Completed D-STOP Projects

The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models

Project ID: 101
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 9/30/13
Completion Date: 9/30/14

Project Summary     Read the report (PDF).

The composite marginal likelihood (CML) inference approach is a relatively simple approach that can be used when the full likelihood function is practically infeasible to evaluate due to underlying complex dependencies. The history of the approach may be traced back to the pseudo-likelihood approach of Besag (1974) for modeling spatial data, and has found traction in a variety of fields since, including genetics, spatial statistics, longitudinal analyses, and multivariate modeling. However, the CML method has found little coverage in the econometrics field, especially in discrete choice modeling. This project will fill this gap by identifying the value and potential applications of the method in discrete dependent variable modeling as well as mixed discrete and continuous dependent variable model systems.

Objective

A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling

Project ID: 102
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 9/30/13
Completion Date: 9/30/14

Project Summary     Read the report (PDF).

We propose a new multinomial probit-based model formulation for integrated choice and latent variable (ICLV) models, which has several important advantages relative to the traditional logit kernel-based ICLV formulation. Combining this multinomial probit (MNP)-based ICLV model formulation with Bhat’s maximum approximate composite marginal likelihood (MACML) inference approach resolves the specification and estimation challenges that are typically encountered with the traditional ICLV formulation estimated using simulation approaches. Our proposed approach can provide very substantial computational time advantages, because the dimensionality of integration in the log-likelihood function is independent of the number of latent variables. Further, our proposed approach easily accommodates ordinal indicators for the latent variables, as well as combinations of ordinal and continuous response indicators. The approach can be extended in a relatively straightforward fashion to also include nominal indicator variables. A simulation exercise in the virtual context of travel mode choice will be designed to evaluate the ability of the MACML approach to recover model parameters.

Objectives

Transit Demand and Routing after Autonomous Vehicle Availability

Project ID: 104
University: University of Texas at Austin
Principal Investigator: Stephen Boyles
Date Started: 1/1/14
Completion Date: 12/31/15

Project Summary     Read the report (PDF).

Autonomous vehicles (AVs) create the potential for improvements in traffic operations as well as new behaviors for travelers such as car sharing among trips through driverless repositioning. Most studies on AVs have focused on technology or traffic operations, and the impact of AVs on planning is currently unknown. Development of a planning model integrating AV improvements to traffic operations and the impact of new traveler behavior options will soon be of practical interest as AVs are currently test-driven on public roads. The altered traveler preferences may affect mode choice, leading to changes in transit demand and transit provider cost. An analysis of the model on metropolitan planning data will provide predictions on the impact of general AV ownership on network conditions.

Objectives

Semi-Autonomous Parking for Enhanced Safety and Efficiency

Project ID: 105
University: University of Texas at Austin
Principal Investigator: Sriram Vishwanath
Start Date: 4/10/14
Expected Completion Date: UPDATED: 06/30/2017

Project Summary     Read the report (PDF).

This project focuses on the use of tools from a combination of computer vision and localization based navigation schemes to aid the process of efficient and safe parking of vehicles in high density parking spaces. The principles of collision avoidance, simultaneous localization and mapping together with vision based actuation in robotics will be used to enable this functionality.

Objectives

Combining Millimeter-Wave Radar and Communication Paradigms for Automotive Applications: A Signal Processing Approach

Project ID: 106
University: University of Texas at Austin
Principal Investigators: Robert Heath and Chandra Bhat
Date Started: 9/1/14
Completion Date: 5/31/16

Project Summary      Read the report (PDF).

This project proposes to develop a conceptual mathematical model for combined paradigm of millimeter-wave communication and radar using a signal processing perspective. In particular, it will explore and investigate different possible signal frameworks for joint communication and radar paradigms, both with simultaneous or non-simultaneous applications. For these mathematical frameworks, novel algorithms will be developed targeting automotive applications. Our algorithms will leverage the performance of the joint paradigm by sharing information between the radar and communications signal frameworks. These algorithms will be further optimized to meet varied performance objectives in both rural and urban areas. This would require identifying the scenarios of interest in transportation environments. A design-trade off analysis will be carried out to meet the conflicting requirements of both the signal frameworks.
This project will also address the challenges unique to the combined mathematical framework such as beamforming, signal design and mutual interference. Furthermore, the performance of the combined paradigm would be compared with the individual signal frameworks of radar and communication. This will involve a detailed survey, mathematical model development and simulation of both these systems separately. The outcomes of the project are expected to dramatically improve safety for vehicles, bicycles, and pedestrians in all weather conditions and on all roadways.

Objectives

Coherence Time and Beam Alignment for mmWave Vehicular Communications

Project ID: 107
University: University of Texas at Austin
Principal Investigator: Robert Heath
Date Started: 9/1/14
Completion Date: 10/31/15

Project Summary     Read the report (PDF).

The goal of this project is to develop a learning based approach to significantly reduce the overhead leveraging side information including user positioning information and network geometry. After initial design, we use offline learning to construct an initial mapping from user feedback to the beam selection. During operation, new data are collected and used to refine the initial mapping. The online learning part can be viewed as a multi-armed bandit (MAB) problem and solved by leveraging the existing results on the subject.

Objectives

Improved Traffic Operations through Real-Time Data Collection and Control

Project ID: 108
University: University of Texas at Austin
Principal Investigators: Stephen Boyles and Sanjay Shakkottai
Date Started: 6/1/14
Completion Date: 5/31/16

Project Summary     Read the report (PDF).

New data collection technologies enable real-time traffic control more precise and efficient than what was earlier possible. This project develops novel control strategies based on this data, with an emphasis on two types of traffic control: (1) signalized intersection control, where cycle lengths and phasing may be adjusted based on observed demands and coordination with nearby intersections, and (2) pricing strategies, where tolls are adjusted in real time based on observed demand, in order to influence travelers to avoid congested areas. Both of these share a common methodological basis of adjusting traffic controls to prioritize particular vehicles to minimize congestion, accounting for human behavior and learning. The project will involve combining wireless routing algorithms with traffic engineering knowledge to create innovative control policies.

Objectives

Models for High Dimensional Mixed Regression

Project ID: 109
University: University of Texas at Austin
Principal Investigators: Constantine Caramanis and Chandra Bhat
Date Started: 9/30/13
Completion Date: 9/30/2016

Project Summary     Read the report (PDF).

We propose to consider the mixed regression problem in high dimensions, under adversarial and stochastic noise. We will consider convex optimization-based formulations with the aim of showing that it provably recovers the true solution. This agenda will seek to provide upper bounds on the recovery errors for both arbitrary noise and stochastic noise settings. We also will seek matching minimax lower bounds (up to log factors), showing that under certain assumptions, our algorithm is information-theoretically optimal. Our preliminary results represent the first (and currently only known) tractable algorithm guaranteeing successful recovery with tight bounds on recovery errors and sample complexity.
Mixture models treat observed data as a superposition of simple statistical processes. Thus they are particularly relevant in the transportation setting, when city-wide phenomena are often mixtures of simple processes (cut-through traffic, intra-city movement, etc.).

Objectives

Streaming PCA with Many Missing Entries

Project ID: 110
University: University of Texas at Austin
Principal Investigator: Constantine Caramanis
Date Started: 9/30/13
Completion Date: 12/31/15

Project Summary     Read the report (PDF).

