This document summarizes a presentation about planning for a world with connected and automated vehicles. Some key points include:
- Automated vehicles will have major impacts on traffic such as increased road capacity through platooning and new traffic control strategies.
- Models are needed to simulate these impacts at large regional scales given technological and regulatory uncertainties.
- Reservation-based intersections show potential to dramatically reduce delays, but their impacts depend on factors like route choice and asymmetric demand.
- Allowing empty automated vehicles to reposition could smooth traffic flows compared to not repositioning empty vehicles.
Forget last mile travel - a new modality rising!Peter Biczók
First and last mile transport discussion is outdated. The Dutch national transport model shows that once passengers can rely on a bike hire system at the activity end of the trip, the attractiveness of transit soars. A new, interdependent! modality, bike-transit has been established that is more attractive and more socio-economical that the (automated) private car.
Barter on What is Success in Urban Transport?Paul Barter
Presented to Junior College geography students at Temasek JC, Singapore, 11 August 2010. Discusses for a general audience competing ideas about how to define 'success' in urban transport policy. Warns to be careful what you wish for. Wanting faster traffic and cheaper driving can be traps.
Webinar: Some Observations on BRT in North America… and ElsewhereBRTCoE
BRT systems can take many forms depending on local needs and environments. Key lessons from implementations include the importance of institutional support, comprehensive service planning, and clear communications. While full BRT features may not be possible initially, phased approaches that start with basic improvements and add elements over time have been successful. As the technology advances, BRT is becoming more attractive and effective through features like guided lanes, hybrid vehicles, and improved stations.
This document summarizes the key points made in a presentation about modeling transportation systems for an automated vehicle world. It discusses how autonomous vehicles could impact traffic flow by allowing closer vehicle spacing, potentially increasing road capacity. It also explores how autonomous vehicles may affect traveler choices and demand for different modes like transit or personal vehicles. New opportunities for real-time traffic control are presented. The document emphasizes that transportation models need to account for interactions between the supply of infrastructure and demand from travelers to adequately capture impacts of autonomous vehicles.
IRJET- A Novel Approach for Intelligent Transportation Systems with Traffic J...IRJET Journal
This document presents a novel approach for intelligent transportation systems to mitigate traffic jams using vehicle-to-vehicle (V2V) communication. Specifically, it proposes an efficient algorithm to detect phantom jams, which are traffic jams that form for no obvious reason. The algorithm uses a fuzzy inference system integrated with a V2V-based phantom jam detection computation. Simulations using real and synthetic traffic data show the approach reduces average travel time by up to 9% and 4.9% compared to a baseline method, at penetration rates of 10% V2V equipped vehicles. The system design, implementation details, and experimental results evaluating phantom jam mitigation are described.
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.
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/
Forget last mile travel - a new modality rising!Peter Biczók
First and last mile transport discussion is outdated. The Dutch national transport model shows that once passengers can rely on a bike hire system at the activity end of the trip, the attractiveness of transit soars. A new, interdependent! modality, bike-transit has been established that is more attractive and more socio-economical that the (automated) private car.
Barter on What is Success in Urban Transport?Paul Barter
Presented to Junior College geography students at Temasek JC, Singapore, 11 August 2010. Discusses for a general audience competing ideas about how to define 'success' in urban transport policy. Warns to be careful what you wish for. Wanting faster traffic and cheaper driving can be traps.
Webinar: Some Observations on BRT in North America… and ElsewhereBRTCoE
BRT systems can take many forms depending on local needs and environments. Key lessons from implementations include the importance of institutional support, comprehensive service planning, and clear communications. While full BRT features may not be possible initially, phased approaches that start with basic improvements and add elements over time have been successful. As the technology advances, BRT is becoming more attractive and effective through features like guided lanes, hybrid vehicles, and improved stations.
This document summarizes the key points made in a presentation about modeling transportation systems for an automated vehicle world. It discusses how autonomous vehicles could impact traffic flow by allowing closer vehicle spacing, potentially increasing road capacity. It also explores how autonomous vehicles may affect traveler choices and demand for different modes like transit or personal vehicles. New opportunities for real-time traffic control are presented. The document emphasizes that transportation models need to account for interactions between the supply of infrastructure and demand from travelers to adequately capture impacts of autonomous vehicles.
