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Paper

Simulation-Based Analysis of Different Curb Space Type Allocations on Curb Performance

 
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Publication: Transportmetrica B: Transport Dynamics
Volume: 11 (1)
Pages: 1384-1405
Publication Date: 2023
Summary:

Curbspace is a limited resource in urban areas. Delivery, ridehailing and passenger vehicles must compete for spaces at the curb. Cities are increasingly adjusting curb rules and allocating curb spaces for uses other than short-term paid parking, yet they lack the tools or data needed to make informed decisions. In this research, we analyze and quantify the impacts of different curb use allocations on curb performance through simulation. Three metrics are developed to evaluate the performance of the curb, covering productivity and accessibility of passengers and goods, and CO2 emissions. The metrics are calculated for each scenario across a range of input parameters (traffic volume, parking rate, vehicle dwell time, and street design speed) and compared to a baseline scenario. This work can inform policy decisions by providing municipalities tools to analyze various curb management strategies and choose the ones that produce results more in line with their policy goals.

Authors: Thomas MaxnerDr. Andisheh RanjbariŞeyma Güneş, Chase Dowling (Pacific Northwest National Laboratory)
Recommended Citation:
Thomas Maxner, Andisheh Ranjbari, Chase P. Dowling & Şeyma Güneş (2023) Simulation-based analysis of different curb space type allocations on curb performance, Transportmetrica B: Transport Dynamics, 11:1, 1384-1405, DOI: 10.1080/21680566.2023.2212324

A Data-Driven Simulation Tool for Dynamic Curb Planning and Management

Project Budget: $2.9M (UW amount: $500k)

Lead Institution:

  • Pacific Northwest National Lab (PNNL)

Partner Institutions:

  • Urban Freight Lab (UFL), University of Washington
  • Lawrence Berkeley National Laboratory (LBNL)
  • Lacuna Technologies, Inc. (Lacuna)
  • National Renewable Energy Laboratory (NREL)

Summary:

Curbs are a critical interfacing layer between movement and arrival in urban areas—the layer at which people and goods transition from travel to arrival—representing a primary point of resistance when joining and leaving the transportation network. Traditionally, curb spaces are statically supplied, priced, and zoned for specific usage (e.g., paid parking, commercial/passenger loading, or bus stops). In response to the growing demand for curb space, some cities are starting to be more intentional about defining curb usage. Examples of curb demand include not only traditional parking and delivery needs, but today include things like curb access requirements generated by micro delivery services, active transportation modes, and transportation network companies. And now due to the pandemic, increased demand comes from food/grocery pick-up/drop-off activities, as well as outdoor business use of curb space (e.g., outdoor restaurant seating).

Heightened demand and changing expectations for finite curb resources necessitates the implementation of new and dynamic curb management capabilities so that local decision-makers have the tools needed to improve occupancy and throughput while reducing the types of traffic disruptions that result from parking search and space maneuvering activities.

However, municipalities and cities currently lack tools that allow them to simulate the effectiveness of potential dynamic curb management policies to understand how the available control variables (e.g. price or curb space supply) can be modified to influence curb usage outcomes. On the other hand, transportation authorities and fleet managers lack the needed signage or communication platforms to effectively communicate the availability of curb space for a specified use, price, and time at scales beyond centralized lots and garages.

This project aims to develop a city-scale dynamic curb use simulation tool and an open-source curb management platform. The envisioned simulation and management capabilities will include dynamically and concurrently controlling price, number of spaces, allowed parking duration, time of use or reservation, and curb space use type (e.g., dynamic curb space rezoning based on supply and demand).

Project Objectives:

Project objectives include the following:

  • Objective 1:  The team will develop a microscale curb simulation tool to model behavior of individual vehicles with different purposes at the curb along a blockface over time of day, accounting for price, supply, function, and maximum parking time.
  • Objective 2: The team will integrate the microscale simulation tool with the LBNL’s mesoscale (city-scale) traffic simulation tool, BEAM, for simulating traffic impacts of alternative curb management strategies and their effects on citywide and regional traffic, in terms of (1) travel time, (2) throughput (people and goods) into and out of urban centers, (3) reduced energy use and emissions (from parking search and congestion), and (4) curb space utilization.
  • Objective 3: The team will develop a dynamic curbspace allocation controller for various curb users, either municipal or commercial, for the purpose of a demonstration and pilot.
  • Objective 4: The team will design, implement and test a curbside resource usage platform for fleet vehicles communications at commercial vehicle load zones (CVLZs), passenger load zones (PLZs), and transit stops.
  • Objective 5: The team will perform demonstrations with stakeholder agencies and provide pathways to practice for promising curb allocation policies.
Report

Final Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

 
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Publication Date: 2022
Summary:

This three-year project supported by the U.S. Department of Energy Vehicle Technologies Office has the potential to radically improve the urban freight system in ways that help both the public and private sectors. Working from 2018-2021, project researchers at the University of Washington’s Urban Freight Lab and collaborators at the Pacific Northwest National Laboratory have produced key data, tested technologies in complex urban settings, developed a prototype parking availability app, and helped close major knowledge gaps.

