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Biking the Goods: How North American Cities Can Prepare for and Promote Large-Scale Adoption

With the rise in demand for home deliveries and the boom of the e-bike market in the U.S., cargo cycles are becoming the alternative mode of transporting goods in urban areas. However, many U.S. cities are struggling to decide how to safely integrate this new mode of transportation into the pre-existing urban environment.

In response, the Urban Freight Lab is developing a white paper on how cities can prepare for and promote large-scale adoption of cargo cycle transportation. Sponsors include freight logistics providers, bicycle industry leaders, and agencies Bosch eBike Systems, Fleet Cycles, Gazelle USA, Michelin North America, Inc., Net Zero Logistics, the Seattle Department of Transportation, and Urban Arrow.

The Urban Freight Lab is internationally recognized as a leader in urban freight research, housing a unique and innovative workgroup of private and public stakeholders and academic researchers working together to study and solve urban freight challenges. The Urban Freight Lab has previously worked on evaluating, studying, and deploying cargo cycles in Asia and the U.S, and is recognized as an expert leader in North America on cargo cycle research.

Objectives
The objectives of the white paper are the following:

  1. Define and understand what types of cargo bikes exist in North America, their main features, how they are operated, and the infrastructure they need.
  2. Identify opportunities for and challenges to large-scale adoption of cargo cycles in North American cities.
  3. Learn from case studies of U.S. cities’ approaches to regulating and promoting cargo cycles.
  4. Provide recommendations for how cities can safely recognize, enable and encourage large-scale adoption of cargo bikes, including infrastructure, policy, and regulatory approaches.
Technical Report

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

 
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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.

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).

Biking for Goods: A Case Study on the Seattle Pedaling Relief Project

1. Introduction
One of the disruptions brought by the COVID-19 pandemic was the reduction of in-store shopping, and the consequent increase in online shopping and home deliveries. However, not everyone had equal access to online shopping and home-delivery services. Customers relying on food banks were forced to shop in-store even during the pandemic. In 2020, the Cascade Bicycle Club started the Pedaling Relief Project (PRP) – a not-for-profit home delivery service run by volunteers using bikes to pick up food at food banks and deliver to food bank customers, among other services.

The Urban Freight Lab collaborates with the Cascade Bicycle Club (CBC) to study and improve PRP operations. For this work, students in Prof. Anne Goodchild’s Transportation Engineering course on Transportation Logistics (CET 587) are undertaking a case study: to analyze the transport and logistics system of the Pedaling Relief Project and provide recommendations for how to improve operations.

2. Background
2.1. Food rescue at a glance
An estimated 94,500 tons of food from Seattle business establishments end up in compost and landfills each year, while many members of our community remain food insecure. The process of food rescuing consists of the gleaning of edible food from business establishments – called donor businesses such as grocery stores, restaurants, and commissary kitchens – that otherwise would enter the waste stream and be re-distributed to local food programs. Hunger relief agencies, also referred to as food banks, are non-profit organizations that collect rescued food, either directly from businesses or through food rescue distributors (such as Food Lifeline or Northeast Harvest) and re-distribute it to the community through meal programs, walk-ins, and pop-up food pantries, student backpack programs, among others.

Read more about the Seattle food rescue system in SCTL’s report (2020) on “Improving Food Rescue in Seattle: What Can Be Learned from a Supply Chain View?

2.2. Pedaling Relief Project
In 2020 the Cascade Bicycle Club started the Pedaling Relief Project (PRP), a volunteer-based program that collaborates with local food banks to offer three main types of services — (1) grocery delivery, (2) food rescue, (3) little free pantry restocking — coordinating a network of volunteers on bikes.

  1. Grocery delivery (GD) service consists of picking up grocery bags from food banks and performing delivery routes, distributing food to food bank customers that asked for home delivery services.
  2. Food rescue (FR) services support the existing distributors by picking up food at business establishments and carrying rescued food to local food banks.
  3. Little free pantries restocking (LFPR) services consist of picking up food at local food banks and carrying it to neighborhood micro pantries –containers placed on local streets and open to everyone to store food from donors to whoever needs it. Learn more about the Little free pantries project on thelittlefreepantries.org.

Volunteers use their own bikes, with some cargo carry capacity, or can request a bike trailer or cargo bike from the Cascade Bicycle Club.

