Abstract
The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.
Highlights
Transportation demand modeling has advanced in many ways in recent years, including activity-based modeling and simulation modeling [1]
Recent work completed by the University of Texas Center for Transportation Research presents modifications to a four-step model in order to accommodate two new features: ride-hailing services and autonomous vehicles (AV) [2]. e main changes developed in this work involved splitting households into two groups—AV households and non-AV households—setting potentially different trip generation rates for the new AV households; allowing for AV households to engage in longer trips due to a reduction of value of travel time; the inclusion of ride-hailing as new mode choice stage; and the use of different values of passengercar-equivalent for AV trips to represent the gains in capacities brought by the use of AVs
When Connected and autonomous vehicles (CAVs) effectively increase the capacity of freeways and arterials, traffic can reroute to rebalance densities
Summary
Received 23 February 2020; Revised 9 July 2020; Accepted 28 July 2020; Published 14 August 2020. Erefore, very little is known about their potential impacts on traffic. Researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. Such models do not yet integrate any expected impacts from CAV deployments. The authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. There are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit
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