Projects

PI's Background: Dr. Yushin Ahn

Project's date: December, 2024

Abstract: Lately, numerous State transportation agencies have been incorporating Mobile Laser Scanning technologies into their operation. Integrated Mobile Laser Scanning with camera, GNSS and IMU, in the current terms, Mobile Terrestrial Laser Scanning (MTLS) yields comprehensive field data in the form of a point cloud. When applied on roadways, this technology enables the 3D visualization, identification, and extraction of various roadside assets, all facilitated by skilled operators.

From our prior FSTI project focused on traffic sign detection using Mobile LiDAR point cloud, we successfully obtained 8 miles' worth of MTLS data from the Caltrans Office of Land Surveyors and their contacts. Our findings demonstrated the detectability of traffic signs within the data, accurately positioning them on the map. Additionally, our research highlighted the potential of leveraging MTLS data for the enhanced detection of various roadside features, including concrete barriers, road markings, slopes, and more.. This study delves into three key aspects. Firstly, it explores the extraction of cross-sectional features like lane slope (or crown), and superelevation on horizontal curves. Secondly, it involves the assessment of road conditions, including profiling along the lanes and identifying damaged road sections. Lastly, the study explores the application of deep learning for point cloud classification in MTLS data.

Report Coming Soon

PI's Background: Dr. Hovannes Kulhandjian

Project's date: December, 2024

Abstract: The scorching wildfires of 2017 and 2018 cast California into a devastating inferno, seizing national attention and leaving entire communities in ruins. The ferocious Thomas fire, tearing through Ventura and Santa Barbara Counties, and the relentless Tubbs fire, laying waste to Napa, Sonoma, and Lake Counties, unleashed destruction upon more than 7,200 structures and devoured a staggering 318,000 acres in 2017. Then, in 2018, the Woolsey fire's unforgiving blaze scarred 1,990 structures across nearly 97,000 acres in Los Angeles and Ventura Counties [1]. The state faced a historic wildfire season in 2020, including the August Complex Fire, which surpassed the Mendocino Complex as the largest recorded wildfire in California's history.

These wildfires not only resulted in substantial property damage and loss of life but also have severe environmental impacts, affecting air quality, wildlife, and ecosystems across the state. The scale and frequency of these wildfires highlight the urgency for innovative approaches in wildfire prevention, early detection, and efficient response strategies to mitigate future catastrophes.

Several research works have been conducted on wildfire detection, spread estimation, wildfire evacuation, search and rescue operations [2-5]. However, to the best of our knowledge there are no unified framework that tries to address several of those important issues simultaneously on a single framework. We are confident that implementing our proposed framework would significantly benefit wildfire control authorities.

Report Coming Soon

PI's Background: Dr. Jaymin Kwon

Project's date: December, 2024

Abstract: The main objective of the StarTraq 2024 project is to create safer communities and provide greater opportunities for the promotion of active transportation modes such as biking and walking, increasing access to transit. Through research on spatio-temporal analyses of roadside transportation-related air quality, people of all abilities and socioeconomic levels can enjoy the accessibility of transit and choices of healthier transit to improve their quality of life. The study aims to provide public information on transportation-related environmental exposure in a timely and relevant so that people can use the knowledge for decision-making of design and application of new materials and technologies to improve the public health of Californians. The proposed StarTraq 2024 project will construct the geospatial time-series database that connects and illustrates the historical changes in air quality impacted by transportation-related air pollutants (TRAPs), commercial/private vehicle miles traveled (VMT), roadway infrastructure network/land use in urban and rural areas, regional socio- economic data, and human disease and health data.

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PI's Background: Dr. Jorge Pesantez

Project's date: December, 2024

Abstract: The main objective of this project is to develop and apply a predictive model to forecast traffic congestion events using deep neural network architectures. Based on a predictive model that reports future traffic conditions using public data sets, the project goal is to provide an effective model to city planners and transit agencies for enhancing planning and management activities. This proposed project encompasses the objective by developing an effective tool to optimize passenger and freight movements with innovative data models and advanced congestions management tools.

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PI's Background: Professor Holly Sowles

Project's date: December, 2024

Abstract: As a continuation from Fall 2023, the Intelligent Interior Design Visualization Lab proposes to complete the design development of the remaining four bike shelters for the Fresno City Bike Campus Project. Additionally, the Vis Lab will create a heat island toolkit which is a framework to analyze multiple design strategies that mitigate extreme temperatures on surfaces. We will continue creating complementary bike shelters that act as backdrops to the Scribbles Bike Sculptures while complementing the local district's architectural aesthetics. Each design outcome offers shade, charging stations, water access, and bike repair facilities. The heat island toolkit will analyze and attempt to reduce extreme temperatures on the pathways and at the trailheads of the Fresno City Bike Campus. After developing the toolkit, we will engage local districts in a democratized design process to gather local design preferences for community placemaking. After determining the preferred toolkit for each district, we will 3D print modular prototypes of both the toolkits and the bike shelters.

Report Coming Soon

PI's Background: Dr. Yertai Tanai

Project's date: December, 2024

Abstract: As an important economic hub and the fifth largest city in California, Fresno faces many challenges that one would not expect to encounter in the heart of the San Joaquin Valley, one of the most productive agricultural regions in the world. One of such challenge lies on Blackstone Avenue, an over eight-mile-long commercial corridor that connected the downtown core to the expanding suburbs. It has been known for crime, poverty, and vacant buildings that together speak of failed land use policy and lack of development ingenuity.

In a transformative move, in 2015, Fresno Area Express (FAX) embarked on a constructive journey by initiating the construction of a 15.7-mile Bus Rapid Transit (BRT) line known as the Q Line. The Q Line connects North Fresno, downtown Fresno, and Fresno's southeast growth area via Blackstone Avenue and Kings Canyon Road. The BRT service replaced existing local bus service in the corridor and offered decreased travel times through fewer stops and more frequent service. The project included transit signal priority, queue jump lanes, 27 stations, 17 low-floor compressed natural gas low-emission BRT vehicles, boarding platforms, fare machines, real time passenger information, and special branding to create a unique identity for the service. The Q Line began service in February 2018 and has since ridership surpassed 2.5 million passengers.In a transformative move, in 2015, Fresno Area Express (FAX) embarked on a constructive journey by initiating the construction of a 15.7-mile Bus Rapid Transit (BRT) line known as the Q Line. The Q Line connects North Fresno, downtown Fresno, and Fresno's southeast growth area via Blackstone Avenue and Kings Canyon Road. The BRT service replaced existing local bus service in the corridor and offered decreased travel times through fewer stops and more frequent service. The project included transit signal priority, queue jump lanes, 27 stations, 17 low-floor compressed natural gas low-emission BRT vehicles, boarding platforms, fare machines, real time passenger information, and special branding to create a unique identity for the service. The Q Line began service in February 2018 and has since ridership surpassed 2.5 million passengers.

Report Coming Soon

PI's Background: Dr. Shahab Tayeb

Project's date: December, 2024

Abstract: The Internet of Vehicles (IoV) aims to facilitate the next generation of Connected and Autonomous Vehicles (CAV). In line with Objective 1 “Leverage new technologies, including vehicle automation, and innovative processes to achieve a seamless, multimodal surface transportation system that integrates with other “smart city” investments”, this project proposes the acquisition of hardware and software testbed for intra-vehicular networks. The adoption of Automotive Ethernet aligns with the objective of leveraging new technologies and innovative processes in the context of a seamless, multimodal surface transportation system integrated with smart city investments. It provides a technologically advanced and standardized communication framework that supports the goals of safety, efficiency, and connectivity within the evolving landscape of transportation and smart city initiatives.