IRJET- A Novel Approach for Intelligent Transportation Systems with Traffic J...IRJET Journal
This document presents a novel approach for intelligent transportation systems to mitigate traffic jams using vehicle-to-vehicle (V2V) communication. Specifically, it proposes an efficient algorithm to detect phantom jams, which are traffic jams that form for no obvious reason. The algorithm uses a fuzzy inference system integrated with a V2V-based phantom jam detection computation. Simulations using real and synthetic traffic data show the approach reduces average travel time by up to 9% and 4.9% compared to a baseline method, at penetration rates of 10% V2V equipped vehicles. The system design, implementation details, and experimental results evaluating phantom jam mitigation are described.
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.
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/
Getting proactive rather than reactive in av infrastructure planning, peter h...JumpingJaq
Three scenarios for autonomous vehicle infrastructure planning are proposed: 1) widespread privately owned autonomous vehicles, 2) popular autonomous vehicle sharing reduces private ownership, and 3) a hybrid model of some ownership and some sharing. Transport models could test these scenarios by modifying vehicle ownership and adding autonomous vehicles as a new transport mode. Modeling issues include representing priority autonomous vehicle lanes, circulation patterns, and impacts on congestion, travel demands, and public transit. Published modeling of autonomous vehicles suggests they could significantly increase road capacity and speeds but also vehicle travel if shared autonomous vehicles are not widely adopted.
Presentation on advance traffic engineering.pptxEtahEneji1
This presentation was done to fulfil the course requirement for the pursuit of my M. ENG on the course title: Advanced traffic engineering Course code : (CIV 8331).
Course Lecturer : ENGR. PROF H. M. AlHASSAN
Making the Most of Long-Range Models for Automated and Connected Vehicle Planning poster was presented at the 95th Annual Transportation Research Board (TRB) Annual Meeting in January 2016.
Automated/Connected Vehicle technology (AV/CV) is expected to have significant impacts on travel behavior. The
potential transformative nature of these technologies to alter
or influence future travel behavior and demand is quite significant.
Simulation Based Assignment in PTV Visum - TRB 2017Michael Oliver
A brief introduction to the new Simulation-Based dynamic Assignment (SBA) released in PTV Visum 16, as presented at the Virginia and Washington DC Joint SimCap Meeting, TRB 2017.
This document summarizes a presentation on dynamic traffic modeling applications and research frontiers. It discusses how dynamic traffic assignment models integrate supply-side factors like road closures and demand-side factors like driver route choices to find traffic flow equilibriums. The document outlines applications of dynamic modeling to work zone impact analysis and emissions modeling. It also explores research frontiers involving easier model calibration, integration with activity-based models, and modeling impacts of autonomous vehicles.
The Future of Mixed-Autonomy Traffic (AIS302) - AWS re:Invent 2018Amazon Web Services
How will self-driving cars change urban mobility patterns? This talk examines scientific contributions in the field of reinforcement learning, presented in the context of enabling mixed-autonomy mobility—the gradual and complex integration of autonomous vehicles into existing traffic systems. We explore the potential impact of a small fraction of autonomous vehicles on low-level traffic flow dynamics, using novel techniques in model-free deep reinforcement learning. We share examples in the context of a new open-source computational platform and state-of-the-art microsimulation tools with deep-reinforcement libraries.
Austin Transit Partnership (ATP) unveiled 5 new light rail alternatives for Project Connect on an open house March 21, 2023. These alternatives differ greatly from the original plan proposed to voters in 2020 when the project was overwhelmingly authorized through a property tax increase. The original plan promised an underground light rail system downtown and an airport connection, now both seem to be unlikely.
ATP must re-evaluate core principles of the project to stay on budget, deliver transit connectivity promised to the voters, and create the backbone for a 21st century transit system for the region.
Light rail is too expensive, too slow, lacks regional expansion potential, and will be instantly outdated when implemented.
eBRT is already authorized by the ballot language and the contract with the voters. No additional elections are required for this change. When paired with the future potential of AEV transit, this approach provides the best solution for Austin today and in the future.
eBRT provides a reliable system backstop if AEV technology does not advance as quickly as projected. eBRT by itself would provide better, faster, and cheaper to operate service than LRT.
An AEV system with a tunneled backbone will have major equity benefits across the City and regionally, replacing existing transit lines with superior service.
To maximize the project benefit, the system must provide regional connectivity in addition to connectivity with the City of Austin. The lower cost per mile to deploy eBRT and AEV enables a larger and more connected system to be built today and in the future.
