Technical Report

An Evaluation of Bicycle Safety Impacts of Seattle’s Commercial Vehicle Load Zones

Publication Date: 2015

Summary:

The Seattle Department of Transportation (SDOT) partnered with the University of Washington to explore how commercial vehicle parking in Seattle’s downtown area affects the safety of bicyclists. The hypothesis was that increased truck access to SDOT’s commercial vehicle loading zones (CVLZs) can positively contribute to bicycle safety. Because CVLZs provide truck drivers with more access to legal parking, their presence could reduce incidences of trucks parking illegally in the street or blocking bicycle lanes, thus reducing the necessity for bicyclists to maneuver around them. This research explored this hypothesis by using four methods, an analysis of bike-trucks accident data, interviews with bicyclists and truck drivers who frequently travel in downtown Seattle, analysis of video recordings of cyclists riding downtown, and observations of truck loading/unloading operations downtown.

The research determined that from bicyclists’ perspectives, illegally parked trucks were a more serious problem than the locations of CVLZs. Therefore, increasing the availability of legal truck parking should have a positive effect on bicyclist safety and level of stress. When trucks park in the bike lane, cyclists are required to maneuver into the stream of traffic, increasing level of exposure and accident risk. Similarly, both the cyclist interviews and video data indicated that construction sites are problematic locations for illegally parked trucks blocking cyclist travel lanes. Better enforcement of parking regulations near construction sites and better site planning would help alleviate a significant amount of conflict between cyclists and parked trucks.

Loading zones on higher speed or busy streets or in areas where cyclists travel downhill increase the danger of those areas. In some areas, it may be possible to relocate loading zones around the corner, onto less busy side streets, to eliminate the need for cyclists to choose between merging into a busy lane to pass a truck or passing close enough to the truck that the delivery operations may put obstacles in the bicyclist’s path. If loading zones are moved, the zones should be situated at the beginning of the block and should allow drivers to still reach the businesses they are serving quickly and without having to maneuver or cross a street. This will encourage the use of the loading zone as opposed to illegal parking.

Authors: Polina ButrinaDr. Ed McCormackDr. Anne Goodchild, Jerome Drescher

Recommended Citation:
Butrina, Polina, Edward McCormack, Anne Goodchild, and Jerome Drescher. "An Evaluation of Bicycle Safety Impacts of Seattle’s Commercial Vehicle Load Zones." (2016).

The Final 50 Feet of the Urban Goods Delivery System: Pilot Test of an Innovative Improvement Strategy

Background

We are living at the convergence of the rise of e-commerce and fast growing cities. Surging growth in U.S. online sales has averaged more than 15% year-over-year since 2010. Total e-commerce sales for 2016 were estimated at $394.9 billion, an increase of 15.1 percent from 2015. This is a huge gain when compared to total retail sales in 2016, which only increased 2.9 percent from 2015. E-commerce sales in 2016 accounted for 8.1 percent of total sales, while accounting for 7.3 percent of total sales in 2015.

This is causing tremendous pressure on local governments to rethink the way they manage street curb parking and alley operations for trucks and other delivery vehicles, and on building operators to plan for the influx of online goods. City managers and policy makers are grappling with high demand for scarce road, curb and sidewalk space, and multiple competing uses. But rapidly growing cities lack data-based evidence for the strategies they are considering to support e-commerce and business vitality, while managing limited parking in street space that is also needed for transit, pedestrians, cars, bikes and trucks.

The Final 50 Feet is the project’s shorthand designation for the last leg of the delivery process, which:

Begins when a truck stops at a city-owned Commercial Vehicle Load Zone or alley, or in a privately-owned freight bay or loading dock in a building;
May extend along sidewalks or through traffic lanes; and
Ends where the end customer takes receipt of delivery.

Research Project

The purpose of the research project is to pilot test a promising strategy to reduce the number of failed first delivery attempts in urban buildings. The test will take place in the Seattle Municipal Tower. It will serve as a case study for transportation and urban planning professionals seeking to reduce truck trips to urban buildings. Urban Freight Lab identified two promising strategies for the pilot test:

Locker system: smaller to medium sized deliveries can be placed into a locker which will be temporarily installed during our pilot test
Grouped-tenant-floor-drop-off-points for medium sized items if locker is too small or full (4-6 floor groups to be set up by SDOT and Seattle City Light)
People will come and pick up the goods at the designated drop off points
Flyers with information of drop-off-points will be given to the carriers

UFL will evaluate the ability of the standardized second step pilot test to reduce the number of failed first delivery attempts by:

Collecting original data to document the number of failed first delivery attempts before and after the pilot test; and
Comparing them to the pilot test goals.