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PI's Background: Dr. Fariborz M. Tehrani

Project's date: December, 2024

Abstract: This project aims to develop novel approaches to predicting and extending the concrete service life in pavement and bridge systems (Objective 2). Concrete materials provide durable solutions and contribute to the resilience of transportation infrastructure. However, the environmental footprints of cementitious contents raise concerns about their climate change impacts. Hence, enhancing the service life of concrete pavements and bridge decks is vital to reducing embodied energy and greenhouse gas emissions during the lifecycle of transportation systems (Objective 5). The project employs advanced techniques in service life prediction using transport properties of concrete (Objective 3). This project is aligned with Federal Highway Administration (FHWA) Every Day Count (EDC-7) Innovations, including Integrating GHG Assessment and Reduction Targets in Transportation Planning, Enhancing Performance with Internally Cured Concrete (EPIC2), and EPDs for Sustainable Project Delivery.

Report Coming Soon

PI's Background: Dr. Yushin Ahn

Project's date: December, 2023

Abstract: Car-centric urban development patterns have given over vast swathes of land to multilane highways, congested arterials, and sprawling parking lots. These impervious surfaces are perceived as unsightly and alienating to many residents and have negative environmental impacts including the magnification of the urban heat island effect and reduction of downstream water quality. This research explores the use of various image classification methods on high resolution aerial imagery to quantify the percentage of urban areas covered by impervious surfaces. Traditional pixel- and object-based classification methods are compared to novel deep learning classification tools. The results are compared to past studies of impervious coverage in Fresno/Clovis and similar cities using other methodologies. Policymakers can use this information to inform future land use planning.

Report Coming Soon

PI's Background: Dr. Jaymin Kwon

Project's date: December, 2023

Abstract: The main objective of the StarTraq 2023 project is to create safer communities and provide more significant opportunities for active transportation modes such as biking and walking, increasing access to transit. Through research on spatiotemporal analyses of roadside transportation-related air quality, everybody can enjoy the accessibility of transit and choices of healthier transit to improve their quality of life. The study aims to provide public information on transportation-related environmental exposure in a timely and relevant way so that people can use the knowledge for decision-making in designing and applying new materials and technologies to improve Californians’ public health. The proposed StarTraq project will further characterize the relationship of air quality between the federal reference monitoring data and local bike paths in the park and recreational areas, urban bike lanes for air quality, and on-road air quality by developing custom-built mobile air sensors.

Report Coming Soon

PI's Background: Dr. Chihhao Wang

Project's date: December, 2023

Abstract: The objectives of this study encompass a diverse approach. This study aims to measure accessibility to urban opportunities, including jobs and services, while considering the influence of the Cal-High-Speed Rail (CHSR) within Fresno, Merced, and Kings. Furthermore, the research seeks to conduct a thorough pre- and post-implementation analysis of job and service accessibility within these three station cities at the census tract level. This study identifies regions where CHSR has the potential to enhance access to job and service opportunities within daily living spheres and areas where it would not. Ultimately, this research aims to provide transportation planning information for improving overall accessibility and promoting equal development through better connection to the local transpiration networks.

Report Coming Soon

PI's Background: Dr. Hovannes Kulhandjian

Project's date: December, 2023

Abstract: In this study, we propose impersonating the work of the person helping students cross the street by developing a Digital Twin, a Smart Robot that allows pedestrians and cyclists to cross the street. It would detect the presence of a pedestrian and cyclist and the traffic flow using four different sensors appended onto it along with artificial intelligence. It would only cross the street and help the pedestrians/cyclists cross along. If it detects a danger during the crossing operation, it will alarm the driver and the pedestrians/cyclists. The intelligent robot technology we plan to develop can also be used for the second most vulnerable population, older people, to help them cross the street safely at a crossroads without traffic lights. The proposed system can be used both during the day and at night using a LIDAR, a thermal infrared camera, a radar system, and a video camera.

Report Coming Soon

PI's Background: Dr. Christian Wandeler

Project's date: December, 2023

Abstract: This project aims to provide underserved minority students in afterschool programs opportunities to examine transportation careers and engage in community transformation through high-quality educational experiences. The project will build on the resources developed for the Central Valley Transportation Challenge and leverage the online transportation hub (funded by an SB 1 grant in 2020). The project will collaborate with afterschool program providers and connect them with CSU students, transportation, and engineering professionals from university and industry sectors to projects around transportation issues in the afterschool programs.

Objective: 

• Transfer 100 lessons collected by the Fresno State Transportation Institute to an online platform, the online lesson plan hub, and make them accessible to after-school programs.
• Pilot the online lesson plan hub in after-school programs
• Develop five transportation lessons that can be self-managed by students
• Pilot the five self-managed lessons in after-school programs

Report Coming Soon

PI's Background: Dr. Maria Calahorra-Jimenez

Project's date: December, 2023

Abstract: This research project aims to explore performance-based contracts (PBC) as a contracting strategy that might facilitate the application of new materials, design, and technology to address long-term road maintenance in California. This research contributes to objective 3 of the California Senate Bill (SB 1) by (1) identifying the benefits and limitations of PBC compared to traditional contracts and (2) exploring the experience of other states and countries in the implementation of PBC for road maintenance to inform the implementation of these types of contracts in California. This research project will contribute to the knowledge of project delivery and procurement methods by investigating the application of PBC to address long-term road maintenance in California. The results of this project will inform California road agencies in selecting the contracting strategy that best matches their long-term performance goals.

Report Coming Soon

PI's Background: Dr. Manideep Tummalapudi

Project's date: December, 2023

Abstract: The objective aims to address Objective 2 of the California SB1 by identifying and developing tools and approaches that create cost-effective methods to improve the long-term benefits of transportation investments by developing innovative and cost-effective practical strategies to implement emerging inspection technologies for the construction and maintenance of bridges. California SB1 (Senate Bill) aims to “fix neighborhood streets, freeways, and bridges in communities across California” (State of California 2021). To accomplish this task, one of the essential activities is to inspect the condition of highway infrastructure, for which Caltrans spends almost 8% of its maintenance program budget (Caltrans report 2015-120). For example, Caltrans (California Department of Transportation) inspects and maintains 25,000 bridges in the state. Approximately 50% of these bridges have exceeded their design life, and it is essential to check them periodically. Accurate and timely bridge evaluations can help maintain a safe and reliable bridge network while optimizing the cost of repair activities, reducing the overall expenditure on bridges (Kim, Frangopol, & Soliman, 2013). Many emerging technologies, such as geospatial tools, 3D modeling, unmanned aerial systems (UASs), etc., have proved to be efficient in inspection of bridges. However, despite their potential to improve inspection quality and save inspection time and resources, the state DOTs have not yet fully implemented these emerging technologies for inspection activities, which costs all the state DOTs approximately $1.3 billion annually (Zulifqar, Cabieses, Mikhail, & Khan, 2014). Caltrans identified that effectively implementing UASs alone saved them $200,000 in 2021 (Caltrans Efficiency Report, 2020- 21), and being able to implement other technologies effectively would result in much higher savings. Therefore, it is essential through this research to identify emerging inspection technologies used for bridge highway inspection purposes, understand critical success factors for implementation, and develop approaches to implement these technologies effectively at the transportation agencies. In this way, this research supports the goals of SB-1 to improve the long-term benefits of transportation investments.

Report Coming Soon

PI's Background: Dr. Shahab Tayeb

Project's date: December, 2023

Abstract: Blind intersections have high accident rates due to the poor visibility of oncoming traffic, high traffic speeds, and lack of road infrastructure (e.g., stoplights). These intersections are more commonplace in rural and suburban areas with underdeveloped traffic infrastructure. The Internet of Vehicles (IoV) aims to address such safety concerns through a network of connected and autonomous vehicles (CAVs) that intercommunicate. In line with Objective 1, “Leverage new technologies, including vehicle automation and innovative processes to achieve a seamless, multimodal surface transportation system that integrates with other “smart city” investments,” this project proposes a lightweight road-side unit (RSU) tailored to rural and suburban areas, aiming to minimize visibility issues by facilitating communications across such intersections. This is accomplished by creating an RSU based on Software Defined Radio (SDR) and Field-Programmable Gate Array (FPGA) that utilizes an algorithm based on virtual traffic light methodologies. The components of the system will include an FPGA with an adaptive virtual traffic light algorithm, a communication module to monitor inter-vehicular communication, and a solar power system to optimize power usage. The implementation of the proposed system will reduce end-to-end delays. The main objectives of this proposal are threefold: (1) to study the impact of nonvisible communication on rural and suburban blind intersections; (2) to design a hardware implementation of an adaptive virtual traffic light that integrates into the existing IoV; and (3) to drive the reconfigurability of (2) using (1).

