Green loading zones (GLZs) are curb spaces dedicated to the use of electric or alternative fuel (“green”) delivery vehicles. Some US cities have begun piloting GLZs to incentivize companies to purchase and operate more green vehicles. However, there are several questions to be answered prior to a GLZ implementation, including siting, potential users, and their willing to pay. We reviewed best practices for GLZs around the world and surveyed goods delivery companies operating in New York City to collect such information for a future GLZ pilot. The findings suggest the best candidate locations are areas where companies are currently subject to the most parking fines and double parking. Companies expressed willingness to pay for GLZs, as long as deploying green vehicles in the city can offset other cost exposures. Respondents also selected several single-space GLZs spread throughout a neighborhood as the preferred layout.
Research Topic: Curb Management
Curb management involves efficiently managing and regulating the use of curbside spaces along roadways. With the increasing demand for these spaces due to micromobility, transportation network companies (TNCs), ecommerce home delivery, and transit access, the goal is to inventory, optimize, allocate, and oversee curb space usage to maximize mobility, safety, and accessibility for users and needs.
Observing Goods Delivery Activities and Identifying Opportunities to Improve the Design of Commercial Vehicle Load Zones in Seattle
The growth of freight activity is one of the results of urban population growth. The growth of freight means that more commercial vehicles must share finite infrastructure like alleys, loading docks, and yellow curb space. In this research project, curb space is studied in order to better understand the needs of commercial vehicles at the curb. Cities in the United States like Seattle have recognized that there are opportunities to better manage curb space, and have implemented programs such as the Flex Zone Program 2016 in order to do so. In this research paper, I have focused on just one aspect of the curb, which is the yellow curb space reserved for Commercial Vehicle Load Zones (CVLZ). The purpose of this thesis is to observe the needs and activities of courier drivers during deliveries/pickups in Seattle, and incorporate observations into a new design of freight curb space that may better respond to their needs. The new design suggests a system in which curb space is designed for different vehicle dimensions and activities. This is done by including paint, texture/pattern, and signage on the pavement and sidewalk that comfortably accommodate the vehicle and activities around the vehicle. By providing a better designed freight curb space that accounts for the needs and activities observed, the hope is that courier drivers will be less likely to partake in high-risk behavior such as double parking, and spilling over into adjacent transit lanes/pedestrian areas/bikes lanes, by providing better infrastructure for them.
Sheth, Manali (2018). Observing Goods Delivery Activities and Identifying Opportunities to Improve the Design of Commercial Vehicle Load Zones in Seattle. University of Washington Master's Degree Thesis.
A Data-Driven Simulation Tool for Dynamic Curb Planning and Management
Project Budget: $2.9M (UW amount: $500k)
Lead Institution:
- Pacific Northwest National Lab (PNNL)
Partner Institutions:
- Urban Freight Lab (UFL), University of Washington
- Lawrence Berkeley National Laboratory (LBNL)
- Lacuna Technologies, Inc. (Lacuna)
- National Renewable Energy Laboratory (NREL)
Summary:
Curbs are a critical interfacing layer between movement and arrival in urban areas—the layer at which people and goods transition from travel to arrival—representing a primary point of resistance when joining and leaving the transportation network. Traditionally, curb spaces are statically supplied, priced, and zoned for specific usage (e.g., paid parking, commercial/passenger loading, or bus stops). In response to the growing demand for curb space, some cities are starting to be more intentional about defining curb usage. Examples of curb demand include not only traditional parking and delivery needs, but today include things like curb access requirements generated by micro delivery services, active transportation modes, and transportation network companies. And now due to the pandemic, increased demand comes from food/grocery pick-up/drop-off activities, as well as outdoor business use of curb space (e.g., outdoor restaurant seating).
Heightened demand and changing expectations for finite curb resources necessitates the implementation of new and dynamic curb management capabilities so that local decision-makers have the tools needed to improve occupancy and throughput while reducing the types of traffic disruptions that result from parking search and space maneuvering activities.
