Automated Traffic Control Paradigms: Thinking Beyond Signals

This document summarizes the key points made in a presentation about modeling transportation systems for an automated vehicle world. It discusses how autonomous vehicles could impact traffic flow by allowing closer vehicle spacing, potentially increasing road capacity. It also explores how autonomous vehicles may affect traveler choices and demand for different modes like transit or personal vehicles. New opportunities for real-time traffic control are presented. The document emphasizes that transportation models need to account for interactions between the supply of infrastructure and demand from travelers to adequately capture impacts of autonomous vehicles.

Technology

Modeling for an Automated Vehicle World
Stephen Boyles
Assistant Professor
Civil, Architectural & Environmental Engineering
The University of Texas at Austin
March 2, 2015
D-STOP Symposium
Austin, TX

What do we mean by “automated”?
This talk is about future possibilities of fully-autonomous
vehicles which do not require human interaction at all.

Why is everybody talking about AVs?
Enormous opportunities for improving mobility, reducing
congestion, improving safety, etc.

The technology for AVs is imminent...
...policymaking is the likely bottleneck, not technology.

So, there’s no time like the present to plan.
While AVs present great opportunities, it is not clear that these
opportunities will be realized.
?

Talk outline:
1. How can we model impacts of AVs on congestion?
2. How can we model impacts of AVs on traveler choices?
3. What are the new opportunities for traffic control?
4. Implications for transportation modeling in the future

Transportation models can roughly be
grouped into “supply” and “demand”
“Supply” side
Traffic jams
Signalized control
Contraflow lanes
“Demand” side
Route choice
Traveler information
Mode choice
Autonomous vehicles interact very heavily with both of these.

“Supply” side
Traffic jams
Signalized control
Contraflow lanes
“Demand” side
Route choice
Traveler information
Mode choice
These two “sides” interact with each other heavily, and an
equilibrium concept is often used to reconcile them.
Travel choices determine congestion, but congestion affects
travel choices.

On a highway, AVs can increase capacity because they require
less following distance.

In traffic flow theory, this will change the shape of the
“fundamental diagram” relating vehicle density to flow.
0
1000
2000
3000
4000
5000
6000
7000
8000
0 50 100 150 200 250 300
Flow(veh/hr)
Density (veh/mi)
0.25 0.5
1 1.5
Levin & Boyles (2015, under review)

At intersections, we can do even better.
Dresner & Stone (2010)

Levin & Boyles (2014)
These high-detail simulation models can also be approximated
for use in models with hundreds of intersections.
Reservation-based intersections in DTA Model

Will AVs induce new demand?
How will they affect choice of destination? Parking? Transit?
vs.

Trip generation
Productions and attractions
Trip distribution
Person-trips per origin-destination
Mode choice
Origin-destination trips per mode
Traffic assignment
Routes and flows at user equilibrium
feedback

Transit
Park at destination
• Parking fee
Return to origin
• Fuel costs
logit
model
minimum
cost
Personal vehicle
Person trips

Downtown Austin network
– 88 zones
– 634 nodes
– 1574 links
– 62836 trips
– 84 bus routes
10 value of time classes
Levin & Boyles (2015)

Levin & Boyles (2015)
20
21
22
23
24
25
26
27
28
29
0 2 4 6 8 10
Avg.linktraveltime(sec)
Number of classes with autonomous vehicles
Effects on traffic

Levin & Boyles (2015)
Effects on transit
14000
15000
16000
17000
18000
19000
20000
21000
0 2 4 6 8 10
Transitdemand(persontrips)
Number of classes with autonomous vehicles

In the future, we may see the distinction between “operations”
and “planning” start to blur.
Planning: Long-term time horizon; future
forecasts or scenarios; alternatives
analysis and project rankings
Operations: Present-day modeling; real-
time observation and control; travel
information provision
The future: Future planning and
policy analysis accounting for
real-time control technologies

Conclusions
• AVs present tremendous opportunity, but must be carefully
planned for.

Conclusions
• AVs present tremendous opportunity, but must be carefully
planned for.
• Emerging modeling techniques can help guide policy.

Conclusions
• AVs present tremendous opportunity, but must be carefully
planned for.
• Emerging modeling techniques can help guide policy.
• The future is now!

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Online platforms are emerging as a powerful mechanism for matching resources to requests. In the setting of freight, the requests arrive from shippers, who have a diverse collection of goods. The resources are supplied by shippers (trucks), and have various physical constraints (driver’s route preferences, carrying capacity, geographic preferences, etc.). Online platforms are emerging that (a) learn the characteristics of shippers and carriers, and (b) efficiently match goods to trucks based on such learning. Our project will develop algorithms for such online resource allocation. This is a challenging problem, due to the complexity of the learning tasks. Such algorithms can have considerable impact on efficiently using trucking resources.