Paper

Commercial Vehicle Driver Behaviors and Decision Making: Lessons Learned from Urban Ridealongs

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URL: https://doi.org/10.1177/03611981211003575

Publication: Transportation Research Record: Journal of the Transportation Research Board

Volume: 2675 (9)

Pages: 608-619

Publication Date: 2021

Summary:

As ecommerce and urban deliveries spike, cities grapple with managing urban freight more actively. To manage urban deliveries effectively, city planners and policy makers need to better understand driver behaviors and the challenges they experience in making deliveries.

In this study, we collected data on commercial vehicle (CV) driver behaviors by performing ridealongs with various logistics carriers. Ridealongs were performed in Seattle, Washington, covering a range of vehicles (cars, vans, and trucks), goods (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail). Observers collected qualitative observations and quantitative data on trip and dwell times, while also tracking vehicles with global positioning system devices.

The results showed that, on average, urban CVs spent 80% of their daily operating time parked. The study also found that, unlike the common belief, drivers (especially those operating heavier vehicles) parked in authorized parking locations, with only less than 5% of stops occurring in the travel lane. Dwell times associated with authorized parking locations were significantly longer than those of other parking locations, and mail and heavy goods deliveries generally had longer dwell times.

We also identified three main criteria CV drivers used for choosing a parking location: avoiding unsafe maneuvers, minimizing conflicts with other users of the road, and competition with other commercial drivers.

The results provide estimates for trip times, dwell times, and parking choice types, as well as insights into why those decisions are made and the factors affecting driver choices.

In recent years, cities have changed their approach toward managing urban freight vehicles. Passive regulations, such as limiting delivery vehicles’ road and curb use to given time windows or areas have been replaced by active management through designing policies for deploying more commercial vehicle (CV) load zones, pay-per-use load zone pricing, curb reservations, and parking information systems. The goal is to reduce the negative externalities produced by urban freight vehicles, such as noise and emissions, traffic congestion, and unauthorized parking, while guaranteeing goods flow in dense urban areas. To accomplish this goal, planners need to have an understanding of the fundamental parking decision-making process and behaviors of CV drivers.

Two main difficulties are encountered when CV driver behaviors are analyzed. First, freight movement in urban areas is a very heterogeneous phenomenon. Drivers face numerous challenges and have to adopt different travel and parking behaviors to navigate the complex urban network and perform deliveries and pick-ups. Therefore, researchers and policy makers find it harder to identify common behaviors and responses to policy actions for freight vehicles than for passenger vehicles. Second, there is a lack of available data. Most data on CV movements are collected by private carriers, who use them to make business decisions and therefore rarely release them to the public. Lack of data results in a lack of fundamental knowledge of the urban freight system, inhibiting policy makers’ ability to make data-driven decisions.

The urban freight literature discusses research that has employed various data collection techniques to study CV driver behaviors. Cherrett et al. reviewed 30 UK surveys on urban delivery activity and performed empirical analyses on delivery rates, time-of-day choice, types of vehicles used to perform deliveries, and dwell time distribution, among others. The surveys reviewed were mostly establishment-based, capturing driver behaviors at specific locations and times of the day. Allen et al. performed a more comprehensive investigation, reviewing different survey techniques used to study urban freight activities, including driver surveys, field observations, vehicle trip diaries, and global positioning system (GPS) traces. Driver surveys collect data on driver activities and are usually performed through in-person interviews with drivers outside their working hours or at roadside at specific locations. In-person interviews provide valuable insights into driver choices and decisions but are often limited by the locations at which the interviews occur or might not reflect actual choices because they are done outside the driver work context. Vehicle trip diaries involve drivers recording their daily activities while field observations entail observing driver activities at specific locations and establishments; neither collects insights into the challenges that drivers face during their trips and how they make certain decisions. The same limitations hold true for data collected through GPS traces. Allen et al. mentioned the collection of travel diaries by surveyors traveling in vehicles with drivers performing deliveries and pick-ups as another data collection technique that could provide useful insights into how deliveries/pick-ups are performed. However, they acknowledged that collecting this type of data is cumbersome because of the difficulty of obtaining permission from carriers and the large effort needed to coordinate data collection.