However, municipalities and cities currently lack tools that allow them to simulate the effectiveness of potential dynamic curb management policies to understand how the available control variables (e.g. price or curb space supply) can be modified to influence curb usage outcomes. On the other hand, transportation authorities and fleet managers lack the needed signage or communication platforms to effectively communicate the availability of curb space for a specified use, price, and time at scales beyond centralized lots and garages.
This project aims to develop a city-scale dynamic curb use simulation tool and an open-source curb management platform. The envisioned simulation and management capabilities will include dynamically and concurrently controlling price, number of spaces, allowed parking duration, time of use or reservation, and curb space use type (e.g., dynamic curb space rezoning based on supply and demand).
Project Objectives:
Project objectives include the following:
- Objective 1: The team will develop a microscale curb simulation tool to model behavior of individual vehicles with different purposes at the curb along a blockface over time of day, accounting for price, supply, function, and maximum parking time.
- Objective 2: The team will integrate the microscale simulation tool with the LBNL’s mesoscale (city-scale) traffic simulation tool, BEAM, for simulating traffic impacts of alternative curb management strategies and their effects on citywide and regional traffic, in terms of (1) travel time, (2) throughput (people and goods) into and out of urban centers, (3) reduced energy use and emissions (from parking search and congestion), and (4) curb space utilization.
- Objective 3: The team will develop a dynamic curbspace allocation controller for various curb users, either municipal or commercial, for the purpose of a demonstration and pilot.
- Objective 4: The team will design, implement and test a curbside resource usage platform for fleet vehicles communications at commercial vehicle load zones (CVLZs), passenger load zones (PLZs), and transit stops.
- Objective 5: The team will perform demonstrations with stakeholder agencies and provide pathways to practice for promising curb allocation policies.
Giving Curb Visibility to Delivery Drivers
Dalla Chiara, Giacomo and Anne Goodchild. Giving Curb Visibility to Delivery Drivers. Intersections + Identities: State of Transportation Planning 2022, 134-143.
Curbing Conflicts: Curb Allocation Change Project Report
Like many congested cities, Seattle is grappling with how best to manage the increasing use of ride-hailing services by Transportation Network Companies (TNCs) like Uber and Lyft. According to a 2018 Seattle Times analysis, TNC ridership in the Seattle region has grown to more than five times the level it was in the beginning of 2015, providing, on average, more than 91,000 rides a day in 2018. And the newspaper reports Uber and Lyft trips are heavily concentrated in the city’s densest neighborhoods, where nearly 40,000 rides a day start in ZIP codes covering downtown, Belltown, Capitol Hill and South Lake Union.
This University of Washington (UW) study focuses on a strategy to manage TNC driver stops when picking up and dropping off passengers to improve traffic flow in the South Lake Union (SLU) area. SLU is the site of the main campus for Amazon, the online retail company. The site is known to generate a large number of TNC trips, and Amazon reports high rates of ride-hailing use for employee commutes. This study also found that vehicle picking-up/dropping-off passengers make up a significant share of total vehicle activity in SLU. The center city neighborhood is characterized by multiple construction sites, slow speed limits (25 mph), and heavy vehicle and pedestrian traffic.
Broad concerns about congestion, safety, and effective curb use led to this study, conducted by researchers at the UW’s Urban Freight Lab and Sustainable Transportation Lab. Amazon specifically was concerned about scarcity of curb space where TNC drivers could legally and readily stop to pick up and drop off passengers. Without dedicated load/unload curb space, TNC vehicles stop and wait at paid parking spots, other unauthorized curb spots, or in the travel lane itself, potentially blocking or slowing traffic. To try to mitigate the impacts of passenger pick-up/drop-off activity on traffic, the city proposed a strategy of increasing passenger loading zone (PLZ) spaces while Uber and Lyft implemented a geofence, which directs their drivers and passengers to designated pick-up and drop-off locations on a block. (Normally, drivers pick up or drop off passengers at any address a rider requests via the ride-hailing app.)