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Through this project, the research team will leverage the computing resources and expertise at UT to develop a “data discovery environment” for transportation data to aid decision-making. Many efforts focus on leveraging transportation data to help travelers make decisions, but less thought has gone into a framework for using big data to help transportation agency staff and decision makers. The team will start by building the DDE for the Central Texas region, in collaboration with the local MPO, the City of Austin, and the local transit agency. Initially, the project will focus on creating more meaning from existing data sources, and as the project progresses, it will grow to include more novel data sources and methods. The data platform will be web-based and part of the research includes not only building the tool but developing appropriate protocols for access and governance.

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Collaborative Sensing for Automated Vehicles

Advanced driver assistance systems (ADAS) are a key technology for improving road safety. But both current and proposed ADAS are limited in important ways. Vision- and lidar-based ADAS performs poorly in heavy rain, snow, or fog. Lack of vehicle situational awareness due to these sensing limitations will unfortunately be the cause of many accidents, including fatalities, for connected and automated vehicles in the years to come. The goal of this research is to develop and test a sensing strategy with robust perception: No blind spots, applicable to all driveable environments, and available in all weather conditions. We believe there are three key requirements for collaborative all-weather sensing: – Precise vehicle positioning within a common reference frame – Decimeter-accurate vision and radar mapping – A means of quantifying the benefits of collaborative sensing

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Vehicular radar and communication are the two primary means of using radio frequency (RF) signals in transportation systems. Automotive radars provide high-resolution sensing using proprietary waveforms in millimeter wave (mmWave) bands and vehicular communications allow vehicles to exchange safety messages or raw sensor data. Both the techniques can be used for applications such as forward collision warning, cooperative adaptive cruise control, and pre-crash applications.

Statistical Inference Using Stochastic Gradient Descent

Many areas of machine learning and data mining focus on point estimates of key parameters. In transportation, however, the inherent variance, and, critically, the need to understand the limits of that variance and the impact it may have, have long been understood to be important. Indeed, variance and other risk measures that capture the cost of the spread around the mean, are critical factors in understanding how people act. Thus they are critical for prediction, as well as for purposes of long term planning, where controlling risk may be equally important to controlling the mean (the point estimate). There has been tremendous progress on large scale optimization techniques to enable the solution of large scale machine learning and data analytics problems. Stochastic Gradient Descent and its variants is probably the most-used large-scale optimization technique for learning. This has not yet seen an impact on the problem of statistical inference — namely, obtaining distributional information that might allow us to control the variance and hence the risk of certain solutions.

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Investigation and findings on reservation-based intersections and managed lanes Real-Time Signal Control and Traffic Stability Congestion on urban arterials is largely centered around intersection control. Traditional traffic signal schemes are limited in their ability to adapt in real time to traffic conditions or by their ability to coordinate with each other to ensure adequate performance. Specifically, there is a tension between adaptivity (as with actuated signals) and coordination through pre-timed signals (signal progression). We propose to investigate whether routing protocols in telecommunications networks can be applied to resolve these problems. Specifically, the backpressure algorithm of Tassiulas & Emphremides (1992) can ensure system stability through decentralized control under relatively weak regularity conditions. It is as yet unknown whether this algorithm can be adapted to traffic signal systems, and if so, what modifications are needed. Traffic systems differ in several significant ways from telecommunication networks: each intersection approach has relatively few queues (lanes) that must be shared among traffic to various definitions. First-in, first-out constraints lead to head-of-line blocking effects, traffic waves move at a much slower speed than data packets, and traffic queues are tightly limited by physical space (finite buffers). Determining whether (and how) the backpressure concept can be adapted to traffic networks requires significant research, and has the potential to dramatically improve signal performance. Improved Models for Managed Lane Operations Managed lanes (ML) are increasingly being considered as a tool to mitigate congestion on highways with limited areas for capacity expansion. Managed lanes are dynamically priced based on the congestion level, and can be set either with the objective of maximum utilization (e.g., a public operator) or profit maximization (e.g., a private operator). Optimization models for determining these pricing policies make restrictive assumptions about the layout of these corridors (often a single entrance and exit) or knowledge of traveler characteristics on behalf of the modeler (e.g., distribution of willingness to pay). Developing new models to address these issues would allow for better utilization of these facilities.

SAVES general overview

Professor Robert W. Heath Jr. is the director of UT SAVES (Situation-Aware Vehicular Engineering Systems), which combines expertise in wireless communications, signal processing, and transportation research. UT SAVES collaborates with automotive companies like Honda R&D Americas on projects involving sensing, communication, and analytics for applications such as automated driving. Membership provides access to UT SAVES research and facilities, including graduate research assistants and experimental capabilities in areas like millimeter wave communication and sensor fusion. Current research projects focus on cooperative sensing, vehicle-to-everything communication, and applying 5G cellular networks to driving assistance technologies.

D-STOP Overview April 2018

The Business Advisory Council meeting covered the following topics in 3 sentences or less: The meeting covered updates on education and workforce development programs at the Engineering Education and Research Center including summer internships and distinguished lectures. Research updates were provided on 30 completed projects and 18 ongoing projects covering topics like connected corridors and autonomous vehicles. New proposed research was presented on topics such as video data analytics, traffic signal optimization, and modeling willingness to share trips in autonomous vehicles.

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