Research

Evaluation of Sidewalk Autonomous Delivery Robot Interactions with Pedestrians and Bicyclists

Posted by ab3824 on September 29, 2022

Sponsor: U.S. Department of Transportation, Pacific Southwest Region 9 UTC

PI: Steven R. Gehrke

Co-PIs: Edward J. Smaglik and Brendan J. Russo

Project Report: https://www.metrans.org/assets/research/psr_finalreport_21-16.pdf

Information and communication technology advancements and an increased demand for contactless deliveries after the Covid-19 pandemic outbreak resulted in the growing adoption of automated delivery services. Across university campuses, the deployment of sidewalk autonomous delivery robots (SADRs) has provided students, staff, and faculty a convenient last-mile delivery option. However, SADRs traverse campuses… Read more

Analysis of the Safety Impacts of the Left-In Left-Out Median Opening Treatment at Intersections/Driveways

Posted by ma3932 on November 30, 2021

Sponsor: City of Scottsdale

PI: Dr. Brendan Russo

Dates: 01/01/2021-11/30/2021

Project Report:
https://www.scottsdaleaz.gov/Assets/ScottsdaleAZ/Transportation/Analysis+Left+In+Left+Out+-+2021.pdf

Over the past few decades the City of Scottsdale, Arizona has installed left-in/left-out (LILO) median opening treatments at approximately sixty intersections/driveways; typically at three-leg intersections or driveways on arterials with medians where the minor leg is stop-controlled. The treatment is relatively unique and consists of a channelizing island with an acceleration length provided for vehicles turning left out of the minor road (these left-turning vehicles then merge with major… Read more

Automated Identification of Traffic Detector Malfunctions

Posted by sj765 on December 30, 2020

Sponsor: Oregon Department of Transportation (ODOT)

PI: Dr. Edward Smaglik, Co-PI: Dr. Brendan Russo

Project Report: https://www.oregon.gov/odot/Programs/ResearchDocuments/SPR837DetectorMalfunctionFinalReport.pdf

Start Date: January, 2020

Proper operation of transportation facilities is dependent upon reliable, accurate traffic sensing equipment; faulty detection equipment can result in unsafe and/or inefficient operation. A recent ODOT project, SPR-781 entitled, “Improving Adaptive / Responsive Signal Control Performance: Implications of Non-Invasive Detection and Legacy Timing Practices” uncovered wide spread issues of data quality irrespective of detector technology (within… Read more

Evaluation of Emerging Transportation Technologies: KHA Emergency Vehicle Preemption in the City of Mesa / Maricopa County

Posted by sj765 on

Sponsor: Maricopa Association of Governments

PI: Dr. Edward Smaglik, Co-PI: Dr. Brendan Russo

Start Date: February, 2020

As a member of the Tri-University Task Force (NAU, ASU, and UA), NAU is leading an evaluation of Kimley-Horn and Associates’ Smart Priority technology, funded by the Maricopa Association of Governments and installed at intersections in the City of Mesa, Arizona and Maricopa County.  The product is a software module that interfaces directly between the Fire Department’s Computer Aided… Read more

Evaluation of Emerging Transportation Technologies: Rhythm Engineering Cyclops Cameras in the City of Peoria

Posted by sj765 on December 29, 2020

Sponsor: Maricopa Association of Governments

PI: Dr. Edward Smaglik, Co-PI: Dr. Brendan Russo

Start Date: January, 2020

As a member of the Tri-University Task Force (NAU, ASU, and UA), NAU is leading an evaluation of Rhythm Engineering’s code|Green hardware and software products, funded by the Maricopa Association of Governments and installed in the City of Peoria, Arizona.  The product is a camera and software combination that can develop presence detection, vehicle counts, and timing plans.  The… Read more

Development of Cost-Effective Sensing Systems and Analytics (CeSSA) to Monitor Roadway Conditions and Mobility Safety

Posted by sj765 on

Sponsor: PSR9 UTC

PI: Dr. Chun-Hsing Ho

Project Report: https://www.metrans.org/assets/research/psr-19-20_ho_final-report.pdf

Dates: 01/06/2020-01/05/2021

The project presents a pavement sensing system along with a list of promising computing models that can be used to predict pavement conditions using a vehicle-based sensing technology. The project started with data acquisition obtained from the previous field data collection followed by a series of data computing using machine leaning methods to determine a promising computing algorithm. Subsequently, statistical analyses were performed to evaluate the… Read more

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