By providing ample designated pick-up and drop-off spots along the curb, the thinking goes, TNC drivers would reduce the frequency with which they stop in the travel lane to pick up or drop off passengers and the time they stay stopped there. By these measures, this study’s findings show the approach was successful. But it is important to note that the strategy is not a silver bullet for solving traffic congestion—nor is it designed as such. It is also important to note that any initiative to manage use of curbs and roads (by TNCs or others) is part of a city’s broader transportation policy framework and goals.
For this study, researchers analyzed an array of data on street and curb activity along three block-faces on Boren Ave N in December 2018 and January 2019. At a minimum, data were collected during the morning and afternoon peak travel times (with some collected 24 hours a day). The research team collected data using video and sensor technology as well as in-person observation. Researchers also surveyed TNC passengers for demographic, trip-related and satisfaction data. The five Amazon buildings in the area studied house roughly 8,650 employees. Researchers collected data in three stages. Phase 1, the study baseline, was before PLZs were added and geofencing started. Phase 2 was after the new PLZs were added, expanding total PLZ curb length from 20 feet (easily filled by one to two vehicles) to 274 feet. Phase 3 was after geofencing was added to the expanded PLZs. The added PLZ spaces were open to any passenger vehicle—not just TNC vehicles—weekdays from 7am to 10am and 2pm to 7pm. (Permitted food trucks were authorized from 10am to 2pm.)
Note that while other cities can learn from this analysis, the findings apply to streets with comparable traffic speed, mix of roadway users, and street design.
The study’s main findings include:
- A significant percentage of vehicles performing a pick-up/drop-off stop in the travel lane. Those in-lane stops appear connected to the lack of available designated curb space: Adding PLZs and geofencing increased driver compliance in stopping at the curb versus stopping in the travel lane to load and unload passengers. But it was not lack of curb space alone that influenced driver activity: Between 7 percent and 10 percent of drivers still stopped in the travel lane even when PLZs were empty. After adding PLZs and geofencing, in-lane stops fell from 20 percent to 14 percent for pick-ups and from 16 percent to 15 percent for drop-offs.
- Adding PLZs and geofencing reduced the average amount of time drivers stopped to load and unload passengers. For example, 90 percent of drop-offs took less than 1 minute 12 seconds, 42 seconds faster than the average with the added PLZs alone.
- While curb occupancy increased after adding PLZs and geofencing, occupancy results show the current allocation of PLZ spaces is more than what is needed to meet observed demand: Average PLZ occupancy remained under 20 percent after PLZ expansion, even during peak commute hours.
- Vehicles picking-up/dropping-off passengers account for a significant share of total traffic volume in the study area: during peak hours the observed average percentage of vehicles performing a pick-up/drop-off with respect to the total traffic volume was 29 percent (in Phase 1), 32 percent (in Phase 2) and 39 percent (in Phase 3).
- High volumes of pedestrians (400-500 per hour on average) cross the street at points where there was no crosswalk. Passengers picked-up/dropped-off constituted a fraction (five to seven percent) of those pedestrians, but high rates of passengers (30 to 40 percent) cross the street at non-crosswalk locations.
- Adding PLZs and geofencing did not have a significant impact on traffic safety. Researchers found no significant change in the number of observed conflicts from baseline to the addition of PLZs and geofencing. Conflicts are situations where a vehicle, bike, or pedestrian is interrupted, forced to alter their path, or engaged in a near-miss situation. Conflicts include vehicles passing in the oncoming traffic lane. • Adding PLZs and geofencing also did not produce a significant impact on roadway travel speed.
- Of the 116 TNC passengers surveyed in the study area:
- Roughly 40 percent to 50 percent said their trip was work related. More than half said they used ride-hailing service at least once a week and 70 percent or more used TNC alone (versus in combination with other transportation options) to get from their origin to their destination.
