1
|
- February 23, 2015
- Ben McKeever and Govind Vadakpat
- DMA Webinar Series
- MMITSS Bundle
|
2
|
- Ben McKeever
- Team Leader, Transportation Operations Applications, FHWA R&D
- DMA Program Overview
- MMITSS Bundle Overview
- Prototype Description and Current Project Status
- Govind Vadakpat
- Research Transportation Specialist, Transportation Operations Applications,
FHWA R&D
- Current Project Status of Impact Assessment
- Testing Results and Impacts/Benefits from IA
- Stakeholder Q&A
- We can only answer the questions related to the DMA program.
- We cannot answer any questions related to the CV Pilots.
|
3
|
|
4
|
- Vision
- Expedite development, testing, commercialization, and deployment of innovative
mobility application
- maximize system productivity
- enhance mobility of individuals within the system
- Objectives
- Create applications using frequently collected and rapidly disseminated
multi-source data from connected travelers, vehicles (automobiles,
transit, freight) and infrastructure
- Develop and assess applications showing potential to improve nature,
accuracy, precision and/or speed of dynamic decision
- Demonstrate promising applications predicted to significantly improve
capability of transportation system
- Determine required infrastructure for transformative applications
implementation, along with associated costs and benefits
- Project Partners
- Strong internal and external participation
- ITS JPO, FTA, FHWA R&D, FHWA Office of Operations, FMCSA, NHTSA,
FHWA Office of Safety
|
5
|
- Challenge 1 (Technical Soundness)
Are the DMA bundles technically sound and deployment-ready?
- Create a “trail” of systems engineering documents (e.g., ConOps, SyRs)
- Share code from open source bundle prototype development
(OSADP website: http://www.itsforge.net/)
- Demonstrate bundle prototypes (in isolation)
- Field test integrated deployment concepts from across CV programs
- Challenge 2 (Transformative Impact)
Are DMA bundle-related benefits big enough to warrant deployment?
- Engage stakeholders to set transformative impact measures and goals
- Assess whether prototype show impact when demonstrated
- Estimate benefits associated with broader deployment
- Utilize analytic testbeds to identify synergistic bundle combinations
|
6
|
|
7
|
|
8
|
|
9
|
- Objectives
- To develop a comprehensive traffic signal system that services multiple
modes of transportation including
- Passenger vehicles,
- Transit
- Emergency vehicles
- Freight fleets (e.g. Trucks)
- Pedestrians
- To demonstrate the developed Multi-Modal Intelligent Traffic Signal System
|
10
|
- Next generation of traffic signal systems that seeks to provide a
comprehensive traffic information framework to service all modes of
transportation:
- Intelligent Traffic Signal System (I-SIG)
- Transit Signal Priority (TSP) and Freight Signal Priority (FSP)
- Emergency Vehicle Preemption (PREEMPT)
- Mobile Accessible Pedestrian Signal System (PED-SIG)
|
11
|
- Sponsors
- Pooled Fund Project
- FHWA
- Virginia DOT/UVA
- Maricopa County DOT
- Caltrans
- Minnesota DOT
- Florida DOT
- Michigan DOT
- …
- Technical Team
- University of Arizona (Prime)
- University of California Berkeley (PATH)
- Savari
- Econolite
|
12
|
|
13
|
|
14
|
|
15
|
|
16
|
- Anthem, AZ
- 6 Intersections + 1 Diamond Interchange
- Equipped with RSE, Controller (ASC/3), and multiple OBE’s
- MAP & SPaT at every intersection
- MMITSS applications at every intersection
- Northern CA
- 11 Intersection along Camino Real
- Equipped with RSEs, Controller (2070 ATC/Caltrans software), multiple
OBE’s
|
17
|
- Intelligent Traffic Control based on Awareness of Equipped Vehicles
- Signal actuation, gap out, extension, dilemma zone protection
- Pedestrians, Disabled Pedestrians
- Signal coordination, congestion control
- Traffic State, Flow, and Performance Observation
- Priority Control for EV, Transit, Trucks, and other Special Vehicles
- Smartphone application for Pedestrians
|
18
|
- Intelligent Traffic Control
- Responsible for allocation of available green, given priority control
constraints (coordination, priority requests)
- Responsible for providing Dilemma Zone protection
|
19
|
- Priority Control Architecture
|
20
|
- Performance Observer
- Derived from BSM Data (Trajectories)
- Process Trajectories to compute observed
- Delay (Average, Variability), Travel Time (Average, Variability), and
Traffic States (Queue Length)
- Performance Measures Used for
- Monitoring and Assessment, DSRC Performance, and Section Level Control
(Coordination Updates)
- Approach
- Defining appropriate performance measures and metrics
- Traditional: Overall Vehicle Delay, Number of Stops, Throughput,
Maximum Queue Length, Total Travel Time
- More recent: Time to Service, Queue Service Time
- Connected Vehicle System: DSRC Range, Packet Drop
- Understanding how improvements to one mode may impact another mode
- Understanding how connected vehicles information, can help to estimate
performance measures
|
21
|
- Pedestrian Smartphone App
|
22
|
- Institutional
- Requires training for engineering and staff to understand technology
and system operation
- Technical
- DSRC Range from multiple nearby RSE’s broadcasting MAP and SPaT data
require OBE algorithms to determine which MAP and SPaT is relevant
- Current BSM specification doesn’t contain Mode information
- Current SSM (Signal Status Message) doesn’t acknowledge all Signal
Request Messages (SRM) – only acknowledges one
- How to overcome
- Multiple MAP’s resolved algorithmically
- Working with SAE DSRC Technical Committee on BSM, SSM and other issues
|
23
|
- Supporting documentation available at - http://www.its.dot.gov/pilots/pilots_mobility.htm
- MMITSS Final ConOps
- Multi-Modal Intelligent Traffic Signal System Final System Requirements
Document
- Multi-Modal Intelligent Traffic Signal System - System Design
- Code from open source MMITSS prototype developments will be available
at: http://www.itsforge.net/ in April, 2015
- Research Data will be available at RDE website: https://www.its-rde.net/
|
24
|
|
25
|
- Separate Contract for Impact Assessment
- The MMITSS Impacts Assessment includes two major tasks
- Field data analyses utilizing the data collected from two MMITSS
prototypes
- Simulation analyses to assess the performance of MMITSS applications at
two prototype sites and a third site
- Field data vs. Simulation study
- The simulation study will compare and confirm the findings of the field
data.
