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Updated: 59 min 15 sec ago

Institute of Transportation Studies Friday Seminar, Aug 29

59 min 15 sec ago
The presentation explores how the segregation of distinct vehicle classes on a roadway can improve travel conditions for all of the classes. Insights come using freeway carpool lanes as case studies. Spatiotemporal study of real sites shows (i) how the activation of a continuous-access carpool lane triggers reductions in vehicle lane-changing maneuvers, and (ii) how the reduced lane-changing can “smooth” and increase bottleneck discharge flows in a freeway’s regular lanes. Theoretical analysis predicts that, thanks to this smoothing effect, even underused carpool lanes can diminish both the people-hours and the vehicle-hours traveled on a freeway. Relevance to bus lanes is briefly discussed. Further insights come via critiques of certain practices that degrade the effectiveness of carpool lanes. Spatiotemporal traffic data reveal that a policy aimed at improving carpool-lane speeds has backfired, owing to a friction effect. The policy mandates the eviction of select fuel-efficient hybrid vehicles from carpool lanes. These evictions have caused queues to expand in regular lanes during the rush. And these expanded queues, in turn, slow vehicles in the adjacent carpool lanes. Spatiotemporal data further show that efforts to combat the friction effect by deploying limited-access carpool lanes can also backfire, because the designs for these lanes are prone to creating bottlenecks.

Institute of Transportation Studies Friday Seminar, Sep 5

59 min 15 sec ago
We present methods to predict the time required for an ambulance to drive to the scene of an emergency. This forecast is critical for deciding how many ambulances should be deployed at a given time, where they should be stationed, and which ambulance should be dispatched to an emergency. Specifically, we predict the distribution of lights-and-sirens ambulance driving time on an arbitrary route in a road network, using automatic vehicle location data and trip information from previous ambulance trips. We train a statistical model using a computationally efficient procedure; challenges include the large size of the network and the lack of trips in the data that follow the route of interest. We demonstrate the operational impact of our methods using data from Toronto Emergency Medical Services, and discuss ongoing efforts to incorporate our methods into a software package used by ambulance services.

Institute of Transportation Studies Friday Seminar, Sep 12

59 min 15 sec ago
Roadway infrastructures, including pavements, bridges, and signs are deteriorating rapidly due to material aging, improper usage, harsh environments, and damages resulting from natural or man-made hazards. With the advancement of sensor technologies, it become feasible to collect the large-scale in-field detailed infrastructure data, such as 3D pavement surface data, using high-performance cameras, lasers, LiDARs, and Inertial Navigation System (INS) to gain better insight understanding of the large-scale in-filed infrastructure behavior. This talk first presents a framework for the sensor-based and spatially-enabled next generation Intelligent and sustainable infrastructure management system, including the key components of data acquisition, automatic information extraction, data integration, and intelligent infrastructure management. An intelligent sensing system has been developed, using 2D Imaging, Laser, LiDAR, and GPS/GIS Technologies with artificial intelligent and pattern recognition to automatically detect pavement surface distress, including rutting, cracking, raveling, etc. along with their detailed level characteristics for determining pavement health condition. The availability of high-resolution roadway images, 3D pavement surface data, and 3D LiDAR data has brought us a great opportunity and new challenges. This calls for a new concept to model this detailed level of big data for revealing new values for infrastructure management. First, we need to effectively extract valuable decision-support from this big data. For cracking, an innovative crack fundamental element (CFE) model that is a topological representation of cracks to support crack classification, diagnosis, and intelligent pavement management will be presented; this CFE provides researchers a mathematical foundation for modeling large-scale, in-field pavement/infrastructure crack characteristics to study crack propagation behavior at multiple scales will be presented. Examples of developing an innovative and sustainable pavement preservation method and developing intelligent crack sealing planning using emerging sensor technologies will also be presented.

Wireless Sensor Networks for Flash Flood and Traffic Monitoring in Urban Environments, Sep 24

59 min 15 sec ago
This talk describes a new architecture for distributed flash flood and traffic monitoring in cities using combined Eulerian and Lagrangian sensing. Unlike current traffic sensor networks, the architecture maintains user privacy by using a distributed computing approach.