- Most responded positively to the added PLZs and geofence: 79 percent rated their pick-up satisfactory and 100 percent rated their drop-off satisfactory as compared to 72 percent and 89 percent in the baseline.
- Nearly half said they would have taken transit and one-third would have walked if ride-hailing was not available.
- 40 percent requested a shared TNC vehicle in Phase 1 and 47 percent in Phase 3.
The study suggests that while vehicles picking-up/dropping-off passengers account for a significant share of traffic volume in SLU, they are not the primary cause of congestion. Myriad factors impact neighborhood congestion, including high vehicle volume overall and bottlenecks moving out of the neighborhood onto regional arterials. As researchers observed in the afternoon peak, these bottlenecks cause spillbacks onto local streets. Amazon garages exit vehicles onto streets that then feed into these clogged arterials.
Regarding traffic safety in SLU, this study was not designed to assess whether TNC driver behavior on average is safer or less safe than that of other vehicles. It is important to understand the safety and speed findings in the context of the SLU traffic environment. Drivers tend to drive at relatively slow speeds, navigating around high pedestrian and jaywalking volumes, and seem relatively comfortable stopping in the middle of the street for short periods of time. Due to the nature of area traffic, this seems to have relatively little impact on other drivers. Drivers appear to anticipate both this behavior and the high volumes of vehicles moving onto/off the curb and into/out of driveways and alleys.
Whether the strategy this study analyzed is recommended depends on a city’s transportation goals and approach. The researchers found the increased PLZ allocation and geofencing strategy worked in that it improved driver compliance, reduced dwell times, and boosted TNC user satisfaction. However, this may encourage commuters to use TNC. The passenger survey clearly shows that TNC service is attracting passengers who would have otherwise walked or used transit. While in the short term the increased PLZs and geofencing had a positive effect on traffic, if this induces TNC demand, there could be larger, more negative long-term consequences. If the end goal is to reduce traffic congestion, measures to reduce—rather than encourage—TNC and passenger car use as the predominant mode of commuting will yield the most substantial benefits.
In the news:
The Seattle Times: Seattle Uber and Lyft drivers often stop in the street to pick up or drop off riders. Here’s a way to reduce that.
Goodchild, Anne. Giacomo dalla Chiara. Jose Luis Machado. Andisheh Ranjbari. (2019) Curb Allocation Change Project.
Managing Increasing Demand for Curb Space in the City of the Future
This research aims to develop innovative methods for managing curb lane function and curb access. The rapid rise of autonomous vehicles (AV), on-demand transportation, and e-commerce goods deliveries, as well as increased cycling rates and transit use, is increasing demand for curb space resulting in competition between modes, failed goods deliveries, roadway and curbside congestion, and illegal parking.
The research findings will improve mobility by increasing the understanding of existing curb usage and provide new solutions to city officials, planners, and engineers responsible for managing this scarce resource in the future.
The research team will work closely with several cities in the PacTrans region to ensure the study’s relevance to their needs, and that the results will be broadly applicable for other cities.
This research will allow for the development of innovative curb space designs and ensure that our urban street system may operate more efficiently, safely, and reliably for both goods and people.
Can Real-Time Curb Availability Information Improve Urban Delivery Efficiency?
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 affects 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 downtown Seattle. 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.
Giacomo Dalla Chiara, Klaas Fiete Krutein, and Anne Goodchild (2022). Can Real-Time Curb Availability Information Improve Urban Delivery Efficiency? 9th International Urban Freight Conference (INUF), Long Beach, CA May 2022.
An Evaluation of Bicycle Safety Impacts of Seattle’s Commercial Vehicle Load Zones
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.
Butrina, Polina, Edward McCormack, Anne Goodchild, and Jerome Drescher. "An Evaluation of Bicycle Safety Impacts of Seattle’s Commercial Vehicle Load Zones." (2016).
