Recent initiatives in the U.S., Asia, and Europe have pointed to the potential benefits of using real-time simulation models and performance measure techniques for enhancement and operation of Decision Support Systems (DSS) for use in Integrated Corridor Management (ICM).
The real time analytics required for ICM go far beyond what is typically required for more traditional types of transportation operations where single network management is the focus. An emerging analytical tool is a Real-Time, Multimodal Decision Support System (RTMDSS). These systems are information systems that support multimodal, transportation operational decision-making in real-time. An RTMDSS is an interactive, software-intensive system that gathers data from multiple relevant real-time data sources and knowledge bases. It uses these data, along with models, processes or analyses to implement context-specific actions and recommendations to assist managers in the process of collaboratively managing a multimodal transportation network to increase system efficiency and improve individual mobility, providing safe, reliable, and secure movement of goods and people. A recent USDOT project assessed the emerging opportunities for RTMDSS in transportation operations and developed a generic concept of operations for a RTMDSS.
A recent NCHRP Domestic Scan team reviewed the implementation of ICM across the U.S. The Scan Team Report reviews various ICM deployments, including an analysis of real-time models for planning and operations for ICM, and the implementation of DSS for ICM. As part of the final report, an ICM Capability Maturity Model (CMM) was developed which covers six process areas for ICM, including DSS.
The objective of this research is to develop guidance on the planning, design, deployment and on-going operations of a real-time DSS and simulation based on the user needs of the regions implementing ICM. The guidance will include: (1) a comprehensive report of existing studies and data on operational impacts of real-time simulation and DSS for use ICM with focus areas on data fusion, prediction, simulation, approaches to event response, and institutional coordination; and (2) a guidebook on ICM DSS and simulation for regions, including data needs, methodologies, and benefits.
This research will require the following tasks:
Task 1. Literature Review. Conduct a comprehensive survey (literature review, surveys, interviews, etc.) of existing studies and data on use of simulation in Decision Support Systems for ICM and Smart City initiatives in the U.S. and abroad, and more broadly on the use of DSS in Transportation Systems Management and Operations (TSM&O) related topics. The synthesis will also cover the corresponding lifecycle costs, human resource demands and skills required, and institutional and management challenges with operating and maintaining a DSS.
Task 2. Summary Report. Develop a comprehensive summary report of existing studies and data on operational impacts and costs, resource requirements, and challenges of operating and maintaining a DSS and real-time simulation model.
Task 3. Enhancement and Data Collection Plan. Develop an overall Methodology Enhancement Plan and Data Collection Plan for the effort needed to validate, test, and expand the current understanding and capabilities of real-time simulation for DSS for an ICM implementation. The plans can be organized along four focus areas, namely (A) institutional coordination, (B) real-time data and performance measurement, (C) modeling and traveler behavior, and (D) staffing needs. The research topics presented here are based on gaps and needs identified in the ICM process. They can be reduced or expanded based on needs by others, funding availability, criticality of mission among others.
- Focus Area A, Institutional Coordination. There must be open communication and cooperation among agencies to operate the assets within an integrated corridor. This can be done informally (i.e., operational personnel share information and coordinate responses among agencies) or more formally (i.e., through intergovernmental agreements or MOUs that define roles and responsibilities). Some areas have been successful using high-level ITS cooperative MOUs, while others have developed ICM-specific MOUs. The following institutional challenges are of particular interest: changes in maintenance practices (particularly, quick replacement of defective data collection equipment and installation and testing standards for sensor equipment); institutional arrangements and approaches for successful system management; changes in planning practices to focus on non-recurrent congestion and real-time traffic management; and operational organization models for ICM and DSS, including recommended language for MOUs.
- Focus Area B, Real-time Data and Performance Management. Data needs and standards for performance measures and performance management are critical for DSS and simulation models for ICM. Potential topics to cover in this focus area include: detector layout optimal monitoring and identification of traffic patterns, data collection technologies and sources, data fusion of multiple data sources (including public and private data), risk analysis on the impact of equipment failure (including detectors, communications devices and systems, hardware, software, agency communication), data exchange protocols and data standards, data filtering, techniques to address data gaps and missing data, and standardization of performance measures (for operators, decision-makers, and travelers), multiple objective decision making (e.g., mobility improvement, emissions reduction, emergency vehicle access and response time), and the incorporation of big data (including changes in the capability and the demand for performance management).
- Focus Area C, Modeling and Traveler Behavior. This task will look at the model requirements for simulation and compare the various DSS software platforms to include real-time simulation. Potential topics to cover include: need for a hybrid solution between analytic (model-based) DSS and artificial intelligence-based DSS; development of time-dependent origin-destination patterns in real-time; automation of real-time calibration and DSS self-learning; comparative review of strengths and weaknesses of existing DSS software platforms; inclusion of dynamic multimodality in real-time using a unified cost function across highway and transit; impacts of ICM on travel behavior (including mode shift, route diversion, temporal shift); key decision variables (e.g., values of time, travel time reliability); and provision of information to travelers.
- Focus Area D, Staffing Needs. The creation of a DSS potentially requires new skill sets and training for the development and ongoing operation and maintenance of a DSS. Information on the knowledge, skills, and abilities (KSA) and relevant training needs for a successful implementation and ongoing operation of the ICM/DSS will be needed.
Task 4. Data Collection. Perform data collection following the approach detailed in the Data Collection Plan developed in Tasks 3-6. Data shall be cleaned, organized, and documented in a form suitable to proceed with Task 5.
Task 5. Conduct Research. Develop methodologies following the approach detailed in the Methodology Enhancement Plan developed in Tasks 3. Demonstrate the data collected and methodology improvements in a simulated environment, and produce estimates of additional benefits resulting from the improved methods and data.
Task 6. Knowledge Transfer. Make best practices, data and methodologies available to the transportation community. Develop an ICM and DSS Guidance Document on the key items and best practices to consider when implementing a DSS for Transportation Management and ICM. This would build upon the ICM Implementation Guide, developed by USDOT for the ICM program – but look specifically at the DSS and simulation aspects of ICM.
Note: The AASHTO Standing Committee on Research directed that the deliverables should go beyond a guidebook to provide material that is useful to agencies in developing and deploying DSS. Customizable systems engineering documents should be valuable. Software can be problematic and development should only be pursued if issues related to its development, implementation, and support can be resolved.