�ݺ�ߣshows by User: ctrutaustin

�ݺ�ߣshows by User: ctrutaustin / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: ctrutaustin / Wed, 28 Nov 2018 22:10:57 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: ctrutaustin Flying with SAVES /slideshow/flying-with-saves/124314240 55gonzalezprelcicflyingwithsaves-181128221057
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:57 GMT /slideshow/flying-with-saves/124314240 ctrutaustin@slideshare.net(ctrutaustin) Flying with SAVES ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/55gonzalezprelcicflyingwithsaves-181128221057-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

Flying with SAVES from Center for Transportation Research - UT Austin

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1 Regret of Queueing Bandits /slideshow/regret-of-queueing-bandits-124314236/124314236 54shakkottaibandits-181128221054
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:54 GMT /slideshow/regret-of-queueing-bandits-124314236/124314236 ctrutaustin@slideshare.net(ctrutaustin) Regret of Queueing Bandits ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/54shakkottaibandits-181128221054-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

Regret of Queueing Bandits from Center for Transportation Research - UT Austin

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1 Advances in Millimeter Wave for V2X /slideshow/advances-in-millimeter-wave-for-v2x-124314232/124314232 53heathadvancesinmmwave-181128221054
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:54 GMT /slideshow/advances-in-millimeter-wave-for-v2x-124314232/124314232 ctrutaustin@slideshare.net(ctrutaustin) Advances in Millimeter Wave for V2X ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/53heathadvancesinmmwave-181128221054-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

Advances in Millimeter Wave for V2X from Center for Transportation Research - UT Austin

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1 Collaborative Sensing and Heterogeneous Networking Leveraging Vehicular Fleets /slideshow/collaborative-sensing-and-heterogeneous-networking-leveraging-vehicular-fleets/124314230 52deveciana-collaborative-sensing-181128221053
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:53 GMT /slideshow/collaborative-sensing-and-heterogeneous-networking-leveraging-vehicular-fleets/124314230 ctrutaustin@slideshare.net(ctrutaustin) Collaborative Sensing and Heterogeneous Networking Leveraging Vehicular Fleets ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/52deveciana-collaborative-sensing-181128221053-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

Collaborative Sensing and Heterogeneous Networking Leveraging Vehicular Fleets from Center for Transportation Research - UT Austin

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1 Collaborative Sensing for Automated Vehicles /ctrutaustin/collaborative-sensing-for-automated-vehicles-124314229 51humphreyssavesvisit2018-181128221051
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:51 GMT /ctrutaustin/collaborative-sensing-for-automated-vehicles-124314229 ctrutaustin@slideshare.net(ctrutaustin) Collaborative Sensing for Automated Vehicles ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/51humphreyssavesvisit2018-181128221051-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

Collaborative Sensing for Automated Vehicles from Center for Transportation Research - UT Austin

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1 Statistical Inference Using Stochastic Gradient Descent /slideshow/statistical-inference-using-stochastic-gradient-descent-124314214/124314214 44constantine-dstop-2018-181128221033
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:33 GMT /slideshow/statistical-inference-using-stochastic-gradient-descent-124314214/124314214 ctrutaustin@slideshare.net(ctrutaustin) Statistical Inference Using Stochastic Gradient Descent ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/44constantine-dstop-2018-181128221033-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

Statistical Inference Using Stochastic Gradient Descent from Center for Transportation Research - UT Austin

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1 CAV/Mixed Transportation Modeling /slideshow/cavmixed-transportation-modeling-124314211/124314211 43kuhrjk-dstop-bac-181128221033
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:33 GMT /slideshow/cavmixed-transportation-modeling-124314211/124314211 ctrutaustin@slideshare.net(ctrutaustin) CAV/Mixed Transportation Modeling ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/43kuhrjk-dstop-bac-181128221033-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

CAV/Mixed Transportation Modeling from Center for Transportation Research - UT Austin

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1 Real-time Signal Control and Traffic Stability / Improved Models for Managed Lanes Operations /slideshow/realtime-signal-control-and-traffic-stability-improved-models-for-managed-lanes-operations/124314210 42boyles-181128221031
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:31 GMT /slideshow/realtime-signal-control-and-traffic-stability-improved-models-for-managed-lanes-operations/124314210 ctrutaustin@slideshare.net(ctrutaustin) Real-time Signal Control and Traffic Stability / Improved Models for Managed Lanes Operations ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/42boyles-181128221031-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

Real-time Signal Control and Traffic Stability / Improved Models for Managed Lanes Operations from Center for Transportation Research - UT Austin