This is an opportunity to cement Austin as the global center for transit innovation.
Impacts of Automated Vehicles - Guidance for Australian and New Zealand Road ...JumpingJaq
This document discusses key actions that road agencies can take to support automated vehicles. It identifies considerations around physical infrastructure, such as ensuring consistency of signs, lines, and asset management. Digital infrastructure needs like vehicle localization and cellular coverage are also addressed. The document recommends road agencies provide consistent guidelines for issues like road works and certification of routes. It suggests road agencies could facilitate more efficient use of networks and optimize infrastructure use as new automated vehicle technologies emerge.
STEP Conference 2015 - Liz Bates, York City Council - Delivering York's 3rd A...STEP_scotland
This document summarizes York, UK's 3rd Air Quality Action Plan. It discusses how the city's historic road network designed for horses and chariots contributes to air quality issues today with more buses, lorries and cars. Previous plans focused on modal shift but did not go far enough. The new plan proposes a Clean Air Zone to control bus emissions based on frequency in the city center. It also discusses converting tour buses and some bus routes to electric vehicles, expanding electric vehicle infrastructure, and developing policies to reduce emissions from new developments and taxis. The goal is to improve air quality and public health in York.
The document discusses improving public transportation service quality through advanced technologies. It describes various public transportation modes and technologies such as vehicle-to-vehicle communication, GPS tracking of buses and trains, lightweight transit buses, transit signal priority, improved intermodal hubs, tracking available parking spaces, and automated rail track inspection. These technologies can help increase reliability, reduce travel times, improve schedules, and enhance the overall user experience of public transportation systems.
April 9 VTA Mountain View Open House project display boardsSCVTA
These were the boards on display at the April 9, 2014 Open Houses in Mountain View about VTA's underway and planned projects in the area. Learn more about the meetings at http://www.vta.org/News-and-Media/Connect-with-VTA/Open-Houses-in-Mountain-View-Generate-Excitement-About-Double-Tracking
This document discusses an online budget path planning mobile application. It aims to analyze budget approaches for tours by determining travel costs in real-time using personalized traveler information from smartphones. The application would help travelers understand costs for different transportation options like trains and flights. It collects data from multiple sources to compare to exhaustive search methods while maintaining service quality. Emergency helpline numbers are also provided for different places in India.
A Dynamic Vehicular Traffic Control Using Ant Colony And Traffic Light Optimi...Kristen Carter
This document proposes a dynamic vehicular traffic control system using ant colony optimization and optimized traffic lights. It aims to reduce traffic congestion in urban areas. The system divides the road network into cells and uses artificial ants to guide vehicles along the least congested paths within each cell. It also proposes a new method for optimizing traffic light timing at intersections based on real-time vehicle count data collected from vehicles and traffic lights using VANET technology. Simulation results using the DIVERT simulator show that the proposed traffic light optimization method improves average vehicle speed and reduces waiting times and stopped vehicles at intersections compared to a system with usual fixed-duration traffic lights.
This presentation was made by Phil Carter of ARUP, at the Shared and App Based Transport Innovation seminar, organised by the Institute for Sensible Transport.
This document discusses the potential impacts of autonomous vehicles on cities. It notes that AVs could significantly improve safety by removing human error, increase road capacity, and provide new mobility options. However, it also raises challenges around planning, legislation, public acceptance, and generational issues. A key point is that the price of accessing AVs will influence traffic volumes, public transport use, car ownership patterns, and urban structure. If prices are low it could lead to sprawl, but higher prices may concentrate development and constrain vehicle miles traveled. The document argues cities must carefully consider these impacts to shape a sustainable vision for autonomous vehicles.