This study aims to fill that gap by collecting data on driver decision-making behaviors through observations made while riding along with CV drivers. A systematic approach was taken to observe and collect data on last-mile deliveries, combining both qualitative observations and quantitative data from GPS traces. The ridealongs were performed with various delivery companies in Seattle, Washington, covering a range of vehicle types (cars, vans, and trucks), goods types (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail).

The data collected will not only add to the existing literature by providing estimates of trip times, parking choice types, time and distance spent cruising for parking, and parking dwell times but will also provide insights into why those decisions are made and the factors affecting driver choices.

The objectives of this study are to provide a better understanding of CV driver behaviors and to identify common and unique challenges they experience in performing the last mile. These findings will help city planners, policy makers, and delivery companies work together better to address those challenges and improve urban delivery efficiency.

The next section of this paper describes the relevant literature on empirical urban freight behavior studies. The following section then introduces the ridealongs performed and the data collection methods employed. Next, analysis of the data and qualitative observations from the ridealongs are described, and the results are discussed in five overarching categories: the time spent in and out of the vehicle, parking location choice, the reasons behind those choices, parking cruising time, and factors affecting dwell time.

Authors: Dr. Giacomo Dalla ChiaraFiete KruteinDr. Andisheh RanjbariDr. Anne Goodchild

Recommended Citation:
Chiara, Giacomo Dalla, Krutein, Klaas Fiete, Ranjbari, Andisheh, & Goodchild, Anne. (2021). Understanding Urban Commercial Vehicle Driver Behaviors and Decision Making. Transportation Research Record, 2675(9), 608-619. https://doi.org/10.1177/03611981211003575

Chapter

Are Cities’ Delivery Spaces in the Right Places? Mapping Truck Load/Unload Locations

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URL: https://doi.org/10.1002/9781119425526.ch21

Publication: City Logistics 2: Modeling and Planning Initiatives (Proceedings of the 2017 International Conference on City Logistics)

Volume: 2

Pages: 351-368

Publication Date: 2018

Summary:

Two converging trends – the rise of e‐commerce and urban population growth – challenge cities facing competing uses for road, curb and alley space. The University of Washington has formed a living Urban Freight Lab to solve city logistics problems that cross private and public sector boundaries. To assess the capacity of the city’s truck load/unload spaces, the lab collected GIS coordinates for private truck loading bays, and combined them with public GIS layers to create a comprehensive map of the city’s truck parking infrastructure. The chapter offers a practical approach to identify useful existent urban GIS data for little or no cost; collect original granular urban truck data for private freight bays and loading docks; and overlay the existing GIS layers and a new layer to study city‐wide truck parking capacity. The Urban Freight Lab’s first research project is addressing the “Final 50 Feet” of the urban delivery system.

Authors: Barbara IvanovDr. Ed McCormackDr. Anne GoodchildJosé Luis Machado LeónGabriela Girón-Valderrama, Anne Moudon, Jason Scully

Recommended Citation:
Goodchild, Anne, Barb Ivanov, Ed McCormack, Anne Moudon, Jason Scully, José Machado Leon, and Gabriela Giron Valderrama. Are Cities' Delivery Spaces in the Right Places? Mapping Truck Load/Unload Locations: Modeling and Planning Initiatives. City Logistics 2: Modeling and Planning Initiatives (2018): 351-368. 10.1002/9781119425526.ch21

The Final 50 Feet of the Urban Goods Delivery System: Documenting Loading Bays, Demonstrating Parcel Lockers’ Proof of Concept & Tracking Curb Use in Seattle’s Interconnected Load/Unload Network (Task Order 2)

Part of the Final 50 Feet Research Program, this project contains: a curb occupancy study, a survey of First and Capitol Hill Loading Bays, a pilot test at Seattle Municipal Tower, and the development of a toolkit.

