Getting more cargo e-bikes on the street as a last-mile freight delivery option will require a strategic approach that goes beyond basic bike-friendliness.
<p><strong>Portland city planners rethink land use and the transportation network as the rise of e-commerce increases congestion and puts climate goals at risk.</strong></p>
Prototype PNNL, UW webapp predicts parking space availability for delivery drivers
A collaboration between Pacific Northwest National Laboratory (PNNL) and the University of Washington’s Urban Freight Lab has developed a prototype webapp that combines smart sensors and machine learning to predict parking space availability. The prototype is ready for initial testing to help commercial delivery drivers find open spaces without expending fuel and losing time.
The webapp combines curbside maps with data from nearly 300 sensors placed within 74 parking spaces in commercial and passenger loading zones from 1
st to 3
rd avenues and between Battery and Stewart streets in the Belltown neighborhood in downtown Seattle.
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From Curb to Doorstep: Driving Efficiencies for Delivering Goods
Ten blocks of the Belltown neighborhood in downtown Seattle house an eclectic mix of retail stores, apartments, and restaurants that require daily deliveries of goods-from fresh seafood for the evening’s dinner special to the latest gadget from Amazon Prime.
In this bustling and often congested urban area, trucks fully packed with goods are on tight delivery schedules. However, circling the blocks to find an open parking zone in the “final 50 feet”-the sweet spot for the most efficient deliveries-can put drivers behind schedule.
Soon, there will be a webapp for that. In a collaboration between Pacific Northwest National Laboratory (PNNL) and the University of Washington’s Urban Freight Lab, a prototype webapp has been developed that combines smart sensors and machine learning to predict parking space availability. The prototype is ready for initial testing to help commercial delivery drivers find o