We propose to consider the streaming memory-constrained principal component analysis (PCA) problem with missing entries, where the available storage is linear in the dimensionality of the problem, and each vector has so many missing entries that matrix completion is not possible. For this problem, we propose a method based on a block power update approach introduced in our previous work. We show on synthetic as well as benchmark data sets that our approach outperforms existing approaches for streaming PCA by a significant margin for several interesting problem settings. We also consider the popular spiked covariance model with randomly missing entries, and obtain the first known global convergence guarantees for this problem. We show that our method converges to the true “spike” using a number of samples that is linear in the dimension of the data. Moreover, our memory requirement is also linear in the ambient dimension. Thus, both memory and sample complexity have optimal scaling with dimension.
Streaming PCA is extremely relevant in the setting where the resolution of our sensors outpaces our ability to store massive amounts of data. This is precisely the setting we face as we increase the amount of intelligence and high precision/resolution sensors on the fleet of private and commercial vehicles.

Objective

Greedy Subspace Clustering

Project ID: 111
University: University of Texas at Austin
Principal Investigator: Constantine Caramanis
Date Started: 12/1/14
Completion Date: 9/30/16

Project Summary     Read the report (PDF).

We propose to consider the problem of subspace clustering: given points that lie on or near the union of many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies sets of points close to the same subspace and uses the sets to estimate the subspaces. As the geometric structure of the clusters (linear subspaces) forbids proper performance of general distance based approaches such as K-means, many model-specific methods have been proposed. In this paper, we provide new simple and efficient algorithms for this problem. Our statistical analysis shows that the algorithms are guaranteed exact (perfect) clustering performance under certain conditions on the number of points and the affinity between subspaces. These conditions are weaker than those considered in the standard statistical literature. Experimental results on synthetic data generated from the standard unions of subspaces model demonstrate our theory. We also show that our algorithm performs competitively against state-of-the-art algorithms on real-world applications such as motion segmentation and face clustering, but with much simpler implementation and lower computational cost.

Objective

An Empirical Investigation into the Time-Use and Activity Patterns of Dual-Earner Couples With and Without Young Children

Project ID: 112
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 3/1/14
Completion Date: 9/30/15

Project Summary      Read the report (PDF).

This project examines the time-use patterns of adults in dual-earner households with and without children as a function of several individual and household socio-demographics and employment characteristics. A disaggregate activity purpose classification including both in-home and out-of-home activity pursuits will be used because of the travel demand relevance of out-of-home pursuits, as well as to examine both mobility-related and general time-use related social exclusion and time poverty issues. The study uses the Nested Multiple Discrete Continuous Extreme Value (NMDCEV) model, which recognizes that time-decisions entail the choice of participating in one or more activity purposes along with the amount of time to invest in each chosen activity purpose, and allows generic correlation structures to account for common unobserved factors that might impact the choice of multiple alternatives. The 2010 American Time Use Survey (ATUS) data is used for the empirical analysis.

Objectives

A New Generalized Heterogeneous Data Model (GHDM) to Jointly Model Mixed Types of Dependent Variables

Project ID: 113
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 3/1/14
Completion Date: 9/30/15

Project Summary     Read the report (PDF).

This project examines the time-use patterns of adults in dual-earner households with and without children as a function of several individual and household socio-demographics and employment characteristics. A disaggregate activity purpose classification including both in-home and out-of-home activity pursuits will be used because of the travel demand relevance of out-of-home pursuits, as well as to examine both mobility-related and general time-use related social exclusion and time poverty issues. The study uses the Nested Multiple Discrete Continuous Extreme Value (MDCNEV) model, which recognizes that time-decisions entail the choice of participating in one or more activity purposes along with the amount of time to invest in each chosen activity purpose, and allows generic correlation structures to account for common unobserved factors that might impact the choice of multiple alternatives. The 2010 American Time Use Survey (ATUS) data is used for the empirical analysis.

Objective

A New Spatial (Social) Interaction Discrete Choice Model Accommodating for Unobserved Effects due to Endogenous Network Formation

Project ID: 114
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 3/1/14
Completion Date: 9/30/15

Project Summary     Read the report (PDF).

This project formulates a model that extends the traditional panel discrete choice model to include social/spatial dependencies in the form of dyadic interactions between each pair of decision-makers. In addition, the formulation accommodates spatial correlation effects as well as allows a global spatial structure to be placed on the individual-specific unobserved response sensitivity to exogenous variables. We interpret these latter two effects, sometimes referred to as spatial drift effects, as originating from endogenous group formation. To our knowledge, we are the first to suggest this endogenous group formation interpretation for spatial drift effects in the social/spatial interactions literature. The formulation is motivated in a travel mode choice context, but is applicable in a wide variety of other empirical contexts.

Objective

High-precision GPS Vehicle Tracking to Improve Safety

Project ID: 115
University: University of Texas at Austin
Principal Investigators: Jennifer Duthie and Todd Humphreys
Date Started: 1/15/15
Completion Date: 8/31/16

Project Summary   Read the report (PDF).

Commercial Global Positioning System (GPS) devices are being used in transportation for applications including vehicle navigation, traffic monitoring, and tracking commercial and public transit vehicles. The current state-of-practice technology in GPS devices typically has 10-meter accuracy and can properly answer the needs in the above applications. When it comes to safety, where driver behavior is important, new technologies for high-precision (i.e., centimeter-level) mobility detection are required. It is with high-precision GPS traces that safety can be evaluated by identifying when drivers are drowsy or distracted, and anticipating problems before they occur. Through this project, the team will build low-cost high-precision GPS devices, obtain GPS traces by placing the devices on buses, and analyzing the traces to identify driver behavior indicators that could anticipate a safety concern before it occurs.

Objective

Infrastructure-Informed Travel Sheds

Project ID: 116
University: University of Texas at Austin
Principal Investigator: Jennifer Duthie
Date Started: 9/1/14
Completion Date: 12/31/15

Project Summary     Read the full report (PDF).

An infrastructure-informed index is needed for pedestrians and bicyclists to relate the natural and built environment with its impact on perceived travel distance and time. The objective is to develop an easy-to-use metric for use at all levels, allowing transportation planners to make better-informed decisions when planning or redeveloping a city or area. Building off of previous research efforts, attributes were determined and weighted to capture the characteristics of a link, then summed to create the infrastructure informed index for pedestrians and bicyclists, respectively. Pedestrian perception data collected by the PI previously was used to determine the attributes. The indices were then visualized using ArcGIS mapping tools, creating a service area around specific origin or destination points to see the effective area a pedestrian or bicyclist can travel taking into account the effects of the infrastructure along the route.

Objectives

The Formulation and Estimation of a Spatial Skew-Normal Generalized Ordered-Response Model

Project ID: 117
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 1/1/15
Completion Date: 6/30/16

Project Summary      Read the report (PDF).

Ordered-response (OR) choice models are now widely used in many different disciplines, including sociology, biology, political science, marketing, and transportation. OR models may be used when analyzing ordinal discrete outcome data that may be considered as manifestations of an underlying scale that is endowed with a natural ordering. In this proposal, we will use the GOR structure as the starting point, and extend the formulation in two different directions. The first direction relates to the distribution of the kernel error distribution, and the second relates to spatial dependence. We will apply the proposed model to examine the determinants of bicycling frequency using data from the Puget Sound Regional Council in the state of Washington.

