Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
I-32 The conventional planning response to the uncertainties introduced by new technologies is to: â¢ Update the models used to forecast future land use and travel behavior, and â¢ Use the updated models to test various potential futures (scenario planning). In essence, agencies continue to plan as before, but now consider a host of new potential eventualities, providing decision makers with tables of potential and possible results. Two reports illustrate how land use and travel forecasting models can be updated to reflect the new technologies and how they can be employed in the planning process to explain potential future impacts of new technologies: â¢ NCHRP Research Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance is a compre- hensive guide to how different land use and travel modeling systems should be updated and employed to evaluate connected, automated, and fully autonomous vehicles (CAVs). The authors focus on CAVs, but much of what they recommend can be extrapolated to other technologies. The NCHRP report focuses on the approaches to updating models without identifying specific values for parameters (Zmud et al. 2018). â¢ âGuidance for Assessing Planning Impacts and Opportunities of Automated, Connected, Electric and Shared-Use Vehicles,â provided to MPOs by the Florida DOT, suggests specific values for model parameters and identifies specific future scenarios to be tested (Florida DOT 2018). The Florida DOT guidance focuses on CAVs, but its recommendations can be extrapolated to other technologies. 6.1 NCHRP Research Report 896âUpdating Forecasting Models NCHRP Research Report 896 states that CAVs are likely to affect travel demand modeling through lower travel costs, increased safety (travel-time reliability), increased highway capacity, and reduced impedances for trip making. The report points out the challenge to modeling posed by the uncertainty of the timeline for adoption of CAVs. The report also provides lists of travel model parameters that should be considered for modification under each modeling system (trip-based, activity-based, and strategic models), and it proposes various ways agencies may employ the models to address uncertainty. Three approaches merit brief descriptions: â¢ Assumption-based scenario planning, which bundles the assumptions into a variety of scenarios, is the simplest approach. The agency puts together plausible combinations of assumptions and runs the analysis on each scenario. The results for the various scenarios are reported without assigning a specific probability to each scenario. C H A P T E R 6 Improve Planning Tools and Processes
Improve Planning Tools and Processes I-33 â¢ More complex approaches, such as ârobust decision making,â stress test the proposed infrastructure improvements under a variety of potential future scenarios. â¢ The dynamic adaptive pathways planning (DAPP) approach recognizes that agencies can make course corrections as the future unfolds. More details can be found in the report. The greatest challenge identified by NCHRP Research Report 896 is communicating the uncer- tainty to decision makers. The report stresses the importance of using the right language to communicate the results. 6.2 Florida DOT Guidance to MPOs In its guidance to MPOs, the Florida DOT (2018) recommends that they take the follow- ing steps: â¢ Adopt performance measures consistent with agency goals for measuring the impacts of new technologies. â¢ Use scenario planning to forecast the impacts of fully autonomous CAVs (including EVs) on the highway system and infrastructure needs. Six scenarios are identified for testing: â Slow roll, minimal plausible change; â Managed autonomous lane network; â Ultimate driver assist, ultra-connectivity; â Niche service growth (high AV/CV in certain cases); â Competing fleets (automated fleets compete); and â RoboTransit (automated MaaS). â¢ Modify the travel demand model inputs according to each scenario. Modifications might include the following: â Increase capacities by area type and facility type; â Decrease terminal times for central business districts and fringe areas; â Increase auto trip generation as appropriate; â Manually shift some transit trips to AV mode; or â Flatten the home-based work friction factor curves to promote longer commute trips. The guidebook notes the following (Florida DOT 2018): It still may not yet be possible to determine the impact of ACES (automated, autonomous, connected, electric, shared vehicles) in terms of setting and measuring specific performance measures with any degree of certainty for the foreseeable future. But all that means, as FHWA noted in Planning for Connected and Automated Vehicles, is that performance-based planning in an age of ACES requires implementing projects based on estimated outcomes coupled with repetitively and regularly evaluating results as new data and data sources come online that are pertinent to ACES performance measures. This implies a de-emphasis of precise predictions and suggests that ACES metrics might be best thought of, at least initially, as âaction brackets.â