Report Coming Soon

PI's Background: Dr. Aly Tawfik

Project's date: December, 2022

Abstract: Freight transportation represents a significant amount of traffic and all its associated externalities, such as traffic safety, congestion, energy demand, greenhouse, and air pollution emissions, and infrastructure costs. However, it also plays a primary role in the supply chains and the costs and availability of goods and is a major player of the economy. This study aims to identify, assess, and utilize different data sources to uncover and understand the patterns and movements of the different types of freight in different counties of the San Joaquin Valley. The San Joaquin Valley region consists of 8 counties naming San Joaquin, Stanislaus, Marced, Madera, Fresno, Kings, and Tulare. This research has explored some major datasets consisting of freight data such as GTA, PIERS, and Streetlight Data Insights for the year 2019, i.e., to get a clear insight on what the actual movement of freight looked like pre-covid. The primary softwares used for this analysis are MS Excel, MS Access, ArcGIS, and Jacob Streetlight Insight. This research investigated all modes of freight transportation for domestic and international trade which are air, water, rail, and road. Findings of this research are valuable for multiple different governments as well as private agencies for various use cases such as development of transportation infrastructure, freight business, and environment assessments.

Full Report (PDF)

PI's Background: Dr. Aly Tawfik

Project's date: May, 2022

Abstract: Over the past decades, different kinds of surveys have traditionally been the primary source of data for understanding travel demand (patterns and behaviors) in a region, and for developing the transportation planning models and designing the transportation infrastructure. However, the substantial evolution of communication technologies and the large market penetration of smartphones over the last decade have opened the door for novel types of data: cellphone trace big data. While traditional surveys will continue to provide value and answers that are not possible by cellphone trace big data, applications of this novel data source in transportation have been consistently growing and are expected to only grow further. This study utilizes cellphone trace big data (from Streetlight Data) to uncover the spatio-temporal distribution of travel demand (e.g. trips by mode) in the urban as well as rural areas in Fresno County, California. The study visualizes Origin-Destination (OD) patterns and OD trends by mode in the region and contrasts them with the existing transportation infrastructure. The study demonstrates the potential value of this novel data source as it provides additional and valuable information that can significantly improve our ability for understanding travel demand, and plan and design more efficient transportation systems to meet this travel demand.

Learning Objectives:
Articulate the advantages and limitations of travel surveys and cellphone trace big data for transportation planning Discuss methods for using cellphone trace big data to understand travel demand in urban and rural areas
Use cellphone trace big data to investigate the suitability of the existing transportation infrastructure for serving its travel demand

Report Coming Soon

PI's Background: Dr. Aly Tawfik

Project's Start date: December, 2022

Abstract: The San Joaquin Valley (SJV) Electric Tractor Development & Demonstration project aims to analyze and demonstrate the potential benefits of utilizing the advanced technology of electric tractors and trucks in agricultural applications as an alternative to their diesel-powered counterparts. Fresno State Transportation Institute (FSTI) collaborated as the third-party data analyst with Project Clean Air, Humming Bird EV (HBEV), and Moonlight Companies to implement the SJV Electric Tractor Development & Demonstration for the California Air Resources Board (ARB) Off-Road Advanced Technology Demonstration Projects program. The contribution of FSTI in the project can be summarized in the following tasks: Providing a baseline report for current tractor fleet use and energy use at Moonlight Companies, Working directly with HBEV, the original equipment manufacturer, Moonlight Companies, the leading demonstration site, and other demonstration sites to collect necessary data for final analysis, Preparing a comprehensive plan for the collection of data in compliance with California ARB’s requirements, Designing data collection tools (i.e. surveys and logs) to collect and capture all required data, Assigning personnel to conduct field studies to observe use, operations, fueling process, and continuously efficiencies of the electric tractor versus the conventional fueled tractors, Working with Project Clean Air to provide data for quarterly and final reports.

Report Coming Soon

PI's Background: Dr. Aly Tawfik

Project's date: December 2022

Abstract: Traffic has a significant impact on public health. Traffic-related air pollution affects the health of individuals, especially when it comes to respiratory and cardiovascular health. A large number of people die due to respiratory and cardiovascular-related complications each year. According to the World Health Organization, 17.9 million people died because of cardiovascular diseases in 2016, representing 31% of all global death. Although the impact of the traffic- related air pollution on individuals is evident, the relationship between traffic-related air pollution and public health has been less investigated in the census tract level. The main goal of the study was to determine whether there was a relation between the health of a community, the traffic within them, and how they connect. Traffic and health data of the entire Fresno County were collected and then were analyzed at the census tract level. The results of this research showed a significant effect of traffic congestion on public health in the census tract level. The findings of this study could help in future planning and in the allocation of funds to help communities with health problems.

Report Coming Soon

PI's Background: Dr. Aly Tawfik

Project's date: May, 2022

Abstract: Traffic congestion is possibly one of the major negative impacts of travel. A large number of people are adversely affected by congestion each day in California and worldwide. In addition to global impacts such as greenhouse gas emissions and energy demand, traffic congestion significantly affects the direct population in areas where it occurs. It causes environmental degradation, significant loss of productive time, health impacts, and significant economic impacts. Measuring and identifying the causes of traffic congestion are necessary steps for understanding and reducing congestion. This study attempts to uncover some of the major causes of traffic congestion. The study analyzes traffic congestion in six major metropolitan areas in California: San Francisco and the Bay Area, Los Angeles, San Diego, Sacramento, Fresno and Bakersfield. The study investigated the possible impact of 35 different attributes on different measures of traffic congestion in these six metropolitan areas. Different attributes were acquired from a variety of data sources (e.g. road networks were collected from local Metropolitan Planning Organizations, MPOs). ArcGIS was utilized for most of the attribute computations. The 35 attributes are classified into 5 main groups: infrastructure attributes (e.g. roadway or lane miles normalized by population or area), connectivity attributes (e.g. roadway links or nodes normalized by area), socioeconomic attributes (e.g. median income and population density), weather attributes (e.g. days of precipitation), and trip attributes (e.g. number of carpools per household). Four different measures of traffic congestion were utilized: Travel Time Index, Roadway Congestion Index, Delay per Auto Commuter, and Cost per Auto Commuter. The results of this work indicated that the roadway miles per population and the number of roadway links per area were the two most highly and consistently significant variables in determining congestion. Roadway miles per population had a negative relationship with traffic congestion and the roadway links per area had a positive relationship. Results of this work could be helpful in understanding and addressing traffic congestion in urban areas.

Report Coming Soon

PI's Background: Dr. Hongwei Dong

Project's date: May, 2022

Abstract: The concept of smart transportation has received a lot of scholarly attention during the last decade. New mobility technologies and innovations, such as autonomous vehicles, electric cars, and shared mobilities, are revolutionizing the transportation industry and transforming American cities. To date, both the research and the implementation of smart transportation technologies have primarily focused on large urban and metropolitan areas. We have a limited understanding of the application and development of smart transportation technologies in small cities and rural areas. Can the promise of smart transportation be extended to small urban and rural communities? Will small urban and rural communities be left behind the smart transportation revolution? The objective of this study is to evaluate the development of smart transportation and smart city technologies in small urban and rural communities in California’s Central Valley, particularly those in Fresno County.