Objective

A Latent Class Multiple Constraint Multiple Discrete-Continuous Extreme Value Model of Time Use and Goods Consumption

Project ID: 118
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 1/1/15
Completion Date: 6/30/16

Project Summary    Read the report (PDF).

The aim of this proposal is to develop a microeconomic time-use framework that (a) accommodates technological relationships between time allocated to activities and goods consumption, and (b) proposed a discrete distribution for the response coefficients. This latent class model will be able to identify different segments of the population, each one of them with different effects of the exogenous variables on time allocation, activity participation, and goods consumption. This endogenous segmentation will be compared in a comprehensive fashion with the typical segmented estimation of microeconomic time use models (of the type discussed in the first paragraph of this abstract) from a theoretical, conceptual, and empirical data fit standpoint. The empirical analysis will be pursued using a 2012 Dutch data set on weekly time use and good expenditure.

Objective

A Comprehensive Dwelling Unit Choice Model Accommodating Psychological Constructs Within A Search Strategy for Consideration Set Formation

Project ID: 119
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 1/1/15
Completion Date: 12/31/15

Project Summary     Read the report (PDF).

This project adopts a dwelling unit level of analysis and considers a probabilistic choice set generation approach for residential choice modeling. In doing so, we accommodate the fact that housing choices involve both characteristics of the dwelling unit and its location, while also mimicking the search process that underlies housing decisions. In particular, we proposed to model a complete range of dwelling unit choices that include tenure type (rent or own), housing type (single family detached, single family attached, or apartment complex), number of bedrooms, number of bathrooms, number of storeys (one or multiple), square footage of the house, lot size, housing costs, density of residential neighborhood, and commute distance. Bhat’s (2014) generalized heterogeneous data model (GHDM) system will be used to accommodate the different types of dependent outcomes associated with housing choices, while capturing jointness caused by unobserved factors. The proposed analytic framework will be applied to study housing choices using data derived from the 2009 American Housing Survey (AHS), sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau.

Objective

On Accommodating Spatial Interactions in a Generalized Heterogeneous Data Model (GHDM) of Mixed Types of Dependent Variables

Project ID: 120
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Date Started: 1/1/15
Completion Date: 12/31/15

Project Summary     Read the report (PDF).

Multi-dimensional dependent outcome models are of interest in several fields, including land-use and transportation, biology, finance, and econometrics, just to name a few. The primary motivation for modeling dependent outcomes jointly is that there may be common underlying unobserved factors (attitudes, values, and lifestyle factors) of decision-makers that impact multiple dependent outcomes simultaneously. Even as there has been increasing emphasis on mixed data outcome modeling, there also has been a growing interest in accommodating spatial (and social) dependency effects among decision-makers in mixed data modeling. This is because spatial/social interactions can be exploited by decision-makers to achieve desired system end-states. In the current project, we use the important insight that the analyst can generate spatial dependence across multiple and mixed outcomes by specifying spatial dependence in the “soft” psychological construct (latent) variables underlying the many outcomes.

Objective

Transportation Data Discovery Environment

Project ID: 121
University: University of Texas at Austin
Principal Investigator: Natalia Ruiz Juri
Start Date: 1/16/15
Expected Completion Date: 8/31/18

Project Summary     Read the report (PDF).

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. The research team features the diverse skill sets of modelers Itamar Gal, Ken Perrine, Natalia Ruiz Juri, and Bruno Chiquini.

Objective

Travel Modeling in an Era of Connected and Automated Transportation Systems: An Investigation in the Dallas-Fort Worth Area

Project ID: 122
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Completion Date: 12/31/16

Project Summary      Read the report (PDF).

There is substantial anticipation and excitement in the area of connected/automated vehicles (CAVs) and transportation systems in terms of their potential to improve transportation safety and enhance mobility and accessibility. CAVs can have a substantial impact on travel patterns and roadway performance, and on mobile source-emissions, fundamentally altering strategic planning decision. Within this context, and given that metropolitan planning organizations (MPOs) incorporate a multi-decade (20 or more years) planning horizon in their long-term planning process, it is important that NCTCOG considers the implications of CAVs early on.The research team includes Natalia Ruiz Juri, James Kuhr, Mason Gemar, and Jen Duthie.

Objective

 Analyzing Millimeter Wave Vehicular Communication Systems in Urban Areas

Project ID: 123
University: University of Texas at Austin
Principal Investigator: Robert Heath
Start Date: 5/1/2016
Expected Completion Date: 5/31/2017

Project Summary      Read the report (PDF).

The project aims at developing a tractable model by stochastic geometry to analyze the performance of these vehicular networks in urban environments and the effects of the on line-of-sight (LOS) distance, non-line-of-sight (NLOS) interferers. Given the intensity of the streets and transmitters as well as the statistics of vehicular mobility, it is possible to evaluate the vehicular network in terms of coverage probability and capacity. These results will reveal insights about how to give the optimal strategy of deploying the base stations under different vehicular environment.

Objectives

Exploiting DSRC Information to Reduce Millimeter Wave Beam Alignment Overhead in Vehicular Environments

Project ID: 124
University: University of Texas at Austin
Principal Investigator: Robert Heath
Start Date: 5/1/2016
Expected Completion Date: 5/31/2017

Project Summary      Read the report (PDF).

This project aims at developing an efficient beam alignment algorithm for millimeter wave (mmWave) vehicular communications taking advantage of the side information available in vehicular context. Due to its propagation characteristics, the antenna gains from directional beams are necessary to maintain link quality, but these gains can be achieved only when beams are properly aligned. Conventional methods, such as the one proposed in the IEEE 802.11ad standard, do not use any side information and must endure large overhead that is unacceptable in mobile environments. In this project, we will focus on the DSRC, which exchanges basic safety messages that convey potential useful information for beam alignment such as position, speed, and acceleration of each vehicle. The outcome of this project is an algorithm with two components. The first one will use position information to efficiently perform initial beam alignment, while the second one will make use of kinematic information (e.g., velocity) to maintain the alignment. This algorithm will be essential in providing robust mmWave links that support high data rates to enable automated driving.

Objectives

Real-Time Signal Control and Traffic Stability

Project ID: 125
University: University of Texas at Austin
Principal Investigator: Stephen Boyles
Start Date: 5/1/2016
Expected Completion Date: 8/31/2019

Project Summary      Read the report (PDF).

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.

Online Learning for Freight—An Examination of Queueing Regret

Project ID: 127
University: University of Texas at Austin
Principal Investigator: Sanjay Shakkottai
Completion Date: 12/31/16

Project Summary     Read the report (PDF).

As recently noted by The Economist, a large part of the freight trucking market is unconsolidated. There is a major opportunity to develop online platforms that match individual consumers (people who need freight delivery) with the resource suppliers (individual trucks and truckers). Such platforms can be game-changers, and dramatically improve the efficiency of freight haulage. At the core of this platform is a matching algorithm. This needs to learn from online data—learn both current supply (available trucks and their preferences) and demand (packages to be transported)—and match supply and demand in an online manner. Our research will focus on developing matching algorithms for this setting.

Objectives

Large-Scale Linear Programs in Planning and Prediction

Project ID: 128
University: University of Texas at Austin
Principal Investigator: Constantine Caramanis
Start Date: 4/1/2016
Expected Completion Date: 6/30/2017

Project Summary     Read the report (PDF).