TTI Research Scientist, Katie Turnbull, presented on this active research project at the 2016 Smart Transport Symposium in Austin, Texas. Learn more by visiting the TxDOT project page: http://library.ctr.utexas.edu/Presto/content/Detail.aspx?q=MC02ODc1&ctID=M2UxNzg5YmEtYzMyZS00ZjBlLWIyODctYzljMzQ3ZmVmOWFl&rID=MzYy&qcf=&ph=VHJ1ZQ==&bckToL=VHJ1ZQ==&
Ness's Chief Innovation Officer, Kuruvilla Mathew, gives his expert take on how Swarm Intelligence can be employed to fix traffic problems and prevent "Carmageddon"
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/
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/
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Getting proactive rather than reactive in av infrastructure planning, peter h...JumpingJaq
Three scenarios for autonomous vehicle infrastructure planning are proposed: 1) widespread privately owned autonomous vehicles, 2) popular autonomous vehicle sharing reduces private ownership, and 3) a hybrid model of some ownership and some sharing. Transport models could test these scenarios by modifying vehicle ownership and adding autonomous vehicles as a new transport mode. Modeling issues include representing priority autonomous vehicle lanes, circulation patterns, and impacts on congestion, travel demands, and public transit. Published modeling of autonomous vehicles suggests they could significantly increase road capacity and speeds but also vehicle travel if shared autonomous vehicles are not widely adopted.
Presentation on advance traffic engineering.pptxEtahEneji1
This presentation was done to fulfil the course requirement for the pursuit of my M. ENG on the course title: Advanced traffic engineering Course code : (CIV 8331).
Course Lecturer : ENGR. PROF H. M. AlHASSAN
Making the Most of Long-Range Models for Automated and Connected Vehicle Planning poster was presented at the 95th Annual Transportation Research Board (TRB) Annual Meeting in January 2016.
Automated/Connected Vehicle technology (AV/CV) is expected to have significant impacts on travel behavior. The
potential transformative nature of these technologies to alter
or influence future travel behavior and demand is quite significant.
Simulation Based Assignment in PTV Visum - TRB 2017Michael Oliver
A brief introduction to the new Simulation-Based dynamic Assignment (SBA) released in PTV Visum 16, as presented at the Virginia and Washington DC Joint SimCap Meeting, TRB 2017.
This document summarizes a presentation on dynamic traffic modeling applications and research frontiers. It discusses how dynamic traffic assignment models integrate supply-side factors like road closures and demand-side factors like driver route choices to find traffic flow equilibriums. The document outlines applications of dynamic modeling to work zone impact analysis and emissions modeling. It also explores research frontiers involving easier model calibration, integration with activity-based models, and modeling impacts of autonomous vehicles.
The Future of Mixed-Autonomy Traffic (AIS302) - AWS re:Invent 2018Amazon Web Services
How will self-driving cars change urban mobility patterns? This talk examines scientific contributions in the field of reinforcement learning, presented in the context of enabling mixed-autonomy mobility—the gradual and complex integration of autonomous vehicles into existing traffic systems. We explore the potential impact of a small fraction of autonomous vehicles on low-level traffic flow dynamics, using novel techniques in model-free deep reinforcement learning. We share examples in the context of a new open-source computational platform and state-of-the-art microsimulation tools with deep-reinforcement libraries.
Austin Transit Partnership (ATP) unveiled 5 new light rail alternatives for Project Connect on an open house March 21, 2023. These alternatives differ greatly from the original plan proposed to voters in 2020 when the project was overwhelmingly authorized through a property tax increase. The original plan promised an underground light rail system downtown and an airport connection, now both seem to be unlikely.
ATP must re-evaluate core principles of the project to stay on budget, deliver transit connectivity promised to the voters, and create the backbone for a 21st century transit system for the region.
Light rail is too expensive, too slow, lacks regional expansion potential, and will be instantly outdated when implemented.
eBRT is already authorized by the ballot language and the contract with the voters. No additional elections are required for this change. When paired with the future potential of AEV transit, this approach provides the best solution for Austin today and in the future.
eBRT provides a reliable system backstop if AEV technology does not advance as quickly as projected. eBRT by itself would provide better, faster, and cheaper to operate service than LRT.
An AEV system with a tunneled backbone will have major equity benefits across the City and regionally, replacing existing transit lines with superior service.
To maximize the project benefit, the system must provide regional connectivity in addition to connectivity with the City of Austin. The lower cost per mile to deploy eBRT and AEV enables a larger and more connected system to be built today and in the future.
This is an opportunity to cement Austin as the global center for transit innovation.
Impacts of Automated Vehicles - Guidance for Australian and New Zealand Road ...JumpingJaq
This document discusses key actions that road agencies can take to support automated vehicles. It identifies considerations around physical infrastructure, such as ensuring consistency of signs, lines, and asset management. Digital infrastructure needs like vehicle localization and cellular coverage are also addressed. The document recommends road agencies provide consistent guidelines for issues like road works and certification of routes. It suggests road agencies could facilitate more efficient use of networks and optimize infrastructure use as new automated vehicle technologies emerge.
