Edward McCormack

ITS Devices Used to Collect Truck Data for Performance Benchmarks

This paper documents the development of data collection methodologies that can be used to measure truck movements along specific roadway corridors in Washington State cost-effectively. The intent of this study was to design and test methodologies that could provide information to ascertain the performance of freight mobility roadway improvement projects. The benchmarks created would be used to report on speed and volume improvements that resulted from completed roadway projects. One technology tested consisted of Commercial Vehicle Information System and Networks electronic truck transponders, which were mounted on the windshields of approximately 30,000 trucks traveling in Washington. These transponders were used at weigh stations across the state to improve the efficiency of truck regulatory compliance checks. With transponder reads from sites anywhere in the state being linked through software, the transponder-equipped trucks can become a travel time probe fleet. The second technology tested involved G...

Travel Impacts of Mixed Land Use Neighborhoods in Seattle, Washington

In response to suburban transportation problems, developers and planners have suggested that mixing land uses can reduce automobile dependency by making more goods and services available within walking, biking, and short driving distances. This view has resulted in a neotraditional planning movement that promotes neighborhoods designed with traditional characteristics including a mix of land uses. However, few studies have empirically explored the transportation implications for these neighborhoods. This issue is addressed by using a travel diary collected in three greater Seattle area neighborhoods characterized by neotraditional neighborhood elements including mixed land use. These data were compared with those collected in an identical diary from individuals throughout the region. It was found that residents of the mixed land use study neighborhoods in Seattle traveled 28 percent fewer kilometers (miles) than residents in adjacent areas and up to 120 percent fewer kilometers than residents in suburban areas. This trend of lower travel distances held across different socioeconomic characteristics. However, the differences in travel distances among the areas were not seen when travel time was considered. The daily travel time was about 90 min/person (including walking), regardless of where that person lived and that person’s socioeconomic status. One implication of this finding is that if a neotraditional neighborhood development does make shopping and other chores less time-consuming, there may simply be more time in the travel budget for additional regional travel. This suggests that travel from the neotraditional neighborhoods needs to be examined in a regional context.

Processing Commercial Global Positioning System Data to Develop a Web-Based Truck Performance Measures Program

Although trucks move larger volumes of goods than other modes of transportation, public agencies know little about their travel patterns and how the roadway network performs for trucks. Trucking companies use data from the Global Positioning System (GPS) provided by commercial vendors to dispatch and track their equipment. This research collected GPS data from approximately 2,500 trucks in the Puget Sound, Washington, region and evaluated the feasibility of processing these data to support a statewide network performance measures program. The program monitors truck travel time and system reliability and will guide freight investment decisions by public agencies. While other studies have used a limited number of project-specific GPS devices to collect frequent location readings, which permit a fine-grained analysis of specific roadway segments, this study used data that involved less frequent readings but that were collected from a larger number of trucks for more than a year. Automated processing was used to clean and format the data, which encompassed millions of data points. Because a performance measurement program ultimately monitored trips generated by trucks as they travel between origins and destinations, an algorithm was developed to extract this information and geocode each trucks location to the roadway network and to traffic analysis zones. Measures were developed to quantify truck travel characteristics and performance between zones. To simplify the process and provide a better communications platform for the analysis, the researchers developed a Google Maps–based online system to compute the measures and show the trucks’ routes graphically.

Using Truck Probe GPS Data to Identify and Rank Roadway Bottlenecks

This paper describes the development of a systematic methodology for identifying and ranking bottlenecks using probe data collected by commercial global positioning system fleet management devices mounted on trucks. These data are processed in a geographic information system and assigned to a roadway network to provide performance measures for individual segments. The authors hypothesized that truck speed distributions on these segments can be represented by either a unimodal or bimodal probability density function and proposed a new reliability measure for evaluating roadway performance. Travel performance was classified into three categories: unreliable, reliably fast, and reliably slow. A mixture of two Gaussian distributions was identified as the best fit for the overall distribution of truck speed data. Roadway bottlenecks were ranked on the basis of both the reliability and congestion measurements. The method was used to evaluate the performance of Washington state roadway segments, and proved efficient at identifying and ranking truck bottlenecks.

Measuring Truck Travel Time Reliability Using Truck Probe GPS Data

Truck probe data collected by global positioning system (GPS) devices has gained increased attention as a source of truck mobility data, including measuring truck travel time reliability. Most reliability studies that apply GPS data are based on travel time observations retrieved from GPS data. The major challenges to using GPS data are small, nonrandom observation sets and low reading frequency. In contrast, using GPS spot speed (instantaneous speed recorded by GPS devices) directly can address these concerns. However, a recently introduced GPS spot-speed-based reliability metric that uses speed distribution does not provide a numerical value that would allow for a quantitative evaluation. In light of this, the research described in this article improves the current GPS spot speed distribution-based reliability approach by calculating the speed distribution coefficient of variation. An empirical investigation of truck travel time reliability on Interstate 5 in Seattle, WA, is performed. In addition, correlations are provided between the improved approach and a number of commonly used reliability measures. The reliability measures are not highly correlated, demonstrating that different measures provide different conclusions for the same underlying data and traffic conditions. The advantages and disadvantages of each measure are discussed and recommendations of the appropriate measures for different applications are presented.

