Paper

Do Commercial Vehicles Cruise for Parking? Empirical Evidence from Seattle

URL: https://doi.org/10.1016/j.tranpol.2020.06.013

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.

Authors: Dr. Anne GoodchildDr. Giacomo Dalla Chiara

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

Report

Analysis of Online Shopping and Shopping Travel Behaviors in West Seattle

Download PDF (1.27 MB)

Publication Date: 2023

Summary:

The purpose of this research is to explore consumers’ online shopping and in-person shopping travel behaviors and the factors affecting these behaviors within the geographical context of the study area of West Seattle.

West Seattle is a peninsula located southwest of downtown Seattle, Washington State. In March 2020, the West Seattle High Bridge, the main bridge connecting the peninsula to the rest of the city, was closed to traffic due to its increased rate of structural deterioration. The closure resulted in most of the traffic being re-distributed across other bridges, forcing many travelers to re-route their trips in and out of the peninsula. At about the same time, the COVID-19 pandemic caused business-shuttering lockdowns. Both events fundamentally changed the nature of shopping and the urban logistics system of the study area.

The Seattle Department of Transportation (SDOT) engaged the Urban Freight Lab (UFL) at the University of Washington to conduct research to understand current freight movements and goods demands in West Seattle and identify challenges related to the bridge closure to inform data-driven mitigation strategies. The study took place in two phases: the first phase documented the challenges experienced by local businesses and carriers through a series of interviews and quantified the freight trip generated by land use in the case study area1 ; the second phase, described in the current report, performed an online survey to understand online shopping and in-person shopping travel behaviors for West Seattle residents.

The main objectives of the current study are twofold:

Describe online shopping and shopping travel consumer behaviors for West Seattle residents.
Understand what factors influence consumer shopping behaviors, from accessibility to local stores, to the characteristics of goods purchased, to socio-economic factors.

Methods

To address these objectives, the research team designed an online questionnaire that was advertised through a West Seattle Bridge Closure-related SDOT newsletter and other local online media outlets during the spring and summer of 2022. The questionnaire asked respondents about their socioeconomic conditions (age, income, education, etc.), where they live and their access to transportation (vehicle ownership and types of vehicles), their online shopping behavior, the impact of the West Seattle High Bridge closure on their shopping habits, and about their most recent purchase for a given category of goods among clothing items, groceries, restaurant food, and household supplies. The questionnaire was collected anonymously, and no personally identifiable information was collected. A total of 1,262 responses were collected, and after data processing, the final sample data consisted of 919 responses, corresponding approximately to 1 percent of the study area population.

Comparing the socioeconomic characteristics of the sample with those of the West Seattle study population it should be noted that individuals identifying themselves as white and female and of older age were oversampled, while individuals with lower than a college degree and with annual income less than $50,000 were under-sampled. Therefore, the sample in general is more representative of a more affluent, older population.

Key Findings

The key findings are summarized as follows:

Online shopping is widespread for clothing items and restaurant food.

Respondents receive on average 5 deliveries per week, across all goods categories. 38.7 percent of the respondents reported performing their most recent shopping activity online, considering all goods categories. However, the frequency of online shopping varied across different goods categories. Most of the respondents that purchased groceries or household supplies reported having shopped in person (89 and 75 percent of the respondents respectively), while, in contrast, for those that purchased restaurant food and clothing items, two-thirds of respondents reported buying online in both categories. Online shopping is widespread in the clothing and restaurant food markets, but less in grocery and household supplies markets. Of the consumers that shopped online for restaurant food, 76 percent of them decided to travel to take out (also referred to as curbside pickup), and only 24 percent of them chose to have the meal delivered directly to their home.

Online shopping is more widespread among mobility-impaired individuals

Participants were asked whether they had a disability that limited physical activities such as carrying, walking, lifting, etc. Of the 918 participants, 98 (11%) responded that they did have a disability that fit this description. The share of respondents that shop online was higher among mobility-impaired individuals (30 percent online for delivery and 19 percent online for pick-up) compared to individuals that did not report any mobility impairment (23 percent online for delivery and 12 percent online for pick-up).