Private Loading Bays and Docks Inventory Study

Taken together with the Urban Freight Lab’s earlier private infrastructure inventory (Seattle Center City Alley Infrastructure Inventory and Occupancy Study 2018) in Downtown Seattle, Uptown, and South Lake Union, this report finalizes the creation of a comprehensive Center City inventory of private loading/unloading infrastructure.

To the research team’s knowledge, Seattle is the first city to maintain a database with the location and features of private loading/unloading infrastructure (meaning, out of the public right of way). This matters because these facilities are privately owned and managed, cities lack information about them—information critical to urban planning. The private infrastructure has been a missing piece of the urban freight management puzzle. The work in this report helps complete that puzzle and advance efforts to make urban freight delivery more efficient in increasingly dense, constrained cities, such as Seattle.

Key Findings from Private Loading Bays and Docks Inventory

Data collectors in this study identified, examined, and collected key data on 92 private loading docks, bays and areas across 421 city blocks in the neighborhoods of Capitol Hill, First Hill, and a small segment of the International District east of I-5. The earlier inventory in Downtown Seattle, Uptown, and South Lake Union had proportionally more than twice the density of private infrastructure of Capitol Hill and First Hill documented in this report. This finding is unsurprising. While all the inventoried neighborhoods are in the broad Center City area, they are fundamentally different neighborhoods with different built environments, land use, and density. Variable demand for private infrastructure—and the resulting supply—stems from those differences.

Researchers found that a trust relationship with the private sector is essential to reduce uncertainty in this type of work. UPS’ collaboration helped reduce uncertainty in the total inventory from 33% to less than 1%.

Curb Occupancy Study

This study gives the city on-the-ground data on the current use and operational capacity of the curb for commercial vehicles, documenting vehicle parking behavior in a three-by-three city block grid around each of five prototype Center City buildings: a hotel, a high-rise office building, an historical building, a retail center, and a residential tower. These buildings were intentionally chosen to deepen the city’s understanding of the Center City; they were part of UFL’s earlier SDOT-sponsored research tracking how goods move vertically within a building in the Final 50 Feet of the goods delivery system.

Significantly, this study captured the parking behavior of commercial vehicles everywhere along the curb as well as the parking activities of all vehicles (including passenger vehicles) in commercial vehicle loading zones (CVLZs.) The research team documented: (1) which types of vehicles parked in CVLZs and for how long, and; (2) how long commercial vehicles (CVs) parked in CVLZs, in metered parking, and in passenger load zones (PLZ) and other unauthorized spaces. (Passenger vehicles in this study were not treated as commercial vehicles, due to challenges in systematically identifying whether passenger vehicles were making deliveries or otherwise carrying a commercial permit.)

Key Findings from Curb Occupancy Study

Commercial and passenger vehicle drivers use CVLZs and PLZs fluidly: commercial vehicles are parking in PLZs and passenger vehicles are parking in CVLZs.
Most commercial vehicle (CV) demand is for short-term parking: 15 or 30 minutes.
Thirty-six percent of the total CVs parked along the curb were service CVs, showing the importance of factoring their behavior and future demand into urban parking schemes.
Forty-one percent of commercial vehicles parked in unauthorized locations. But a much higher percentage parked in unauthorized areas near the two retail centers (55% – 65%) when compared to the predominantly office and residential areas (27% – 30%). The research team found that curb parking behavior is associated with granular, building-level urban land use. This occurred even as other factors such as the total number, length and ratio of CVLZs versus PLZs varied widely across the five study areas.

Seattle Municipal Tower Common Carrier Locker Pilot

The UFL’s 2017 research (The Final 50 Feet Urban Goods Delivery System Research Scan and Data Collection Project) documented that of the 20 total minutes delivery drivers spent on average in the 62-story Seattle Municipal Tower, 12.2 of those minutes were spent going floor-to-floor in freight elevators and door-to-door to tenants on multiple floors. The UFL recognized that cutting those two steps from the delivery process could slash delivery time in the Tower by more than half—which would translate into a substantial reduction in truck dwell time.

