Special Issue

The Curb Lane

URL: https://www.sciencedirect.com/journal/transportation-research-part-a-policy-and-practice/special-issue/10F7Q5183NN

Publication: Transportation Research Part A: Policy and Practice

Publication Date: 2021

Summary:

Efforts to regulate the curb also suffer from a lack of publicly accessible data on both the demand and supply of curb space. Cities often do not have the technical expertise to develop a curb data collection and data-sharing strategy. In addition, the private individuals and companies that generate most of the curb-use data often withhold them from public use to protect proprietary information and personal user data.

However, new uses of data sources, such as the Global Positioning System (GPS) and cellular networks, as well as the implementation of wide networks of IoT devices, are enabling the “digitization” of the curb, allowing cities to gain a better understanding of curb use as well as ways to change their approach toward curb space management.

In a way, the revolution in curb space management has already started. Many cities are re-inventing their role from passively regulating on-street parking to dynamically allocating and managing the curb, both physically and digitally, to serve many different users. Geofencing and time-dependent allocation of curb space facilitate efficient passenger pickup and drop off. Parking information systems and pay-for-parking apps enable dynamic parking allocation and pricing. We believe this is the right time for scientific research to “catch up” with current changes and to develop new analytical tools for curb space management. Such efforts are the focus of this special issue on curb lane analysis and policy.

Authors: Dr. Anne GoodchildDr. Giacomo Dalla ChiaraDr. Andisheh Ranjbari, Susan Shaheen (University of California, Berkeley), Donald Shoup (UCLA)

Recommended Citation:
Special Issue: The Curb Lane. Transportation Research Part A: Policy and Practice | ScienceDirect.com by Elsevier.

Paper

Understanding Urban Commercial Vehicle Driver Behaviors and Decision Making

Download PDF (1.85 MB)

URL: https://journals.sagepub.com/doi/pdf/10.1177/03611981211003575

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

Volume: 2675 (9)

Publication Date: 2021

Summary:

As e-commerce 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 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 (1), 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 policymakers 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 (2). Lack of data results in a lack of fundamental knowledge of the urban freight system, inhibiting policy makers’ ability to make data-driven decisions (3).

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. (5) 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. (5) 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:
Dalla Chiara, G., Krutein, K. F., Ranjbari, A., & Goodchild, A. (2021). Understanding Urban Commercial Vehicle Driver Behaviors and Decision Making. Transportation Research Record: Journal of the Transportation Research Board, 036119812110035. https://doi.org/10.1177/03611981211003575.

Technical Report

Insights from Driver Parking Decisions in a Truck Simulator to Inform Curb Management Decisions

Download PDF (3.37 MB)

URL: http://hdl.handle.net/1773/49802

Publication Date: 2023

Summary:

Millions of people who live and work in cities purchase goods online. As ecommerce and urban deliveries spike, there is an increasing demand for curbside loading and unloading space. To better manage city curb spaces for urban freight, city planners and decision makers need to understand commercial vehicle driver behaviors and the factors they consider when parking at the curb.

Urban freight transportation is a diverse phenomenon. Commercial vehicle drivers must overcome several obstacles and adapt to various rules and policies to properly navigate the intricate metropolitan network and make deliveries and pick-ups. However, other road users and occasionally municipal planners generally view them as contributing considerably to urban congestio, responsible for unauthorized parking, double parking, and exceeding their legal parking time.

These realities reflect the need for a thorough comprehension of commercial vehicle operators’ core decision-making procedures and parking habits to inform and adjust curb management policies and procedures. However, more robust corroborated literature on the subject is needed. The information used in these studies is typically obtained from empirical field research, which, while valuable, is limited to certain situations and case scenarios. Therefore, to improve the operation of urban transportation networks, it is necessary to study commercial vehicle drivers’ parking behavior in a controlled environment.

This project used a heavy vehicle driving simulator to examine commercial vehicle drivers’ curbside parking behaviors in various environments in shared urban areas. Also observed were the interactions between commercial vehicle drivers and other road users.