Large-scale linear programs are at the core of many traffic-related optimization problems in both planning and prediction. Moreover, many of these involve significant uncertainty, and hence are modeled using either chance constraints, or robust optimization. Chance constraints and robust optimization are by now classical approaches for dealing with uncertainty. The ultimate goal in each of these areas, is to find an explicit convex reformulation that provides some approximation to the original (uncertain) optimization problem. The work in these areas has helped us obtain a nearly comprehensive understanding of when convex reformulations (and approximations) are possible, and what the quality of the approximation is. Yet little has been said about truly tractable solutions—solutions where running time for the uncertain problem is comparable (perhaps even less than!) the time to solve the problem without any uncertainty. As networks grow in size, and our ability to capture more data rapidly increases, it is of paramount importance to rethink our theory of robust and uncertain optimization for transportation applications, to one that is computationally oriented.

Objective

Imputing Missing Data via Sparse Reconstruction Techniques

Project ID: 129
University: University of Texas at Austin
Principal Investigator: Constantine Caramanis
Start Date: 4/1/2016
Expected Completion Date: 6/30/2017

Project Summary     Read the report (PDF).

The State of Texas does not currently have an automated approach for estimating volumes for links without counts. This research project proposes the development of an automated system to efficiently estimate the traffic volumes on uncounted links, in the event of rare disturbances of the typical traffic flow. The idea we plan to leverage is that the road network provides a mixing effect, whereby localized disturbances (accidents, flooding, road damage, etc.) have an impact whose effect can be measured at many places across the city. This forms the important analog to the well-known uncertainty principle, whereby a signal cannot be sparse in both the time and frequency domains—a result that is critically utilized in the signal reconstruction algorithms for fMRI.

Objectives

Using Collected Data to Improve Dynamic Traffic Assignment Modeling

Project ID: 130
University: University of Texas at Austin
Principal Investigator: Natalia Ruiz Juri
Start Date: 4/1/2016
Expected Completion Date: 8/31/18

Project Summary     Read the report (PDF).

Traffic assignment models depend on collected data for calibration and validation. The City of Austin is currently deploying an array of sensors and other equipment to collect new sources of data for the City’s transportation system. In addition, other area agencies are collecting large quantities of data and are contributing to the trend of deploying new equipment in the field. Collecting and ultimately using this data to inform transportation network models will result in improved accuracy and can enhance the capabilities of the Center for Transportation’s dynamic traffic assignment tools. In addition, the data collection and analysis will spur new research opportunities.

Objective

Accommodating a Flexible Response Heterogeneity Distribution in Choice Models of Human Behavior for Transportation Planning

Project ID: 131
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Start Date: 5/1/2016
Expected Completion Date: 12/31/17

Project Summary     Read the report (PDF).

In this project, we will formulate a copula-based framework to accommodate non-normal continuous mixing distributions in the MNP model. This approach will allow a multivariate mixing distribution that can combine any continuous distributional shape for each coefficient, including (but not limited to) the skew-normal distribution. The procedure will be based on generating a multivariate continuous distribution through the use of specified parametric univariate continuous coefficient distributions (that can be different for different coefficients) combined with a Gaussian Copula. This research will also propose two alternative estimation procedures for the new model. The effectiveness of the formulation and inference approach will be demonstrated through simulations and an empirical application.

Objective

Internet of Moving Things using Full Duplex Mesh Networks

Project ID: 132
University: University of Texas at Austin
Principal Investigator: Sriram Vishwanath
Start Date: 5/1/16
Expected Completion Date: 12/31/2018

Project Summary     Read the summary (PDF).

Through years of research, we have developed true full duplex communication systems (transmission and reception in the same band at the same time) using novel off the shelf components. Such radios are unique in their ability to listen while transmitting at the same frequency at the same time. Although other full duplex technologies exist (from work conducted at Stanford, Rice and Columbia), these technologies are typically antenna or custom-chip based. Our solution is unique in that it is based on off-the-shelf discrete components together with software. This ability to build a software-centric full duplex solution has many advantages, including low-cost, rapid reconfigurability, and agility. Full duplex radios are able to listen and talk simultaneously, making them ideally suited for mesh networking applications. Conventional mesh networking is highly prone to poor performance due to massive overheads and rigidity. Full duplex radios are much more flexible and adaptable, and can perform tasks such as handoff and scheduling in a low-overhead, rapid manner. This makes them ideally suited to be the basis for the Internet of Moving Things (IoMT). IoMT aims to connect all moving (and static) objects with one another—buses, cars, people, even their pets—without using a cellular or satellite backbone. It enables vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) connectivity in a much more seamless fashion than currently thought possible. Full duplex based IoMT will provide low-cost connectivity between people, while helping us understand how people and vehicles move.

Objective

Performance Trade-Off for a Joint Automotive Radar and Communication System

Project ID: 133
University: University of Texas at Austin
Principal Investigator: Robert Heath
Start Date: 5/1/2016
Expected Completion Date: 5/31/2017

Project Summary     Read the report (PDF).

The IEEE 802.11ad waveform can be used for automotive radar by exploiting the Golay complementary sequences in the preamble of a frame. The performance of radar, however, is limited by the preamble structure. In this paper, we propose an adaptive preamble design that permits a trade-off between radar parameters’ estimation accuracy and communication rate. To quantify this trade-off, we propose a minimum mean square error (MMSE) metric based on rate distortion theory. The simulation results demonstrate that by adapting the preamble structure, we can achieve decimeter-level range mean square error (MSE) per symbol duration and gigabit per second (Gbps) data rates simultaneously for a distance up to 280 m.

Cybersecurity Challenges and Pathways in the Context of Connected Vehicle Systems

Project ID: 134
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Start Date: 5/1/2016
Expected Completion Date: 12/31/17

Project Summary     Read the report (PDF).

This project focuses on one specific challenge: improving the security of data flow in VANETs (Vehicle Ad Hoc Networks). VANETs are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication. They represent a class of Mobile Ad Hoc Networks (MANETs), which are distributed, self-organizing communication networks built up from traveling vehicles. VANETs can be utilized for a broad range of safety and non-safety applications. This project will synthesize various attacks/threats that may be encountered by VANETs in the real world, and identify possible countermeasures to eliminate (or at least reduce the intensity of) such threats. We will highlight the limitations of current security measures and demonstrate a real-world application of a new security protocol that overcomes some of the limitations of existing security protocols.

Objective

Evaluation of Routing Protocols for Vehicular Ad hoc Networks (VANETs) in Connected Transportation Systems

Project ID: 135
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Start Date: 5/1/2016
Expected Completion Date: 12/31/17

Project Summary     Read the report (PDF).

While the design of an efficient and reliable routing protocol is central to VANET performance, this is a challenging task because of rapidly changing topology, frequent disconnection, patterned mobility and involved propagation streams. In addition, routing protocol performance varies substantially depending on the density and mobility present in the network, as well as the topography of the test site (e.g., presence of high-rise buildings and trees) and the radio parameters (e.g., carrier frequency, transmission power, and bandwidth). There are three main objectives in protocol design: reliable packet transmission with minimum delay, maximum throughput, and low communication overhead. Most existing routing protocols address only one or two of these objectives. In this project, we propose the use of hybrid techniques (of existing routing protocols) to simultaneously address all three objectives. We will then evaluate the existing and hybrid routing protocols using two areas as test beds: the Austin downtown area (high vehicular traffic with multiple intersections and the presence of high rise buildings) and a stretch of I-35 outside Austin city limits (low to medium traffic density with high vehicular mobility). Note that, due to safety and financial considerations, we will not actually test the many routing protocols directly in the field, but rather simulate the two test beds in open source VANET simulators.