- The simulation study will identify the most beneficial operation
conditions for each scenario which can be identified by a combination
of specific traffic demand levels.
|
26
|
- Experimental Design of IA Plan
|
27
|
- Impact Assessment Approach
- Field test will be recreated in the simulation environment.
- The simulation output will be compared with data from the field tests
to properly calibrate the models.
- The simulation environment will be customized to match the traffic
signal controller interface, communications environment, and priority algorithms.
- Major simulation variables include
- Throughput Volumes, Market Penetration of Connected Vehicles, and
Traffic Composition
- IA study will identify the most beneficial operation conditions for each
operational scenario, which can be identified through a combination of
specific traffic demand levels and other simulation variables.
|
28
|
- Operational Scenarios
- I-SIG: Basic Signal Actuation
- I-SIG: Coordinated Section of Signals
- I-SIG: Dilemma Zone Protection
- TSP: Basic Transit Signal Priority
- TSP: Extended Transit Signal Priority
- PED-SIG: Equipped, Non-Motorized Traveler
- FSP: Basic Freight Signal Priority Scenario
- FSP: Coordinated Freight Signal Priority along a Truck Arterial
- PRE-EMPT: Single Intersection Emergency Vehicle Priority/Preemptions
- Bundled Scenarios
- Transit Signal Priority and Emergency Vehicle Priority at a Single
Intersection
- Connected Passenger Cars and Transit Vehicles Operation in a
Coordinated Section of Signals
|
29
|
- The Bundled Scenarios Evaluates
- How the MMITSS system will provide a hierarchical level of priority
- A priority policy objective function considers the weight factors of
modes, the delay of modes, the weight factors of coordination and
actuation flexibility, coordination delay, and actuation time factor.
- Hypotheses in IA Plan
- The basic components of the connected vehicle system (RSE and OBE) is
configured properly, powered-on, and communicating with the
infrastructure.
- The priority server can accurately predict the arrival time to the
intersection of requesting vehicles and need to deal with multiple
signal priority requests.
- The MMITSS system has an intelligent algorithm for providing priority
signal for priority requests based on a hierarchical level of priority.
- Multiple signalized intersections are equipped with RSEs and have
RSE-to-RSE communication enabled.
|
30
|
- MMITSS IA Preliminary Results
|
31
|
- MMITSS I-SIG effective at reducing total delay in network at 25% and above
under regular traffic condition
|
32
|
- MMITSS I-SIG effective at reducing total delay in network under semi-saturated
conditions (10% of demand increase)
|
33
|
- MMITSS I-SIG effective at reducing total delay in network under saturated
conditions (25% of demand increase)
|
34
|
- MMITSS effective at reducing delay and travel time of priority vehicles
if priority can be allocated to the desired mode of travel (e.g. TSP or
FSP)
|
35
|
- MMITSS effective at reducing delay and travel time of priority vehicles
if priority can be allocated to the desired mode of travel (e.g. TSP or
FSP)
|
36
|
- The PERM MEAS component will allow observation of modal performance
- Nomadic devices are envisioned for PED SIG, But it is possible that they
could be used for priority applications (TSP, FSP, and EVP)
- Level of Market Penetration
- Possible Effects of Communication Errors and Latency
- The marginal benefit with data from existing sensors
- The modal benefits of connected vehicle data are critical
- Both communication media have potential benefits for deployment.
- Cellular for EVP might be subject to network outages under extreme
events (e.g. hurricane, earthquake, terrorist attack,…)
- Potential Near-, Mid- and Long-term Deployment Impacts
|
37
|
- Prototype and Demonstration
- Field test in AZ in early March 2015
- Field test in CA in Summer 2015
- Post code and data on OSADP and RDE
- Finalize the Project Report in June 2015
- Impact Assessment
- AZ Field data evaluation in March 2015
- Simulation evaluation in April 2015
- Final report in July 2015
|
38
|
|