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1 Sharing Novel Data Sources to Promote Innovation Through Collaboration: Case Studies in Austin, TX /slideshow/sharing-novel-data-sources-to-promote-innovation-through-collaboration-case-studies-in-austin-tx-124314208/124314208 41ruizd-stop2018-181128221031
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:30 GMT /slideshow/sharing-novel-data-sources-to-promote-innovation-through-collaboration-case-studies-in-austin-tx-124314208/124314208 ctrutaustin@slideshare.net(ctrutaustin) Sharing Novel Data Sources to Promote Innovation Through Collaboration: Case Studies in Austin, TX ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/41ruizd-stop2018-181128221031-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

Sharing Novel Data Sources to Promote Innovation Through Collaboration: Case Studies in Austin, TX from Center for Transportation Research - UT Austin

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1 UT SAVES: Situation Aware Vehicular Engineering Systems /slideshow/ut-saves-situation-aware-vehicular-engineering-systems/124314203 3saveswncgdstop-181128221026
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]>
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/]]> Wed, 28 Nov 2018 22:10:26 GMT /slideshow/ut-saves-situation-aware-vehicular-engineering-systems/124314203 ctrutaustin@slideshare.net(ctrutaustin) UT SAVES: Situation Aware Vehicular Engineering Systems ctrutaustin Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3saveswncgdstop-181128221026-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/

UT SAVES: Situation Aware Vehicular Engineering Systems from Center for Transportation Research - UT Austin

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1 Regret of Queueing Bandits /slideshow/regret-of-queueing-bandits/103062823 09shakkottaiapril18-180626040029
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.]]>
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.]]> Tue, 26 Jun 2018 04:00:29 GMT /slideshow/regret-of-queueing-bandits/103062823 ctrutaustin@slideshare.net(ctrutaustin) Regret of Queueing Bandits ctrutaustin 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/09shakkottaiapril18-180626040029-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Regret of Queueing Bandits from Center for Transportation Research - UT Austin

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1 Sharing Novel Data Sources to Promote Innovation through Collaboration: Case Studies in Austin TX /slideshow/sharing-novel-data-sources-to-promote-innovation-through-collaboration-case-studies-in-austin-tx/103062466 08ruizapril2018-180626035727
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.]]>
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.]]> Tue, 26 Jun 2018 03:57:27 GMT /slideshow/sharing-novel-data-sources-to-promote-innovation-through-collaboration-case-studies-in-austin-tx/103062466 ctrutaustin@slideshare.net(ctrutaustin) Sharing Novel Data Sources to Promote Innovation through Collaboration: Case Studies in Austin TX ctrutaustin 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/08ruizapril2018-180626035727-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Sharing Novel Data Sources to Promote Innovation through Collaboration: Case Studies in Austin TX from Center for Transportation Research - UT Austin

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1 CAV/Mixed Transportation Modeling /slideshow/cavmixed-transportation-modeling/103061467 07kuhrapril18-180626034810
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.]]>
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.]]> Tue, 26 Jun 2018 03:48:10 GMT /slideshow/cavmixed-transportation-modeling/103061467 ctrutaustin@slideshare.net(ctrutaustin) CAV/Mixed Transportation Modeling ctrutaustin 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/07kuhrapril18-180626034810-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

CAV/Mixed Transportation Modeling from Center for Transportation Research - UT Austin

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1 Collaborative Sensing for Automated Vehicles /ctrutaustin/collaborative-sensing-for-automated-vehicles 06humphreysapril18-180626034311
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]]>
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]]> Tue, 26 Jun 2018 03:43:11 GMT /ctrutaustin/collaborative-sensing-for-automated-vehicles ctrutaustin@slideshare.net(ctrutaustin) Collaborative Sensing for Automated Vehicles ctrutaustin 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 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/06humphreysapril18-180626034311-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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

Collaborative Sensing for Automated Vehicles from Center for Transportation Research - UT Austin

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1 Advances in Millimeter Wave for V2X /slideshow/advances-in-millimeter-wave-for-v2x/103060240 05heathapril18-180626033746
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.]]>
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.]]> Tue, 26 Jun 2018 03:37:46 GMT /slideshow/advances-in-millimeter-wave-for-v2x/103060240 ctrutaustin@slideshare.net(ctrutaustin) Advances in Millimeter Wave for V2X ctrutaustin 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/05heathapril18-180626033746-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Advances in Millimeter Wave for V2X from Center for Transportation Research - UT Austin

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1 Statistical Inference Using Stochastic Gradient Descent /slideshow/statistical-inference-using-stochastic-gradient-descent/103058843 04constantineapril18-180626032528
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.]]>
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.]]> Tue, 26 Jun 2018 03:25:27 GMT /slideshow/statistical-inference-using-stochastic-gradient-descent/103058843 ctrutaustin@slideshare.net(ctrutaustin) Statistical Inference Using Stochastic Gradient Descent ctrutaustin 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/04constantineapril18-180626032528-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Statistical Inference Using Stochastic Gradient Descent from Center for Transportation Research - UT Austin

