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1 Planners and modelers are concerned with long-range forecasting of travel and related consequences in urban regions. Forecasting the impacts of connected and automated vehicles (CAVs) on transportation systems is challenging. Automated technologies in vehicles, efficient communi- cations between vehicles and infrastructure, and a market shift toward economical and flexible shared mobility fleets will transform the current landscape of personal mobility and goods movement. The difficulty of predicting the exact timing, magnitude, type, and locations of the dis- ruptive changes that will result from CAVs will pose new risk for infra- structure investment decisions. Change may occur quickly for certain modes, while for other modes it may take decades to realize the impacts and obtain market stability. For instance, transit systems may soon be affected as shared rides and comprehensive mobility-as-a-service (MaaS) platforms grow; however, impacts on parking and land use changes may take many years. Given that long-range forecasts are made for 20 to 30 years into the future, planners can expect that this system of technologies will, by that future time, have a significant impact on the transportation system and travel choices. This stand-alone executive summary introduces the information presented in NCHRP Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles. That report presents guidance related to updates to model- ing and forecasting tools used by planners and modelers in state departments of transportation (DOTs) and regional metropolitan planning organizations (MPOs). Planners and modelers will find this guidance necessary to more appropriately account for the impacts of CAVs on trans- portation supply, road capacity, and travel demand components. The authors acknowledge that considerations in the CAV space are changing rapidly and that this report may need updating in the next 3 to 5 years. CAVs Are Changing the Traditional Travel Forecasting Paradigm In the strict sense, modeling is a mathematical representation of data by using formulaic expressions. Models designed to both predict and test future scenarios can only be as accurate as the formulas calibrated to match observed data and the forecast independent variables, such as the future number of households. In addition to data, however, the model designâthe struc- ture that defines the independent and dependent variables and the process by which the mobility environment is simulatedâis also critical to the effectiveness of forecasts in providing valuable information to decision makers. Forecasting Travel Behavior in the Context of Connected and Automated Vehicles Disruption is upon us. As a planner or modeler, how should you respond? This executive summary and its source, NCHRP Research Report 896, Volume 2: Guidance, pro- vide information about how state departments of transportation and metropolitan planning organizations can begin accounting for CAVs in planning and modeling activities.
2 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles In the past several decades, travel forecasting models have been predicated on the assumption that past trends in travel behavior and choices will continue two or three decades into the future with only minor alterations. This paradigm of trans- portation modeling has been effective because of relative stabil- ity being observed in travel behavior over time. Two modes of personal transportationâprivate auto (or walking/biking) and public transportation [bus or rail, and, to some limited extent, taxis and transportation network companies (TNCs)]âhave remained the primary mobility choices. Models can be cali- brated to observed choices reflected in surveyed data, and growth can be applied. This process results in a rational fore- cast level of demand calibrated to observed data and validated by existing usage levels. The forecast level of demand is then compared with system supply represented by facility capacity to determine future system performance. The process of calibrat- ing models to observed data, validating the modeling outcomes to existing (or rather, immediate past) conditions, and apply- ing future growth to produce one potential future outcome has been a long-standing paradigm in the United States. Recent mobility and technology innovations are prompting a change to the existing fore- cast modeling paradigm. CAVs and shared autonomous vehicles (SAVs) could change mobility significantly. Existing models do not have CAV or SAV modes, nor do they reflect behavioral impacts of deployment of CAVs and SAVs. Critical Considerations for Planning and Modeling Expected areas of impact of CAVs on travel behavior are critical considerations for planners and modelers. These areas of impact can be categorized as follows: â¢ Transportation cost impacts. Transportation cost is a very uncertain impact area. The eco- nomics of CAV technology imply that vehicle cost will certainly rise, but the cost per mile may decrease. More specifically, costs of vehicles that include highly automated technology will need to be recouped by manufac- turers, so the cost per vehicle is likely to increase. However, the cost per trip may decline if fleet services of driverless vehicles prevail in the market. Thus, the overall transportation cost to the consumer is uncertain and is most likely tied to vehicle ownership versus distributed vehicle ownership, vehicle club membership, or ride sharing. â¢ Transportation safety impacts. A reduction in crashes would improve the reliability of travel times and reduce property damage, injuries, and fatalities. Improved reliabil- ity would increase the utility of CAVs, growing their market share. â¢ Vehicle operations impacts. Much research has focused on the impact of connecting vehicles through dedicated short- range communications (DSRC) or wireless technology into platoons of vehicles, which would dramatically shorten head- way space and thereby improve coordinated acceleration and Zapp2Photo/Shutterstock.com jamesteohart/Shutterstock.com
Forecasting Travel Behavior in the Context of Connected and Automated Vehicles 3 vehicle throughput. The overall impact would be to increase capacity, with most esti- mates arriving at a doubling of existing roadway capacities. However, because platooning requires increased space, the prospect of increased capacity where formation and dissolu- tion of platoons is frequent may be diminished. In the longer term, the prospects of positive impacts from vehicle operations of an automated fleet are expected to be impressive. While groups of platooned vehicles may or may not improve intersection and freeway operations, the coordination of flow through the concept of synchronized arrivals and reserved time and space may indeed prove to reduce queuing and congestion. â¢ Electrification (fuel) impacts. If SAVs are used, the user would not need to be concerned with refueling the vehicle because the fleet owner/operator would use optimizing algorithms to reduce cost. This would have an enormous impact on the petroleum industry, but also on fuel delivery services, gas station land use, and the need for strategically located electric refueling stations. â¢ Personal mobility and convenience impacts. If SAVs prevail in the marketplace as the U.S. population ages, the prospect of older adults, young teens, and mobility-limited persons gaining increased transportation freedom could greatly improve. However, these individu- als could also benefit in a scenario of privately owned CAVs if the cost of the vehicles is not prohibitive. The impact on the (dis)utility and (in)convenience of travel timeâa key com- ponent of the value of travel timeâfrom CAV technology is also somewhat uncertain. A key modeling issue is how the perception of the cost of travel time will change if more drivers become passengers in SAVs and if the capital cost of vehicle ownership is replaced by paying capital, maintenance, and operations costs on a per-trip basis. AVs encompass a range of automated technologies, from relatively simple driver assistance systems to fully autonomous or self-driving vehicles. CVs have internal devices that connect to other vehicles, other road users, or back-end infrastructure.