Driving is the predominant shopping travel mode

Of the sample of respondents, 96 percent reported having access to a motorized vehicle within their household. Driving is also the most common shopping mode of in-person travel, with 81.3 percent of respondents reporting that they drove to a store to shop. Walking is a distant second preferred shopping travel mode, with 13.1 percent of respondents reporting having walked to a store. Biking and public transit were rarely adopted as a shopping travel mode, together they were observed 5.6 percent of the time. Though included as a travel option, only 1 participant reported using a rideshare vehicle to shop.

Electrification in West Seattle

Of the respondents that have access to a motorized vehicle in their households, 9.8 percent of them reported owning an electric vehicle. Car ownership is much more widespread than bike ownership, with 51.6 percent of the respondents reporting having access to a bike. Among these, 15.5 percent of them said that at least one of their bikes is electric.

The 10-minute city

The average walking time across all types of goods purchased was 10 minutes. The average driving time, for those respondents that reported driving to a store, was also about 10 minutes, except for those who reported purchasing clothing items, which reported on average of 27-minute trip time (both using a private car or using public transit). The longest travel times are seen mostly for respondents that took public transit as a shopping travel mode.

Living in proximity to stores reduces driving and online deliveries

A higher number of stores within a 10-minute walking distance (0.5 miles) is correlated with a higher number of consumers choosing to walk to a store, compared to those that chose to drive to a store or that shopped online. This is true across all goods types, but it is more significantly seen in grocery shopping. Moreover, accessibility to commercial establishments at a walking distance has a stronger impact on reducing the likelihood of driving, and at a lesser magnitude, reduces the propensity of shopping online.

Delivery to the doorstep is the most common destination for online deliveries

For those that chose to buy online, the most common delivery destination was at the respondents’ home doorstep (84 percent of respondents reported receiving online deliveries at home). The second most frequently used delivery destination was parcel lockers (15 percent of respondents), with 12 percent of respondents making use of private lockers, while only 3 percent made use of public lockers. The remaining one percent received deliveries at other destinations (e.g. office or nearby store).

The West Seattle High Bridge closure incentivized local shopping

When asked about the impacts of the West Seattle Bridge closure on individual online and shopping travel behaviors, more respondents reported buying more locally and online, vs. traveling farther for shopping and buying in person.

Authors: Dr. Anne GoodchildDr. Giacomo Dalla ChiaraRishi VermaKelly Rula

Recommended Citation:
Goodchild, A., Dalla Chiara, G., Verma, R., Rula, K. (2023) Analysis of Online Shopping and Shopping Travel Behaviors in West Seattle, Urban Freight Lab.

Report

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

Download PDF (3.99 MB)

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.

Technical Report

Freight and Transit Lane Case Study

Download PDF (3.57 MB)

Publication Date: 2020

Summary:

The Seattle Department of Transportation (SDOT) engaged the Urban Freight Lab at the Supply Chain Transportation and Logistics Center at the University of Washington to conduct research on the impacts of a freight and transit (FAT) lane that was implemented in January 2019 in Seattle. To improve freight mobility in the City of Seattle and realize the objectives included in the city’s Freight Master Plan (FMP), the FAT lane was opened upon the closing of the Alaskan Way Viaduct.

The objective of this study was therefore to evaluate the performance and utilization of the FAT lane. Street camera video recordings from two separate intersection locations were used for this research.

Vehicles were categorized into ten different groups, including drayage with container and drayage without container, to capture their different behavior. Drayage vehicles are vehicles transporting cargo to a warehouse or to another port. Human data reducers used street camera videos to count vehicles in those ten designated groups.

The results of the traffic volume analysis showed that transit vehicles chose the FAT lane over the general purpose lane at ratios of higher than 90 percent. By the time of day, transit vehicle volumes in the FAT lane followed a different pattern than freight vehicles. Transit vehicle volumes peaked around afternoon rush hours, but freight activity decreased during that same time. Some freight vehicles used the FAT lane, but their ratio in the FAT lane decreased when bus volumes increased. The ratio of unauthorized vehicles in the FAT lane increased during congestion.

Further analysis described in this report included a multinomial logistic regression model to estimate the factors influencing the choice of FAT lane over the regular lane. The results showed that lane choice was dependent on the day of week, time of day, vehicle type, and location features. Density, as a measure of congestion, was found to be statistically insignificant for the model.

