Ongoing D-STOP Projects

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

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

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: 8/31/2018

Project Summary

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.

Objective

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

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

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

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

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

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: 5/31/2018

Project Summary

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: 5/31/2018

Project Summary

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

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

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: 8/31/2018

Project Summary

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.

Objective

In this project, we will study dynamic pricing models for managed lanes. We will focus on two aspects of dynamic pricing: (a) utilizing real time traffic measurements to inform parameters of the pricing model, and (b) developing a optimal pricing formulation for managed lanes with multiple entrances and exits.

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

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.

V2I Managed Lanes Test Bed

Project ID: 142
University: University of Texas at Austin
Principal Investigator: James Kuhr
Start Date: 9/1/2017
Expected Completion Date: 09/30/2018

Project Summary

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

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

Transit Policy in the Context of New Transportation Paradigms

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

Project Summary

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.