Objective

Spatial Correlation Estimation of Millimeter Vehicular Communication Channels Using Out-of-Band Information

Project ID: 136
University: University of Texas at Austin
Principal Investigator: Robert Heath
Start Date:
5/1/2017
Expected Completion Date: 5/31/2019

Project Summary    Read the report (PDF).

Wireless channels in vehicular communications systems rapidly vary due to the fast changes of their topology. Obtaining reliable instantaneous information about the propagation channel is invariably important in wireless communications. It is more challenging in vehicular communication systems especially at millimeter wave (mmWave) bands since the problem is further exacerbated by the hardware constraints required for mmWave systems. For example, a small number of RF chains and low-resolution ADCs enforce to limit to the number of measurements for and the direct access to the channels between transceivers.

Compared to the instantaneous channel information, the second order statistics (or the spatial correlation) of the channels vary slowly so it is relatively not hard to obtain them in general. In the case of mmWave systems, however, even acquiring the second order statics is still difficult because of the lack of direct access to the channel and possibly many transmit/receive antennas with hybrid beamforming architectures – inducing high training overhead. Therefore, to overcome the problem, we aim at developing a framework to leverage the second order statistics of out-of-band channels and to estimate mmWave channel correlations by using them. Specifically, algorithms will be developed to fetch out-of-band information from sub-6 GHz channels use this information for mmWave channel correlation estimation.

Objective

Joint Millimeter-Wave Communication and Radar for Automotive Applications

Project ID: 137
University: University of Texas at Austin
Principal Investigator: Robert Heath
Start Date:
5/1/2017
Expected Completion Date: 5/31/2019

Project Summary   Read the report (PDF).

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.

The project aims at developing a mmWave vehicular joint system that exploits the same waveform for both communication and radar operations, thus allowing hardware reuse. The use of a standard mmWave waveform, which provides access to a large bandwidth, will lead to significant advantages in terms of higher data rates for communication and better accuracy/resolution for radar operation compared with approaches based on sub-6 GHz frequencies. Our initial work was to propose the idea of using IEEE 802.11ad for a joint vehicular communication and radar system. This allows us to exploit the same spectrum and to leverage shared hardware based on the mmWave consumer WLAN standard. The approach is reasonable because the most prevalent vehicular communication standard, dedicated short-range communications (DSRC), is based on the WLAN standard. In this project, we propose to refine previously developed algorithms and develop a further framework and associated algorithm to better achieve vehicular radar and communication objectives. These algorithms shall be used for designing modified IEEE 802.11ad and associated processing techniques (e.g., by exploiting sparsity) that permit both radar and communication functionalities in a multi-target scenario. The results will allow insights about how to design an optimal joint waveform for different vehicular scenarios to meet the continuously growing performance requirements of a more advanced assisted driving and future autonomous driving.

Objective

Cooperative Mapping for Automated Vehicles

Project ID: 138
University: University of Texas at Austin
Principal Investigators: Todd Humphreys and Robert Heath; Guarav Bansal of Toyota ITC
Start Date: 1/1/2017
Expected Completion Date: 9/29/2017

Project Summary     Read the report (PDF).

Localization is essential for automated vehicles, even for simple tasks such as lane- keeping. Some automated vehicle systems use their sensors to perceive their surroundings on-the-fly, such as the early variants of the Tesla Autopilot, while others such as the Waymo car navigate within a prior map. The latter approach is beneficial in that it helps the system to expect the expected, that is, it relieves the system of perceiving static features. However, making and updating such accurate prior maps using a specialized vehicle fleet is expensive and cumbersome. A key enabler for large-scale up-to-date maps will be enlisting the help of the very vehicles who need the map—consumer vehicles—to build and update the map. This project explores the possibility of using multiple vehicles equipped with the kinds of sensors that are (or will be) common on cars (optical cameras, radar, IMU, and GNSS) to perform cooperative SLAM for improving and updating a point-feature map 3D map of the environment.

Objective

ADAS Enhanced by 5G Connectivity

Project ID: 139
University: University of Texas at Austin
Principal Investigators: Todd Humphreys and Robert Heath
Start Date: 9/30/2017
Expected Completion Date: 9/30/2018

Project Summary     Read the report (PDF).

Advanced driver assistance systems (ADAS) are a key technology for improved traffic safety. Long before fully automated vehicles arrive in significant numbers, ADAS will see high penetration and substantially reduce accident rates. Toyota and Honda have both committed to focusing on “hands on the wheel, eyes on the road” ADAS long before (perhaps up to a decade) introducing higher levels of automation to consumers. This is a philosophy that resonates with the PI and co-investigator of this project.

Connectivity between vehicles, and between vehicles and infrastructure, makes ADAS more effective by enabling vehicles to “see” around corners and through other vehicles. But connectivity via DSRC, the 802.11-based standard that will likely be mandated by 2020, can become congested when a large number of vehicles, cyclists, and pedestrians congregate near intersections in urban areas. Moreover, DSRC does not offer the bandwidth for sharing of raw, or lightly-processed, sensor data between vehicles or from infrastructure to vehicles. In fact, in all likelihood, DSRC message traffic will be limited to the basic safety message, a low-rate, low-latency message that communicates a vehicle’s or cyclist’s or pedestrian’s current position and velocity to others in the vicinity. And even this message will become unreliable if too many DSRC transmitters find themselves fighting for slots in which to transmit, such as will occur in urban areas with heavy foot and vehicular traffic.

This project aims to study how emerging 5G technology can be used to “supercharge” ADAS by releasing it from the limitations of DSRC. How can ADAS benefit from the sub-10-ms latency, the 100 Mbps per-user download data rate, and the high connection density that 5G promises?

Objective

Partnering with an automotive OEM (either Toyota or Honda), we intend to study the following:

  1. How can infrastructure-mounted cameras, and possibly infrastructure-mounted radar units, be used to provide better situational awareness to in-vehicle ADAS? Is it practical to send raw images or radar returns from roadside sensors to vehicle ADAS via 5G?
  2. Can the benefit of infrastructure-aided ADAS be quantified in terms of a reduction in risk (as defined by a cost-probability product)?
  3. How does vehicle positioning improve if GNSS corrections are sent with low latency over 5G, as opposed to no corrections with the standard positioning service?
  4. Can UAVs play a role in helping a vehicle piece together a fuller picture of its surroundings? Might individual vehicles be equipped with their own “eyes in the sky” UAV, to be deployed as necessary to fill in gaps in the vehicle’s instantaneous area map (including the location and movement of pedestrians, cyclists, etc.)?

Improved Models for Managed Lane Operations

Project ID: 140
University: University of Texas at Austin
Principal Investigator: Stephen Boyles
Start Date: 9/1/2017
Expected Completion Date: 8/31/2019

Project Summary      Read the report (PDF).

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.

Capturing the Impacts of Ride-sourcing and HOVs

Project ID: 141
University: University of Texas at Austin
Principal Investigator: Chandra Bhat, Natalia Ruiz Juri, and James Kuhr
Start Date: 9/1/2017
Expected Completion Date: 09/30/2018

Project Summary     Read the report (PDF).