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1 Status of two projects: Real-time Signal Control and Traffic Stability; Improved Models for Managed Lane Operations /slideshow/status-of-two-projects-realtime-signal-control-and-traffic-stability-improved-models-for-managed-lane-operations/103058290 03boylesapril18-180626032021
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.]]>
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.]]> Tue, 26 Jun 2018 03:20:20 GMT /slideshow/status-of-two-projects-realtime-signal-control-and-traffic-stability-improved-models-for-managed-lane-operations/103058290 ctrutaustin@slideshare.net(ctrutaustin) Status of two projects: Real-time Signal Control and Traffic Stability; Improved Models for Managed Lane Operations ctrutaustin 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/03boylesapril18-180626032021-thumbnail.jpg?width=120&height=120&fit=bounds" /> 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.

Status of two projects: Real-time Signal Control and Traffic Stability; Improved Models for Managed Lane Operations from Center for Transportation Research - UT Austin

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1 SAVES general overview /slideshow/saves-general-overview/103057443 02savesoverviewapril18-180626031240
This presentation provides a broad view of the SAVES initiatives and technology.]]>
This presentation provides a broad view of the SAVES initiatives and technology.]]> Tue, 26 Jun 2018 03:12:40 GMT /slideshow/saves-general-overview/103057443 ctrutaustin@slideshare.net(ctrutaustin) SAVES general overview ctrutaustin This presentation provides a broad view of the SAVES initiatives and technology. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/02savesoverviewapril18-180626031240-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation provides a broad view of the SAVES initiatives and technology.

SAVES general overview from Center for Transportation Research - UT Austin

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1 D-STOP Overview April 2018 /slideshow/dstop-overview-april-2018/103057140 01dstopoverviewapril18-180626031010
General overview of D-STOP given at the April 2018 Business Advisory Council meeting]]>
General overview of D-STOP given at the April 2018 Business Advisory Council meeting]]> Tue, 26 Jun 2018 03:10:10 GMT /slideshow/dstop-overview-april-2018/103057140 ctrutaustin@slideshare.net(ctrutaustin) D-STOP Overview April 2018 ctrutaustin General overview of D-STOP given at the April 2018 Business Advisory Council meeting <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/01dstopoverviewapril18-180626031010-thumbnail.jpg?width=120&height=120&fit=bounds" /> General overview of D-STOP given at the April 2018 Business Advisory Council meeting

D-STOP Overview April 2018 from Center for Transportation Research - UT Austin

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1 Managing Mobility during Design-Build Highway Construction: Successes and Lessons Learned /slideshow/managing-mobility-during-designbuild-highway-construction-successes-and-lessons-learned/96626058 pruner-180510153317
Presentation for the April 2018 CTR Symposium by Kris Pruner http://ctr.utexas.edu/ctr-symp/]]>
Presentation for the April 2018 CTR Symposium by Kris Pruner http://ctr.utexas.edu/ctr-symp/]]> Thu, 10 May 2018 15:33:17 GMT /slideshow/managing-mobility-during-designbuild-highway-construction-successes-and-lessons-learned/96626058 ctrutaustin@slideshare.net(ctrutaustin) Managing Mobility during Design-Build Highway Construction: Successes and Lessons Learned ctrutaustin Presentation for the April 2018 CTR Symposium by Kris Pruner http://ctr.utexas.edu/ctr-symp/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pruner-180510153317-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation for the April 2018 CTR Symposium by Kris Pruner http://ctr.utexas.edu/ctr-symp/

Managing Mobility during Design-Build Highway Construction: Successes and Lessons Learned from Center for Transportation Research - UT Austin

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1 https://cdn.slidesharecdn.com/profile-photo-ctrutaustin-48x48.jpg?cb=1595604065 The Center for Transportation Research is a nationally recognized research institution focusing on transportation research, education, and public service. Current and ongoing projects address virtually all aspects of transportation, including economics, multimodal systems, traffic congestion relief, transportation policy, materials, structures, transit, environmental impacts, driver behavior, land use, geometric design, accessibility, and pavements. ctr.utexas.edu https://cdn.slidesharecdn.com/ss_thumbnails/55gonzalezprelcicflyingwithsaves-181128221057-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/flying-with-saves/124314240 Flying with SAVES https://cdn.slidesharecdn.com/ss_thumbnails/54shakkottaibandits-181128221054-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/regret-of-queueing-bandits-124314236/124314236 Regret of Queueing Ban... https://cdn.slidesharecdn.com/ss_thumbnails/53heathadvancesinmmwave-181128221054-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/advances-in-millimeter-wave-for-v2x-124314232/124314232 Advances in Millimeter...