10,748 research outputs found
Reconstructing the Traffic State by Fusion of Heterogeneous Data
We present an advanced interpolation method for estimating smooth
spatiotemporal profiles for local highway traffic variables such as flow, speed
and density. The method is based on stationary detector data as typically
collected by traffic control centres, and may be augmented by floating car data
or other traffic information. The resulting profiles display transitions
between free and congested traffic in great detail, as well as fine structures
such as stop-and-go waves. We establish the accuracy and robustness of the
method and demonstrate three potential applications: 1. compensation for gaps
in data caused by detector failure; 2. separation of noise from dynamic traffic
information; and 3. the fusion of floating car data with stationary detector
data.Comment: For more information see http://www.mtreiber.de or
http://www.akesting.d
Highlights of the DVRPC FY2017 Transportation Improvement Program (TIP) for Pennsylvania (FY2017-2020)
The highlights for the DVRPC FY2017 Transportation Improvement Program (TIP) for Pennsylvania briefly describes the region's TIP as a federally required, Multi-modal, four year contrained program of planned transportation infrastructure investment. It contains a summary listing of all transit, highway, bridge, bicycle, pedestrian, and freight related projects in DVRPC's Pennsylvania region which will seek federal funding in fiscal years (FY) 2017 to 2020. The Highloights document includes a finacial summary of costs by country an by operator, as well as a section on commenting on the Draft DVRPC FY2017 TIP for Pennsylvania.
Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology
INE/AUTC 10.0
Regional Data Archiving and Management for Northeast Illinois
This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor
and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the
regional transportation agencies, from both technical and business perspectives, about building such a comprehensive
transportation information system. Several implementation alternatives are identified and analyzed. This research is carried
out in three phases.
In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a
thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect
information on all data elements that they store, including the format, system, and granularity. Their perception of a data
archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the
database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and
examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We
estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the
hardware, software, labor, and resource requirements. We also identify possible revenue opportunities.
A few implementation options for the archive system are summarized in this report; namely:
1. System hosted by a partnering agency
2. System contracted to a university
3. System contracted to a national laboratory
4. System outsourced to a service provider
The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe
Automated Driving Systems Data Acquisition and Processing Platform
This paper presents an automated driving system (ADS) data acquisition and
processing platform for vehicle trajectory extraction, reconstruction, and
evaluation based on connected automated vehicle (CAV) cooperative perception.
This platform presents a holistic pipeline from the raw advanced sensory data
collection to data processing, which can process the sensor data from multiple
CAVs and extract the objects' Identity (ID) number, position, speed, and
orientation information in the map and Frenet coordinates. First, the ADS data
acquisition and analytics platform are presented. Specifically, the
experimental CAVs platform and sensor configuration are shown, and the
processing software, including a deep-learning-based object detection algorithm
using LiDAR information, a late fusion scheme to leverage cooperative
perception to fuse the detected objects from multiple CAVs, and a multi-object
tracking method is introduced. To further enhance the object detection and
tracking results, high definition maps consisting of point cloud and vector
maps are generated and forwarded to a world model to filter out the objects off
the road and extract the objects' coordinates in Frenet coordinates and the
lane information. In addition, a post-processing method is proposed to refine
trajectories from the object tracking algorithms. Aiming to tackle the ID
switch issue of the object tracking algorithm, a fuzzy-logic-based approach is
proposed to detect the discontinuous trajectories of the same object. Finally,
results, including object detection and tracking and a late fusion scheme, are
presented, and the post-processing algorithm's improvements in noise level and
outlier removal are discussed, confirming the functionality and effectiveness
of the proposed holistic data collection and processing platform
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