Research Keyword: Commercial vehicle and truck movements

The movement of a truck or vehicle that meet one or more of the following requirements: (1) vehicle weight, weight rating, gross combination weight, or gross combination weight rating exceeding 10,001 pounds, (2) designed and used to transport over eight passengers and is doing so for compensation, (3) designed and used to transport more than 15 passengers (including the driver), and is not receiving compensation, and (4) is used to transport hazardous materials.

Dataset

Developing Better Curb Management Strategies through Understanding Commercial Vehicle Driver Parking Behavior in a Simulated Environment

URL: https://doi.org/10.7910/DVN/HVAUT3

Publication: Harvard Dataverse

Publication Date: 2023

Summary:

Three different data types were obtained from Oregon State Driving and Bicycling Simulator Laboratory for purpose of this report and they are as follow:

Speed data consists of subject number, average speed, minimum speed, and all the independent variables. Speed data were collected based on the truck’s speed while driving through a certain scenario (out of 24). For each scenario, the average and minimum speed (mph) of 12 drivers were recorded along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals).
Eye tracking data consists of subject number, total fixation duration (TFD) in milliseconds, area of interest (AOI), and all the independent variables. TFD data were collected while the truck driver maneuvers through a certain scenario (out of 24). For each scenario, the TFD for each AOI was recorded for 11 subjects along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals). AOI represent the area of interest that a driver fixates for a certain of time to generate the total fixation duration.
Eye tracking data consists of subject number, GSR in peaks per minute, and all the independent variables. GSR data were collected while the truck driver maneuvers through a certain scenario (1 out of 24). For each scenario, the peaks per minute data was recorded for 11 subjects along each segment (scenario) from the start of the road to 150 feet before the intersection (traffic signals). Peaks per minute represents the emotional arousal (i.e., something is scary, threating, joyful, etc.) that a driver generates when reacting to a particular event. Fourteen participants were recruited, two of them had a simulator sickness so they were excluded from the data and the analysis. While there are no quality or consistency issues with this data set, it should be noted that the sample is on the smaller side and that should be considered when interpreting derived results. The average values were calculated to apply robust statistical analysis for such data (speed and lateral position). As the experiment consists of 2x2x2x3 factorial design, each participant had to driver through 24 scenarios; therefore, 288 scenario observations were obtained and recorded in the excel file.

Authors: Dr. Anne GoodchildDr. Ed McCormackDr. Andisheh Ranjbari, David Hurwitz

Recommended Citation:
Goodchild, Anne; McCormack, Ed; Ranjbari, Andisheh; Hurwitz, David, 2023, "Developing Better Curb Management Strategies through Understanding Commercial Vehicle Driver Parking Behavior in a Simulated Environment", Harvard Dataverse. https://doi.org/10.7910/DVN/HVAUT3.

Paper

Evaluation of Bicyclist Physiological Response and Visual Attention in Commercial Vehicle Loading Zones

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URL: https://doi.org/10.1016/j.jsr.2023.11.018

Publication: Journal of Safety Research

Publication Date: 2023

Summary:

With growing freight operations throughout the world, there is a push for transportation systems to accommodate trucks during loading and unloading operations. Currently, many urban locations do not provide loading and unloading zones, which results in trucks parking in places that obstruct bicyclist’s roadway infrastructure (e.g., bicycle lanes).

Method
To understand the implications of these truck operations, a bicycle simulation experiment was designed to evaluate the impact of commercial vehicle loading and unloading activities on safe and efficient bicycle operations in a shared urban roadway environment. A fully counterbalanced, partially randomized, factorial design was chosen to explore three independent variables: commercial vehicle loading zone (CVLZ) sizes with three levels (i.e., no CVLZ, Min CVLZ, and Max CVLZ), courier position with three levels (i.e., no courier, behind the truck, beside the truck), and with and without loading accessories. Bicyclist’s physiological response and eye tracking were used as performance measures. Data were obtained from 48 participants, resulting in 864 observations in 18 experimental scenarios using linear mixed-effects models (LMM).