This report provides compelling evidence of the effectiveness of a new urban goods delivery system strategy: common carrier lockers that create parcel delivery density and provide secure delivery locations in public spaces. Parcel lockers are widely available secure, automated, self-service storage systems that are typically owned by a single retailer or delivery firm and placed inside private property. In contrast, common carrier lockers are open to multiple retailers and delivery carriers. This pilot, which placed a common carrier locker system in the 62-floor Seattle Municipal Tower for ten days in spring 2018, was intentionally carried out in a public space.

Key Findings from Seattle Municipal Tower Common Carrier Locker Pilot

The common carrier locker both reduced total delivery time by 78% when compared to traditional floor-to-floor, door-to-door delivery method and cut the number of failed first parcel deliveries to zero.

Paper

Urban Delivery Company Needs and Preferences for Green Loading Zones Implementation: A Case Study of NYC

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URL: https://doi.org/10.1061/9780784484340.030

Publication: Proceedings of American Society of Civil Engineers (ASCE) Transportation and Development Conference 2022: Transportation Planning and Workforce Development

Publication Date: 2022

Summary:

(This project is part of the Urban Freight Lab’s Technical Assistance Program, where UFL contributes to the project by providing 1:1 match funds in terms of staff and/or research assistants to complete project tasks.)

Green Loading Zones (GLZs) are curb spaces dedicated to the use of electric or alternative fuel (“green”) delivery vehicles. Some U.S. cities have begun piloting GLZs to incentivize companies to purchase and operate more green vehicles. However, there are several questions to be answered prior to a GLZ implementation, including siting, potential users and their willingness to pay. We reviewed best practices for GLZs around the world, and surveyed goods delivery companies operating in New York City to collect such information for a future GLZ pilot. The findings suggest the best candidate locations are areas where companies are currently subject to the most parking fines and double parking. Companies expressed willingness to pay for GLZs, as long as deploying green vehicles in the city can offset other cost exposures. Respondents also selected several single-space GLZs spread throughout a neighborhood as the preferred layout.

Authors: Thomas MaxnerNota GoulianouDr. Andisheh RanjbariDr. Anne Goodchild

Recommended Citation:
Maxner, T., Goulianou, P., Ranjbari, A., and Goodchild, A. (2022). "Studying Urban Delivery Company Needs and Preferences for Green Loading Zones Implementation: A Case Study of NYC", In Proceedings of ASCE Transportation and Development Conference (Forthcoming), Seattle, WA.

Dynamically Managed Curb Space Pilot

Transportation Network Company (TNC) usage in Seattle has been increasing every quarter since 2015 when the City of Seattle Department of Transportation (SDOT) began collecting data. TNC trips exceeded 20 million in 2017, a 46% increase from total reported trips in 2016. This has led to concerns about congestion and pedestrian safety as cars and people take risks to connect at the curb and in the right-of-way. By providing additional curb capacity through increased passenger loading zones and directing customers via in-app messaging, the City may be able to reduce congestion and unsafe vehicle/people movements during peak traffic and late-night hours.

Other cities have attempted to study the impacts of increased usage of passenger loading zones (e.g., San Francisco, Washington D.C.), with varying success, but no standard methodology exists for cities to assess the potential for reallocated curb space and the subsequent impacts of those changes. SDOT is taking a data-driven approach to curb reallocation and traffic network impacts, modeling the work SDOT has done to quantify demand in paid parking areas and set rates accordingly. The main goals of this pilot are three-fold: increase pedestrian safety, minimize congestion impacts on the larger transportation network, and build a scalable methodology for assessment and implementation of curb allocation to accommodate this new mobility service.

The Supply Chain Transportation & Logistics Center and SDOT will work in collaboration with employers, transit operators, and TNCs to test a variety of strategies to mitigate the traffic impacts of TNC pick-ups on the greater transportation network and improve safety for passengers and drivers. Strategies include increasing the number of passenger loading zones in high-traffic pick-up areas and geofenced pick-up or black-out areas. Curb and street use data will be collected under each alternative and compared to baseline data.

Report

NYC Zero-Emissions Urban Freight and Green Loading Zones Market Research

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Publication Date: 2022

Summary:

In an effort to reduce emissions from last-mile deliveries and incentivize green vehicle adoption, The New York City Department of Transportation (NYC DOT) is seeking to implement a Green Loading Zone (GLZ) pilot program. A Green Loading Zone is curb space designated for the sole use of “green” vehicles, which could include electric and alternative fuel vehicles as well as other zero-emission delivery modes like electric-assist cargo bikes. To inform decisions about the program’s siting and regulations, this study was conducted by the University of Washington’s Urban Freight Lab (UFL) in collaboration with NYC DOT under the UFL’s Technical Assistance Program.

