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

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

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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.

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

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

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

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

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