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53 C H A P T E R 5 This research project focused on assessing and develop- ing strategies for obtaining comprehensive truck-activity data. The key objectives were to gauge the current state of the practice for obtaining and reporting truck-activity data, identify the issues and limitations in current sources, develop data-gathering strategies with a chance of success at over- coming current challenges, and pull together implementa- tion scenarios for a subset of recommended strategies. To do so, the research team reviewed literature, evaluated datasets, and interviewed key informants. 5.1 Innovative Approaches This research identified key gaps in truck-activity data on VMT, ton/ton-miles, value/value-miles, O/D flows, vehicle speeds, and transportation costs. Although there are sources for these data, they are highly fragmented and users need to piece together information from many sources to answer key policy questions. The most comprehensive source, the CFS, is conducted only every 5 years, making it difficult to accu- rately model the intervening years. The CFS also lacks neces- sary information at finer levels of geography. This research reviewed the feasibility of strategies for obtaining truck- activity data that would provide improved coverage of key data elements and the trucking industry universe (i.e., for- hire carriers, owner-operators, shipper-owned trucking for long-distance and local geographies). The strategies assessed included improvements to existing Census-based surveys (like the CFS and T&W surveys), the creation of new surveys (a VIUS-like survey and an indus- try supply-chain survey), identification of new sources of operations data (like GPS or LPR data), better use of MCMIS records and R. L. Polk data, and an ABM approach. In identi- fying the menu of strategies for which feasibility assessments would be performed, the research team focused on improv- ing existing approaches rather than on creating something entirely new. Total feasibility scores across the feasibility reviews high- lighted the likely chances of successful implementation of the following three approaches: â¢ New VIUS-Like Survey would provide vehicle activity (for pickups, minivans, or other light vans, light single-unit trucks, heavy single-unit trucks, and truck-tractors) by range, products carried, VMT, industry, and type of opera- tor at the national and state levels. Using either informa- tion from MCMIS or R. L. Polk as a sampling frame would enlarge coverage of the trucking industry universe. This approach would use the same basic survey design, process- ing, reporting procedures as the historic VIUS. An addi- tional improvement would be to model the new VIUS-like survey after the CVUS, which uses onboard equipment to capture trip making and could create a harmonized U.S.- Canadian survey. It is possible that this survey could be implemented within 5 years, benefiting from the Canadian process, but resolving financial considerations within this timeframe would be challenging. â¢ National Freight GPS Framework that amassed GPS data derived from truck movements would provide VMT data and O/D flows by time of day. Appended with other attributes to become multi-attributed traces, one can obtain or calculate these data by vehicle type, commodity type, load type, aver- age load, route, time of day, speed, or average speed by the truck stop, corridor, metro area, state, multi-state region, or national levels. A new app, My Trip Matters (MTM), could be an answer to the challenges of identifying the best type of GPS device and of gaining cooperation among truckers, while providing protections for privacy and security. Using MTM, truckers would opt-in to the data program. Within the next 5 years, the entire MTM program could be up and run- ning, including development of a location service app and set-up of the necessary cloud-based operating and process- ing environment. Federal legislative action may be necessary to empower FHWA to collect GPS trace data from trucks. Conclusions and Future Research Needs
54 â¢ Agent-Based Modeling has the possibility of representing a large part of the trucking industry universe, both inter- regional as well as intraregional truck movements. There is also the significant possibility of filling gaps in VMT and O/D flow data. It is conceivable that if successfully imple- mented, ABM also could be used to generate here-to-fore lacking information on supply chains (i.e., the interactions among shippers, receivers, and intermediaries). The ABM models specifically simulate the simultaneous operations and interactions of multiple agents (firms and individu- als) that could provide the best source of information on trucking activities. However, the best-case scenario for this strategy is one of long-term development. The research team suggests that resources be devoted to further the development of these three strategies for obtain- ing truck-activity data. 5.2 Additional Research Needs The feasibility reviews conducted as part of this research resulted in suggestions regarding other strategies reviewed in this report. These include the following: â¢ Expansion of the Trucking and Warehousing Survey. The Trucking and Warehousing Survey represents the most comprehensive set of transportation industries available, with reporting on an annual and quarterly basis. Its weak- ness is that it does not encompass shipper-owned vehicle fleets engaged in own-account haulage. The research team suggests that university-based or other researchers consider deriving modeled information on VMT, tonnage, and value for carriers and owner-operators from this survey. â¢ Design of a New Industry-Based Supply-Chain Survey. Supply-chain data is a major gap in freight transportation data. There is currently no data program responsible for capturing, analyzing, and delivering supply-chain informa- tion. Opportunities for further research include (1) explor- atory research on developing a typology of supply chains for survey purposes and (2) qualitative research on how shippers choose modes and routes. â¢ Creation of an Operations Data Analysis Platform. Operations data is one of the richest sources of timely and detailed data about trucks traveling on public infrastruc- ture. These data represent private, for-hire (TL, LTL), heavy and tractor-trailer, and light or delivery services trucks. Although extensively collected by states and reported to FHWA, the currently generated reports do not include VMT and do not use weigh-in-motion (WIM) data to look at freight flows. â¢ Obtaining Data from License Plate Readers (LPRs). LPR is an image-processing technology used to identify vehicles by their license plates. LPR systems use cameras, computer hardware, and software to capture an image of a license plate, recognize its characters by converting them into readable text, and check the license plate against des- ignated databases for identification. Although a subset of the data from such systemsâspecifically, large volumes of observations of different commercial vehicles at dif- ferent locations and points in timeâcould be analyzed to develop information about truck travel patterns, there are a number of technical and institutional issues that severely limit its applicability to obtaining truck-activity data at the present time. This strategy may be most beneficial if moni- tored for ongoing developments that could make it more feasible in future years. 