Authors: Urban Freight Lab

Recommended Citation:
Urban Freight Lab (2020). Freight and Transit Lane Case Study.

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

Paper

An Agent-Based Simulation Assessment Of Freight Parking Demand Management Strategies For Large Urban Freight Generators

Download PDF (3.76 MB)

URL: https://doi.org/10.1016/j.rtbm.2022.100804

Publication: Research in Transportation Business & Management

Volume: 42

Publication Date: 2022

Summary:

A growing body of research looks specifically at freight vehicle parking choices for purposes of deliveries to street retail, and choice impacts on travel time/uncertainty, congestion, and emissions. However, little attention was given to large urban freight traffic generators, e.g., shopping malls and commercial buildings with offices and retail. These pose different challenges to manage freight vehicle parking demand, due to the limited parking options. To study these, we propose an agent-based simulation approach which integrates data-driven parking-choice models and a demand/supply simulation model. A case study compares demand management strategies (DMS), influencing parking choices, and their impact in reducing freight vehicle parking externalities, such as traffic congestion. DMS include changes to parking capacity, availability, and pricing as well as services (centralized receiving) and technology-based solutions (directed parking). The case study for a commercial region in Singapore shows DMS can improve travel time, parking costs, emission levels and reducing the queuing. This study contributes with a generalizable method, and to local understanding of technology and policy potential. The latter can be of value for managers of large traffic generators and public authorities as a way to understand to select suitable DMS.

Authors: Dr. Giacomo Dalla Chiara, Andre Alho, Simon Oh, Ravi Seshadri, Wen Han Chong, Takanori Sakai, Lynette Cheah, Moshe Ben-Akiva

Recommended Citation:
Alho, A., Oh, S., Seshadri, R., Dalla Chiara, G., Chong, W. H., Sakai, T., Cheah, L., & Ben-Akiva, M. (2022). An agent-based simulation assessment of freight parking demand management strategies for large urban freight generators. Research in Transportation Business & Management, 42, 100804. https://doi.org/10.1016/j.rtbm.2022.100804

Technical Report

Developing Design Guidelines for Commercial Vehicle Envelopes on Urban Streets (Technical Report)

Download PDF (5.66 MB)

URL: https://rosap.ntl.bts.gov/view/dot/55776

Publication Date: 2020

Summary:

This report presents research to improve the understanding of curb space and delivery needs in urban areas. Observations of delivery operations to determine vehicle type, loading actions, door locations, and accessories used were conducted. Once common practices had been identified, then simulated loading activities were measured to quantify different types of loading space requirements around commercial vehicles. This resulted in a robust measurement of the operating envelope required to reduce conflicts between truck loading and unloading activities with adjacent pedestrian, bicycle, and motor vehicle activities.

A bicycling simulator experiment examined bicycle and truck interactions in a variety of CVLZ designs. The experiment was completed by 50 participants. The bicycling simulator collected data regarding a participant’s velocity, lane position, and acceleration. Three independent variables were included in this experiment: pavement marking (No, Minimum, or Recommended CVLZ), Courier Position (none, behind vehicle, on driver’s side), and Accessory (none or hand truck). The results support the development of commercial loading zone design recommendations that will allow our urban street system to operate more efficiently, safely, and reliably for all users.

As urban populations and freight activities grow, there is continued pressure for multiple modes to share urban streets and compete for curb space. Cities are recognizing curb space as valuable public real estate that must be better understood and designed in order to improve the quality of life for residents and the transportation systems of cities.

Current commercial vehicle load zones are not well designed to accommodate safe, efficient, and reliable deliveries. Commercial vehicles using urban curbside loading zones are not typically provided with a consistent envelope, or a space allocation adjacent to the vehicle for deliveries. While completing loading and unloading activities, drivers are required to walk around the vehicle, extend ramps and handling equipment, and maneuver goods; these activities require space around the vehicle. But these unique space needs of delivery trucks are not commonly acknowledged by or incorporated in current urban design practices. Due to this lack of a truck envelope, drivers of commercial vehicles are observed using pedestrian pathways and bicycling infrastructure for unloading activities as well as walking in traffic lanes. These actions put themselves, and other road users in direct conflict and potentially in harm’s way.