The Final 50 Feet of the Urban Goods Delivery System: Pilot Test of an Innovative Improvement Strategy
Background
We are living at the convergence of the rise of e-commerce and fast growing cities. Surging growth in U.S. online sales has averaged more than 15% year-over-year since 2010. Total e-commerce sales for 2016 were estimated at $394.9 billion, an increase of 15.1 percent from 2015. This is a huge gain when compared to total retail sales in 2016, which only increased 2.9 percent from 2015. E-commerce sales in 2016 accounted for 8.1 percent of total sales, while accounting for 7.3 percent of total sales in 2015.
This is causing tremendous pressure on local governments to rethink the way they manage street curb parking and alley operations for trucks and other delivery vehicles, and on building operators to plan for the influx of online goods. City managers and policy makers are grappling with high demand for scarce road, curb and sidewalk space, and multiple competing uses. But rapidly growing cities lack data-based evidence for the strategies they are considering to support e-commerce and business vitality, while managing limited parking in street space that is also needed for transit, pedestrians, cars, bikes and trucks.
The Final 50 Feet is the project’s shorthand designation for the last leg of the delivery process, which:
- Begins when a truck stops at a city-owned Commercial Vehicle Load Zone or alley, or in a privately-owned freight bay or loading dock in a building;
- May extend along sidewalks or through traffic lanes; and
- Ends where the end customer takes receipt of delivery.
Research Project
The purpose of the research project is to pilot test a promising strategy to reduce the number of failed first delivery attempts in urban buildings. The test will take place in the Seattle Municipal Tower. It will serve as a case study for transportation and urban planning professionals seeking to reduce truck trips to urban buildings. Urban Freight Lab identified two promising strategies for the pilot test:
- Locker system: smaller to medium sized deliveries can be placed into a locker which will be temporarily installed during our pilot test
- Grouped-tenant-floor-drop-off-points for medium sized items if locker is too small or full (4-6 floor groups to be set up by SDOT and Seattle City Light)
- People will come and pick up the goods at the designated drop off points
- Flyers with information of drop-off-points will be given to the carriers
UFL will evaluate the ability of the standardized second step pilot test to reduce the number of failed first delivery attempts by:
- Collecting original data to document the number of failed first delivery attempts before and after the pilot test; and
- Comparing them to the pilot test goals.
Commercial Vehicle Driver Behaviors and Decision Making: Lessons Learned from Urban Ridealongs
As ecommerce and urban deliveries spike, cities grapple with managing urban freight more actively. To manage urban deliveries effectively, city planners and policy makers need to better understand driver behaviors and the challenges they experience in making deliveries.
In this study, we collected data on commercial vehicle (CV) driver behaviors by performing ridealongs with various logistics carriers. Ridealongs were performed in Seattle, Washington, covering a range of vehicles (cars, vans, and trucks), goods (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail). Observers collected qualitative observations and quantitative data on trip and dwell times, while also tracking vehicles with global positioning system devices.
The results showed that, on average, urban CVs spent 80% of their daily operating time parked. The study also found that, unlike the common belief, drivers (especially those operating heavier vehicles) parked in authorized parking locations, with only less than 5% of stops occurring in the travel lane. Dwell times associated with authorized parking locations were significantly longer than those of other parking locations, and mail and heavy goods deliveries generally had longer dwell times.
We also identified three main criteria CV drivers used for choosing a parking location: avoiding unsafe maneuvers, minimizing conflicts with other users of the road, and competition with other commercial drivers.
The results provide estimates for trip times, dwell times, and parking choice types, as well as insights into why those decisions are made and the factors affecting driver choices.