One new paradigm of particular interest is the possibility of a mobility shift to heavy reliance on ride-sourcing services provided by transportation network companies (TNCs). Ride-sourcing services allow users to contact and utilize third parties for rides. After just over seven years of exposure to TNCs in the United States, there is market evidence of general widespread acceptance for ride-sourcing as an innovative and valuable augmentation to transportation systems.

Still, while the supply market may be showing overt signs of embracing the ride-sourcing paradigm, consumer attitudes towards adoption remain uncertain. Moreover, the potential system-wide impacts of a drastic shift to autonomous ride-sourcing require a careful examination. Unintended outcomes, such as a significant increase in vehicles-miles-traveled, may lead to additional congestion, which can in turn affect choices such as residential location and trip making patterns. In this context, the use of behavioral models is critical to comprehensively asses the evolution of ride-sharing and HOVs.

Objective

Conduct a survey in Texas – either in Austin or another large metropolitan area. This could be done via a new survey or leveraging existing survey instruments and technologies. We plan to conduct at least two waves of a panel survey (longitudinal data collection), a year apart to help analyze the change in attitudes and preferences towards adoption of ride-sourcing and autonomous vehicles over time. The use of web-based/mobile technologies to facilitate the surveying process and the corresponding data analyses will be considered. The latter may also facilitate continuous/repeated surveying, and potentially the transferability of survey instruments. The results of this survey will be cataloged in a report and be used to inform the future construction of behavioral models.

Explorations to Inform V2I Managed Lanes Design and Development

Project ID: 142
University: University of Texas at Austin
Principal Investigator: Natalia Ruiz Juri
Start Date: 9/1/2017
Expected Completion Date: 02/28/2019

Project Summary

 Report forthcoming: This report was partially funded also by Cintra (as matching funds to D-STOP), and the authors are discussing options with both Cintra and USDOT regarding how to report the results of this research.

Vehicle-to-everything (V2X) technology connects vehicles, infrastructure, and any other communicating devices. By sending and receiving short communications, known as Basic Safety Messages (BSMs), vehicles will receive key information about their surroundings that can be relayed to their drivers to aid safe operations. The potential for safety applications under a mature V2X system is tremendous: through use of a mature system, NHTSA studies indicate that 81 percent of all unimpaired crashes could be avoided.

Research is critical to better understand how communication technologies may support or limit the potential impacts of connected vehicles. The project will investigate vehicle-to-infrastructure (V2I) communication technology to be installed along connected roadways, including DSRC, LTE and potential 5G technologies, and investigate V2I privacy and security threats.

Objective

Transition Period from Today to Fully Autonomous

Project ID: 143
University: University of Texas at Austin
Principal Investigator: Natalia Ruiz Juri, Chandra Bhat, and James Kuhr
Start Date: 9/1/2017
Expected Completion Date: 09/30/2018

Project Summary      Read the report (PDF).

Thanks to innovations from Silicon Valley, what was once thought to be a timeline that would introduce autonomous vehicles by 2035 has turned into a race to produce autonomous vehicles as fast as possible; now, the horizon for commercially available autonomous vehicles appears to be at the beginning of the next decade. However, CAVs will coexist with regular vehicles for several decades, and understanding traffic patterns during the transition period is critical to support planning and operations decisions. While behavioral modeling tools may be used to conduct such an assessment, it is also important to consider that models may require substantial changes in order to capture the impact of CAVs on traffic flow.

This research will work to understand the challenges of capturing the impacts of CAVS for different traffic models. Propose adequate methodologies to incorporate CAVs into the traffic models used in this project and estimate model parameters to reflect the impact of CAVs in the selected traffic model.

Objective

Statistical Inference Using Stochastic Gradient Descent

Project ID: 144
University: University of Texas at Austin
Principal Investigator: Constantine Caramanis
Start Date: 3/1/2017
Expected Completion Date: 8/31/2018

Project Summary   Read the report (PDF).

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.

Objective

Clustering and Classification

Project ID: 145
University: University of Texas at Austin
Principal Investigator: Constantine Caramanis
Start Date: 4/1/2017
Expected Completion Date: 9/1/2018

Project Summary   Read the report (PDF).

Clustering is a fundamental methodology for transportation applications. This is true, in part, because we are trying to learn from and predict the behavior of a system that combines many different types of behaviors (drivers). Much about clustering, including fast rates of convergence, are still quite poorly understood.

Objective

We plan to investigate minimax bounds for classification and clustering error in the setting where co-variates are drawn from a mixture of two isotropic Gaussian distributions. Here, we define clustering error in a discriminative fashion, demonstrating fundamental connections between classification (supervised) and clustering (unsupervised). For both classification and clustering, our lower bounds show that without enough samples, the best any classifier or clustering rule can do is close to random guessing. For classification, as part of our upper bound analysis, we will attempt to show that Fisher’s linear discriminant achieves a fast minimax rate Θ(1/n) with enough samples n. For clustering, as part of our upper bound analysis, in our work we would like to show that a clustering rule constructed using principal component analysis achieves the minimax rate with enough samples.

Our preliminary work suggests that something precisely along these lines may be true.

We will also seek to provide lower and upper bounds for the high-dimensional sparse setting where the dimensionality of the covariates p is potentially larger than the number of samples n, but where the difference between the Gaussian means is sparse.

Bandit Algorithms for Online Learning and Resource Allocation

Project ID: 146
University: University of Texas at Austin
Principal Investigator: Sanjay Shakkottai and Stephen Boyles
Start Date: 01/01/2017
Expected Completion Date: 08/31/2018

Project Summary      Read the report (PDF).

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.

Objectives

We will develop mathematical models for learning such complex tasks. There are several technical directions we will explore. First, we will develop low-dimensional latent models that can potentially simplify the sample complexity of such online learning. With such models, we will develop algorithms that exploit latent structure, as well as develop theoretical guarantees for these algorithms.

A second exciting direction we will pursue is to continually optimize the policy for making resource allocation decisions. The high-level idea is that policies “leak” information about other policies. Specifically, if the efficiency with a small collection of policies is understood, it is plausible that we can predict the behavior of other policies even when we have not implemented the others. We will explore formal mechanisms for leveraging such intuition, and develop algorithms for online policy optimizations.

V2X Spectrum Resource Allocation for Sensing and Communications

Project ID: 147
University: University of Texas at Austin
Principal Investigator: Sanjay Shakkottai 
Start Date: 01/01/2017
Expected Completion Date: 08/31/2018

Project Summary      Read the report (PDF).

It is expected that emerging wireless networks will be densified, and be able to support high throughput and low latency data along with infrastructure-supported sensing needs for vehicular traffic. Network densification, where dense overlapping spatial coverage is implemented using infrastructure nodes, is necessary to reliably support these needs. However, such densification will result in high energy usage, which is undesirable. We develop a research agenda for managing infrastructure node states for switching between active and inactive states to save energy, and when active, dynamically switching between sensing and communications to effectively serve both objectives (e.g. objectives being low latency for communications, and mean-square error for sensing fidelity).