Results
Results from the LMMs suggest that loading zone size and courier position had the greatest effect on bicyclist’s physiological responses. Bicyclists had approximately two peaks-per-minute higher when riding in the condition that included no CVLZ and courier on the side compared to the base conditions (i.e., Max CVLZ and no courier). Additionally, when the courier was beside the truck, bicyclist’s eye fixation durations (sec) were one (s) greater than when the courier was located behind the truck, indicating that bicyclists were more alert as they passed by the courier. The presence of accessories had the lowest influence on both bicyclists’ physiological response and eye tracking measures.

Practical Applications
These findings could support better roadway and CVLZ design guidelines, which will allow our urban street system to operate more efficiently, safely, and reliable for all users.

Authors: Dr. Ed McCormackDr. Anne Goodchild, Hisham Jashami, Douglas Cobb, Ivan Sinkus, Yujun Liu, David Hurwitz

Recommended Citation:
Jashami, Hisham, Douglas Cobb, Ivan Sinkus, Yujun Liu, Edward McCormack, Anne Goodchild, and David Hurwitz. “Evaluation of Bicyclist Physiological Response and Visual Attention in Commercial Vehicle Loading Zones.” Journal of Safety Research. Elsevier BV, December 2023. https://doi.org/10.1016/j.jsr.2023.11.018

Report

The Final 50 Feet of the Urban Goods Delivery System (Final Report)

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Publication Date: 2018

Summary:

Urban Freight Lab’s foundational report is the first assessment in any American city of the privately-owned and operated elements of the Final 50 Feet of goods delivery supply chains (the end of the supply chain, where delivery drivers must locate both parking and end customers). These include curb parking spaces, private truck freight bays and loading docks, street design, traffic control, and delivery policies and operations within buildings.

Goods delivery is an essential but little-noticed activity in urban areas. For the last 40 years, deliveries have been mostly performed by a private sector shipping industry that operates within general city traffic conditions. However, in recent years e-commerce has created a rapid increase in deliveries, which implies an explosion of activity in the future.

Meeting current and future demand is creating unprecedented challenges for shippers to meet both increased volumes and increasing customer expectations for efficient and timely delivery. Anecdotal evidence suggests that increasing demand is overwhelming goods delivery infrastructure and operations. Delivery vehicles parked in travel lanes, unloading taking place on crowded sidewalks, and commercial truck noise during late night and early morning hours are familiar stories in urban areas.

These conditions are noticeable throughout the City of Seattle as our population and employment rapidly increase. However, goods delivery issues are particularly problematic in Seattle’s high-density areas of Downtown, Belltown, South Lake Union, Pioneer Square, First Hill, Capitol Hill and Queen Anne, described as Seattle’s “Center City”. Urban goods transportation makes our economy and quality of life possible.

As the Seattle Department of Transportation (SDOT) responds to the many travel challenges of a complex urban environment, we recognize that goods delivery needs to be better understood and supported to retain the vitality and livability of our busiest neighborhoods.

U.S. cities do not have much information about the urban goods delivery system. While public agencies have data on city streets, public transportation and designated curbside parking, the “final 50 feet” in goods delivery also utilizes private vehicles, private loading facilities, and privately-owned and operated buildings outside the traditional realm of urban planning.

Bridging the information gap between the public and private sectors requires a new way of thinking about urban systems. Specifically, it requires trusted data sharing between public and private partners, and a data-driven approach to asking and answering the right questions, to successfully meet modern urban goods delivery needs.

The Urban Freight Lab (UFL) provides a standing forum to solve a range of short-term as well as long-term strategic urban goods problem solving, that provides evidence of effectiveness before strategies are widely implemented in the City.

Authors: Urban Freight Lab

Recommended Citation:
Supply Chain Transportation & Logistics Center. (2018) The Final 50 Feet of the Urban Goods Delivery System.