STEP Conference 2015 - Liz Bates, York City Council - Delivering York's 3rd A...STEP_scotland
This document summarizes York, UK's 3rd Air Quality Action Plan. It discusses how the city's historic road network designed for horses and chariots contributes to air quality issues today with more buses, lorries and cars. Previous plans focused on modal shift but did not go far enough. The new plan proposes a Clean Air Zone to control bus emissions based on frequency in the city center. It also discusses converting tour buses and some bus routes to electric vehicles, expanding electric vehicle infrastructure, and developing policies to reduce emissions from new developments and taxis. The goal is to improve air quality and public health in York.
The document discusses improving public transportation service quality through advanced technologies. It describes various public transportation modes and technologies such as vehicle-to-vehicle communication, GPS tracking of buses and trains, lightweight transit buses, transit signal priority, improved intermodal hubs, tracking available parking spaces, and automated rail track inspection. These technologies can help increase reliability, reduce travel times, improve schedules, and enhance the overall user experience of public transportation systems.
April 9 VTA Mountain View Open House project display boardsSCVTA
These were the boards on display at the April 9, 2014 Open Houses in Mountain View about VTA's underway and planned projects in the area. Learn more about the meetings at http://www.vta.org/News-and-Media/Connect-with-VTA/Open-Houses-in-Mountain-View-Generate-Excitement-About-Double-Tracking
This document discusses an online budget path planning mobile application. It aims to analyze budget approaches for tours by determining travel costs in real-time using personalized traveler information from smartphones. The application would help travelers understand costs for different transportation options like trains and flights. It collects data from multiple sources to compare to exhaustive search methods while maintaining service quality. Emergency helpline numbers are also provided for different places in India.
A Dynamic Vehicular Traffic Control Using Ant Colony And Traffic Light Optimi...Kristen Carter
This document proposes a dynamic vehicular traffic control system using ant colony optimization and optimized traffic lights. It aims to reduce traffic congestion in urban areas. The system divides the road network into cells and uses artificial ants to guide vehicles along the least congested paths within each cell. It also proposes a new method for optimizing traffic light timing at intersections based on real-time vehicle count data collected from vehicles and traffic lights using VANET technology. Simulation results using the DIVERT simulator show that the proposed traffic light optimization method improves average vehicle speed and reduces waiting times and stopped vehicles at intersections compared to a system with usual fixed-duration traffic lights.
This presentation was made by Phil Carter of ARUP, at the Shared and App Based Transport Innovation seminar, organised by the Institute for Sensible Transport.
This document discusses the potential impacts of autonomous vehicles on cities. It notes that AVs could significantly improve safety by removing human error, increase road capacity, and provide new mobility options. However, it also raises challenges around planning, legislation, public acceptance, and generational issues. A key point is that the price of accessing AVs will influence traffic volumes, public transport use, car ownership patterns, and urban structure. If prices are low it could lead to sprawl, but higher prices may concentrate development and constrain vehicle miles traveled. The document argues cities must carefully consider these impacts to shape a sustainable vision for autonomous vehicles.
TTI Research Scientist, Katie Turnbull, presented on this active research project at the 2016 Smart Transport Symposium in Austin, Texas. Learn more by visiting the TxDOT project page: http://library.ctr.utexas.edu/Presto/content/Detail.aspx?q=MC02ODc1&ctID=M2UxNzg5YmEtYzMyZS00ZjBlLWIyODctYzljMzQ3ZmVmOWFl&rID=MzYy&qcf=&ph=VHJ1ZQ==&bckToL=VHJ1ZQ==&
Ness's Chief Innovation Officer, Kuruvilla Mathew, gives his expert take on how Swarm Intelligence can be employed to fix traffic problems and prevent "Carmageddon"
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/
Similar to Planning for a World of Connected and Automated Vehicles (20)
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/
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/
This document discusses ongoing research projects related to collaborative sensing and heterogeneous networking leveraging vehicular fleets. Specifically, it discusses:
1) How increased cluster density of vehicles improves overall data rates and reduces variability in individual user rates.
2) Modeling what collaborative sensing systems can "see" or be aware of in obstructed environments and how coverage benefits scale with increased penetration of collaborative vehicles.
3) Developing optimal information sharing policies to maximize situational awareness for autonomous nodes in resource-constrained network environments.