In addition to fast time-varying channels, the challenge here is that mode switching incurs a penalty. When an infrastructure node’s state changes (e.g. between active and inactive), there are penalties due to state migration, signaling to update V2I associations (a vehicle needs to be re-associated with some other infrastructure node), and related backhaul costs. Further, some of these operations have a hysteresis penalty as well (time lag due to such migration processes). This motivates the need for new scheduling mechanisms, that dynamically manage infrastructure nodes, and overall guarantees good network performance.

Objectives

We will develop mathematical models for describing infrastructure modes, along with queueing modes to describe traffic demands. These models will be used to develop scheduling algorithms that optimize network performance with switching costs.

A novel aspect of this research will be in the use of online learning algorithms that work in synergy with the queue-length based scheduling algorithms. The learning algorithms will learn good activation and mode patterns over time, and continually work in tandem with the channel allocation algorithms to jointly optimize network goals.

The outcomes of this study will be new learning-cum-scheduling algorithms for V2I networks.

A New Microeconomic Theory-Based Model for Ranking Data

Project ID: 148
University: University of Texas at Austin
Principal Investigator: Chandra Bhat
Start Date: 5/1/2017
Expected Completion Date: 09/30/2018

Project Summary  Read the report (PDF).

This project will address the importance of flexible specifications for the utility kernel error terms for rank-ordered data models. We also explore why, just like in the rank-ordered logit (ROL) model, a mixed ROL that superimposes a distribution on the variable coefficients cannot be expected to resolve the problem of unstable coefficients across rank depths. Also, extending the mixed ROL in the ways that the ROL has been extended result in the corresponding models not being based on microeconomic theory. We instead adopt a finite-mixture approach to specify random coefficients on the variables as well as on the kernel error term, while using a multivariate normal distribution (including the kernel error term) within each mixture. As importantly, we propose the use of a robust composite marginal likelihood (CML) approach that guarantees estimator consistency under usual regularity conditions, while also entailing no more than the evaluation of bivariate cumulative normal distribution functions in the case of cross-sectional data, regardless of the number of random coefficients or number of alternatives. In the case of repeated ranking exercises, as is typical in stated preference surveys, we propose the MACML approach, which again entails the evaluation of no more than two-dimensional cumulative normal distribution functions. We demonstrate an application of our formulation and estimation approach to study bicycle commute route choice. The use of non-motorized modes for commuting presents many benefits both to society (reduction in congestion and vehicle emissions) and to the individual (health benefits from an active lifestyle). Therefore, encouraging the use of bicycles through the provision of adequate infrastructure is of vital importance. In order to better plan for such infrastructure, it is necessary to first understand how bicyclists make route decisions, what their preferences regarding route attributes (such as pavement condition, presence of big uphills, or travel time) are, and what determines such preferences.

Objective

The following tasks will be undertaken during the course of the project:

  1. Develop the econometric formulation for finite-mixture ROL approach.
  2. Develop an estimation approach for the proposed model.
  3. Conduct an empirical application of the proposed model.
  4. Discuss the main takeaways of our application and how they can be applied to transportation planning.

Megaregional Trends of Passenger and Freight Movement: Evidence from National Transportation Data Sources

Project ID: 149
University: University of Texas at Austin
Principal Investigator: Ming Zhang and Chandra Bhat
Start Date: 5/15/2017
Expected Completion Date: 09/30/2018

Project Summary  Read the report (PDF).

In January 2017 USDOT designated thirteen Beyond Traffic Innovation Centers (BTICs) throughout the country. The BTICs set a clear focus on transportation research, education, and technology transfers in US megaregions. Megaregion is a concept originated from geography in the 1960s. The concept was rejuvenated at the turn of this century by urban and regional planners for spatial planning and research. The designation of BTICs signifies the prime time for transportation-centered megaregion research and policy making. UT Austin is home to one of the thirteen BTICs and is deemed to participate in and lead the megaregion efforts. The proposed research aims to study megaregional trends of passenger and freight movement by exploring national transportation databases and travel surveys. The research output is expected to help further conceptualize megaregion from the transportation planning perspective and identify trends and issues of megaregional mobility.

Objective

Transit Policy in the Context of New Transportation Paradigms

Project ID: 150
University: University of Texas at Austin
Principal Investigator: James Kuhr, Chandra Bhat, and Natalia Ruiz Juri
Start Date: 9/1/2017
Expected Completion Date: 09/30/2018

Project Summary    Read the report (PDF).

With changing transportation paradigms, there is significant potential for a shift in the balance between the overall population use of, and reliance on, ridesharing services versus traditional transportation options such as personal car ownership or transit use. This shift could lead to a realignment of the bulk of the responsibility for mobility to private entities and away from individual citizens and public entities. Today, as supplemental to the multitude of transportation options that are available, the availability, or lack thereof, of ridesharing services produces low to minimal risk to the traveling public. However, in a future in which ridesharing is optimally (widely) employed, the current independent nature of ridesharing services will influence wider community transit services. This problem statement explores the effects of new types of transportation on transit through the creation of several plausible future scenarios, and what policy decisions could potentially be made to ensure that transit is optimally employed.

Objective

Utilizing other D-STOP supported surveys (and potentially conducting our own as needed) that will identify attitudes towards ridesharing and various other methods of carpooling/car sharing that could serve as a competitor to transit services, we will work to analyze the potential effects of new transportation paradigms on transit system ridership. We will then identify key policy considerations for transit service providers in the next 10 years.

Video Data Analytics for Safer and More Efficient Mobility

Project ID: 151
University: University of Texas at Austin
Principal Investigator: Natalia Ruiz Juri
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).

Identifying and tracking objects in video data frames has become significantly easier in recent years, thanks to advances in artificial intelligence and greater access to increased computing power. Some commercially available products allow transportation agencies to leverage new video data analysis capabilities to support traffic analysis, operations, and safety applications. However, such products often require the use of dedicated hardware which is typically costly.

However, many transportation agencies own extensive networks of pan-zoom-tilt cameras, used mostly for traffic monitoring. The analysis of the traffic streams produced by such cameras can potentially yield valuable data sets for a number of planning and operation applications, and support analyses which are not possible using data from simpler sensors.

Data-driven, Real-time Traffic Signal Optimization: A Distributed Approach

Project ID: 152
University: University of Texas at Austin
Principal Investigator:  Stephen Boyles
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).
The vehicular transportation system is essentially a distributed computing system, but one with very limited communication and coordination abilities at present. These limitations are especially acute in light of the ambiguities of human interaction on the road and as the current standard system for signal coordination. We propose to build on prior development of an improved system for traffic control, based on improved, reliable communication between a traffic signal controller and vehicles. We also aim to study how, in both partially and fully connected environments, the concepts of distributed computer operating systems, communication networks, and optimization algorithms can be leveraged to improve such a control system. The goal is a highly abstracted and modular system that can be implemented by any city to optimize their traffic control systems using a high-performance computing cluster. In this regard, we aim to create an adaptive intersection control system that greatly improves upon traditional adaptive systems: one which leverages the power of computing to provide performance optimizations humans could never deliver.

Real-time, Targeted Incentives for Strategic Travelers

Project ID: 153
University: University of Texas at Austin
Principal Investigator:  Stephen Boyles
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).
New communication technologies allow targeted network control aimed at reducing congestion by identifying, in real time, alternative routes for travelers. In current practice, such measures involve messages broadcast to all (or many) travelers, or tolls which are assessed identically among vehicles. A more finessed approach has the potential of reducing congestion while alleviating some of the social equity concerns with tolling, or concerns about over-reaction to real-time travel information.