Paper

How to Improve Urban Delivery Routes’ Efficiency Considering Cruising for Parking Delays

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URL: https://dx.doi.org/10.2139/ssrn.4183322

Publication Date: 2022

Summary:

This paper explores the value of providing parking availability data in urban environments for commercial vehicle deliveries. The research investigated how historic cruising and parking delay data can be leveraged to improve the routes of carriers in urban environments to increase cost efficiency. To do so, the research developed a methodology consisting of a travel time prediction model and a routing model to account for parking delay estimates. The method was applied both to a real-world case study to show its immediate application potential and to a synthetic data set to identify environments and route characteristics that would most benefit from considering this information.

Results from the real-world data set showed a mean total drive time savings of 1.5 percent. The synthetic data set showed a potential mean total drive time savings of 21.6 percent, with routes with fewer stops, a homogeneous spatial distribution, and a higher cruising time standard deviation showing the largest savings potential at up to 62.3 percent. The results demonstrated that higher visibility of curb activity for commercial vehicles can reduce time per vehicle spent in urban environments, which can decrease the impact on congestion and space use in cities.

Authors: Fiete KruteinDr. Giacomo Dalla ChiaraDr. Anne Goodchild, Todor Dimitrov (University of Washington Paul G. Allen School of Computer Science & Engineering)

Recommended Citation:
Krutein, Klaas Fiete and Dalla Chiara, Giacomo and Dimitrov, Todor and Goodchild, Anne, How to Improve Urban Delivery Routes' Efficiency Considering Cruising for Parking Delays. http://dx.doi.org/10.2139/ssrn.4183322

Paper

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

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

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

Volume: 2675 (9)

Pages: 608-619

Publication Date: 2021

Summary:

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

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

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

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

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

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

Paper

Understanding Urban Commercial Vehicle Driver Behaviors and Decision Making

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

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

Volume: 2675 (9)

Publication Date: 2021

Summary:

As e-commerce and urban deliveries spike, cities grapple with managing urban freight more actively. To manage urban deliveries effectively, city planners and policy makers need to better understand driver behaviors and the challenges they experience in making deliveries. In this study, we collected data on commercial vehicle (CV) driver behaviors by performing ridealongs with various logistics carriers. Ridealongs were performed in Seattle, Washington, covering a range of vehicles (cars, vans, and trucks), goods (parcels, mail, beverages, and printed materials), and customer types (residential, office, large and small retail). Observers collected qualitative observations and quantitative data on trip and dwell times, while also tracking vehicles with global positioning system devices.

The results showed that, on average, urban CVs spent 80% of their daily operating time parked. The study also found that, unlike the common belief, drivers (especially those operating heavier vehicles) parked in authorized parking locations, with less than 5% of stops occurring in the travel lane. Dwell times associated with authorized parking locations were significantly longer than those of other parking locations, and mail and heavy goods deliveries generally had longer dwell times. We also identified three main criteria CV drivers used for choosing a parking location: avoiding unsafe maneuvers, minimizing conflicts with other users of the road, and competition with other commercial drivers. The results provide estimates for trip times, dwell times, and parking choice types, as well as insights into why those decisions are made and the factors affecting driver choices.

The goal is to reduce the negative externalities produced by urban freight vehicles, such as noise and emissions, traffic congestion, and unauthorized parking while guaranteeing goods flow in dense urban areas. To accomplish this goal, planners need to have an understanding of the fundamental parking decision-making process and behaviors of CV drivers.

Two main difficulties are encountered when CV driver behaviors are analyzed. First, freight movement in urban areas is a very heterogeneous phenomenon. Drivers face numerous challenges and have to adopt different travel and parking behaviors to navigate the complex urban network and perform deliveries and pick-ups. Therefore, researchers and policymakers find it harder to identify common behaviors and responses to policy actions for freight vehicles than for passenger vehicles. Second, there is a lack of available data. Most data on CV movements are collected by private carriers, who use them to make business decisions and therefore rarely release them to the public (2). Lack of data results in a lack of fundamental knowledge of the urban freight system, inhibiting policy makers’ ability to make data-driven decisions (3).