This project improves our understanding of curb space requirements and delivery needs in urban areas. The research approach involved the observation of delivery activities operations to measure the envelope required for different vehicle types, loading actions, door locations, and accessories. Once the envelope was determined the (simulator was used).

Common loading and unloading practices and where freight activities occurred in relationship to trucks (sides, back, or front) were initially identified by observing twenty-five curbside deliveries in urban Seattle. The research team next collaborated with three delivery companies with active operations in urban areas. These companies proved access to their facilities, nine different urban delivery vehicles, and a variety of loading accessories. The research team initially recorded the commercial vehicle’s closed vehicle footprint without any possible extensions engaged. Next the open vehicle footprint was measured when all vehicle parts such as doors, lift gates, and ramps were extended for delivery operations. Finally, the active vehicle footprint was recorded as the companies’ drivers simulated deliveries which allowed the research team to observe and precisely measure driver and accessory paths around the vehicle.

This process resulted in robust measurements, tailored to different types of truck configurations, loading equipment and accessories, of the operating envelope around a commercial vehicle. These measurements, added to the foot print of a user-selected delivery truck sizes, provides the envelope needed to reduce conflicts between truck loading and unloading activities and adjacent pedestrian, bicycle, and motor vehicle activities.

A bicycling simulator experiment examined bicycle and truck interactions in a variety of CVLZ designs. The experiment was successfully completed by 50 participants. The bicycling simulator collected data regarding a participant’s velocity, lane position, and acceleration.

Three independent variables were included in this experiment: pavement marking (No, Minimum, or Recommended CVLZ), Courier Position (none, behind vehicle, on driver’s side), and Accessory (none or hand truck). Several summary observations resulted from the bicycling simulator experiment:

A bicyclist passing by no loading zone (truck is obstructing bike lane) or minimum loading zone (truck next to the bike lane without a buffer) had a significantly lower speed than a bicyclist passing a preferred loading zone (truck has an extra buffer). A smaller loading zone had a ix decreasing effect on mean speed, with a courier exiting on the driver side of the truck causing the lowest mean speed.
A courier on the driver’s side of the truck had an increasing effect on mean lateral position, with a no CVLZ causing the highest divergence from the right edge of the bike lane. Consequently, bicyclists shifted their position toward the left edge of bike lane and into the adjacent travel lane. Moreover, some bicyclists used the crosswalk to avoid the delivery truck and the travel lane.
In the presence of a courier on the driver’s side of the truck, the minimum CVLZ tended to be the most disruptive for bicyclists since they tended to depart from the bike lane toward the adjacent vehicular travel lane.
When the bicyclist approached a delivery vehicle parked in the bicycle lane, they had to choose between using the travel lane or the sidewalk. About one third of participants decided to use the sidewalk.

From our results, commercial loading zone best practice envelope recommendations can be developed that will allow our urban street system to operate more efficiently, safely, and reliably for all users

Authors: Dr. Ed McCormackDr. Anne GoodchildManali Sheth, David S. Hurwitz, Hisham Jashami, Douglas P. Cobb

Recommended Citation:
McCormack, Ed. Anne Goodchild, Manali Sheth, et.al. (2020). Developing Design Guidelines for Commercial Vehicle Envelopes on Urban Streets.

West Seattle Bridge Case Study (Phase II)

This project is a continuation of the West Seattle Bridge Case Study Phase I.

Background:
West Seattle (WS) is an area of the city of Seattle, Washington, located on a peninsula west of the Duwamish waterway and east of the Puget Sound. In March 2020, the West Seattle High Bridge (WSHB), the main bridge connecting WS to the rest of the city, was closed to traffic due to its increasing rate of structural deterioration.

The Seattle Department of Transportation (SDOT) has engaged the Supply Chain Transportation and Logistics Center (SCTL) at the University of Washington, to conduct research to understand current freight movements and freight demands in WS and identify challenges related to the bridge closure to inform data-driven mitigation strategies.

