An estimation of total vehicle travel reduction in the case of telecommuting. Detailed analyses using an activity-based modeling approach.

Transportation Demand Management (TDM) is often referred to as a management strategy adopted by transport planners with the overall and wider goal to increase transport system efficiency. One of the possible measures that can be adopted in TDM is the implementation of telecommuting. A significant number of studies have been conducted in the past to evaluate the effect of telecommuting on peak-period trips. However it is less studied in literature whether teleworking also effectively and significantly reduces total vehicle travel. Indeed, travel reductions may be partly offset by additional vehicle trips conducted during the telecommuting time period, which normally would have been made during normal commuting as part of a trip chain. For this reason, a conventional modeling approach (approach 1) that has been taken from literature was adopted in this paper to calculate total kilometers of travel saved in the case telecommuting (assuming different shares of telecommuters) would materialize. The model is adapted and calculated for the specifics of the Flanders area. Second, the paper also introduces the use of an activity-based modeling approach (approach 2) to evaluate the effect of telecommuting. The real-life representation of Flanders is also embedded in this activity-based simulation model trough the existence of over six million agents; each agent representing one member of the Flemish population. Because of this characteristic, a similar share of telecommuters can be simulated as in approach 1, enabling for a detailed comparison in terms of total kilometres of travel saved using the activity-based model. However, the potential power of the activity-based model is much wider, since it will be possible to predict which activities are conducted where, when, for how long, with whom, and the transport mode involved. Furthermore, the detailed prediction of every activity-travel schedule of every agent allows for more detailed explanations of travel offsets that can be calculated through the availability of complex statistics in the model. Examples of statistics that will be shown in the paper are the distribution of non-work activities conducted during teleworking, number of tours and number of trip chains. Results show that the activity-based model achieves similar results as the conventional modelling approach but enables a far more detailed calculation of statistics, and thus a potentially much richer framework for interpretation and explanation of results. The innovation achieved in this paper is multiple. First of all, it is the first time a fully operational activity-based model is really applied in practice for the Flanders area through the implementation of a concrete TDM scenario. Second, and in extension to the first contribution, it is -to the best of the authors’ knowledge- also the first implementation of an activity- based model for telecommuting. Finally, we also believe it is unique in this paper that an operational activity-based framework is externally validated by means of another completely different model, both calibrated for the same application and application area.