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/
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/
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.
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.
This document discusses modeling strategies for autonomous and connected vehicles. It proposes modifying traditional four-step transportation models to account for autonomous vehicle adoption rates and different trip types. Autonomous vehicle passenger car equivalents and flow ratios are modeled based on vehicle speed, market penetration, and other factors. The document also describes plans for a 4G deployment test bed to demonstrate connected vehicle technologies on managed lanes in Dallas-Fort Worth and Virginia.
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
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.
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.
Professor Robert W. Heath Jr. is the director of UT SAVES (Situation-Aware Vehicular Engineering Systems), which combines expertise in wireless communications, signal processing, and transportation research. UT SAVES collaborates with automotive companies like Honda R&D Americas on projects involving sensing, communication, and analytics for applications such as automated driving. Membership provides access to UT SAVES research and facilities, including graduate research assistants and experimental capabilities in areas like millimeter wave communication and sensor fusion. Current research projects focus on cooperative sensing, vehicle-to-everything communication, and applying 5G cellular networks to driving assistance technologies.
The Business Advisory Council meeting covered the following topics in 3 sentences or less:
The meeting covered updates on education and workforce development programs at the Engineering Education and Research Center including summer internships and distinguished lectures. Research updates were provided on 30 completed projects and 18 ongoing projects covering topics like connected corridors and autonomous vehicles. New proposed research was presented on topics such as video data analytics, traffic signal optimization, and modeling willingness to share trips in autonomous vehicles.
The document discusses managing mobility during the design-build reconstruction of the Dallas Horseshoe highway interchange project. It describes the project's high traffic volumes and constraints. It highlights the contractor's successes in maintaining access and maximizing work during limited closures. It stresses the importance of collaboration between the agency and contractor in developing traffic control plans and finding solutions to difficult situations.
The document summarizes research on the use of natural pozzolans and reclaimed/remediated fly ashes in concrete. Key findings include:
1) Natural pozzolans like pumice and metakaolin reduced heat of hydration and provided good strength and ASR resistance, while zeolites and shale also performed well.
2) Reclaimed and remediated fly ashes reduced heat of hydration and met ASTM standards, with fineness impacting performance.
3) Future research will assess blended fly ashes and develop rapid screening tests for supplementary cementitious materials.
1) Laboratory fatigue tests and field monitoring of in-service high mast illumination poles (HMIPs) in Texas found that pre-existing cracks at the base of galvanized steel poles can grow over time due to wind-induced vibration.
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Planning for a World of Connected and Automated Vehicles
1. Planning for a World of Connected and Automated
Vehicles
Stephen D. Boyles
Associate Professor
Civil, Architectural & Environmental Engineering
The University of Texas at Austin
April 12, 2018
Planning for AVs Boyles
2. The world is changing...
Planning for AVs Introduction Boyles
3. Automated vehicles (AVs) are a perfect example of a “disruptive
technology.”
Planning for AVs Introduction Boyles
4. Automated vehicles (AVs) are a perfect example of a “disruptive
technology.”
This talk focuses on traffic operations, and traveler behavior.
Planning for AVs Introduction Boyles
5. What are the traffic impacts of AVs?
Planning for AVs Introduction Boyles
6. What are the traffic impacts of AVs?
Platooning: If AVs have faster reaction times or communicate with other
vehicles, they can follow at shorter distances.
Planning for AVs Introduction Boyles
7. What are the traffic impacts of AVs?
Platooning: If AVs have faster reaction times or communicate with other
vehicles, they can follow at shorter distances.
New traffic control: Dynamic lane allocation; “microtolling” and
incentives; optimal real-time routing, etc.
Planning for AVs Introduction Boyles
8. What are the traffic impacts of AVs?
Platooning: If AVs have faster reaction times or communicate with other
vehicles, they can follow at shorter distances.
New traffic control: Dynamic lane allocation; “microtolling” and
incentives; optimal real-time routing, etc.
New intersection treatments: The reservation-based intersection.
Planning for AVs Introduction Boyles
9. What are the traffic impacts of AVs?
Platooning: If AVs have faster reaction times or communicate with other
vehicles, they can follow at shorter distances.
New traffic control: Dynamic lane allocation; “microtolling” and
incentives; optimal real-time routing, etc.
New intersection treatments: The reservation-based intersection.