The study consists of three sources of information, focusing primarily on input from potential GLZ users, i.e., delivery companies. An online survey of these stakeholders was conducted, garnering 13 responses from 8 types of companies. Interviews were conducted with a parcel carrier and an electric vehicle manufacturer. Additionally, similar programs from around the world were researched to help identify current practices. The major findings are summarized below, followed by recommendations for siting, usage restriction and pricing of GLZs. It is important to note that these recommendations are based on the survey and interview findings and thus on benefits to delivery companies. However, other important factors such as environmental justice, land use patterns, and budgetary constraints should be considered when implementing GLZs.

Literature Review Findings

Green Loading Zones are a relatively novel approach to incentivizing electric vehicle (EV) adoption. Two relevant pilot programs exist in the United States, one in Santa Monica, CA and the other one in Los Angeles, CA. Both are “zero-emission” delivery programs, meaning alternative fuel vehicles that reduce emissions (compared to fossil fuel vehicles) are not included in the pilot’s parking benefits (dedicated spaces and free parking). Other cities including Washington, DC and Vancouver, Canada are also creating truck-only zones and dedicating parking to EVs in their efforts to reduce emissions. Bremen, Germany also has a similar program called an Environmental Loading Point.

Many cities in Europe are implementing low- or zero-emission zones. These are different than GLZs in that entire cities or sections of cities are restricted to vehicles that meet certain emissions criteria. London, Paris, and 13 Dutch municipalities are all implementing low-emission zones. These zones have achieved some success in reducing greenhouse gas emissions: in London, CO2 from vehicles has been reduced by 13 percent. Companies operating in those cities have opted to purchase cleaner vehicles or to replace trucks with alternative modes like cargo bikes. In addition to demonstrating similar goals as NYC DOT, these programs provide insights to the siting and structure of GLZs. Loading zones have been selected based on equity concerns, delivery demand, and commercial density. Every city in the literature review has installed specific signage for the programs to clearly convey the regulations involved.

Survey and interview Findings

A range of company types replied to the survey: parcel carriers (large shippers), small shippers, e-commerce and retail companies, freight distributors, a truck dealer, a liquid fuel delivery company, and a logistics NYC association (answering on behalf of members). The majority of these companies will be increasing their fleet sizes over the next ten years, and most plan to increase the share of EVs in their fleets while doing so. A smaller share (4 of 13) also plans to increase their share of alternative fuel vehicles. The most cited reasons for increasing fleet size and green vehicle share are: 1) internal sustainability goals, 2) social responsibility, and 3) new vehicles/models coming to the market.

Green vehicle adoption is not without its challenges. For EV adoption specifically, companies identified three major barriers: 1) competition in the EV market, 2) electric grid requirements upstream of company-owned facilities, and 3) lack of adequate government-supported purchasing subsidies. To overcome these barriers, respondents would like larger or more government purchasing incentives and reduced toll or parking rates for EVs. However, the majority of companies also expressed a willingness to pay for GLZs at similar rates to other commercial loading zones.

As for area coverage, all respondents deliver to Manhattan, Queens, and Brooklyn. 11 of 13 deliver to Staten Island and the Bronx as well. All EV and cargo bike operators deliver to Manhattan, whereas only one EV operator and one cargo bike operator deliver to all five boroughs of NYC. Respondents deliver at all times of day, but the busiest times are between 9:00AM and 4:00PM (stated by 8 of 13 respondents). Peak periods are busiest for four companies in the morning (6:00AM-9:00AM) and six companies in the evening (4:00PM-9:00PM).

The interviews supported findings from the survey. Both interviewed companies have a vested interest in reducing their environmental footprint and plan to use or produce exclusively zero-emission vehicles by 2050 (carrier) or 2035 (manufacturer). However, they noted challenges to electrifying entire fleets for cities. Charging infrastructure needs to be expanded, but incentives are also needed (parking benefits, subsidies, expedited permitting) to make the market viable for many delivery companies.

