1. TRB Managed Lanes Committee 2017 Research Needs Workshop

2. Data Management for Managed Lanes (1)
Integrated Corridor Management
ICM-2
Data needs and standards for ICM (a wider net than what you see in a FOC) operations and traveler information, especially focused on managed lanes operations, communications via multimedia, communications links across modes, across rideshare resources. Much more broad than freeway management.
Questions to be answered: · What are the Standard Operating Procedures (SOP) for multi-modal and multi-agency data integration · What are the Standard Operating Agreements (SOA) for multi-modal and multi-agency data integration · Standards for archive and real time operations and management? · What are the advantages and disadvantages of centralized vs. distributed data models? · What are the best practice models for using private sector data? · What are advantages of using data standards (e.g., TMDD) especially with deep data? · What are the issues with use of General Transit Feed Specification (GTFS)?
Driver perspective information provision along the corridor
Pivot off of Freeway operations circular and identify anything not being answered · What other shared data or multimodal information would be required to improve ICM? · What type of information do travelers need to alter their behavior (e.g., are travel times, comparative travel times, or parking status enough to change behavior)?

3. Dynamic Traveler Information for ML (2)
Freeway Traffic Control Devices
TCD-1
What is the most appropriate and effective methodology for presenting dynamic information to road users under high speed freeway conditions?
How do changes in speed affect the methodologies for delivery of the message selected?
How much information (3-lines, LED, color, how dynamic is the message)
How does the information affect the individual decision making process (impacts on decision, distraction, safety, etc.)?
How does this transition to automated vehicles
Potentially combine with #16
Texas is looking at network component
Providing travel information for multi-segment trips (different rules)
6-7 seconds to look at a sign
Can’t do all O-Ds
Information overload (too much information)
Israel research indicated that this can have significant safety impacts
What are the most effective and safest methods for communicating real-time information to road users through in-vehicle or personal communication devices?
What is the reaction of the drivers that has yet to be addressed?
Decision times and distances, context of sign overlays, etc.
Are they responding to these messages and responding to these messages or waiting until the last minute
How do drivers react to these inputs?
What is the stakeholder’s perspective of variable speed limits other ATM approaches?

4. Predicting Near Term Performance (3)
Performance Data, Monitoring & Management
PM- 4
Develop a methodology for predicting performance measures from field data and analytical tools, as part of a decision support system in the TMC.
Freeway operations is handling the metrics (e.g. speed, reliability, person throughput, etc.)
What is the typical trends on a Wednesday at 4pm (i.e. Google average)
Dynamic predictive performance from the instantaneous and evolving field data
Expectation is that managed lanes will perform better, so therefore we need to better anticipate changes in very near term conditions.
Waze is doing this (building the future based upon the past)
Vendor shootouts turning forecasting tools into data management systems
Impacts of sunset, weather, pavement condition, lane closures, etc.
How does this impact safety?
This could involve comparative studies
Predicting traffic and roadway conditions
How does incident management and detection fit into this?
Issues with toll caps and percent of exempt vehicles
40% of are low emission exempt vehicles in some areas
Use of real-time decision support systems. Questions to be answered: · What are the best practices for integrating traffic simulation into real-time decision support? ·   What are effective artificial intelligence approaches? · How can automated decision making be implemented and what are the human interfaces needed? · What are the challenges with real-time prediction of traffic conditions and impacts of response plans?

5. Dynamic Operations and AV Impacts (4)
Managed Lanes
ML-10
Applications of ATM on managed lanes to regulate speeds and manage queues.
Link to AV group. Speed harmonization and dynamic speed speed/acceleration control (beyond dynamic speed limits). Managing congestion in response to queues. Merging behavior. What about managing these items in a non-AV world, then mixed, then automated in stages.
Impacts of merging and access (lane changes for ingress/egress)
Mixed traffic of AVs with conventionally controlled vehicles (vehicle-vehicle interaction)
HDR Modeler Roads software designed to control the vehicle with respect to merging
Implications of machine learning with AVs and connected vehicles, ability to read dynamic messages, how much infrastructure needs to be deployed, can you depend on wired/wireless communication without signs, addressing security and reliability (more fiber/copper).
Conflicts may arise between the implementation of ATM strategies and impacts on safety
Modular lanes

6. Mobile vs. Stationary and Big Data (5)
Detection & Surveillance
DS- 5
With the continued increase in coverage provided by motorist’s GPS-equipped cell phones, an examination of the future of point sensors on freeways and other limited access highways is timely.
While continued use of the point detectors for traffic signal control appears needed in the future, it is not as clear that mass deployment of these sensors may be required to detect incidents or slow moving traffic conditions.
Data streams, communications bandwidth, infrastructure requirements, regulatory impacts, jurisdictional issues, privacy, data storage and retention cost,
Point vs mobile sensors
DSRC messages
What about places with no mobile coverage?
Cell phones can transmit under most conditions
Bluetooth
Develop techniques for analysis of data from traveler’s mobile devices (“big data”) to obtain performance measures. Assess the usability of the data for obtaining additional information not typically gathered through conventional approaches (e.g., origin-destination information, trip chaining).
Project level planning and decision making with respect to managed lanes
Comparing to conventional planning modeling and mechanisms
Bigger picture on how planning and simulation models work for managed lanes
Link this back to the financial analysis and economic interests
Socio-economic analysis of original planning model to corridor and then response to managed lane system
Data modeling and visualization tools to support ICM planning and evaluation. Questions to be answered: · What are best approaches to measure and calculate common corridor performance measures? · VMT, VHT, PMT, VHT, transit on time performance, freight on time performance, economic impacts, environmental impacts, safety impacts? · How do we consider multiple objectives from different organizations? · What tools are best for visualizing multi-modal, multi-agency, and multi-objective performance monitoring? · What are approaches to determine optimal data needs for ICM operational objectives? · What are approaches to understand real time changes in demand at the outer parts of the travel shed? · What are approaches to understand real time changes in demand at the outer parts of the travel shed?

7. Demand Management and ML (6)
Managed Lanes
ML-11
Managed lanes are congested
What incentives change behavior in aggregate and real-time?
No connection to real-time travel demand model
How it affects tolling and revenue studies
Microscopic perspective on travel behavior
CUTR 95 Express etc.
How does this interplay with Uber and services that could game the system (incentive aggregation technology futures, etc.)
Real-time TDM strategies to reduce or better manage peak demand for managed lanes and transit services
Toll caps, public satisfaction, no real-time travel demand model,
Working with transportation network companies (e.g., real-time ride sharing vendor for real-time discounts on bill)

8. Geometric Design (7)
Geometric Design

9. Choice Modeling (8)
Who uses managed lanes and why. How do we predict consumer behavior. Choice modeling for lane management - response to pricing as a precursor to near term response and regional modeling (access eligibility, pricing, tools to communicate)
Integration into regional modeling and mesoscopic planning tools (ABM+DTA, etc.)
When to change from general purpose to managed lane and when to change to priced lanes