The experiment was successfully completed by 12 participants. Five independent variables were included in this experiment: number of lanes (two-lane and four-lane roads), bike lane existence, passenger vehicle parking space availability, commercial vehicle loading zones (CVLZs) (no CVLZ, occupied CVLZs, and unoccupied CVLZs), and parking time (short-term parking: 3 to 5 minutes and long-term parking: 20 to 60 minutes). The heavy vehicle driving simulator also collected data regarding participants’ driving speed, eye movement, and stress level.

Results from the heavy vehicle driving simulator experiment indicated that the presence of a bike lane had significant effects on commercial vehicle drivers’ parking decisions., but only a slight effect on fixation duration times. The average fixation duration time, representing how long participants looked at a particular object, on the road with a bike lane was 4.81 seconds, whereas it was 5.25 seconds on roads without a bike lane. Results also showed that the frequency of illegal parking (not parking in the CVLZs) was greater during short-term parking activities, occurring 60 times (45 percent of parking maneuvers). Delivery times also had a slight effect on commercial vehicles’ speed while searching for parking (short-term parking was 17.7 mph; long term parking was 17.2 mph) and on drivers’ level of stress (short-term parking was 8.16 peaks/mins; long-term parking was 8.36 peaks/mins). Seven percent of participants chose to park in the travel lane, which suggested that commercial vehicle operators prioritize minimizing their walking distance to the destination over the violation of parking regulations.

The limited sample size demonstrated the value of our experimental approach but limited the strength of the recommendations that can be applied to practice. With that limitation acknowledged, our preliminary recommendations for city planners include infrastructure installation (i.e., convex mirrors installed at the curbside and CVLZ signs) to help drivers more easily identify legal parking spaces, and pavement markings (i.e., CVLZs, buffered bike lanes) to improve safety when parking. Parking time limits and buffers for bike lanes could improve efficient operation and safety for cyclists and other road users.

For future work, larger sample sizes should be collected. Additional factors could be considered, such as increased traffic flow, pedestrian traffic, conflicts among multiple delivery vehicles simultaneously, various curb use type allocations, and different curb policies and enforcement. Including a larger variety of commercial vehicle sizes and loading, zone sizes would also be of value. A combination of field observations and a driving simulator study could also help validate this investigation’s outcomes.

Authors: Dr. Andisheh RanjbariDr. Anne GoodchildDr. Ed McCormackRishi Verma, David S. Hurwitz (Oregon State University), Yujun Liu (Oregon State University), Hisham Jashami (Oregon State University)

Recommended Citation:
Goodchild, A., McCormack, E., Hurwitz, D., Ranjbari, A., Verma, R., Liu, Y., & Jashami, H. (2023). Insights from Driver Parking Decisions in a Truck Simulator to Inform Curb Management Decisions. PacTrans.

Empirical Analysis of Commercial Vehicle Dwell Times Around Freight-Attracting Urban Buildings in Downtown Seattle

Download PDF (4.24 MB)

URL: https://doi.org/10.1016/j.tra.2021.02.019

Publication: Transportation Research Part A: Policy and Practice

Volume: 147

Pages: 320-338

Publication Date: 2021

Summary:

This study aims to identify factors correlated with dwell time for commercial vehicles (the time that delivery workers spend performing out-of-vehicle activities while parked). While restricting vehicle dwell time is widely used to manage commercial vehicle parking behavior, there is insufficient data to help assess the effectiveness of these restrictions, which makes it difficult for policymakers to account for the complexity of commercial vehicle parking behavior.

This is accomplished by using generalized linear models with data collected from five buildings that are known to include commercial vehicle activities in the downtown area of Seattle, Washington, USA. Our models showed that dwell times for buildings with concierge services tended to be shorter. Deliveries of documents also tended to have shorter dwell times than oversized supplies deliveries. Passenger vehicle deliveries had shorter dwell times than deliveries made with vehicles with roll-up doors or swing doors (e.g., vans and trucks). When there were deliveries made to multiple locations within a building, the dwell times were significantly longer than dwell times made to one location in a building. The findings from the presented models demonstrate the potential for improving future parking policies for commercial vehicles by considering data collected from different building types, delivered goods, and vehicle types.