2.3. Cargo Bikes
Cargo bikes are two/three/four-wheel bikes with some cargo-carrying capacity. They are increasingly used as an alternative mode to trucks and vans to transport goods in urban areas. Cargo bikes are often supported by an electric motor that assists the driver when pedaling. Compared to internal combustion engine vehicles, cargo bikes do not produce tailpipe emissions and they consume less energy than electric vans (Verlinghieri et al., 2021). They also offer several operational advantages: they are more agile in navigating urban road traffic, they can use alternative road infrastructure such as bike lanes and sidewalks to drive and park, they can park closer to their delivery destination, reducing walking distances and parking dwell times (Dalla Chiara et al., 2020).

3. Project instructions

The CBC provided access to anonymous data on the PRP operations for the exclusive use of the 2022 CET 587 course student cohort final projects. Students are asked to individually perform empirical research using the provided data and/or self-collected data on the PRP operations with the following objectives:

  • Empirically analyze and describe PRP operations.
  • Provide recommendations on what actions can be taken to improve PRP operations.

Projects will meet the following two requirements:

  • Use the provided data and/or self-collected and/or publicly sourced data to perform empirical analysis
  • Provide justified and concrete recommendations on how to improve the PRP.
  • Complete deliverables 1 and 2 (see below), which consist of 2 presentations, a project proposal, and a final project report.

Project progress timeline and deliverables:

Weeks Progress & Deliverables
1-2 Become familiar with R language programming; PRP background and data
3 CBC gives a guest lecture about PRP
4-5 Project proposal; 2-minute lightning talk about the project proposal
Deliverable 1: 1-page project proposal
6-10 Implement proposed methodology and perform research
11 Each student will give a 15-minute presentation of the main results of the project
Deliverable 2: Final report
The following are potential project directions:
  • Analyze current routes performed by volunteers. How can they be improved? Get the work done more quickly, or with fewer bikes?
  • Analyze data from little free pantries restocking. Collect additional data on the use of Little Free Pantries by manual observations or by installing sensors in a few of them. Can we model demand and supply for food donations?
  • Collect and analyze GPS data by signing up and performing some of the PRP routes yourself. What type of infrastructure do cargo bikes need and how does street and curb use behavior differ between cargo bikes and vans? What can the city do to better support this type of activity?
  • Analyze volunteers’ behaviors data. Is it possible to model the supply of volunteers? Can you simulate different scenarios of volunteer supply?
  • Develop your own direction with approval.

Students will be provided with a base dataset on PRP operations. Students are encouraged to use other datasets self-collected or from public data sources (e.g. check out the SDOT Open Data Portal), to share ideas in class and among each other, to use as much as possible class time, guest lectures and office hours to ask questions and share ideas.

1: 1-page project proposal and 2-minute lightning talk describing motivation, project objective(s) and research question(s), proposed methodology (data to use/collect, methods to implement), and expected results.

2: Final report and 10-minute presentation describing data used, including sample size and sample statistics, how data collection was performed, empirical analysis performed using data and results from the analysis, and conclusions, key findings, and key recommendations.

Paper

Measuring Delivery Route Cost Trade-Offs Between Electric-Assist Cargo Bicycles and Delivery Trucks in Dense Urban Areas

 
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Publication: European Transport Research Review
Volume: 11
Publication Date: 2019
Summary:

Introduction

Completing urban freight deliveries is increasingly a challenge in congested urban areas, particularly when delivery trucks are required to meet time windows. Depending on the route characteristics, Electric Assist (EA) cargo bicycles may serve as an economically viable alternative to delivery trucks. The purpose of this paper is to compare the delivery route cost trade-offs between box delivery trucks and EA cargo bicycles that have the same route and delivery characteristics, and to explore the question, under what conditions do EA cargo bikes perform at a lower cost than typical delivery trucks?

Methods

The independent variables, constant variables, and assumptions used for the cost function comparison model were gathered through data collection and a literature review. A delivery route in Seattle was observed and used as the base case; the same route was then modeled using EA cargo bicycles.

Four separate delivery scenarios were modeled to evaluate how the following independent route characteristics would impact delivery route cost – distance between a distribution center (DC) and a neighborhood, number of stops, distance between each stop, and number of parcels per stop.