In recent years, cities have changed their approach toward managing urban freight vehicles. Passive regulations, such as limiting delivery vehicles’ road and curb use to given time windows or areas have been replaced by active management through designing policies for deploying more commercial vehicle (CV) load zones, pay-per-use load zone pricing, curb reservations, and parking information systems. The goal is to reduce the negative externalities produced by urban freight vehicles, such as noise and emissions, traffic congestion, and unauthorized parking, while guaranteeing goods flow in dense urban areas. To accomplish this goal, planners need to have an understanding of the fundamental parking decision-making process and behaviors of CV drivers.
Two main difficulties are encountered when CV driver behaviors are analyzed. First, freight movement in urban areas is a very heterogeneous phenomenon. Drivers face numerous challenges and have to adopt different travel and parking behaviors to navigate the complex urban network and perform deliveries and pick-ups. Therefore, researchers and policy makers find it harder to identify common behaviors and responses to policy actions for freight vehicles than for passenger vehicles. Second, there is a lack of available data. Most data on CV movements are collected by private carriers, who use them to make business decisions and therefore rarely release them to the public. Lack of data results in a lack of fundamental knowledge of the urban freight system, inhibiting policy makers’ ability to make data-driven decisions.
The urban freight literature discusses research that has employed various data collection techniques to study CV driver behaviors. Cherrett et al. reviewed 30 UK surveys on urban delivery activity and performed empirical analyses on delivery rates, time-of-day choice, types of vehicles used to perform deliveries, and dwell time distribution, among others. The surveys reviewed were mostly establishment-based, capturing driver behaviors at specific locations and times of the day. Allen et al. performed a more comprehensive investigation, reviewing different survey techniques used to study urban freight activities, including driver surveys, field observations, vehicle trip diaries, and global positioning system (GPS) traces. Driver surveys collect data on driver activities and are usually performed through in-person interviews with drivers outside their working hours or at roadside at specific locations. In-person interviews provide valuable insights into driver choices and decisions but are often limited by the locations at which the interviews occur or might not reflect actual choices because they are done outside the driver work context. Vehicle trip diaries involve drivers recording their daily activities while field observations entail observing driver activities at specific locations and establishments; neither collects insights into the challenges that drivers face during their trips and how they make certain decisions. The same limitations hold true for data collected through GPS traces. Allen et al. mentioned the collection of travel diaries by surveyors traveling in vehicles with drivers performing deliveries and pick-ups as another data collection technique that could provide useful insights into how deliveries/pick-ups are performed. However, they acknowledged that collecting this type of data is cumbersome because of the difficulty of obtaining permission from carriers and the large effort needed to coordinate data collection.
This study aims to fill that gap by collecting data on driver decision-making behaviors through observations made while riding along with CV drivers. A systematic approach was taken to observe and collect data on last-mile deliveries, combining both qualitative observations and quantitative data from GPS traces. The ridealongs were performed with various delivery companies in Seattle, Washington, covering a range of vehicle types (cars, vans, and trucks), goods types (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail).
The data collected will not only add to the existing literature by providing estimates of trip times, parking choice types, time and distance spent cruising for parking, and parking dwell times but will also provide insights into why those decisions are made and the factors affecting driver choices.
The objectives of this study are to provide a better understanding of CV driver behaviors and to identify common and unique challenges they experience in performing the last mile. These findings will help city planners, policy makers, and delivery companies work together better to address those challenges and improve urban delivery efficiency.
The next section of this paper describes the relevant literature on empirical urban freight behavior studies. The following section then introduces the ridealongs performed and the data collection methods employed. Next, analysis of the data and qualitative observations from the ridealongs are described, and the results are discussed in five overarching categories: the time spent in and out of the vehicle, parking location choice, the reasons behind those choices, parking cruising time, and factors affecting dwell time.
Chiara, Giacomo Dalla, Krutein, Klaas Fiete, Ranjbari, Andisheh, & Goodchild, Anne. (2021). Understanding Urban Commercial Vehicle Driver Behaviors and Decision Making. Transportation Research Record, 2675(9), 608-619. https://doi.org/10.1177/03611981211003575