5.3 Future Visions This research took an evolutionary approach to identify- ing innovative strategies that could be used to collect truck- activity data. But the future will bring new âdisruptiveâ opportunities for data gathering. To conclude this report, two possibilities for future areas of research are discussed. As mentioned in the last chapter relating to the Internet of Things, both of these opportunities are related to types of connectivity that could become a rich source of information to support modeling of the freight transportation system. 5.3.1 Autonomous Vehicles Self-driving (or autonomous) vehicles are those in which operation of the vehicle occurs without direct driver input to control the steering, acceleration, and braking. Autono- mous vehicles are designed so that the driver is not expected to constantly monitor the roadway while operating in self- driving mode. A product to decrease motor vehicle accidents, several states, including Nevada, California, and Florida, have enacted legislation that expressly permits operation of self-driving vehicles under certain conditions. As many as 20 other states are considering similar legislation. These experi- mental vehicles are at the highest levels of a wide range of automation. The highest levels of automation (Levels 3 and 4) depend on âconnected carâ technology. Vehicles at Level 3 enable the driver to cede full control of all safety-critical functions under certain traffic or environmental conditions. The Google car is an example of such limited self-driving automation. Vehicles at Level 4 are designed to perform all safety-critical driving functions and monitor roadway conditions for an entire trip. Such a design anticipates that the driver will provide destina- tion or navigation input, but is not expected to be available for control at any time during the trip. This includes both occupied and unoccupied vehicles.
55 The Office of the Assistant Secretary for Research and Tech- nology (formerly the Research and Innovative Technology Administration [RITA] of U.S.DOT) characterizes its vision of âconnected carsâ as requiring âa wireless communications network that includes cars, buses, trucks, trains, traffic sig- nals, cell phones, and other devices. Like the Internet, which provides information connectivity, connected vehicle tech- nology provides a starting point for transportation connec- tivity that will potentially enable countless applications and spawn new industriesâ (Intelligent Transportation Options, 2013a; 2013b). Among vehicles, infrastructure, and wireless devices provide continuous real-time connectivity to all sys- tem users (Intelligent Transportation Systems, 2013b). A convergence of communications and sensor-based tech- nologies enabling autonomous vehicle operation also could be used to obtain information about trucking activity. The two-way flow of information would be from driver to vehicle to infrastructure to other vehicles. Data archiving would occur using cloud-based services. Although the technology is in early stages, NHTSA is conducting research on self-driving vehicles so the agency can establish standards for these vehicles should they become commercially available. The first phase of this research is expected to be completed within the next 4 years. 5.3.2 Product Traceability As with the advent of autonomous vehicles, advances in product traceability technology have largely been driven by safety. For example, food traceability is at the heart of food safety. It involves the ability to identify, at any specified stage of the food chain (from production to distribution), from where the food came (one step back) and where it went (one step forward)âthe so-called âone-up, one-downâ system (OUOD). This necessitates that each lot of each food mate- rial is given a unique identifier, which accompanies it and is recorded at all stages of its progress through its food chain (International Union of Food Science and Technology, 2013). Because multi-ingredient foods may include materials from a variety of food chains and countries, traceability systems of multiple countries are often involved. There are many approaches for unique identification of food and other products. Current practice often involves combining different relevant data fields such as a global trade identification number (GTIN) with a handlerâs production lot or batch number. Other, less used possibilities include serialized GTINs or unique identification numbers (UIDs) as used by the U.S. Department of Defense, or a globally unique identification number (GUID) as used by other man- ufactured product industries. Unique codes may be stored, presented, and transmitted in a variety of ways including ear tags for livestock, printed human-readable data, barcodes, 2D barcodes, and electronically through RFID tags. Commer- cially available hardware and software and solutions provid- ers offer many ways for recording, storing and retrieving data. Tracking devices are not only becoming much less expensive (new devices are known as âthrowawaysâ) but smaller and less intrusive. One technology involves edible bar codes that are the size of a dust speck and thinner than a strand of hair. In October 2013, the Food and Drug Administration issued a report on two pilot projectsâone on tomatoes and one on chicken, peanut butter, and spices used in processed foodâ on how food can be rapidly tracked and traced as well as what types of data are needed and how the data can be made available to FDA (Institute of Food Technologies, 2013). The challenge in using this type of information as a source of truck-activity data is that there is no standardization of data requirements or standardization of recordkeeping. The FDA is in early stages of information collection for rulemaking, which means that any type of national system is at least 7 to 10 years away.