Results

The analysis shows that three of the four modeled route characteristics affect the cost trade-offs between delivery trucks and EA cargo bikes. EA cargo bikes are more cost effective than delivery trucks for deliveries in close proximity to the DC (less than 2 miles for the observed delivery route with 50 parcels per stop and less than 6 miles for the hypothetical delivery route with 10 parcels per stop) and at which there is a high density of residential units and low delivery volumes per stop.

Conclusion

Delivery trucks are more cost effective for greater distances from the DC and for large volume deliveries to one stop.

 

Recommended Citation:
Sheth, Manali, Polina Butrina, Anne Goodchild, and Edward McCormack. "Measuring delivery route cost trade-offs between electric-assist cargo bicycles and delivery trucks in dense urban areas." European Transport Research Review 11, no. 1 (2019): 11.

An Examination of the Impact of Increasing Commercial Parking Utilization on Cyclist Safety in Urban Environments

There is little research on the behavioral interaction between bicycle lanes and commercial vehicle loading zones (CVLZ) in the United States. These interactions are important to understand, to preempt increasing conflicts between truckers and bicyclists. In this study, a bicycling simulator experiment examined bicycle and truck interactions. The experiment was successfully completed by 48 participants. The bicycling simulator collected data regarding a participant’s velocity and lateral position. Three independent variables reflecting common engineering approaches were included in this experiment: pavement marking (L1: white lane markings with no supplemental pavement color, termed white lane markings, L2: white lane markings with solid green pavement applied on the conflict area, termed solid green, and L3: white lane markings with dashed green pavement applied on the conflict area, termed dashed green), signage (L1: No sign and L2: a truck warning sign), and truck maneuver (L1: no truck in CVLZ, L2: truck parked in CVLZ, and L3: truck pulling out of CVLZ).

The results showed that truck presence does have an effect on bicyclist’s performance, and this effect varies based on the engineering and design treatments employed. Of the three independent variables, truck maneuvering had the greatest impact by decreasing mean bicyclist velocity and increasing mean lateral position. It was also observed that when a truck was present in a CVLZ, bicyclists had a lower velocity and lower divergence from right-edge of bike lane on solid green pavement, and a higher divergence from the right-edge of bike lane was observed when a warning sign was present.
Paper

Factors Impacting Bicyclist Lateral Position and Velocity in Proximity to Commercial Vehicle Loading Zones: Application of a Bicycling Simulator

 
Download PDF  (7.70 MB)
Publication: Accident Analysis & Prevention
Volume: 125
Pages: 29-39
Publication Date: 2019
Summary:

There is little research on the behavioral interaction between bicycle lanes and commercial vehicle loading zones (CVLZ) in the United States. These interactions are important to understand, to preempt increasing conflicts between truckers and bicyclists. In this study, a bicycling simulator experiment examined bicycle and truck interactions. The experiment was successfully completed by 48 participants. The bicycling simulator collected data regarding a participant’s velocity and lateral position. Three independent variables reflecting common engineering approaches were included in this experiment: pavement marking (L1: white lane markings with no supplemental pavement color, termed white lane markings, L2: white lane markings with solid green pavement applied on the conflict area, termed solid green, and L3: white lane markings with dashed green pavement applied on the conflict area, termed dashed green), signage (L1: No sign and L2: a truck warning sign), and truck maneuver (L1: no truck in CVLZ, L2: truck parked in CVLZ, and L3: truck pulling out of CVLZ).

The results showed that truck presence does have an effect on bicyclist’s performance, and this effect varies based on the engineering and design treatments employed. Of the three independent variables, truck maneuvering had the greatest impact by decreasing mean bicyclist velocity and increasing mean lateral position. It was also observed that when a truck was present in a CVLZ, bicyclists had a lower velocity and lower divergence from right-edge of bike lane on solid green pavement, and a higher divergence from the right-edge of bike lane was observed when a warning sign was present.

Authors: Manali ShethDr. Anne GoodchildDr. Ed McCormack, Masoud Ghodrat Abadia, David S. Hurwitz
Recommended Citation:
Abadi, Masoud Ghodrat, David S. Hurwitz, Manali Sheth, Edward McCormack, and Anne Goodchild. (2019) Factors Impacting Bicyclist Lateral Position and Velocity in Proximity to Commercial Vehicle Loading Zones: Application of a Bicycling Simulator. Accident Analysis and Prevention, 125, 29–39. https://doi.org/10.1016/j.aap.2019.01.024