Specifically, this study attempts to achieve three goals. First, we survey the state-of-the-art practices of smart transportation technologies in both large and small cities as well as in rural communities in the United States. Second, we evaluate the development of smart transportation and smart city technologies in small urban and rural communities in California’s Central Valley by conducting surveys and interviews with local experts, transportation engineers, urban planners, and policy makers. The first-hand data collected through surveys and interviews are complemented by the second-hand data that are available from local planning agencies and other public data sources such as the U.S. Census Bureau. The first- and second-hand data allow us to evaluate the extent to which smart transportation has been implemented as well as the barriers to implementing smart transportation technologies in small urban and rural communities in the Central Valley. Lastly, we propose policy suggestions based on the successful experiences outside of the Central Valley and our careful evaluation of the development of smart transportation in small urban and rural communities in the Central Valley.

Full Report (PDF)

PI's Background: Dr. Hovannes Kulhandjian

Project's date: November, 2022

Executive Summary: There are over 590,000 bridges strewn across the network of highways stretching across the United States alone. The Federal Highway Act (FHWA) of 1968 mandates that each bridge with a length of 20 feet or more must be inspected at least once every 24 months. Each inspection must align with the criteria outlined by the National Bridge Inspection Standards (NBIS) [1]. Additionally, a bridge inspection will identify major structural issues that require follow-up, quantify the overall condition of the bridge to prioritize capital needs, identify routine maintenance, and catalog a history of the bridge’s condition. Inspecting bridges has been a labor-intensive and very costly process. Drones can potentially cut costs, provide better data, and improve worker safety during bridge inspections. Using drones for bridge inspections dramatically reduces the costs associated with the examination. This research proposes implementing an AI-based bridge and road inspection framework using drones with multiple sensor-collecting capabilities. It is not sufficient to do an inspection using cameras; we plan to utilize an infrared (IR) camera and a high-resolution optical camera. The IR camera can provide more details on the interior structural damages of a bridge than an optical camera, which is ideal for inspecting cracks on the surface of a bridge; in addition to that, our drone inspection system is equipped with a computer on-chip that runs Machine Learning algorithms that enable autonomous driving of the drone and taking images of the bridge or the road structure whenever it detects any damages. Instead of having a person operate the drone, it will self-operate and conduct the inspection process using the advanced AI algorithms we are developing.

Full Report (PDF)

PI's Background: Dr. Scott Peterson

Project's date: December, 2022

Abstract: Pavement performance investigation and evaluation is critical for pavement management systems (PMS) to maintain good driving conditions and prioritize maintenance or rehabilitation efforts and funding (Chun et al, 2021). For this purpose, agencies are interested in the type, extent, and severity of different distresses. The most important distresses include rutting, cracking (fatigue cracking and thermal cracking) for flexible pavement, and cracking and faulting for rigid pavement. There is specialized equipment available for the identification of pavement distresses and quantification of pavement condition. However, the specialized equipment generally has high variability, is subject to detection errors, and requires much labor, or costly efforts (Chun et al, 2021; Huang et al, 2014). Therefore, a reliable, accurate, time efficient, and cost‐effective way to collect pavement condition data and evaluate pavement performance is of interest. Recently, mobile phones and mobile electronic devices began adapting new camera and 3D sensing technologies. In 2020, the Apple iPad Pro and the Apple iPhone 12 Pro gained a new technology with a LiDAR sensor to help improve portrait mode photos in the day and night time. This new LiDAR sensor however has various not intended uses that need to be explored to identify further reaching capabilities that can be applied to many other applications, such as pavement performance.

Report Coming Soon

PI's Background: Dr. Julio Roa

Project's date: December, 2022

Abstract: California is aggressively moving forward with efforts to deploy zero-emission transportation technology to fight climate change. However, to date, the investments California has made with Cap-and-Trade funding have focused on ground transportation and some marine sources. These sources are major contributors to climate change but do not represent the entirety of transportation modes in California. One mode of transport where California is lagging in recognizing the potential to reduce GHG emissions through electrification is air transport with the rapidly emerging development and deployment of zero-emission aircraft powered by battery/hybrid electric motors. There are over 140 public-use airports in California with 32 of those being in the San Joaquin Valley. Many of these airports are in close proximity to growing population and commerce centers, particularly in the San Joaquin Valley, but are underutilized. The development of advanced electric/hybrid electric is opening the door to using these airports for both passenger and freight movement through significantly reduced costs of operation associated with electric propulsion. Strategic investment in the supporting infrastructure to facilitate operations of these new aircraft in conjunction with zero- emission ground vehicles offers the potential to transform these existing airport assets into multi-modal, zero-emission transportation hubs for the communities they are located in; bringing enhanced mobility and increased economic activity to many communities currently isolated due to limited ground transportation connections. The objective of this project is to maximize opportunities for California’s cap-and-trade program to reduce the impact of greenhouse gas emission and transportation on climate change by comparing GHG emissions from both ground and air modes of transportation including evaluating new advances in air mobility being developed using electric/hybrid-electric propulsion for aircraft.

Full Report (PDF)

PI's Background: Dr. Jaymin Kwon

Project's date: December, 2022

Abstract: The San Joaquin Valley (SJV) has been known for poor air quality and high rates of respiratory-related illnesses. Veloz et al. (2020) conducted a study on perceptions about air quality in SJV residents and found that a majority of the participants are worried about air quality. Hence, promoting alternative forms of transportation is critical in our community. In this research, we propose to examine the baseline air pollution exposure of pedestrians, cyclists, drivers, and passengers during transportation. This study will provide valuable findings that are useful for planning transportation facilities more efficiently to protect people, promoting active transportation modes, and increasing awareness of clean air efforts in transportation for healthier communities.

Full Report (PDF)

PI's Background: Dr. Jaymin Kwon

Project's date: December, 2021

Abstract: Promoting alternative forms of transportation is a major focus area in Transportation Planning. Information on pedestrians' and cyclists' exposure to traffic-related air pollutants during commute and utilization of parks and recreational areas is very limited in our Fresno/Clovis area. This proposed research will provide valuable insights and findings that could be used to plan transportation facilities better and promote more active transportation. The purpose of our proposal is to continue and expand the StarTraq 2020 project that is currently funded by the Fresno State Transportation Institute (FSTI) to meet the identified needs of the Fresno/Clovis area, promoting alternative forms of transportation by examining air pollution data collected near roadways and on trails as related to the active transportation modes of walking and bicycling. Through this research, we attempt to broaden the scope of the roadside, on-road, and in-vehicle air quality data from particle pollutants.

Objective: This research attempts to broaden the scope of the roadside, on-road, and in-vehicle air quality data from particle pollutants. The results will provide the baseline exposure levels for public health concerns of different transportation modes. In addition, the information on pedestrians' and cyclists' exposure to traffic-related air pollutants during commute and utilization of parks and recreational areas will be precious to promote the active transportation mode while developing strategies for reducing the risks associated with the mode of transportation via urban planning and policy development.