In project Phase 1 the research team performed a freight trip generation (FTG) estimation and conducted interviews with local business establishments, carriers, and the Port of Seattle. As a result of the FTG modeling, the research team estimated that 94 percent of the freight trips generated by WS are destined to residential buildings. Moreover, the interviews identified disruptions in the supply chains of small and medium-size local businesses as well as carriers facing longer travel times to access the peninsula.

Research Objectives:
In Phase 2 of the project, the research team will shift the focus from business establishments to consumers. In particular, we will explore consumer behavior, defined as how people choose to buy goods and services and where they buy them, to better understand residential demand and accessibility of goods for WS residents.

This study will make use of a consumer survey for Seattle residents to:

Describe consumer behavior and buying habits for Seattle residents, in particular, we will address how (online vs. in-person and with which travel mode), where (locally or not-locally), and how often people shop.
Better understand what drives consumer behavior, in particular how consumer behavior is impacted by urban form (transport infrastructure available, land uses, urban density, etc.), access to transportation, local access to stores, and socioeconomic characteristics.

Tasks:

Gather public datasets and review previous consumer surveys: The research team will review and summarize publicly available datasets that contain information on consumer behaviors and urban form for Seattle residents, for instance, the Puget Sound Regional Council (PSRC) data, the National Household Travel Survey (NHTS), the Freight Trip Generation (FTG) estimates from Phase 1, the Google Maps APIs and the publicly available Seattle Department of Transportation (SDOT) GIS layers. The research team will also scan the scientific literature and reports to inform the design of the survey on consumer behavior.
Survey Design: The research team will design a consumer survey and a method of survey distribution. The survey will include socioeconomic data (e.g. age, gender, income, education, household composition, car ownership), geographical location (where the interviewee lives), consumer behavior (e.g. types of goods purchased, the amount spent, where goods are purchased, mode of travel, whether goods were purchased online or in-person, how often the purchases take place). SDOT will be provided the opportunity to review and give comments on the draft survey before the survey roll-out.
Survey roll-out: The approved survey will be distributed to residents of the agreed study area. The survey will be drafted as an online survey. SDOT will reserve the option to further expand the survey reach, for instance by creating and distributing a paper version of the survey, translating the survey to other languages, use SDOT channels to distribute the survey.
Analysis of survey data: Data from the survey will be analyzed. A descriptive statistical analysis will be performed, addressing questions such as how people consume, how far people travel to purchase goods, what is the preferred mode of transportation for shopping trips, and how frequently people purchase things online vs. in person. A second part of the analysis will focus on understanding the relationship between socioeconomic variables and urban form variables with consumer behavior variables.
Reporting: A final report will be drafted reporting on the survey design and method, a data description, and data analysis addressing the project goals. SDOT will review and confirm the final report before publication on the SCTL website.

Deliverables: Final project report and executive summary

Budget: $60,000
Timeline: January to December 2022

Article

A Framework to Assess Pedestrian Exposure Using Personal Device Data

Download PDF (1.72 MB)

URL: https://doi.org/10.1177/1071181322661319

Publication: Human Factors and Ergonomics Society

Volume: 66 (1)

Pages: 320 - 324

Publication Date: 2022

Summary:

Capturing pedestrian exposure is important to assess the likelihood of a pedestrian-vehicle crash. In this study, we show how data collected on pedestrians using personal electronic devices can provide insights on exposure. This paper presents a framework for capturing exposure using spatial pedestrian movements based on GPS coordinates collected from accelerometers, deﬁned as walking bouts. The process includes extracting and cleaning the walking bouts and then merging other environmental factors. A zero-inﬂated negative binomial model is used to show how the data can be used to predict the likelihood of walking bouts at the intersection level. This information can be used by engineers, designers, and planners in roadway designs to enhance pedestrian safety.

Authors: Haena Kim, Grace Douglas, Linda Ng Boyle, Anne Moudon, Steve Mooney, Brian Saelens, Beth Ebel

Recommended Citation:
Douglas, G., Boyle, L. N., Kim, H., Moudon, A., Mooney, S., Saelens, B., & Ebel, B. (2022). A Framework to Assess Pedestrian Exposure Using Personal Device Data. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. https://doi.org/10.1177/1071181322661319

Technical Report

Transit Corridor Study

Download PDF (6.60 MB)

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