likely to generate a work tour. This assumption has several shortcomings, including:
● It misstates the “rebound” effect of telecommuting, as telecommuters’ non-mandatory activities are not constrained in the simulation by the need to work while at home. Because telecommuters are not working, the results are not realistic, which calls into question the model’s validity.
● It does not facilitate the analysis of the behavior of telecommuters vs. non-telecommuters because it does not explicitly identify telecommuters in the simulation (i.e., the models are silent as to why a worker is not working on the simulation day).

A step towards improving the representation of telecommuting is the subject of this paper. Specifically, the CT-RAMP2 model implemented for large MPOs in Ohio was modified, calibrated, and tested with the following key modifications:
● Telecommuters are explicitly represented in the simulation via a simple choice model that acts on the existing telecommute frequency model, allowing for their analysis.
● Workers working exclusively at home are just as likely to have a mandatory activity pattern (rather than a non-mandatory or at-home pattern) as workers working exclusively outside the home.
● Telecommuters have a mandatory activity pattern and therefore engage in a mandatory tour. The destination for the mandatory tour is the home location.
● Telecommuters are allowed to make stops on the mandatory tours to their at-home workplaces, which allows escorting and grocery shopping to occur at the beginning or end of the workday. This allows us to leverage the activity-scheduling intelligence of CT-RAMP2.
● In CT-RAMP2’s combinatorial mode choice model, telecommuting movements from home to the home-based workplace are tagged as a cost-free telecommuting mode and not assigned to the transportation network. This allows us to leverage the mode choice intelligence of CT-RAMP2 while representing telecommuting accurately.
● A calibration constant in the choice models is exposed to the user allowing easy adjustment of the telecommuting shares in response to dynamic post-pandemic conditions and future scenario testing. These shares are sensitive to the worker’s industry. (i.e., retail workers are much less likely to telecommute.)

The approach described above is a second-best solution. A first best solution would be a broader move to a truer “activity-based” formulation than those currently used in practice. Such an approach would first create a work activity and then locate it (either at home or at the usual workplace). The approach discussed here was simpler but was implemented quickly and responded to Ohio DOT agency needs motivated by the COVID pandemic. This approach is, to the authors’ knowledge, the first time a practical travel model includes an explicit representation of telecommuters engaged in working while at home. 

Presentation File