All the fruits of this project can be harnessed to help cities better understand, support and actively manage truck load/unload operations and their urban freight transport infrastructure. Project learnings and tools can be used to help make goods delivery firms more efficient by reducing miles traveled and the time it takes to complete deliveries, benefitting businesses and residents who rely on the urban freight system for supplies of goods. And, ultimately, these project learnings and tools can be used to make cities more livable by minimizing wasted travel, which, in turn, contributes to reductions in fuel consumption and emissions.

Cities today are challenged to effectively and efficiently manage their infrastructure to absorb the impacts of ever-increasing e-commerce-fueled delivery demand. All delivery trucks need to park somewhere to unload and load. Yet today’s delivery drivers have no visibility on available parking until they arrive at a site, which may be full. That means they can wind up cruising for parking, which wastes time and fuel and contributes to congestion. Once drivers do find parking, the faster they can unload at the spot, the faster they free up space for other drivers, helping others avoid circling for parking. This makes the parking space—and thus the greater load/unload network—more productive.

To this end, the research team successfully met the project’s three goals, developing and piloting strategies and technologies to:

  • Reduce parking-seeking behavior in the study area by 20%
  • Reduce parcel truck dwell time (the time a truck spends in a spot to load/unload) in the study area by 30%
  • Increase curb space, alley space, and private loading bay occupancy rates in the study area

The research team met these goals by creating and piloting on Seattle streets OpenPark, a first-of-its-kind real-time and forecasting curb parking app customized for commercial delivery drivers—giving drivers the “missing link” in their commonly used routing tools that tell them how best to get to delivery locations, but not what parking is available to use when they get there. Installing in-ground sensors on commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) in the 10-block study area in Seattle’s downtown neighborhood of Belltown let researchers glean real-time curb parking data. The research team also met project goals by piloting three parcel lockers in public and private spaces open to any delivery carrier, creating a consolidated delivery hub that lets drivers complete deliveries faster and spend less time parked. Researchers collected and analyzed data to produce the first empirical, robust, statistically significant results as to the impact of the lockers, and app, on on-the-ground operations. In addition to collecting and analyzing sensor and other real-time and historical data, researchers rode along with delivery drivers to confirm real-world routing and parking behavior. Researchers also surveyed building managers on their private loading bay operations to understand how to boost usage.

Key findings that provide needed context for piloting promising urban delivery solutions:

  • After developing a novel model using GPS data to measure parking-seeking behavior, researchers were able to quantify that, on average, a delivery driver spends 28% of travel time searching for parking, totaling on average one hour per day for a parcel delivery driver. This project offers the first empirical proof of delivery drivers’ cruising for parking.
  • While many working models to date have assumed that urban delivery drivers always choose to double-park (unauthorized parking in the travel lane), this study found that behavior is rare: Double parking happened less than 5% of the times drivers parked.
  • That said, drivers do not always park where they are supposed to. The research team found that CVLZ parking took place approximately 50% of the time. The remaining 50% included mostly parking in “unauthorized” curb spaces, including no-parking zones, bus zones, entrances/exits of parking garages, etc.
  • Researcher ride-alongs with delivery drivers revealed parking behaviors other than unauthorized parking that waste valuable time and fuel: re-routing (after failing to find a desired space, giving up and doubling back to the delivery destination later in the day) and queuing (temporarily parking in an alternate location and waiting until the desired space becomes available).
  • Some 13% of all parking events in CVLZ spaces were estimated as overstays; the figure was 80% of all parking events in PLZ spaces. So, the curb is not being used efficiently or as the city intended as many parking events exceed the posted time limit.
  • Meantime, there is unused off-street capacity that could be tapped in Seattle’s Central Business District. Estimates show private loading bays could increase area parking capacity for commercial vehicles by at least 50%. But surveys show reported use of loading bays is low and property managers have little incentive to maximize it. Property managers find curb loading zones more convenient; it seems delivery drivers do, too, as they choose to park at the curb even when loading bay space is available.

Key findings from the technology and strategies employed:

Carriers give commercial drivers routing tools that optimize delivery routes by considering travel distance and (often) traffic patterns—but not details on parking availability. Limited parking availability can lead to significant driver delays through cruising for parking or rerouting, and today’s drivers are largely left on their own to assess and manage their parking situation as they pull up to deliver.

The project team worked closely with the City of Seattle to obtain permission to install parking sensors in the roadway and communications equipment to relay sensor data to project servers. The team also developed a fully functional and open application that offers both real-time parking availability and near-time prediction of parking availability, letting drivers pick forecasts 5, 15, or 30 minutes into the future depending on when the driver expects to arrive at the delivery destination. Drivers can also enter their vehicle length to customize availability information.

