Abstract
The increasing effect of Transport Network Companies (TNCs) in major US cities, allied to the expectation that connected and autonomous vehicles (CAVs) will become the prevailing type of automobile on the streets in the coming decades, requires such a trend to be reflected in our forecasting models. Yet, most of the efforts undertaken in the United States and elsewhere are largely focused on understanding the demand for such types of transportation and have left aside the analysis of crucial differences in envisaged systems, such as pooled versus single-occupancy vehicles, use of CAVs as access modes to mass transit, and the cost corresponding to different levels of service in citywide systems. In this paper we introduce a new algorithm for modeling pooled CAVs and a framework for integrating this into traditional forecasting models. We also present the preliminary results of an application of the proposed methodology to the metropolitan region of Vancouver, British Columbia. The results are promising, although a few implementation choices made for this study have resulted in poor computational performance.
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