Driver surveys collect data on driver activities and are usually performed through in-person interviews with drivers outside their working hours or at roadside at specific locations. In-person interviews provide valuable insights into driver choices and decisions but are often limited by the locations at which the interviews occur or might not reflect actual choices because they are done outside the driver work context. Vehicle trip diaries involve drivers recording their daily activities while field observations entail observing driver activities at specific locations and establishments; neither collects insights into the challenges that drivers face during their trips and how they make certain decisions.

The same limitations hold true for data collected through GPS traces. Allen et al. (5) mentioned the collection of travel diaries by surveyors traveling in vehicles with drivers performing deliveries and pick-ups as another data collection technique that could provide useful insights into how deliveries/pick-ups are performed. However, they acknowledged that collecting this type of data is cumbersome because of the difficulty of obtaining permission from carriers and the large effort needed to coordinate data collection.

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.

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

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

Technical Report

Characterization of Seattle’s Commercial Traffic Patterns: A Greater Downtown Area and Ballard/Interbay Vehicle Count and Evaluation

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Publication Date: 2021

Summary:

Seattle now ranks as the nation’s sixth-fastest growing city and is among the nation’s densest. As the city grows, so do truck volumes — volumes tied to economic growth for Seattle and the region as a whole. But many streets are already at capacity during peak hours and bottleneck conditions are worsening. This project is designed to deliver critical granular baseline data on commercial vehicle movement in two key areas of the city to help the city effectively and efficiently plan for growing freight demand.

This timely research from the Urban Freight Lab (UFL) on behalf of the Seattle Department of Transportation produces Seattle’s first complete estimate of Greater Downtown area traffic volumes. And it offers a detailed analysis of commercial vehicle traffic in and around one of the city’s two major industrial centers, the Ballard-Interbay Northern Manufacturing Industrial Center.

These efforts are significant because the city has lacked a comprehensive estimate of commercial vehicle volumes until now. In the Greater Downtown area, the cordon counts (tracking traffic in and out of 39 entry/exit points) alongside traffic volume estimates will provide a powerful tool for local government to model, evaluate, develop, and refine transportation planning policies. This study lays the groundwork for the first commercial vehicle traffic model that will enable the evaluation of different freight planning and traffic management strategies, economic growth scenarios, and application of new freight vehicle technologies. Ballard-Interbay is slated for major infrastructure projects in the coming years, including new Sound Transit stations and critical bridge replacements. This analysis will help inform these projects, which are critical to an efficient, reliable transportation system for goods and people.

One overall finding merits attention as it suggests the need to update some of the freight network element categories defined in the current Seattle Freight Master Plan. The SCTL research team finds that the volume of smaller commercial vehicles (such as pick-ups, vans, and step vans) is significant in both the Greater Downtown area and Ballard-Interbay, representing more than half of all commercial vehicles observed (54% in the Greater Downtown area and 60% in Ballard-Interbay.) Among those smaller commercial vehicles, it is service vehicles that constitute a significant share of commercial traffic (representing 30% in the Greater Downtown area and 40% in Ballard-Interbay.) Among the myriad possible ramifications of this finding is parking planning. An earlier SCTL research paper (1) found service vehicles tend to have longer dwell times, with 44% of all observed service vehicles parked for more than 30 minutes and 27% parked for an hour or more. Given this study’s finding of service vehicles representing a significant share of commercial traffic volume, these vehicles may have a disproportionate impact on parking space rates at the curb.

Comprehensive planning requires comprehensive data. Yet cities like Seattle often lack the detailed data needed for effective freight planning, from peak hours and fleet composition to activity type and gateways of entry/exit. And if cities do have data, they are often too highly aggregated to be useful for management or planning or suffer from lack of comparability or data confidentiality problems.

Currently, urban traffic volume estimates by Puget Sound agencies are limited in spatial and vehicular detail. For example:

Seattle Department of Transportation (SDOT) is responsible for recording traffic counts through the year on selected arterial streets in Seattle, providing a seasonally adjusted average weekday total vehicle traffic for all lanes at all count locations.
Washington Department of Transportation (WSDOT) provides annual average daily traffic volumes in select locations of their jurisdiction, including the major interstates and state highways in the Seattle area. This data includes truck volume separated into three types: single, double, and triple units.
Puget Sound Regional Council (PSRC) regional truck model has three levels of vehicle classification: light commercial, medium trucks, and heavy trucks. This is based on WSDOT Annual Traffic Flow’s count locations and additional manual counts for model validation through the Puget Sound Region.

