Laserfiche WebLink
Page 208CITY OF SUNNY ISLES BEACH • RFQ #26-07-01 • CONTINUING PROFESSIONAL <br />ARCHITECTURAL AND ENGINEERING CONSULTING SERVICES (CCNA) <br />TAB 4 | APPROACH/METHODOLOGY <br />| TRANSPORTATION/TRANSIT PLANNING <br />The transportation systems in Miami-Dade County and across the nation are becoming increasingly complex, with transformations driven by automated, connected, electric, and shared-use vehicles, as well as other emerging technologies. Our team’s expertise means we are well-equipped to assist the City in evaluating and developing new mobility options, including shared-use, micro-transit, micro-mobility, automated transit, mobility-on-demand, and transportation network companies. These analyses, as we have found through our work with the Miami-Dade TPO, and with local entities such as North Miami Beach, where our Comprehensive Operational Analysis’s recommendations resulted in a change in annual ridership from 17,000 to an estimated 287,000 in three years, require an understanding of available infrastructure, policy, and financial options that are critical to selecting the correct technology to deploy. We can provide options for integrating short- and long-term planning to address future challenges and opportunities better. Corradino understands the FDOT ACES guidance and its application in the Long-Range Planning processes. <br />Corradino recently developed the Impact of Future Technology Document for the 2045 Miami-Dade Long-Range Transportation Plan and is incorporating ACES considerations into the 2045 Gainesville Long-Range Transportation Plan. This work included thorough research into all emerging technologies and their potential impacts on transportation systems, which can <br />assist the City in developing future technology solutions.Our modeling team, at the forefront of technological advancements in modeling, has developed connected/automated vehicle modeling guidelines for FDOT and MPOs/TPOs through our work for the Florida Model Task Force. We understand that it is key to understanding the market penetration rates of these emerging technologies and making necessary adjustments in travel forecasting processes. Adjustments to each step of the model are necessary, including the Trip Generation-to-model assignment. Depending on the type of technology implemented, fully automated vehicles will be able to self-park and provide on-demand pickup for users. These options will tend to increase vehicle miles traveled (VMT). In addition, this may reduce households’ need to own multiple vehicles, thereby lowering auto ownership and, in turn, VMT. Another tradeoff would be the increased use of automated transit and transportation network companies. There is a potential for increased mode split towards these new technological modes. Corradino’s modelers, Srin Varanasi and Ken Kaltenbach, have extensive experience with the Florida Standard Urban Model Structure (FSUTMS) and its applicability. <br />The COVID-19 pandemic accelerated long-term changes in travel behavior that continue to influence transportation planning today. Remote and hybrid work arrangements remain an important consideration in both short- and long-range planning efforts. At the same time, many local governments have expanded the use of virtual meetings and online engagement tools better to accommodate residents, businesses, and development stakeholders. Continued growth in e-commerce and home delivery services has also altered traditional trip patterns and household travel behavior, adding complexity to trip generation and trip-chaining analyses. Because the long-term effects of these trends remain uncertain, Corradino recommends evaluating a range of telecommuting and travel-demand scenarios to understand better their potential impacts and support informed, resilient decision-making. <br />Agencies dedicate a great deal of time and effort to developing and using models to forecast future travel behavior. A key challenge is that current models often do not account for modes or travel choices that don’t yet exist. This makes predicting the effects of autonomous