Demystifying Urban Curbside Freight Management: Strategic Incremental Approach from Washington, D.C.

Abstract

Loading zones and commercial vehicle activity are issues in cities across the country including the District of Columbia. Like other major U.S. cities, the District Department of Transportation (DDOT) has tried a variety of initiatives to alleviate the strain commercial vehicle activity places on the overall transportation system. These include metering or otherwise pricing loading zones and parking, expanding the size and increasing the number of loading zones, increasing the number of metered parking spaces, extending loading and metering time periods, simplifying and standardizing truck parking rules, and encouraging nighttime or off-peak goods delivery. These initiatives have had some success but a more targeted approach was needed, especially to address non-commercial vehicles parking in loading zones and thus pushing delivery vehicles to double park while loading and unloading. As part of the parkDC: Penn Quarter/Chinatown performance parking pilot, DDOT attempted to reduce the number of illegally parked cars blocking loading zones by raising prices to the highest prevailing rate on the blockface and increasing loading zone availability by extending the hours of operation of loading zones. Moving forward, DDOT has identified several next steps including: oversize/overweight vehicle routing, increased and targeted loading zone enforcement, increased disincentives for violations, data-driven modifications to the loading zone program, and modifications to the pay-by-cell program to mitigate non-trucks paying to park in loading zones. As gaps in understanding curbside movement close with increased data availability, the District expects the programs associated with curbside management to evolve to reflect smarter policy, program, and management practices.

Solving the Curb Space Puzzle Through the Development of a Curb Space Allocation Tool

“…In addition to the ITE Guide and NACTO’s Curb Appeal , documented curbside management efforts from other cities were reviewed, including the Cincinnati Curb Study ( 18 ) and two documents from San Francisco: the On-Street Parking Management and Pricing Study ( 19 ) produced by the San Francisco County Transportation Authority, and the SFpark: Pilot Project Evaluation ( 20 ) developed by the San Francisco Municipal Transportation Authority. Additionally, nearby efforts from the District were reviewed, including the parkDC: Penn Quarter/Chinatown Performance Parking Pilot Final Report (parkDC) ( 21 ) and an accompanying freight management study ( 22 ) conducted as part of the parkDC evaluation. These efforts have yet to produce a method that can guide practitioners in applying general curb-prioritization policies or principles to the task of allocating space for different users on a particular block or corridor.…”

Approximate Optimum Curbside Utilisation for Pick-Up and Drop-Off (PUDO) and Parking Demands Using Reinforcement Learning

“…Along with parking, the PUDO operations of the taxi or the ride-sharing mobility contribute to almost 55% of curb use in busy streets [5]. Meanwhile, the curb delivery business is projected to grow 75% by 2040, thanks to the continuously booming ecommerce [6].…”

“…Conventional curbside management imposes regulations to restrain unfettered parking behaviours for the benefit of a balanced demand-supply distribution [6]. However, 34% of passenger vehicle trips are still cruising for parking [9], while, 86% delivery vehicles stop on unauthorised zones [6]. Consequentially, their double-park manoeuvres potentially obstruct traffic flow.…”

“…As the crucial interface between land use and transport systems, curbside represents a scarce asset where various means of mobility compete for room to stop or park [1], [2]. Whilst on-street parking activities [3], [4], Pick-Up and Drop-Offs (PUDOs) passengers, brief docking, and freights loading are emerging as primary and dominant curb activities in recent years [5], [6]. Furthermore, with the uptake of Autonomous Vehicles (AVs), in particular Shared AVs (SAVs), the average Vehicle Kilometres Travelled (VKT), the level of ride-sharing, frequencies of PUDOs and curb delivery are all envisaged to surge drastically [7], [8].…”