In this system, probe vehicles broadcast speed data to local nodes, which estimate vehicles location. Fixed sensors also measure traffic parameters, and all traffic data is forwarded to local coordinator nodes. Using the classical LWR traffic flow model, we show that the traffic reconstruction problem results in a set of MILPs, which can be efficiently solved by all nodes using distributed computing, the coordinator node supervising all computations. With this approach, user privacy is maintained, in the sense that no vehicle track data is forwarded beyond the radio range of the node cluster.


Biography:
Christian Claudel is an assistant professor of Electrical Engineering and Mechanical engineering at KAUST. He received the PhD degree in EECS from UC Berkeley in 2010, and the Ms degree in Plasma Physics from Ecole Normale Superieure de Lyon in 2004. He received the Leon Chua Award from UC Berkeley in 2010 for his work on Mobile Millennium. His research interests include control and estimation of distributed parameter systems, wireless sensor networks and environmental sensing systems

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Live broadcast at http://video.citris.berkeley.edu/playlists/webcast. Ask questions live on Twitter: #CITRISRE. All talks may be viewed on our YouTube channel

The schedule for the semester can be found on the CITRIS site.

Webviewing:
UC Davis: 1065 Kemper Hall
UC Merced: COB 322-Willow
UC Santa Cruz: SOE E2 Building, Room 595B

Registration through eventbrite is required for lunch at UC Berkley.

Institute of Transportation Studies Friday Seminar, Sep 26

59 min 15 sec ago
This talk presents a robust control framework applied to transportation problems in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi equation, we pose the problem of controlling the state of the system on a network link, using boundary flow control, as a Linear Program. Unlike many previously investigated transportation control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e. discontinuities in the state of the system). This framework can handle networked control problems or robust control problems, and is extremely fast, since it leverages the intrinsic properties of the Hamilton-Jacobi equation used to model the state of the system.

Communicating Environmental Risks: Are Ex-Ante Predictions of Benefits and Costs Being Accurately Estimated?, Oct 1

59 min 15 sec ago
Energy and Resources Group Fall 2014 Colloquium Series (ER295)

Institute of Transportation Studies Friday Seminar, Oct 24

59 min 15 sec ago
Forecasts will play an increasingly important role in the the next generation of autonomous and semi-autonomous systems. Applications include transportation, energy, manufacturing and healthcare systems. Predictions of systems dynamics, human behavior and environment conditions can improve safety and performance of the resulting system. However, constraint satisfaction, performance guarantees and real-time computation are challenged by the growing complexity of the engineered system, the human/machine interaction and the uncertainty of the environment where the system operates.

Institute of Transportation Studies Friday Seminar, Oct 31

59 min 15 sec ago
Adaptive traffic signal control systems are an emerging technology for urban arterial operations. This presentation focuses on the current status of adaptive traffic signal control system applications in the U.S. An overview of adaptive signal system deployments is first presented. Some insights of the advantages and drawbacks will be given. The results from some field before-after studies will be discussed. One particular issue is related to how a comparison is made against the time-of-day coordination plans. Potential biases can result if the comparison is against non-optimal time-of-day coordination plans. Signal optimization software does not necessarily generate truly optimized signal timing plans. Errors often result during the timing implementation process due to various factors. The presentation will also include a demo of a Smartphone based application called SMRT (Signal Management and Re-timing Tool) for facilitating field signal timing diagnoses and implementations.

Institute of Transportation Studies Friday Seminar, Nov 7

59 min 15 sec ago
Transportation-related air pollution, GHG emissions and energy problems are a significant issue in the U.S., China, and across the world. The World Health Organization estimates that urban air pollution causes 200,000 deaths per year worldwide and that it will be responsible for 8 million premature deaths from 2000 to 2020. Sacrificing transportation needs for environmental quality is simply infeasible since transportation provides a vital wheel for economic development.

Institute of Transportation Studies Friday Seminar, Dec 12

59 min 15 sec ago
Despite growing interest in urban consumption amenities, little is known about their origin and importance. This paper estimates the consumption value of urban density by combining travel microdata with Google’s local business data. This dataset allows to integrate travel costs into a discrete choice model for restaurants. I find that in high density areas, consumers enjoy large benefits from visiting places that they prefer, and relatively smaller gains from shorter trip time. These results demonstrate the importance of non-tradable consumption in explaining the value of cities, and represent the first estimates of the gains from variety in the service sector.