Evaluation of Smart Phone Weight-Mile Tax Truck Data for Supporting Freight Modeling, Performance Measures and Planning

Oregon is one of the few states that currently charge a commercial truck weight-mile tax (WMT). This research serves to evaluate ancillary applications for a system developed by the Oregon Department of Transportation (ODOT) to simplify WMT collection. The data collection system developed by ODOT – TRUE (Truck Road Use Electronics) – includes a smart phone application with a Global Positioning System (GPS) device and microprocessor. The TRUE data has enormous advantages over GPS data used in previous research due to its level of disaggregation and its potential to differentiate between vehicle and commodity types. This research evaluates the accuracy of the TRUE data and demonstrates the results of its application to develop trip generation rates for a variety of truck types and land use categories. This research also confirms the value of the TRUE data to enhance existing ODOT transportation planning models and performance measures. Bell and Figliozzi 3 INTRODUCTION Reliable freight transportation is particularly important to Oregon due to its geographic location. According to The Oregon Freight Plan (1), Oregon is the ninth most trade-dependent state in the nation and is expected to see significant increases in freight flows in the future. This trade dependency comes with unwanted side effects such as congestion, travel unreliability, environmental and health concerns, and increased transport costs (2) (3). In this regard, recent research stresses the importance of freight performance measures and associated data management processes in sustaining an effective transportation planning system (4) (5). Freight data that might be used for such performance measures is usually incomplete, scarce and expensive to collect. However, a unique and highly promising data source is available through a system recently developed by ODOT to simplify its weight-mile tax (WMT) collection. Oregon is one of the few states that currently charge a commercial truck WMT; Oregon's WMT applies to trucks operating at weights of over 26,000 lbs. In February 2010, the ODOT Motor Carrier Transportation Division (MCTD) implemented a pilot project for the use of Truck Road Use Electronics (TRUE) (6). The TRUE system provides an automated process for WMT collection that reduces the administrative burden on trucking firms and ODOT while also reducing reporting errors and tax avoidance. The system includes a smart phone application with a Global Positioning System (GPS) device and microprocessor. The application can be uploaded to the phones of truck drivers in order to track miles traveled; the data is then sent electronically to ODOT to produce the company's WMT "invoice" which can be paid online. This research serves to evaluate ancillary applications for the TRUE data; applications that address ODOT needs for freight modeling, performance measures, and planning are explored. Previous research related to the use of freight GPS data provides a valuable starting point for this research. However, due to its level of disaggregation and its potential to differentiate between vehicle and commodity types, the TRUE dataset has many unique characteristics and enormous advantages over truck GPS data used in past research. Data collected through TRUE not only provides commercial truck origin-destination, space/time coordinates and trajectories, but also weight class, truck type and commodity codes. Such information can be used to understand the intricacies of freight transportation and to better inform decision makers. This paper will provide a review of relevant past research and associated applications, as well as an evaluation of the applications that will be possible for Oregon given the unique characteristics of the TRUE WMT GPS data. LITERATURE REVIEW Table 1 provides a summary of past academic research that explored applications for commercial truck GPS data. The work reviewed in this table is not intended to be an exhaustive list, but rather, a representation of research relevant to this project. One of the earlier, successful urban scale examples of the use of commercial truck GPS data for transportation engineering applications was performed in Australia (7); in this research, a trip identification algorithm was developed to determine the location of trip ends. The algorithm provided a means to differentiate between "genuine" stops and "false positive" stops (those associated with congestion, signals, etc.). Perhaps one of the more progressive uses of GPS data is associated with research on traffic in the New York City metropolitan area (8). Holguin-Veras et al. used commercial truck GPS data to evaluate the use of financial incentives to shift truck traffic to off-peak hours. In 2011, Wheeler and Figliozzi (2) researched the potential to develop multi-criteria (mobility, cost and emissions) performance measures using truck GPS data. In particular, they showed that loop Bell and