Full Report (PDF)

PI's Background: Dr. Hovannes Kulhandjian

Project's date: December, 2021

Abstract: The goal of this research work is to maximize the detection capabilities of pedestrians, especially at night, by effectively data fusing the information gathered from a thermal camera, a radar sensor and a video camera along with the use of advanced machine learning algorithms to detect and avoid pedestrian collision in real-time. Using this multi- dimensional valuable data, it could make intelligent decisions during different conditions of the road, be it during the day or at night. The proposed system could potentially be imbedded into a smart car system that provides real-time pedestrian detection and alerting mechanism by vibrating the driver’s wheel and display a message on a monitor/dashboard to warn the driver to avoid colliding into the pedestrian. The proposed system can be used both during the day and at night using the combination of thermal infrared camera, a radar system and a video camera. It could also be installed in an autonomous vehicle. The PI has research expertise in the area of sensing combined with Artificial Intelligence. His most recent FSTI research work was based on Artificial Intelligence and Machine Learning. Recently, he presented two conference papers, please refer to [11, 12] one was in a flagship Institute of Electrical and Electronics Engineers (IEEE) Global Communications Conference (GLOBECOM) held in Hawaii while the other was in an International Conference run by the Association for Computing Machinery (ACM) held in Atlanta, Georgia

Objective: One possible solution is to use a video camera, a radar system, or a LIDAR system in a vehicle. More recently, the advancement of thermal IR cameras has shown a potential possible solution. The research on pedestrian detection and avoidance is still in its infancy. Several methods have been explored to detect a pedestrian and avoid an accident. To the best of our knowledge, no prior research work has explored or experimented with the idea of using Data Fusion from multiple sensors (i.e., a thermal infrared camera, radar sensor, and a visible camera) combined with advanced Machine Learning (ML) for pedestrian detection and avoiding mechanize. Therefore, we believe that this research exploration could lead to new Artificial Intelligent-based application tools for drivers that could potentially save lives. We will be exploring state-of-the-art ML techniques combined with data fusion to achieve this objective. The goal of this research work is to maximize the detection capabilities of pedestrians, especially at night, by effectively data fusing the information gathered from a thermal camera, a radar sensor, and a video camera along with the use of advanced machine learning algorithms to detect and avoid pedestrian collision in real-time. Using this multi-dimensional valuable data, it could make intelligent decisions during different conditions of the road, be it during the day or at night. The proposed system could potentially be embedded into a smart vehicle system that provides real-time pedestrian detection and alerting mechanism by vibrating the driver’s wheel and display a message on a monitor/dashboard to warn the driver to avoid colliding into the pedestrian. The proposed system can be used both during the day and at night using the combination of a thermal infrared camera, a radar system, and a video camera. It could also be installed in an autonomous vehicle.

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PI's Background: Dr. Shahab Tayeb

Project's date: December, 2021

Abstract: This study analyzes the security of a common in-vehicle network standard, the Controller Area Network (CAN). Due to its inherent vulnerabilities to various forms of cyber-attacks, CAN implementations can easily be targeted by cybercriminals. Such vulnerabilities range from eavesdropping, where the attacker can read the raw data traversing the vehicle, to spoofing, where the attacker can place fabricated traffic on the network. We, initially, perform a simulation of CAN traffic generation followed by hardware implementation of the same on a test vehicle. Due to the obscure nature of CAN, we reverse-engineered the missing parameters through a series of passive sniffing attacks on the network. Finally, we demonstrate the feasibility of the attack by placing fabricated frames on the CAN.

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PI's Background: Dr. Julio Roa

Project's Start date: December, 2021

Project's End date: May, 2022

Executive Summary:  The objective of this project [A1] seeks to determine how Regional Air Mobility (RAM) using electric/hybrid electric aircraft can provide new high-speed transportation for high priority passenger and cargo movement within Fresno County and connections to coastal urban centers. It is achieved by researching the demand for regional air travel generating an inventory of existing infrastructure, studying new technology available, studying infrastructure requirements from the landside and the airside, and evaluating the potential for integration with, and enhancement of, current and planned ground transportation services. 
Regional Air Mobility (RAM) using electric aircraft will become a reality within the next 10 years and transform both air and ground transportation by offering a new service using existing, underutilized airports that will change them into vibrant hubs for zero emission transportation for the communities they serve. Using small 5-20 passenger all electric or electric-hybrid aircraft, RAM services will provide connectivity for both passengers and freight to communities where ground transportation currently provides the bulk for goods and people movement. 
The San Joaquin Valley is an area of California that could benefit greatly from RAM services due to the large land area of the region, the presence of over 30 general use airports from Bakersfield to Stockton, and the need for improved connectivity for communities within the region with the urban centers in the north and south. In spite of having many small airports near cities in the region, there is currently only limited commercial air service available, leaving most of the region reliant on ground transportation. Understanding the potential of RAM to become an economic engine for communities with these underutilized airport assets was an important outcome for this research. 

Project Objective and Motivation: Advances in electric aircraft development are providing opportunities for new Regional Air Mobility services that can enhance connectivity of regions by using underutilized existing airport infrastructure and integrating use of electrified ground transportation. This research project seeks to determine how RAM using electric/hybrid electric aircraft can provide new high-speed transportation for high priority passenger and cargo movement within Fresno County and connections to coastal urban centers. This project also seeks to study the potential opportunities and challenges to effectively implement (electric/hybrid) RAM in the San Joaquin Valley. 
This study covers a range of topics to help clarify what RAM is and why it matters. It should not be construed as the final, defining source of all knowledge when it comes to possibilities and best ways forward as they relate to RAM initiatives. Rather, this study is intended to start a conversation with the communities that will be participants in what is an evolving RAM deployment process. 
The focus of this study is what is needed in terms of RAM development in order to effectively implement RAM for high-priority cargo and passengers. 

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PI's Background: Dr. Maria Calahorra-Jimenez

Project's Start date: December, 2021

Project's End date: July, 2022

Abstract: Improving long-term performance in highway projects is an imperative goal for public administrations. Project delivery and procurement methods might provide an opportunity to align design and construction processes with this goal. Previous studies have explored whether project delivery methods impact the long-term performance of highway projects. However, these studies did not focus specifically on how core elements within the procurement might relate to long-term performance. Thus, this research aims to fill this gap by exploring to what extent and how long-term evaluation criteria are considered in design-build best-value procurement of highway projects. To this end, content analysis was conducted on 100 projects procured between 2009 and 2019 by 19 DOTs across the U.S. The analysis of 365 evaluation criteria found that (1) roughly 11% of them related to long-term performance. (2) The weight given to these criteria in the overall technical proposal was lower than 30%. (3) Sixty-five percent (65%) of long-term evaluation criteria focused on design while 15% related to materials and technology, respectively. The results of this study are a first steppingstone to initiate a deep exploration of the relationship between procurement practices and actual project performance. Currently, with sustainability and life cycle assessments being top concerns in infrastructure projects, this line of research might be of particular interest to DOTs and highway agencies across the U.S. and worldwide.

Objective: In this research, the objective of the content analysis is twofold. First, it aims to identify to what extent RFPs include long-term performance evaluation criteria. To this end, the researcher used quantitative content analysis. Second, the study seeks to explore how long-term goals and evaluation criteria relate to various assessment categories. To this end, the researchers conducted a qualitative content analysis.

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Project's Start date: Feb 1st 2020

Project's End date: Dec 31st 2020

Executive Summary:

The goal of the Fresno State Transportation Challenge is to create an authentic civic service-learning experience, in which K-12 students and teachers; university students and professors; and community members work together on projects addressing transportation concerns and related issues in the region. During 2019 we piloted the Transportation Challenge process with 9 teachers. In 2020 the goal is to expand, refine, and create structures to sustain the implementation of the Transportation Challenge across subsequent years.

The objectives to meet this goal are as follows: 1) Conduct research on the implementation and expansion of the Transportation Challenge program to identify content and procedural supports and challenges; 2) Develop a pipeline for recruitment and continuous participation of K-8 teachers across the Central Valley; 3) Convene participating teachers and FSTI members to analyze instructional procedures and materials to develop a Transportation Challenge curriculum; and 4) Develop structures for increasing the connections between local educators and FSTI members and resources.

