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

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

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

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

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.)?

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.

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

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

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

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

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

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

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 1960’s. 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