Riverside, California, with a population upwards of 320,000, is one of the most diverse and fastest growing cities in California and as such serves as an ideal model to demonstrate the application of connected infrastructure technology. Significant growth is projected for the next six years that will transform the economic complexion of the city, especially along University Avenue and the Eastside district. The City has designated this area – a highly disadvantaged district where congestion, air pollution, and underutilized space is abundant – as Riverside’s Innovation District.
As a part of the City of Riverside’s proposed Innovation District, CE-CERT researchers will partner with the city to equip a six-mile section of University Avenue between UC Riverside and downtown Riverside with integrated technologies such as connected vehicles and traffic signals, vehicle-to-grid interaction, and new generation air quality sensors. This “innovation corridor” represents a sustainable mobility solution that will significantly benefit California’s disadvantaged communities by creating a healthy, efficient, integrated city for the world to emulate.
Projects
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Smart City Laboratory
AutoTrac Competition
This work focuses on building up a small-scale autonomous vehicle traffic environment for testing connected driving. The small-scale vehicles are modified from RC car, and equipped with onboard computer, 1D Lidar, and fisheye camera. Also, the vehicles are power by Robot Operating System (ROS), and are capable of communicating with each other. Two different tracks are avaliable for testing both freeway and urban scenarios.
Lead Faculty: Zhouqiao Zhao, Xuanpeng Zhao, Zhensong Wei, Yu Jiang, Pingbo Ruan, Shangrui Liu, Dr. Chao Wang, Dr. Guoyuan Wu, Dr. Kanok Boriboonsomsin, Dr. Matthew Barth
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Innovation Corridor
coming soon
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Strategic Growth Council
Coming soon
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Active Transportation
Developing an Aerial-Image-based Approach for Creating Digital Sidewalk Inventories
To support the active mobility, extensive work has been focused on planning, maintaining and enhancing the infrastructure, such as sidewalks. A significant amount of these efforts have to go for the setup and maintenance of the sidewalk inventory on a certain geographic scale (e.g., citywide, statewide). To address the stated problem, it is proposed herein to develop an aerial-image-based approach that can: 1) extract the features of sidewalks based on digital vehicle road network; 2) overlay the initial sidewalk features with aerial imagery and extract aerial images around sidewalk area; 3) apply a machine learning algorithm to classify sidewalks images into two major categories, i.e., concrete-surface present or sidewalks missing and 4) construct a connected sidewalk network in a time-efficient and cost-effective manner. A deep convolutional neural network is applied to classify the extracted sidewalk images. The learning algorithm achieves 97.22% total predication rate for the test set and 92.6% total predication rate in the blind test. The proposed method takes full advantage of available data sources and build on top of existing roadway network to digitize sidewalk.
Lead Faculty: Dr. Ji Lou, Dr. Guoyuan Wu, Dr. Zhensong Wei
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Managed Lanes
Evaluating Alternative Design of Geometric Configuration for High-Occupancy Vehicle (HOV) Facilities in California
Most special-use freeway lanes, such as High Occupancy Vehicle (HOV) lanes, have traditionally been designed with either limited access or continuous access control from the adjacent general-purposed (GP) lanes. Studies have shown the advantages and disadvantages of each design in terms of safety, mobility, environment, and enforcement, among other factors. With a focus on improving the operational performance of HOV facilities, this paper proposes a new design called partially limited access control where the continuous access is mostly designated along the freeway to achieve higher travel speed while buffers between the HOV lane(s) and the adjacent GP lanes are strategically placed on selected freeway segments to accommodate higher throughput on those segments. The placement of buffers primarily aims to reduce the impact of HOV cross-weave flow on the capacity of GP lanes. In this research, a methodology for determining the location and length of buffers in the partially limited access control has been developed. A case study is performed along a 13-mile section of HOV facility on SR-210 E in Southern California, which is coded and evaluated in traffic microsimulation. The results show that the partially limited access control increases the throughput (represented by total vehicle miles traveled or VMT) and decreases the delay (represented by total vehicle hours traveled or VHT) of the freeway as compared with either the limited access or continuous access control. As a result, the overall efficiency (represented by average travel speed calculated as VMT/VHT) of the freeway with partially limited access HOV facility is 21% and 6% higher than that of the freeway with limited access and continuous access HOV facility, respectively, under the baseline traffic demand.
Evaluating Alternative Design of Geometric Configuration for High-Occupancy Vehicle (HOV) Facilities in CaliforniaLead Faculty: Dr. Kanok Boriboonsomsin