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PI's Background: Dr. Jaymin Kwon

Project's Start date: March 1st 2020

Project's End date: Dec 31st 2020

Abstract:  To promote active transportation modes (such as bike ride and walking), and to create safer communities for easier access to transit, it is essential to provide consolidated data-driven transportation information to the public. The relevant and timely information from data facilitates the improvement of decision-making processes for the establishment of public policy and urban planning for sustainable growth, and for promoting public health in the region. For the characterization of the spatial variation of transportation-emitted air pollution in the Fresno/Clovis neighborhood in California, various species of particulate matters emitted from traffic sources were measured using real-time monitors and GPS loggers at over 100 neighborhood walking routes within 58 census tracts from the previous research, Children’s Health to Air Pollution Study - San Joaquin Valley (CHAPS-SJV). Roadside air pollution data show that PM2.5, black carbon, and PAHs were significantly elevated in the neighborhood walking air samples compared to indoor air or the ambient monitoring station in the Central Fresno area due to the immediate source proximity. The simultaneous parallel measurements in two neighborhoods which are distinctively different areas (High diesel High poverty vs. Low diesel Low poverty) showed that the higher pollution levels were observed when more frequent vehicular activities were occurring around the neighborhoods. Elevated PM2.5 concentrations near the roadways were evident with a high volume of traffic and in regions with more unpaved areas. Neighborhood walking air samples were influenced by immediate roadway traffic conditions, such as encounters with diesel trucks, approaching in close proximity to freeways and/or busy roadways, passing cigarette smokers, and gardening activity. The elevated black carbon concentrations occur near the highway corridors and regions with high diesel traffic and high industry.

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PI's Background: Dr. Samer Sarofim

Project's Start date: March 1st, 2020

Project's End date: December 31st, 2020 

Abstract:  This research empirically investigates the need for, and the effective design and content of, a proposed mobile application that is targeted at pedestrians and cyclists in Fresno County. The differential effect of the proposed mobile app name and colors on the target audience opinions was examined. Further, app content and features were evaluated for importance and the likelihood of use. This included design appeal, attractiveness, relevance, ease of navigation, usefulness of functions, personalization and customization, message recipients’ attitudes towards message framing, and intended behaviors related to pedestrian, cyclist, and motorist traffic safety practices. Design mobile application features tested included image aesthetics, coherence and organization, and memorability and distinction. Potential engagement with the mobile app was assessed via measuring the users’ perceived enjoyment while using the app. The behavioral intentions to adopt the app and likelihood to recommend the app were assessed. The willingness to pay for purchasing the app was measured. This research provided evidence that a mobile application designed for pedestrians and cyclists is needed, with high intentions for its adoption. Functions, such as Safety Information, Weather Conditions, Guide to Trails, Events for Walkers and Bikers, and Promotional Offers are deemed important by the target population. This research was conducted in an effort to increase active transportation mode utilization and to enhance the safety of vulnerable road users. The public, city administrators, transportation authorities, and policy makers shall benefit from the results of this study by adapting the design and the features that are proposed in this research and were found appealing and useful for the target vulnerable road user groups. The need of the proposed mobile application and its main functions are established, based on the results of this research, which propagates further steps of implementation by city administrators and transportation authorities.

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PI's Background: Dr Hovannes Kulhandjian. 

Project's Start date: 03 June, 20120

Project's End date: December 31st, 2020

Abstract: According to the National Highway Traffic Safety Administration, in 2017 drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, as well as almost 800 deaths. Through the application of visual and radar sensors combined with machine learning, this research developed a drowsy driver detection system aimed to prevent potentially fatal accidents. The working prototype of Advanced Driver Assistance Systems can be installed in present- day vehicles to detect drowsy drivers with over 95% accuracy. It integrates two types of visual surveillance to examine the driver for signs of drowsiness. A camera is used to monitor the driver’s eyes, mouth and head movement in order to recognize when a discrepancy occurs in the driver's eye blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor in the system allows the driver's head movement to be captured at all times. Through data fusion and deep learning, the system quickly analyzes and classifies a driver's behavior under various conditions in real-time monitoring. This research could be implemented to reduce drowsy driving, thereby, making the roads safer for everyone and ultimately saving lives.

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PI's Background: Dr. Shahab Tayeb

Project's Start date: March 1st, 2020

Project's End date: December 31st, 2020

Abstract: As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms normalization and the use of Min-Max normalization.

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PI's Background: Dr. Christian Wandeler

Project's date: December, 2020

Abstract: The Fresno State Transportation Challenge uses an action civics approach to support K-12 students in developing transportation-related projects that have a positive impact on the community. In 2020 the goal was to expand, refine, and create structures to sustain the implementation of the Transportation Challenge across subsequent years. As a result of the COVID pandemic, the process and goals of the project were adapted. The project was extended into April 2021 and was entirely conducted through remote participation. The focus was on two high schools. The expansion into the high school age bracket was successful and the experience with these two projects will allow for easier expansion in additional high schools in the future. One high school focused on the topic of active mobility, specifically biking, and addressed the challenge of how to get more students to bike to school. The other high school combined the transportation challenge with an economic vitalization project. The students were asked to also develop a modern transportation concept. Both projects exposed high school students to the topic of transportation and expanded awareness of transportation careers. Students also developed important competencies in the domains of problem solving, collaboration, communication, and leadership.

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PI's Background: Dr. Samer Sarofim

Project's published date: June, 2020

Abstract: This research empirically investigated the differential effect of message framing on message recipients’ attitudes and intended behaviors related to pedestrian, cyclist, and motorist traffic safety practices. The framework empirically investigated time horizon (expansive vs. limited) and regulatory focus (prevention vs. promotion) message framing. The time horizon in the message can make someone either think of the future (expansive) or the present (limited). For example, an expansive time horizon message communicates that life is long and directs the focus on the future. On the contrary, a limited time horizon message communicates that life is short and directs the focus on the present moment. The regulatory focus of the message can direct the message recipients’ attention to take certain action to either avoid negative consequences (prevention) or attain positive outcomes (promotion). The research examined the role of the individual difference of perceived personal control on the perceptions of the presented messages and behavioral intentions to adopt safe transportation practices. Various messages were designed to employ a multilayer framing and fit with a 2 (time horizon: expansive vs. limited) x 2 (regulatory focus: promotion vs. prevention) between- subjects design. Findings suggest the messages adopting the expansive and promotion-focused framing combination seem to be more effective and have a higher tendency to induce positive intentions to act safe on the road for both pedestrians and motorists. Also, perceived personal control serves as a positive significant predictor of various safety-related motivations and intended behaviors. This research experimentally studied the differential effects of time horizon and regulatory focus framing on advancing traffic safety throughout effective messaging, an endeavor that shall benefit transportation authorities, city administrators, policymakers, and the general public. The tested message framing can be adopted in various forms, including text message, billboards, road signs, flyers, educational workshops, etc.

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PI's Background: Dr. Aly Tawfik

Project's Start date: December, 2020

Project's End date: July, 2022

Abstract: Electric vehicles (EVs) are a valuable tool in addressing the climate and energy challenges placed on our transportation systems. However, while national and international market shares of EVs have been rising with exponential rates, access to EVs of low-income populations has been significantly slower. This research developed a business model for expanding the EV market to low-income Californians. The team developed the model from qualitative data from various stakeholders, including Electric and Solar Companies, Professional and Community-Based Organizations, State Agencies, research institutions, and more, which enabled insights regarding various barriers that hinder the adoption of EVs. The team also used a state-wide survey to understand the barriers from the point of view of lower income Californians. The business model created from this data can be used by state administrators, policy makers, and social emprises to mitigate the barriers faced by low-income Californians within the EV market.

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PI's Background: Dr. Aly Tawfik

Project's date: December, 2022

Abstract: HSR is one of the largest projects in the nation currently under construction 24stations, 800 miles of rail from Sacramento to San Diego. HSR will impact travel accessibility between cities and areas across the state. Its objective is to Quantify and visualize the impact of HSR on travel time. Compare HSR to other travel modes (roads, bus, air and train) Provide more information to the public on whether investment is valuable. Completed with use of ArcGIS and advanced ArcGIS extensions including Network Analyst and Model Builder to produce: OD Matrices Isochrone Maps.