But none of these existing efforts produce enough detail to understand Seattle’s vehicle movements or connect them with economic activity. To fill the gap, Seattle could consider adopting a standard freight-data reporting system that would emphasize collecting and distributing richer and better data for time-series analysis and other freight forecasting, similar to systems used in cities like Toronto and London. Seattle is a national leader when it comes to freight master plans. This study offers a critical snapshot of the detailed data needed for effective policy and planning, potentially informing everything from road maintenance and traffic signals to electric vehicle charging station sites and possible proposals for congestion pricing. That said, Seattle could benefit greatly from sustained, ongoing detailed data reporting.

Authors: Gabriela Girón-ValderramaDr. Anne Goodchild

Recommended Citation:
Urban Freight Lab (2021). Characterization of Seattle's Commercial Traffic Patterns: A Greater Downtown Area and Ballard/Interbay Vehicle Count and Evaluation.

Paper

Do Commercial Vehicles Cruise for Parking? Empirical Evidence from Seattle

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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

Article

Developing Roadway Performance Measures Using Commercial GPS Data from Trucks

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URL: https://www.researchgate.net/publication/287183573_Developing_roadway_performance_measures_using_commercial_gps_data_from_trucks

Publication: Institute of Transportation Engineers. ITE Journal,

Volume: 84(6)

Pages: 36-40

Publication Date: 2014

Summary:

Global positioning system (GPS) devices that are installed in trucks and used for fleet management are increasingly common. Raw data from these devices present an opportunity for public agencies to use these trucks as probe vehicles to better monitor roadway operations and to quantify transportation system efficiency. Several North American programs have demonstrated that these truck GPS data can be used for a variety of performance measurement applications including locating roadway bottlenecks for trucks, providing travel reliability data, and informing planning and engineering processes. This paper discusses why these private sector GPS truck data are available, suggests how a public agency might acquire these data, provides some examples of the use of these data by transportation organizations, and covers some of the steps needed to make the GPS useful. In a performance measurement program, the GPS-equipped trucks are a small subset of all trucks on the network.

Authors: Dr. Ed McCormack

Recommended Citation:
McCormack, E. (2014). Developing Roadway Performance Measures Using Commercial GPS Data from Trucks. Institute of Transportation Engineers. ITE Journal, 84(6), 36-40.

Technical Report

Cost, Emissions, and Customer Service Trade-Off Analysis In Pickup and Delivery Systems

URL: https://rosap.ntl.bts.gov/view/dot/24904

Publication: Oregon Department of Transportation, Research Section

Publication Date: 2011

Summary:

This research offers a novel formulation for including emissions into fleet assignment and vehicle routing and for the trade-offs faced by fleet operators between cost, emissions, and service quality. This approach enables evaluation of the impact of a variety of internal changes (e.g. time window schemes) and external policies (e.g. spatial restrictions), and enables comparisons of the relative impacts on fleet emissions. To apply the above approach to real fleets, three different case studies were developed. Each of these cases has significant differences in their fleet composition, customers’ requirements, and operational features that provide this research with the opportunity to explore different scenarios.

The research includes estimations of the impact on cost and CO2 and NOX emissions from fleet upgrades, the impact on cost, emissions, and customer wait time when demand density or location changes, and the impact on cost, emissions, and customer wait time from congestion and time window flexibility. Additionally, it shows that any infrastructure use restriction increases cost and emissions. A discussion of the implications for policymakers and fleet operators in a variety of physical and transportation environments is also presented.

Authors: Dr. Anne Goodchild, Felipe Sandoval

Recommended Citation:
Goodchild, A., & Sandoval, F. (2011). Cost, Emissions, and Customer Service Trade-Off Analysis In Pickup and Delivery Systems (No. OR-RD 11-13). Oregon Department of Transportation Research Section.