CHAPTER 5

Summary and Recommended Research

This research report documented the research performed as part of the development of the Guide for Truck Parking Information Management Systems. Key findings of the research include the following:

• The literature review identified several key topic areas that are relevant to TPIMS, including research on truck parking preferences, various hardware/software/technology options associated with TPIMS, and effectiveness of real-world deployments. While this effort looked at international examples, most TPIMS deployments are found in the United States. Various public-sector and private-sector models exist, offering options to deploy TPIMS in traditional rest areas, private-sector parking facilities, and curbside applications.
• The definition of the practice identified key topic areas that are relevant for TPIMS, including an understanding of the practice, policy/planning considerations, various count methodologies, and technology options to support information collection, processing, and distribution. The state of the practice may be defined as a series of lifecycle considerations, which provides an opportunity to utilize best practice approaches such as the systems engineering approach, to develop a TPIMS program.

Recommended Research

The research done in support of the Guide for TPIMS produced information on successful practices as well as findings that suggest areas of further research. The following recommendations may improve the practice of TPIMS development and the resultant quality and availability of parking information.

• Detector and system accuracy is an actively evolving practice. Further research and development is still needed to validate the various detection technologies for accuracy and reliability. This will also inform best practice guidance for designers placing equipment. System accuracy is improving, but additional research and development of software or algorithms processing incoming data may help reduce the need for manual interventions or recalibrations.
• Data exchange standards for TPIMS should be advanced. This will streamline development TPIMS data management and information dissemination, and it will aid with proliferation to information providers and to the truckers that need the information. The MAASTO experience is noted in the Data Sharing Systems subsection of Chapter 3 of the report. Improving interoperability or sharing TPIMS data across jurisdictions may provide related benefits, e.g., port or other intermodal terminal operations, and urban curb management for local freight. A current analogous initiative researchers may refer to is the Work Zone Data Exchange (United States Department of Transportation 2024), wherein standard, open-source, GeoJSON schemas are adopted by many interested agencies and made available to any third-party provider for dissemination. Collaboration beyond Infrastructure Owner-Operators to national stakeholders—e.g., AASHTO, NATSO, OOIDA, ATA—is important for improving parking information dissemination and standardization across all geographies.
• Improve placement and communication timing of parking information. Whether parking availability is communicated by roadside message signs or a push-notification to the driver, timing of this information along a driver’s route is crucial for timely decision-making. Additional research about driver preferences as to when they are alerted to parking availability (e.g., one hour before reaching parking location), or where the information is disseminated (e.g., 60 miles before parking location) is critical to balancing the

• information accuracy with driver utilization of TPIMS. Additionally, incorporating updated parking information as the driver approaches the parking location, whether actual or predictive counts, would be an additional avenue to research.
• Predictive counts—targeted at upstream approaching trucks seeking parking—are mentioned several times in this report, and existing research indicates an interest in implementing these. The barriers that remain to be researched and addressed include both technical and organizational. For example, identifying which count methods and technologies are most amenable, additional data validating predictions against actual, what constitutes sufficient confidence for an agency to report predictive counts, and what fallback processes are needed.