University of Washington

Yinhai Wang

Yegor Malinovskiy

Hao Yang

Nancy L Nihan

Chenxi Liu

Yao-Jan Wu

Ziyuan Pu

Patikhom O Cheevarunothai

Ruimin Ke

A mobile roadway sensing unit can include an image sensor that can sense image data, a network sensor that can acquire wireless signal data, and a weather sensor that can sense weather condition data. The mobile roadway sensing unit can include a processor and a memory. The memory can include instructions executable by the processor for causing the processor to acquire the sensor data including at least one of the image data, the wireless signal data, or the weather condition data from the weather sensor, wherein the sensor data represents one or more current conditions proximate the mobile roadway sensing unit. The processor can generate processed data based on the sensor data The processor can generate, based on the processed data, a notification corresponding to the one or more current conditions. The processor can provide the notification to one or more external electronic devices.

Mobile roadway sensing

A facility comprising methods and systems for discovering travel patterns based at least in part on data collected from a plurality of dynamic and/or mobile sensor devices is disclosed. The mobile sensor devices sense or detect the presence of nearby devices and record information about each detection, such as the location, the date and time, and an identifier of the detected device and provide this information to the facility. Thus, the facility can leverage the communication capabilities of existing mobile devices and networks to provide a broad, wide-ranging, scalable sensor network that detects the presence of individual devices. By recording and analyzing the encounter data received from various mobile sensor devices, the facility can ascertain or estimate the path of devices and their associated users. Given the availability of mobile devices and mobile networks, the disclosed techniques can be deployed without a substantial investment in infrastructure and maintenance.

Travel pattern discovery using mobile device sensors

Systems and methods for detecting and tracking objects, such as motor vehicles, within video data. The systems and method analyze video data, for example, to count objects, determine object speeds, and track the path of objects without relying on the detection and identification of background data within the captured video data. The detection system uses one or more scan lines to generate a spatio-temporal map. A spatio-temporal map is a time progression of a slice of video data representing a history of pixel data corresponding to a scan line. The detection system detects objects in the video data based on intersections of lines within the spatio-temporal map. Once the detection system has detected an object, the detection system may record the detection for counting purposes, display an indication of the object in association with the video data, determine the speed of the object, etc.

Video-based vehicle detection and tracking using spatio-temporal maps

Traffic induction loop simulator (LOOPSIM) software, and a system and method designed for traffic research and education. The system is able to generate output signals in response to previously recorded real traffic events or simulated traffic. If directly connected to a controller cabinet or a traffic event data collection system, LOOPSIM can be used to simulate output signals from loop detectors and to test algorithms executable in the controller, or to test the performance of a traffic data collection/monitoring system. LOOPSIM can simulate different predefined patterns of traffic distributions and replay loop event data recorded by a detector event data collection (DEDAC) system or an advanced loop event data analyzer (ALEDA) at a pace responsive to controller feedback. Using LOOPSIM, many traffic system tests can be conducted for multi-purpose traffic control applications, and traffic arrival patterns can be generated for researchers and educators to test theoretical models.

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Seattle, WA, United States of America

Yinhai Wang