There are legal and technological issues associated with each of these strate-
gies. This talk is viewing them from a “what if” perspective, to guide policy
and plan appropriately.
Planning for AVs Introduction Boyles
10. How can we answer these questions when there is still so much
technological and regulatory uncertainty?
The paradox: the best time to plan is before the technology is here! So,
simulation results should be examined for trends and what-if possibilities,
not treated as a crystal ball.
Planning for AVs Introduction Boyles
11. There may also be unintended consequences.
In the 19th century, improved technology led to more efficient ways to
burn coal.
Planning for AVs Introduction Boyles
12. Making factories more efficient increased coal consumption, rather than
decreased it.
This is known as the Jevons paradox.
Planning for AVs Introduction Boyles
13. Does the Jevons paradox exist in transportation systems?
Planning for AVs Introduction Boyles
14. Does the Jevons paradox exist in transportation systems?
If we make travel more efficient, will demand for travel increase?
Planning for AVs Introduction Boyles
15. Does the Jevons paradox exist in transportation systems?
If we make travel more efficient, will demand for travel increase?
Could induced demand counteract all of the benefits of automated vehicle
technology?
Planning for AVs Introduction Boyles
16. Does the Jevons paradox exist in transportation systems?
If we make travel more efficient, will demand for travel increase?
Could induced demand counteract all of the benefits of automated vehicle
technology?
Transportation systems are complex systems, with many components
which interact heavily with each other.
Planning for AVs Introduction Boyles
19. All of this points to a need to do quantitative modeling of AVs and their
impacts. This talk provides a few examples of how this might be done.
1 Traffic modeling for AV capabilities
2 What if we replace signals with reservation-based control?
3 What if we allow AVs to be “driven empty”?
4 What are the implications for planning today?
Planning for AVs Introduction Boyles
20. Collaborators and Acknowledgements
This talk includes contributions from Dr. Kara Kockelman, Dr. Peter
Stone, Michael Levin, Rahul Patel, Chris Melson, and Hannah Smith.
This research was sponsored by the Texas Department of Transportation,
National Science Foundation, Federal Highway Administration, and the
Data-Supported Transportation Operations and Planning Center.
Planning for AVs Introduction Boyles
22. How might we model the effects of platooning on roadway capacity?
Planning for AVs Traffic modeling for AVs Boyles
23. How might we model the effects of platooning on roadway capacity?
How might we model the effects of dynamic lane allocation?
Planning for AVs Traffic modeling for AVs Boyles
24. How might we model the effects of platooning on roadway capacity?
How might we model the effects of dynamic lane allocation?
How might we model the effects of reservation-based intersections?
Planning for AVs Traffic modeling for AVs Boyles
25. How might we model the effects of platooning on roadway capacity?
How might we model the effects of dynamic lane allocation?
How might we model the effects of reservation-based intersections?
In particular, can we find models simple enough to allow us to simulate large
regions? Small corridor models can omit complex interactions (like elastic
demand)
Planning for AVs Traffic modeling for AVs Boyles
26. Fundamental diagram
k
q
Q(k)
qmax
kc kj
u
The fundamental traffic flow diagram relates vehicle density (veh/mi) to
vehicle flow (veh/hr). The diagram can also produce vehicle speeds and
shockwave speeds.
Planning for AVs Traffic modeling for AVs Boyles
27. Car-following perspective Assume that in congested conditions, the time
headway between vehicles is determined by the safe following distance
(accounting for reaction time)
Then we can derive a new speed-density relationship, and translate this to
a new fundamental diagram.
Planning for AVs Traffic modeling for AVs Boyles
29. In these diagrams, we can directly see the capacity increase. Also, the
congested portion of the diagram has a steeper slope. What does this
mean?
Planning for AVs Traffic modeling for AVs Boyles
30. Cell transmission model
Daganzo’s cell transmission model is a practical way of modeling traffic
flow on large networks, given the shape of the fundamental diagram.
5 4 7
x=0 1 2 3
n(0,t) n(1,t) n(2,t)
3 0
y(1,t) y(2,t)
Roadway segments are divided into cells, and vehicles propagate from one
cell to the next.
Planning for AVs Traffic modeling for AVs Boyles
31. Reservation-based intersections
The first simulation model for reservation-based control was AIM
(Autonomous Intersection Management)
This microsimulator is very detailed, but is too complex to efficiently
model large networks.