Tight-coupling of Vision, Radar, and Carrier-phase Differential GNSS for Robust All-weather Positioning

Project ID: 154
University: University of Texas at Austin
Principal Investigator:  Todd Humphreys
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).

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

Modeling Willingness-to-share Trips in an Autonomous Vehicle Future: A Stochastic Psychological Latent Construct Approach

Project ID: 155
University: University of Texas at Austin
Principal Investigator:  Chandra Bhat
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).

Shared autonomous vehicle (SAV) systems, in which autonomous vehicles (AVs) are owned by transportation network companies that offer Mobility as a Service (MaaS) to customers, are gaining considerable research attention. SAVs have the potential to reduce vehicle ownership and parking requirements, improve traffic conditions, and minimize empty-vehicle travel. However, the extent to which each of these potentials can be achieved depends on consumers’ willingness to adopt such services as well as their disposition to sharing rides. While both MaaS and SAV adoption have received some attention in the literature (for example, Krueger et al., 2016; Rayle et al., 2016; Clewlow and Mishra, 2017; Dias et al., 2017; Lavieri et al., 2017), there is no specific discussion and measurement about willingness-to-share (WTS). The objective of this project is to develop this notion of WTS in the transportation context and propose a measuring and modeling approach to this concept. The developed modeling framework will be used to investigate variations in WTS and value of travel time (VTT) across distinct population segments for different trip purposes. Outcomes from this investigation can contribute to SAV adoption forecasts and guide SAV demand assumptions in traffic models.

Emerging Transportation Mobility Options and Technologies: A Comprehensive Analysis of Consumer Preferences using Survey and Supplementary Data

Project ID: 156
University: University of Texas at Austin
Principal Investigator:  Chandra Bhat
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).

The new emerging transportation landscape includes new mobility options and technologies, such as autonomous vehicles (AVs) and ride-hailing services (like Uber and Lyft). In this project, we will undertake a revealed preference survey of individuals’ current travel choices (including ridehailing) and obtain stated preferences/choices about individuals’ potential responses to new mobility options. The survey data will be combined with other sources of land-use and transportation network data to project travel demand into the future, and develop policy recommendations on how best to harness the potential of new mobility options to make transportation in urban areas safer, more reliable, and more resilient. In particular, the survey’s main objective is to collect a rich set of data that includes information about people’s travel behavior and their perceptions and attitudes towards advanced transportation technologies and mobility options. This survey will be a coordinated effort among four different universities and will collect data in five metropolitan areas (Phoenix, Atlanta, Austin, Houston, and Tampa) regarding users’ perceptions on new transportation technologies and services, allowing for cross-city comparisons. The D-STOP project will be responsible for all aspects of data collection in the Austin and Houston areas, and will contribute to data analysis across all five cities. The research will inform the development of behavioral models of technology adoption capable of reflecting the impacts of these disruptive forces on traveler behavior and values, and provide guidance to policy makers and urban transportation planning agencies in terms of regulations and preparing for the new era of transportation services.

Sensing and Communications in V2V and V2I Settings

Project ID: 157
University: University of Texas at Austin
Principal Investigator:  Sanjay Shakkottai
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).

We consider the wireless scheduling problem of jointly scheduling resources (activating/de-activating and communications/sensing mode selection) at various locations on a vehicle, in order to optimize for both communications and sensing (e.g. through in-band radar). The resources could be antenna resources located at various points on a car (e.g. sides, back-bumper, front). An example scenario would be a car simultaneously engaged in V2V communications (with another car), V2I communications with a base-station, and sensing the environment (for location, obstacles, etc.). The resource allocation task here would be to dynamically select each of the resources for communications or sensing. When considering infrastructure nodes, one could also consider turning off resources to save energy. We build on our earlier research to study a few important new directions:

  1. Dynamic scheduling algorithms that use queue-lengths, channel state and current sensed-state in order to result in smaller delays and improved sensing fidelity. These algorithms would dynamically move modalities (communications/radar) across multiple antenna resources (e.g. different arrays located on multiple locations on a car) to optimize for both communications and sensing.
  2. Online-learning based algorithms to optimize the use of various antenna resources (e.g. learning-based beam pattern optimization for improved sensing).

Online Matching, Black-box Optimization and Hyper-parameter Tuning

Project ID: 158
University: University of Texas at Austin
Principal Investigator:  Sanjay Shakkottai
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).

Machine learning algorithms form an integral part of modern data-driven platforms and systems. In the vehicular setting, examples range from platforms for matching — allocating passengers to vehicles, matching cargo freight carriers – to onboard deep-learning based algorithms for driver-assist. While these algorithms adapt a range of parameters based on new information, what is common is that they typically need certain parameters to be fixed (the hyper-parameters) and are outside the learning framework. Due the high-dimensionality of the parameter space, hyper-parameter tuning (i.e. selecting these hyper-parameters) is a major hurdle in deploying algorithms. We propose a principled approach for search and optimization of hyper-parameters.

Solving Perception Challenges for Autonomous Vehicles Using SGD

Project ID: 159
University: University of Texas at Austin
Principal Investigator:  Constantine Caramanis
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).

All levels of autonomy in vehicles faces a critical problem due to the fragility and lack of robustness of state of the art image classifiers to perturbations in the input image. Specifically, it has been repeatedly shown that classifiers that enjoy extremely high accuracy on test sets and challenge sets, are remarkably susceptible to misclassifying images that have small, but planted, perturbations. Stop signs can be misclassified as yield signs, with modifications that are imperceptible to the casual human observer.

We plan to use techniques and ideas from natural scene statistics, manifold learning and convex optimization, in order to build in robustness to non-perceptually-significant perturbations to images. A perturbation that morphs one image into another (according to human perception), is naturally a perturbation on the manifold of natural images. Adversarial attacks, such as those described above, are off of this manifold. A main challenge is that the desciprtion, while perhaps compelling, is purely at the conceptual level. We will use techniques for learning a compact and usable description of this manifold. We will develop ideas from dictionary learning, convex optimization, manifold learning and natural scene statistics.

Large-scale Optimization with Small-scale Data

Project ID: 160
University: University of Texas at Austin
Principal Investigator:  Constantine Caramanis
Start Date: 9/1/2018
Expected Completion Date: 08/31/2020

Project Summary     Read the report (PDF).

This project takes the same approach as Project 159, but addresses the same pressing problem from a completely different perspective, developing completely different ideas and methodological tools. All levels of autonomy in vehicles face a critical problem due to the fragility and lack of robustness of state of the art image classifiers to perturbations in the input image. Specifically, it has been repeatedly shown that classifiers that enjoy extremely high accuracy on test sets and challenge sets, are remarkably susceptible to misclassifying images that have small, but planted, perturbations. Stop signs can be misclassified as yield signs, with modifications that are imperceptible to the casual human observer.

Many researchers have identified this as a critical problem for neural networks. Our approach grows directly out of a previously funded D-STOP project. In that work, we have been working on developing fast SGD-based algorithms for large scale inference. Our preliminary experiments have demonstrated that those ideas can in fact be used to defend against these so-called adversarial attacks. Our proposal is to study how the natural exploration of the sample space that is drive by our SGD approach can be mapped to implicitly define the natural manifold of images. We conjecture that this is the critical concept for defending against adversarial attacks.