Report Coming Soon

PI's Background: Dr. Aly Tawfik

Project's date: December, 2021

Abstract: Many believe that telecommuting could be a solution for some of the significant adverse impacts of our transportation systems, e.g., traffic congestion, greenhouse gas and air pollution emissions, and energy consumption. Observations may have further strengthened this belief during the first year of the COVID-19 Pandemic when streets were deserted and clean air and wildlife returned to urban areas. Accordingly, this study investigates the legitimacy of this belief. Telecommuting is the Substitution for work regularly performed at the workplace with work at home or a location close to home. It can be performed multiple days per week, for full or partial days. A telecommute framework was adapted to analyze the 2017 National Household Travel Survey dataset. To study the impact of telecommuting, in its various forms, on daily trip counts and daily miles traveled; for urban and rural workers. The derived relationships would indicate if increased telecommuting, expected after the COVID-19 Pandemic, would result in fewer or greater trip counts and miles traveled in urban and rural contexts.

Report Coming Soon

Project's Start date: May 1st 2019

Project's End date: Dec 31st 2019

Abstract:  The goal of the “Fresno State Transportation Challenge” was outreach to schools and community engagement, to provide K-8 students opportunities to learn about transportation and transportation-related careers, and to practice 21st century skills by solving a transportation-related issue in their community. Through the pedagogical frameworks of action civics and eduScrum (a method to facilitate self-managed teamwork with a visual board), teachers and students worked on solving issues in their community. They learned design thinking to identify issues and develop solutions while using eduScrum to manage their work. University students from transportation engineering visited the schools regularly to support the K-8 students in their work on a transportation-related project. The study tested two different formats: summer school and during the regular academic school year. The research question was: What is the impact of the “Fresno State Transportation Challenge” on K-8 students, K-8 teachers, university students, and community members? The research methods involved observations, open interviews, and a final survey of participants. Key findings reveal the Transportation Challenge is suited to teach elementary and middle school students about transportation and transportation-related careers, and to encourage them to apply this knowledge in addressing a transportation-related issue in their community. The involvement of university students had a positive influence on the younger students’ learning in regard to motivation, role modeling, and broadening the perspective of transportation-related careers. The pedagogical approaches of action civics and eduScrum facilitated the development of career skills, such as collaboration, communication, creativity, teamwork, critical thinking, and persistence to overcome challenges. Implications for practice include that leveraging university resources, such as the Fresno State Transportation Institute, can be an effective way to engage K-8 students and teachers in transportation-related authentic learning experience, increase their awareness of transportation-related careers and topics, and develop their career skills.

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PI's Background: Dr. John Walkup

Project's date: December, 2019

Abstract:  This project proposed centers on developing a suite of standards-aligned, rigorous lesson plans for secondary school teachers centered on transportation issues. Each grade level will receive 3-4 lesson plans to cover a complete 2-week unit of study. Each grade level will address a specific major topic within the field of transportation. Examples of such topics will include the societal impact of autonomous vehicles, transportation safety, and traffic flow. The designers of this project believe that education on transportation issues from an early age can elevate awareness of these issues and boost interest in transportation careers. Each lesson plan will span at least one full classroom period; some will span multiple periods. Each lesson plan will be guided by a culminating activity and will include (a) the background knowledge that expected of students, (b) strategies for scaffolding students who do not possess this background knowledge, (c) the facts, skills, concepts, and metacognition address in the lesson plan, and (d) teaching methods likely to prove effective for delivering the lesson plan in the classroom.

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PI's Background: Dr. Samer Sarofim

Project's Start date: April 1st, 2019

Project's End date: December 31st, 2019 

Abstract:  The ultimate objective of this research is to empirically investigate the differential effect of message framing on message recipients’ attitudes and intended behaviors related to pedestrian, cyclists, and motorists traffic safety practices. The framework empirically investigated time horizon (expansive vs. limited) and regulatory focus (prevention vs. promotion) message framing. The role of the individual difference of perceived personal control on the perceptions of the presented messages and behavioral intentions to adopt safety transportation practices was studied. Various messages are designed to employ a multilayer framing and fit with a 2 (time horizon: expansive vs. limited) x 2 (regulatory focus: promotion vs. prevention) between-subjects design. Findings suggest that the messages adopting expansive and promotion-focused framing combination seem to be more effective and has a higher tendency to induce positive intentions to act safe on the road for both pedestrians and motorists. Also, perceived personal control serves as a positive significant predictor of various safety related motivations and intended behaviors. This research experimentally studied the differential effects of time horizon and regulatory focus framing on advancing traffic safety throughout effective messaging, an endeavor that shall benefit the public, transportation authorities, city administrators, and policymakers.

Full Report (PDF)

PI's Background: Dr. Mazen Eldeeb 

Project's Start date: 3/ 1/19 

Project's End date: 12/31/19  

Abstract:  The study addresses two of the main challenges facing combustion modeling for transportation fuels: simultaneous simulation of non-related combustion problems and reducing the computational cost of the modeling process itself. To address the first challenge, researchers determine a characteristic flame time from thermal diffusivity and laminar burning velocity. Researchers examine parametric dependence of flame time and ignition delay time on pressure, temperature and equivalence ratio for methane, based on validated chemical kinetic mechanisms. The study reveals flame time and ignition delay time show similar temperature dependence, flame time has stronger dependence on equivalence ratio and weaker dependence on pressure than ignition delay time. The study also establishes a correlation to predict flame time, and subsequently, burning velocity, based on knowledge of the relevant auto ignition time. Differences between methane, propane and ethanol are also explored. Researchers address the second challenge in a chemical kinetic modeling study of the high-temperature ignition behavior of Tetrahydrofuran (THF), a promising second-generation transportation biofuel. The study implements a Stochastic Species Elimination (SSE) reduction approach to develop multiple skeletal versions of a detailed chemical kinetic model of THF from the literature based on ignition delay time simulations at various pressures and temperatures. The developed skeletal versions are combined into a global skeletal model. The study uses ignition delay time simulations using detailed and skeletal models, with good agreement observed at higher temperatures. Next, researchers use sensitivity analysis to identify the most important reactions responsible for the performance of the skeletal model. Finally, they perform reaction rate parameter modification for such reactions in order to improve the agreement of detailed and reduced model predictions with literature experimental ignition data. This work contributes toward improved understanding and modeling of the oxidation kinetics of conventional and bio-derived transportation biofuels, as well as the estimation of laminar burning velocity that can be encountered in turbulent combustion simulations. This would ultimately contribute into the design of cleaner and more efficient transportation systems, and support the testing and adoption of novel fuels as additives and/or replacement to conventional non-renewable fossil fuels.

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PI's Background: Dr Hovannes Kulhandjian. 

Project's Start date: 03 June, 2019

Project's End date: 27 December, 2019

Abstract:  In this work, we developed a visible light communication (VLC) framework that can be used for Intelligent Transportation Systems (ITS). ITS has been motivated by the need for reducing traffic congestion and offering better user experience in navigation and location-specific services. Recently, VLC has drawn a great deal of attention in the research community, including the development of new applications for ITS. It would be of great use to enable the traffic lights to be able to talk to the vehicles in their proximity and convey important information about the traffic condition. In this project, we developed a framework that can potentially support infrastructure-to-vehicle (I2V) and vehicle-to-infrastructure (V2I) communication. (In our context the infrastructure refers to traffic lights using VLC.) Specifically, traffic lights will be used to not only to order traffic flow, but also to share some important information to the cars. The developed smart traffic light system can provide information about the traffic conditions several blocks down the road and, in case of accidents, this information would be useful for the driver to detour their original route to help reduce congestion and save time. In order to do that we have developed a transmitter circuitry that is composed of an embedded system and optical electronics. In addition, we have developed the receiver circuitry in which the photodiode along with other circuitry is used for detecting and decoding the VLC signal coming from the traffic lights. We have also developed and experimented in a laboratory with a novel optical code-division multiple-access (CDMA) scheme for overloaded optical CDMA transmission in which the optical codes are uniquely decodable. This new coding system could potentially provide higher data rate in the VLC protocol establishment.