Recommendations

The preceding findings informed four key recommendations:

GLZs should be made available to multiple modes: green vehicles and cargo bikes. Adequate curb space might be needed to accommodate multiple step-side vans plus a small vehicle and cargo bikes, but this should be balanced against curb utilization rates and anticipated dwell times to maximize curb use.
Explore piloting GLZs in Lower Manhattan and commercial areas of Midtown Manhattan; they could be the most beneficial locations for the pilot according to survey respondents.
The preferred layout for GLZs is several spaces distributed across multiple blocks.
DOT can charge for the GLZ use. It is recommended that rates not exceed current parking prices in the selected neighborhood, but some companies are willing to pay a modest increase over that rate to avoid parking tickets.

Authors: Thomas MaxnerNota GoulianouDr. Andisheh RanjbariDr. Anne Goodchild

Recommended Citation:
Urban Freight Lab (2022). NYC Zero-Emissions Urban Freight and Green Loading Zones Market Research.

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

The Urban Freight Lab (UFL) received $1.5 million in funding from the U.S. Department of Energy to help goods delivery drivers find a place to park without driving around the block in crowded cities for hours, wasting time and fuel and adding to congestion. The project partners will integrate sensor technologies, develop data platforms to process large data streams, and publish a prototype app to let delivery drivers know when a parking space is open – and when it’s predicted to be open so they can plan to arrive when another truck is leaving.

The UFL will also pilot test common carrier locker systems in public and private load/unload spaces near transit stops. Transit riders, downtown workers, and residents will be able to pick up packages they ordered online from any retailer in a convenient and secure locker in a public plaza or outside their office. The benefits don’t stop there. Common carrier lockers create delivery density that increases the productivity of parking spaces and provides significant commercial efficiencies. They do this by reducing the amount of time it takes delivery people to complete their work. 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, it decreases the time their truck needs to be parked, increasing turnover and adding parking capacity in crowded cities.

This is a timely project as cities are looking for new strategies to accommodate the rapid growth of e-commerce. Online shopping has grown by 15% annually for the past 11 years, and is now 9% of total retail sales in the U.S., with $453.5 billion in revenue in 2017. Many online shoppers want the goods delivery system to bring them whatever they want, where they want it, in one to two hours. At the same time, many cities are replacing goods delivery load/unload spaces with transit and bike lanes. Cities need new load/unload space concepts supported by technology to make the leap to autonomous cars and trucks in the street, and autonomous freight vehicles in the Final 50 Feet of the goods delivery system. The Final 50 feet segment starts when a truck parks in a load/unload space, and includes delivery persons’ activities as they maneuver goods along sidewalks and into urban towers to make their deliveries.

The goals of this project are to:

Reduce parking seeking behavior by 20% in the pilot test area by returning current and predicted load/unload space occupancy information to users on a web-based and/or mobile platform to inform real-time parking decisions.
Reduce parcel truck dwell time in pilot test area locations by 30%, thereby increasing productivity of load/unload spaces near common carrier locker systems.
Increase network and commercial firms’ efficiency by increasing curb and alley space occupancy rates, and underutilized private loading bay occupancy in the p.m. peak, in the pilot test area.

Cost-share partnering organizations are:

Seattle Department of Transportation
Bellevue Department of Transportation
CBRE Seattle
King County Metro Transit
Kroger Company
Puget Sound Clean Air Agency
Sound Transit

Members of the UFL are also participating in the project. Pacific National National Laboratory (PNNL) is a partner, completing several of the project tasks.

Technical Report

Transit Corridor Study

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URL: https://mic.comotion.uw.edu/wp-content/uploads/2021/07/TransitCorridorStudy_InterimReport_June2021.pdf

Publication Date: 2021

Summary:

This study is sponsored by Amazon, Bellevue Transportation department, Challenge Seattle, King County Metro, Seattle Department of Transportation, Sound Transit, and Uber, with support from the Mobility Innovation Center at UW CoMotion.