Authors: Haena KimDr. Anne Goodchild, Linda Ng Boyle

Recommended Citation:
Kim, H., Goodchild, A., & Boyle, L. N. (2021). Empirical analysis of commercial vehicle dwell times around freight-attracting urban buildings in downtown Seattle. Transportation Research Part A: Policy and Practice, 147, 320–338. https://doi.org/10.1016/j.tra.2021.02.019

Presentation

Growth of Ecommerce and Ride-Hailing Services is Reshaping Cities Connecting State and City DOTs, and Transit Agencies for Innovative Solutions

Download Slides PDF (5.26 MB)

URL: https://policy.transportation.org/wp-content/uploads/sites/35/2018/09/Growth-of-E-Commerce-and-Ride-Hailing-Services-Urban-Freight-Lab.pdf

Publication: AASHTO 2018 Joint Policy Conference: Connecting the DOTs

Volume: 19-Jul-18

Publication Date: 2018

Summary:

There is not enough curb capacity, now.

A recent curb parking utilization study in the City of Seattle indicated 90% or higher occupancy rates in Commercial Vehicle Load Zones (CVLZs) for some areas for much of the workday.

The Final Fifty Feet is a new research field.

The Final 50 Feet project is the first time that researchers have analyzed both the street network and cities’ vertical space as one unified goods delivery system. It focuses on:

The use of scarce curb, buildings’ internal loading bays, and alley space
How delivery people move with handcarts through intersections and sidewalks; and
On the delivery processes inside urban towers.

Authors: Barbara Ivanov

Student Thesis and Dissertations

Estimating the Location of Private Infrastructure for Delivery and Pick-Up Operations in Dense Urban Areas

URL: http://hdl.handle.net/1773/42251

Publication Date: 2018

Summary:

The growth of home deliveries, lower inventory levels and just-in-time deliveries drive the fragmentation of freight flows, increased frequency, more delivery addresses and smaller volumes. This leads to trucks inefficiently loaded and consequently more trucks in the road contributing to the growing congestion in cities. According to a study by INRIX and the Texas Transportation Institute, travelers in the U.S. are stuck 42 hours per rush hour commuter in their cars in 2014, that is twice what it was in 1982 and the problem is four times worse than in 1982 for cities of 500,000 people or less [28]. At the same time, a historical lack of integration of the freight transportation system into city planning efforts has left local governments unprepared. Under these circumstances, there is growing need for best practices for freight planning and management in U.S. cities. There is anecdotal evidence that the lack of areas for trucks to park and load/unload freight is one of the main causes of an inefficient urban freight parking infrastructure that leads to illegal parking and more congestion. The problem of lack of parking for freight loading/unloading has been studied with a focus on on-street parking. Meanwhile, the contribution of areas out of the public right of way (i.e. private) such as loading bays in buildings has not benefited from research. More importantly, the location and features of private freight parking are often unknown by local governments due to their private character.

This thesis presents the first predictive tool to estimate the presence of private freight loading/unloading infrastructure based on observable characteristics of property parcels and their buildings. The predictive model classifies parcels with and without these infrastructures using random forest, a supervised machine learning algorithm. The model was developed based on a rich geodatabase of private truck load/unload spaces in the City of Seattle and the King County tax parcel database. The performance of the random forest model was evaluated through cross-validated estimates of the test error. The distribution of the outcome variables is unbalance with over 90% of parcels without private freight infrastructure. To consider the problem of unbalance sample, the optimum model was set to maximize the area under the ROC curve (AUC). The authors investigated the confusion matrix and the model classifier was design to balance the sensitivity and specificity of the model. Model results showed AUC of 81.5%, a true positive rate of 82.1% and a misclassification error of 22.5%.

This research provides an assessment tool that reduces the field work required to develop a quality inventory of private freight loading/unloading infrastructure by targeting the parcel stock and making data collection methods more effective. Local governments can use this research to inform efforts to revise and update delivery operations and regulations of truck parking and loading.

Authors: José Luis Machado León

Recommended Citation:
Machado Leon, Jose Luis. (2018). Estimating the Location of Private Infrastructure for Delivery and Pick-Up Operations in Dense Urban Areas. University of Washington Master's Degree Thesis.