Planning for AVs Traffic modeling for AVs Boyles
32. The conflict region model provides a simpler way to approximate this type
of roadway control.
Each region permits a certain maximum flow rate. Vehicles from each
approach are assigned trajectories as long as all of these limits are
satisfied. Any remaining vehicles are queued.
Planning for AVs Traffic modeling for AVs Boyles
34. Early experiments show that reservation-based systems can offer dramatic
reductions in control delay.
Under oversaturated conditions, delay can be reduced by 1–2 orders of
magnitude.
Planning for AVs Reservation-based intersections Boyles
35. What happens when we apply them on real networks?
Planning for AVs Reservation-based intersections Boyles
36. What happens when we apply them on real networks?
(Real = multiple intersections, asymmetric demand, nonuniform roads,
drivers changing routes, etc.)
Planning for AVs Reservation-based intersections Boyles
38. On this network, replacing signals with reservation-based intersections had
substantial benefits.
Planning for AVs Reservation-based intersections Boyles
39. Lamar & 38th Street
Planning for AVs Reservation-based intersections Boyles
40. On this network, reservations actually performed worse than the signal.
Planning for AVs Reservation-based intersections Boyles
41. This is largely because reservations are granted in first-come, first-serve
order.
Planning for AVs Reservation-based intersections Boyles
42. This is largely because reservations are granted in first-come, first-serve
order.
In a network with “asymmetric” demand and capacity, this may not be the
right strategy.
Planning for AVs Reservation-based intersections Boyles
43. This is largely because reservations are granted in first-come, first-serve
order.
In a network with “asymmetric” demand and capacity, this may not be the
right strategy.
In this kind of network, an optimal reservation can’t be any worse than a
signal — since you can replicate a signal by how you grant reservations.
Planning for AVs Reservation-based intersections Boyles
44. Similar results were seen when applying this to a freeway corridor.
Planning for AVs Reservation-based intersections Boyles
45. Route choice can also cause problems: we can replicate Daganzo’s paradox
with reservation-based controls.
Planning for AVs Reservation-based intersections Boyles
46. Lesson: Used carefully, reservations can dramatically decrease delay in
real networks. Used carelessly, they may not help... and might even make
things worse.
Planning for AVs Reservation-based intersections Boyles
48. Vehicle trip distribution: mixed fleet
0
2000
4000
6000
8000
10000
12000
14000
16000
7:00 AM 7:30 AM 8:00 AM 8:30 AM 9:00 AM 9:30 AM 10:00 AM
Totalvehicletrips
Departure time
Repositioning
No repositioning
Planning for AVs Other AV policies Boyles
49. Average road speeds: mixed fleet
Without
repositioning
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
7:00 AM 7:30 AM 8:00 AM 8:30 AM 9:00 AM 9:30 AM 10:00 AM
Averagespeedratio
Time
Local roads
Arterials and collectors
Freeways
With
repositioning
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
7:00 AM 7:30 AM 8:00 AM 8:30 AM 9:00 AM 9:30 AM 10:00 AM
Averagespeedratio
Time
Local roads
Arterials and collectors
Freeways
Planning for AVs Other AV policies Boyles
50. AVs as a competitor to public transit
Planning for AVs Other AV policies Boyles
52. Automated vehicles have arrived!
The future is bright: There is the potential for substantial
improvements in operations and safety.
Planning for AVs Conclusions Boyles
53. Automated vehicles have arrived!
The future is bright: There is the potential for substantial
improvements in operations and safety.
The future is scary: There may be unintended consequences —
remember the lesson of the Jevons paradox.
Planning for AVs Conclusions Boyles
54. Automated vehicles have arrived!
The future is bright: There is the potential for substantial
improvements in operations and safety.
The future is scary: There may be unintended consequences —
remember the lesson of the Jevons paradox.
The future is now: It is encouraging to see transportation
professionals being proactive about planning for AVs.
Planning for AVs Conclusions Boyles
55. Automated vehicles have arrived!
The future is bright: There is the potential for substantial
improvements in operations and safety.
The future is scary: There may be unintended consequences —
remember the lesson of the Jevons paradox.
The future is now: It is encouraging to see transportation
professionals being proactive about planning for AVs.
The future is ours: Quantitative models play a critical role in
guiding policy and regulation. Transportation systems are complex,
and can behave counterintuitively. Researchers are developing the
tools we need to realize the potential of AVs.
Planning for AVs Conclusions Boyles