After developing, modeling, and piloting the real-time and forecasting parking app, researchers conducted an experiment to determine how use of the app impacted driver behavior and transportation outcomes. They found that:

  • Having access to parking availability via the app resulted in a 28% decrease in the time drivers spent cruising for parking. Exceeding our initial goal of reducing parking seeking behavior by 20%. In the study experiment, all drivers had the same 20-foot delivery van and the same number of randomly sampled delivery addresses in the study area. But some drivers had access to the app; others did not.
  • Preliminary results based on historic routing data show that the use of such a real-time curb parking information and prediction app can reduce route time by approximately 1.5%. An analysis collected historic parking occupancy and cruising information and integrated it into a model that was then used to revise scheduling and routing. This model optimally routed vehicles to minimize total driving and cruising time. However, since the urban environment is complex and consists of many random elements, results based on historic data underly a high amount of randomness. Analysis on synthetic routes suggests including parking availability in routing systems is especially promising for routes with high delivery density and with stops where the cruising time delays vary a lot along the planned time horizon; here, route time savings can reach approximately 20.4% — conditions outlined in the report.
  • The central tradeoff among four approaches to parking app architecture going forward is cost and accuracy. The research team found that it is possible to train machine learning models using only data from curb occupancy sensors and reach a higher than 90% accuracy. Training of state-space models (those using inputs such as time of day, day of the week, and location to predict future parking availability) is computationally inexpensive, but these models offer limited accuracy. In contrast, deep-learning models are highly accurate but computationally expensive and difficult to use on streaming data.

Common carrier lockers create delivery density, helping delivery people complete their work faster. The driver parks next to the locker system, loads packages into it, and returns to the truck. When delivery people spend less time going door-to-door (or floor-to-floor inside a building), it cuts the time their truck needs to be parked, increasing turnover and adding parking capacity in crowded cities. This project piloted and collected data on common carrier lockers in three study area buildings.

From piloting the common carrier parcel lockers, researchers found that:

  • The implementation of the parcel locker allowed delivery drivers to increase productivity: 40%-60% reduction in time spent in the building and 33% reduction in vehicle dwell time at the curb.
Authors: Dr. Anne GoodchildDr. Giacomo Dalla ChiaraFiete KruteinDr. Andisheh RanjbariDr. Ed McCormackElizabeth Guzy, Dr. Vinay Amatya (PNNL), Ms. Amelia Bleeker (PNNL), Dr. Milan Jain (PNNL)
Recommended Citation:
Urban Freight Lab (2022). Final Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.
Article

Giving Curb Visibility to Delivery Drivers

 
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Publication: American Planning Association | 2022 State of Transportation Planning
Pages: 134-143
Publication Date: 2022
Summary:
At the time we are writing this article, hundreds of thousands of delivery vehicles are getting ready to hit the road and travel across U.S. cities to meet the highest peak of demand for ecommerce deliveries during Thanksgiving, Black Friday, and the Christmas holiday season. This mammoth fleet will not only add vehicle miles traveled through urban centers but also increase parking congestion, battling with other vehicles for available curb space.
While the integration of road traffic data with modern navigation systems has seen huge developments in the past decade, drastically changing the way we, and delivery vehicles, navigate through cities, not as much can be said when it comes to parking. The task of finding and securing parking is still left to drivers, and largely unsupported by real-time information or app-based solutions.
Delivery vehicle drivers are affected by curb parking congestion even more than any other driver because delivery drivers have to re-park their vehicles not once or twice, but 10, 20, or even more times during a delivery route.
Our work, discussed in this article, focuses on improving delivery drivers’ lives when it comes to finding available curb space, improving the delivery system, and reducing some of the externalities generated in the process. We first describe what types of parking behaviors delivery drivers adopt when facing a lack of available curb parking, then we will quantify the cost of lack of available parking, estimating how much time delivery drivers spend circling the block and searching for parking. We then discuss how we can improve on that by creating the first curb availability information system – OpenPark.

 

Recommended Citation:
Dalla Chiara, Giacomo and Anne Goodchild. Giving Curb Visibility to Delivery Drivers. Intersections + Identities: State of Transportation Planning 2022, 134-143.
Paper

Do Commercial Vehicles Cruise for Parking? Empirical Evidence from Seattle

 
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Publication: Transport Policy
Volume: 97
Pages: 26-36
Publication Date: 2020
Summary:

Parking cruising is a well-known phenomenon in passenger transportation, and a significant source of congestion and pollution in urban areas. While urban commercial vehicles are known to travel longer distances and to stop more frequently than passenger vehicles, little is known about their parking cruising behavior, nor how parking infrastructure affect such behavior.

In this study we propose a simple method to quantitatively explore the parking cruising behavior of commercial vehicle drivers in urban areas using widely available GPS data, and how urban transport infrastructure impacts parking cruising times.

We apply the method to a sample of 2900 trips performed by a fleet of commercial vehicles, delivering and picking up parcels in Seattle downtown. We obtain an average estimated parking cruising time of 2.3 minutes per trip, contributing on average for 28 percent of total trip time. We also found that cruising for parking decreased as more curb-space was allocated to commercial vehicles load zones and paid parking and as more off-street parking areas were available at trip destinations, whereas it increased as more curb space was allocated to bus zone.

Recommended Citation:
Dalla Chiara, Giacomo, & Goodchild, Anne. (2020) Do Commercial Vehicles Cruise for Parking? Empirical Evidence from Seattle. Transport Policy, 97, 26-36. https://doi.org/10.1016/j.tranpol.2020.06.013