Thesis: Array

Paper

How Cargo Cycle Drivers Use the Urban Transport Infrastructure

Download PDF (10.47 MB)

URL: https://doi.org/10.1016/j.tra.2022.103562

Publication: Transportation Research Part A: Policy and Practice

Volume: 167

Publication Date: 2023

Summary:

Electric cargo cycles are often considered a viable alternative mode for delivering goods in an urban area. However, cities in the U.S. are struggling to regulate cargo cycles, with most authorities applying the same rules used for motorized vehicles or traditional bikes. One reason is the lack of understanding of the relationships between existing regulations, transport infrastructure, and cargo cycle parking and driving behaviors.

In this study, we analyzed a cargo cycle pilot test in Seattle and collected detailed data on the types of infrastructure used for driving and parking. GPS data were augmented by installing a video camera on the cargo cycle and recording the types of infrastructure used (distinguishing between the travel lane, bicycle lane, and sidewalk), the time spent on each type, and the activity performed.

The analysis created a first-of-its-kind, detailed profile of the parking and driving behaviors of a cargo cycle driver. We observed a strong preference for parking (80 percent of the time) and driving (37 percent of the time) on the sidewalk. We also observed that cargo cycle parking was generally short (about 4 min), and the driver parked very close to the delivery address (30 m on average) and made only one delivery. Using a random utility model, we identified the infrastructure design parameters that would incentivize drivers to not use the sidewalk and to drive more on travel and bicycle lanes.

The results from this study can be used to better plan for a future in which cargo cycles are used to make deliveries in urban areas.

Authors: Dr. Giacomo Dalla ChiaraGriffin DonnellyŞeyma GüneşDr. Anne Goodchild

Recommended Citation:
Dalla Chiara, G., Donnelly, G., Gunes, S., & Goodchild, A. (2023). How Cargo Cycle Drivers Use the Urban Transport Infrastructure. Transportation Research Part A: Policy and Practice, 167, 103562. https://doi.org/10.1016/j.tra.2022.103562

Technical Report

Year Two Progress 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

Download PDF (2.46 MB)

Publication: U.S. Department of Energy

Publication Date: 2021

Summary:

The objectives of this project are to develop and implement a technology solution to support research, development, and demonstration of data processing techniques, models, simulations, a smart phone application, and a visual-confirmation system to:

Reduce delivery vehicle parking seeking behavior by approximately 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 areas in Seattle and Bellevue, Washington, by approximately 30%, thereby increasing productivity of load/unload spaces near common carrier locker systems, and
Improve the transportation network (which includes roads, intersections, warehouses, fulfillment centers, etc.) and commercial firms’ efficiency by increasing curb occupancy rates to roughly 80%, and alley space occupancy rates from 46% to 60% during peak hours, and increasing private loading bay occupancy rates in the afternoon peak times, in the pilot test area.

The project team has designed a 3-year plan to achieve the objectives of this project.

In Year 1, the team developed integrated technologies and finalized the pilot test parameters. This involved finalizing the plan for placing sensory devices and common parcel locker systems on public and private property; issuing the request for proposals; selecting vendors; and gaining approvals necessary to execute the plan. The team also developed techniques to preprocess the data streams from the sensor devices, and began to design the prototype smart phone parking app to display real-time load/unload space availability, as well as the truck load/unload space behavior model.

In Year 2, the team executed the implementation plan:

oversaw installation of the in-road sensors, and collecting and processing data,
managed installation, marketing and operations of three common locker systems in the pilot test area,
tested the prototype smart phone parking app with initial data stream, and
developed a truck parking behavior simulation model.

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2021). Year Two Progress Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.

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

Download PDF (7.07 MB)

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.

Technical Report

Year One Progress 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

Download PDF (5.08 MB)

Publication: U.S. Department of Energy

Publication Date: 2019

Summary:

Reduce delivery vehicle parking seeking behavior by approximately 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 areas in Seattle and Bellevue, Washington, by approximately 30%, thereby increasing productivity of load/unload spaces near common carrier locker systems, and
Improve the transportation network (which includes roads, intersections, warehouses, fulfillment centers, etc.) and commercial firms’ efficiency by increasing curb occupancy rates to roughly 80%, and alley space occupancy rates from 46% to 60% during peak hours, and increasing private loading bay occupancy rates in the afternoon peak times, in the pilot test area.

The project team has designed a 3-year plan, as follows, to achieve the objectives of this project.

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2020). Year One Progress Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.