Evaluating the Accuracy of Spot Speed Data from Global Positioning Systems for Estimating Truck Travel Speed

A number of trucking companies use Global Positioning System (GPS) devices for fleet management. Data extracted from these devices can provide valuable traffic information such as spot (instantaneous) speeds and vehicle trajectory. However, the accuracy of GPS spot speeds has not been fully explored, and there is concern about their use for estimating truck travel speed. This concern was addressed by initially comparing GPS spot speeds with speeds estimated from dual-loop detectors. A simple speed estimation method based on GPS spot speeds was devised to estimate link travel speed, and that method was compared with space mean speed estimation based on GPS vehicle location and time data. The analysis demonstrated that aggregated GPS spot speeds generally matched loop detector speeds and captured travel conditions over time and space. Speed estimation based on GPS spot speeds was sufficiently accurate in comparison with space mean speeds, with a mean absolute difference of less than 6%. It is concluded that GPS spot speed data provide an alternative for measuring freight corridor performance and truck travel characteristics.

What transportation modeling needs from a GIS: a conceptual framework

Both the geographic information system (GIS) and transportation modeling environments have seen continually developing analytic concepts and techniques. However, these developments have seldom resulted in the integration of GISs and transportation models. This paper explores the potential inherent in merging of these environments through a systematic investigation of the fundamental basis of integration. To do this, the traditional four step transportation modeling process is extended to include input and output steps. We then define functional components for GIS data handling — data management, manipulation, and analysis. The steps of modeling are matched against the list of GIS data handling functions within a matrix‐based framework. GIS functions that enhance a land‐use based urban transportation modeling process are then categorized. Conclusions are drawn and directions for future developments are discussed.

Assessing the safety effects of cooperative intelligent transport systems: A bowtie analysis approach.

The safety effects of cooperative intelligent transport systems (C-ITS) are mostly unknown and associated with uncertainties, because these systems represent emerging technology. This study proposes a bowtie analysis as a conceptual framework for evaluating the safety effect of cooperative intelligent transport systems. These seek to prevent road traffic accidents or mitigate their consequences. Under the assumption of the potential occurrence of a particular single vehicle accident, three case studies demonstrate the application of the bowtie analysis approach in road traffic safety. The approach utilizes exemplary expert estimates and knowledge from literature on the probability of the occurrence of accident risk factors and of the success of safety measures. Fuzzy set theory is applied to handle uncertainty in expert knowledge. Based on this approach, a useful tool is developed to estimate the effects of safety-related cooperative intelligent transport systems in terms of the expected change in accident occurrence and consequence probability.

Smart growth and goods movement: emerging research agendas

While recent urban planning efforts have focused on the management of growth into developed areas, the research community has not examined the impacts of these development patterns on urban goods movement. Successful implementation of growth strategies has multiple environmental and social benefits but also raises the demand for intra-urban goods movement, potentially increasing conflicts between modes of travel and worsening air quality. Because urban goods movement is critical for economic vitality, understanding the relation between smart growth and goods movement is necessary in the development of appropriate policies. This paper reviews the academic literature and summarizes the results of six focus groups to identify gaps in the state of knowledge and suggest important future research topics in five sub-areas of smart growth related to goods movement: (1) access, parking, and loading zones; (2) road channelization and bicycle and pedestrian facilities; (3) land use; (4) logistics; and (5) network system management.

Small Unmanned Aircraft Evaluated for Avalanche Control

The Washington State Department of Transportations (DOT) snow avalanche control program reduces winter roadway closure times and hazards to motorists. The University of Washington and the Washington State DOT evaluated small unmanned aircraft systems (UASs) as a tool to enhance this program. Because of military investment, UAS technology has dropped in cost as it has become increasingly capable and easier to operate. Commercially available UASs, which fly autonomously, can be operated off a roadway and can collect low-cost, real-time aerial imagery while also carrying payloads. This project conducted a series of test flights involving both fixed- and rotary-wing (helicopter) UASs over a roadway in mountainous terrain. The flights demonstrated that UASs can conduct snowpack and terrain surveillance and can accurately drop explosive charges such as those used to trigger controlled avalanches. The rotary-wing UAS was particularly usable because of its ability to hover, which provided a stable camera platform, and because it required minimal area to land. The reliability of UASs is a concern, and their capabilities may be challenged by mountainous terrain and weather. This problem may be reduced as UASs become either less expensive and more expendable or more reliable and all-weather capable. A major barrier to use of UASs is the need to obtain approval to fly from FAA, a process that can be time-consuming and restrictive. FAA is currently updating its plans to integrate UASs into the national airspace, and a number of technology-based solutions are being considered.