Population and extended economic growth in many Seattle neighborhoods are driving increased demand for private car travel along with transportation services such as ridehailing and on-demand delivery. Together, these trends are adding to existing demand for loading and unloading operations throughout the city, and exacerbating traffic congestion. Anecdotal evidence indicates that passenger/delivery vehicle stops at or next to transit stops can interfere with bus operations, causing longer or more volatile delays. The increased travel times and reduced reliability further erode the attractiveness of transit to travelers. Thus, it is important to understand how transit, ridehailing, and goods delivery vehicles interact in terms of both operations and travel demand.

This project focuses on the analysis of open-source transit data to screen for locations with slow and/or unreliable bus travel times, and couple that data with interference observation, environmental, and traffic-related data to potentially predict the likely causes. We have developed tools to identify transit corridors with high levels of interference from other road users, including passenger cars, ridehailing vehicles and goods delivery vehicles. These tools are applied to transit corridors in Seattle and Bellevue, and methods have been developed to identify likely sources of interference from available data.

We drew on multiple data sources for identifying high-interference corridors in the region, including:

a virtual workshop with participants from beneficiary agencies and stakeholders to solicit input;
an online crowdsourcing survey to engage the community and gather feedback from all road users;
route-level ridership data from King County Metro; and
aggregated pick-up/drop-off data on ridehailing activities from SharedStreets.

Data was consolidated and 10 corridors were selected based on their likelihood of containing interference between buses and other road users, transit ridership levels, and stakeholder and community feedback.

In addition, we have developed a tool for identifying corridors with slow and/or unreliable bus travel times from open-source real-time transit data. We implemented a pipeline for ingesting and analyzing King County Metro’s real-time Generalized Transit Feed Specification data (GTFS-RT) at 10-second intervals. Using this pipeline, active bus coordinate and schedule adherence data has been scraped and stored to an Amazon Web Services (AWS) server since September 2020. We developed efficient methods to aggregate tracked bus locations and assign them to roadway segments, and quantified delays in terms of schedule deviation and ratio of median to free-flow speeds, among other metrics. We have developed a web based visualization tool to display this data, and it is being updated daily with aggregated performance metrics from our database.

To collect ground truth validation data along selected corridors, we implemented an online data collection tool for field observations, and recruited research assistants to observe bus operations along the study corridors and record information on bus traversals and instances of interference. This dataset is analyzed alongside the GTFS-RT data, environmental, and traffic related data to identify instances of delay and predict the likely causes.

Field data was collected for three weeks along eight of the selected corridors in March 2021, but was later paused due to depressed levels of transportation activity during the COVID-19 pandemic and the current unstable condition of travel choices and city traffic (and thus interferences). Preliminary analysis on the collected data revealed that there is not a substantial effect shown in the GTFS-RT data when a bus is interfered with; however, there were not a lot of interference observations in the collected field data. So, it remains to be seen whether the lack of an identifiable effect is due to the lack of ground truth data, lack of precision in the automatic vehicle location system, or the relatively low impact of an interference when compared to the effects of general traffic congestion, signals, and other roadway conditions. A linear regression model was also generated to determine the extent to which roadway characteristics can predict segment performance, which produced mildly predictive results.

As businesses and transit services continue to reopen, there will likely be an increase in the amount of transit interference experienced between buses and other roadway users, which will potentially allow for the gathering of more ground truth validation data. Field observations will resume in late Summer/early Fall 2021 and will continue until enough data is collected to either (1) model connections between observed interference and bus delays in the GTFS-RT data; or (2) determine whether significant delays cannot be linked to observed instances of interference in the study corridors. The GTFS-RT data scraping will continue daily, and summarized in the developed interactive visualization tool.

The major anticipated benefits of the project can be summarized as follows:

This work will help identify network-wide road and route segments with slow and/or unreliable bus travel times. We may also be able to identify main causes of delay in the study corridors.
Moreover, we expect that this work will generate reusable analytical tools that can be applied by local agencies on an ongoing basis, and by other researchers and transportation agencies in their own jurisdictions.
The outcomes of this work will enable identifying corridors with slow and/or unreliable bus travel times as candidates for specific countermeasures to increase transit performance, such as increased enforcement, modified curb use rules, or preferential bus or street use treatments. Targeting such countermeasures towards priority locations will result in faster and more reliable bus operations, and a more efficient transportation network at a lower cost to transit agencies.

Authors: Dr. Andisheh Ranjbari, Zack Aemmer, Borna Arabkhedri, Don MacKenzie