Active Transportation Forecasting: The Critical Need for Activity-Based Modeling Tools By Tom Rossi An activity-based model can be an integral tool in determining the impacts of strategies and investments designed to increase walking and bicycling. C ONSIDERING AMERICA’S real-world needs of the communities great love of the automobile, it’s no surprise that the U.S. Surgeon General has issued a nationwide call to action promoting walking and walkable communities. Despite the proven health benefi ts of an active lifestyle, Americans rely on their personal vehicles for 83 percent of their daily trips while only 10 percent of their travel is by foot and one percent by bicycle. Th ese fi gures could be higher if there were greater infrastructure to support active transportation — a fact noted in a 2014 American Community Survey Report on non-motorized commuting. “Infrastructure that supports bicycling and walking expands transportation options and may complement other forms of transportation by supplementing segments of trips,” the report states. An increasing number of state and urban governments have published goals to boost active transportation in their regions, per a 2016 study by the Alliance for Biking and Walking. To estimate the impact of active transportation growth strategies, it is essential for travel planners to gauge travel preferences and sensitivities of individuals in precise geographic zones. Th ey then can determine infrastructure investments that should spur and support more Americans to transition from cars. Legacy planning tools have been car-centric and based on what, for the most part, have been coarsely aggregated data. Yet, the increasing popularity of granular planning tools such as activity-based demand models is helping planners hone in on creating an infrastructure for a healthier lifestyle while meeting the 40 | Mass Transit | MassTransitmag.com | APRIL/MAY 2017 they serve. Data Authenticity When it comes to data integrity, a model’s output is only as valid as its input. Th e need for reality-based data in transportation demand forecasting is essential. To gather the real-world travel habits and preferences of drivers, household surveys are an established, effi cient means of reaching the auto-dominated U.S. population. As for public transit users, riders can be intercepted en route and surveyed on their travel patterns, routes, trip purpose and other relevant information. Data collection from active travelers, however, is less readily accomplished. Most models depend on trip purposes and traveler characteristics, such as age, gender, household structure, income level, and vehicle availability. But, active transport modeling must also consider infrastructure sensitivities, urban form, and fi negrained temporal and spatial constraints. Surveying a statistically accurate number of walkers and cyclists scattered across geographic regions is highly challenging. Cambridge Systematics NEW TECHNOLOGIES that passively count pedestrians and cyclists are emerging.
MassTransit_AprilMay_2017
To see the actual publication please follow the link above