Figliozzi 4 sensor data may underestimate the impact of congestion on freight travel time reliability. Subsequently, these researchers developed a new methodology and algorithms for combining freight GPS data with loop sensor data to more accurately model congestion and emissions. In later research, corridor level travel time reliability algorithms and programming logic was successfully applied to segment corridors and to estimate travel times for each segment identified (3). Considerable research has also been completed in the state of Washington using commercial truck data from private GPS vendors to develop transportation metrics (9) (10) (11) (12). In general, applications of truck GPS data are limited because the data typically does not differentiate between different truck or commodity types. Further, use of truck GPS data often involves an ongoing cost as it is typically purchased from an outside provider or third party. As a result, researchers have had "limited success" in developing trip generation rates with truck GPS data. It has also been determined that improvements in the data used for previous research would be needed before it could be used for freight transportation modeling (12). The freight performance measure categories included in Table 1 are those that have been receiving increasing attention at both national and state levels. In 2011, the National Cooperative Freight Research Program (NCFRP) released NCFRP Report 10, Performance Measures for Freight Transportation (5). This report proposes a "Balanced Scorecard" framework for a Freight System Report Card with 29 performance measures in six categories. The report suggests that given the ability to disaggregate freight data, the "Balanced Scorecard" framework proposed could be used to analyze the performance of individual links or bridges at the state or local level. The authors also note that a major challenge towards such efforts is the availability of useful data. At the state level, a report completed for ODOT in May 2010 (4) provides recommendations for Oregon freight performance measures. Most of the measures suggested for Oregon overlap with those suggested by NCFRP (see Table 2). As cell phone technology has evolved, so too have efforts towards using these devices in a variety of roles in the transportation sector. A common goal of cell phone applications is the estimation of travel times. A report for the Florida Department of Transportation (13) evaluated travel time measurements that were estimated by five companies using cell phone technology. Estimates were evaluated on the basis of methodology, data filtering and aggregation, reliability of data, and other key measures. The study observed good results in free-flow or fast traffic conditions; the study was inconclusive as to the accuracy of estimations in heavy traffic. In 2000, Zhao (14) reviewed the three most common location detecting technologies – stand-alone (dead reckoning), satellite-based (GPS), and terrestrial-based (navigation systems, cell networks). Zhao concluded that the cell-ID-based method has the worst positional accuracy, while assisted GPS has the best. In 2008, Barbeau et al (15) addressed the tradeoff between data accuracy and battery life, data transmission costs, and burden on the server. The researchers introduced two algorithms, a "Critical Point" algorithm and a "Location-Aware State Machine" algorithm, intended to ensure that the GPS devices did not waste battery power obtaining point fixes that would not enhance the quality of the data. Newer uses that involve smartphones can provide emissions estimates to the user and suggestions to improve driving (16) (17) (18). Among proprietary uses of commercial truck GPS data, a private company, INRIX, anonymously collects GPS data from "probe vehicles", in part through agreements with fleet operators who have GPS devices in their trucks, but also from personal vehicles (individuals who have downloaded the INRIX application to their smart phone) and taxis (19). The data is compiled into average speed profiles for freeways, highways and arterials. INRIX uses the results to provide travel information for a variety of users including individual travelers, Bell and Figliozzi 5 commercial fleets and the public sector. Customers from commercial fleets are provided services such as dispatch services, traffic map overlays, fastest routes, next-day planning and congestion pricing (20). In addition, INRIX provides the majority of the data used to produce the Texas Transportation Institute's Urban Mobility Report (UMR) (19). INRIX also offers a mobile application developer kit (MDK) for smartphone application developers wishing to use INRIX data. A technology with applications similar to ODOT's TRUE system is "Xata Turnpike" (21). Xata Turnpike is a fleet management and optimization technology that provides real time information for commercial trucking companies. Xata charges users a subscription price per vehicle per month. Xata's Electronic On Board Recorder (EOBR) tracks