Full Report (PDF)

Project's Start date: May, 01, 2019

Project's End date: December 31, 2019 

Abstract:  Research on the relationship between urbanicity and physical activity yielded mixed results despite many studies consistently showing that residents tended to undertake more transportation-related physical activity in a more urban environment. This study analyzed the 2017 NHTS data to examine the geographic disparities in physical activity, particularly transportation-related physical activity in the United States. Our analysis suggests the relationship between urbanicity and physical activity demonstrates a flat U- shape in graphed data. Residents are more physically active when they live in the areas from the two ends of the urbanization spectrum: inner cities and inner suburbs of large metropolitan areas and the rural parts of non-metropolitan areas. Suburbanites, particularly mid-ring and outer-ring suburbanites walk the least. Only very slight geographic variation exists in the weekly rates of walk and bike trips that are strictly for exercise. The study revealed greater variation of the weekly rates of walk and bike trips that are for non-exercise purposes. This study suggests a more complicated relationship between urbanicity, active travel, and physical activity in a broader geographic context. More research needs to examine whether and how new urbanist design can promote active travel, particularly active travel strictly for exercise, in rural areas and areas of low urbanicity.

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PI's Background: Dr. Shahab Tayeb

Project's date: December, 2019

Abstract: The Internet of Vehicles (IoV) aims to establish a network of autonomous and connected vehicles that communicate with one another through facilitation led by road-side units (RSUs) and a central trust authority (TA). Messages must be efficiently and securely disseminated to conserve resources and preserve network security. Currently, research in this area lacks consensus about security schemes and methods of disseminating messages. Furthermore, a current deficiency of information regarding resource optimization prevents further efficient development of this network. This paper takes an interdisciplinary approach to these issues by merging both cybersecurity and data science to optimize and secure the network. The proposed method is to apply Prim’s algorithm to an existing vehicular security scheme, Privacy-Preserving Dual Authentication Scheme (PPDAS), to further network efficiency in terms of power and time consumption. When a dual authentication security scheme is in place, the time taken for message dissemination follows a quadratic growth; applying Prim’s algorithm to the security scheme reduces the time to disseminate messages to a linear growth. The number of messages sent was decreased by a magnitude of up to 44.57. Contemporary security schemes are compared with PPDAS to justify the overhead consumption. Through the proposed approach, the usage of network resources, such as power and time, is reduced, which substantially enhances the performance of the vehicular network and allows for the scalability of the IoV.

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PI's Background: Mr. Malshana Wadugurunnehalage

Advisor's Background: Dr. Ajith Weerasinghe

Project's Start date: March 4th, 2019

Project's End date: December 31st, 2019 

Abstract: This project has three main objectives. The first objective is standardizing battery. There are various types of batteries used in the Automotive Industry. When this comes to Electric Vehicles (EV), battery manufacturers are hiding different recipes that they use for manufacturing batteries. This creates numerous limitations, especially in recycling processes and growth of EV demand. In this project, the importance of standardizing on EV battery technologies are discussed. The second objective is exploring battery switching stations. Battery Switching is identified as a promising technique for secure charging and a better way of maintaining the entire EV battery system in a region. In this project, it's identified the importance of having switching stations instead of fast charging. Fast charging leads to battery degradation and its effects on the grid if the power supply is not properly maintained. Considering tremendous benefits, implementing switching stations are widely encouraged throughout this project. The third objective is exploring IoT(Internet of Things) for efficient green energy transportation. Currently, the Internet plays a vital role in the business world. Through the IoT concept, it creates the connection between Electric Vehicles, Switching Stations, and other private and government organizations which interested to access the relevant data. In this way, it’s expected to energize the total battery switching system, bringing more productive and effective by establishing a robust IoT system. In addition to that, ultimately it's expected to make possible pathways to enhance performances of Electric Vehicles in public transportation.

This project aims to demonstrate the possible pathways to implement battery switching approaches for Battery Electric Vehicles (BEV) through the Electric Vehicle (EV) battery standardizing. During the past decade, there was significant interest in EVs and their technologies. Due to the number of studies and experiments which carried out for enhancing total performances of EVs, demand a different type of EVs are grown faster. The growth of different battery technologies has led to improving the primary aspects of Electric Vehicles. As a result of relentless research and developments regarding different battery technologies, the distance travel per single charge was growing day by day. Battery design and manufacturing techniques becoming complex, and battery manufacturers tend to hide their battery chemistry intend to increase the performance of their product stay strong in the competition.

EV batteries carry a different kind of chemical which can be harmful to the environment. Properly managed recycling approaches are essential to establish and maintain robust operation due to the increasing usage of EV batteries. Hiding their recipes of battery manufacturing, create complexity on the recycling process and through the enforcement of established rules and regulations for EV battery standardizing, battery manufacturers will be more open with their manufacturing techniques. Through this project, environmental impact due to increasing usage of EV batteries and possible pathways to create new businesses through recycling will be discussed. EV battery standardizing becoming an important concept, and scientists and engineers are found possible pathways to implement other approaches to establish a different type of business model through the EV battery standardizing. Battery switching stations are one of the promising business models, been introduced and, through this project, the values of such a business model will be discussed. 

Throughout the project, battery switching stations are identified as the most important aspect of harvesting benefits from all around. To make this battery switching process more effective and efficient, Introducing of the Internet of Things (IoT) is one of the major aspects of this project. The suggested network is something that goes beyond the traditional data acquisition and performance monitoring system. Through this system, the vehicle driver, the staff of battery switching station and data center for performance monitoring and system development, will be aware of each and every switched battery. This system will be demonstrated throughout the project and advances will be highlighted. In this way, there are numerous benefits that will be demonstrated and discussed throughout the project. Though it will be a time-constrained project, in the beginning, the further developments will be discussed at the end of the project. Depending on the availability of time and funds, this project will be continued for developing further, meeting its’ all aspects.

Report Coming Soon

PI's Background: Dr. Nancy Van Leuven
Project's Start date: 1 July, 2018
Project's End date: 31 December, 2018

Abstract: Over the past few years, there have been notable trends in the areas of tracking public opinion, especially in public issues such as transportation. Such powerful processes continue to be a critical (and often mandated) component of the democratic process as they help policy makers connect with affected constituencies. This study explores travel trends and transportation preferences of a sample of adults in the California Central Valley of Fresno, an increasingly congested region that is also heavily agricultural and regarded as an expected launching pad for California’s first high- speed rail system. Relying on an e-survey modeled off a statewide polling project, this study used a modified electronic survey as a valuable predictor of public opinion. Findings include preferences skewing toward concern about local issues (road conditions, safety, accessible active transport) and a lack of knowledge about future mobility options (high- speed rail, driverless cars). Based on these results, Fresno should be viewed as a prime area for focused public information campaigns to foster behavior change and attitudes about potential transportation improvements.

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PI's Background: Dr. Aly Tawfik

Project's Start date: December 2018

Abstract: Existing costs show that on average, Americans are spending approximately $.54 per mile for driving individually- owned (IO) vehicles. This value is based on factors such as fuel, maintenance, insurance, registration, parking, tolls and depreciation. Many of these factors will be affected with the arrival of autonomous vehicles (AVs) and more specifically, Shared Autonomous Vehicle (SAV) systems. For the purposes of realizing the advantages of future AVs and SAVs, a better understanding of future costs of travel is necessary. The goal of this research work is to estimate future travel costs using each of the AV and SAV Systems. These cost estimates would be crucial in evaluating the impacts of these technologies on future mobility and society. For this purpose, extensive literature reviews were conducted and a model was developed to analyze and evaluate the future travel costs using each of the AV and SAV systems. Results of this work indicate that future travel costs using SAV systems are likely to be substantially lower than those of using AV systems. This may lead to a disruption that can ultimately influence consumers to switch from owning a vehicle to on- demand mobility services.

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PI's Background: Dr. Douglas Singleton

Project's Start date: December, 2018

Abstract: This research focused on developing a suite of standards-aligned, rigorous lesson plans for secondary school teachers centered on transportation issues. The first phase of the grant project focused on training a cadre of prospective K-12 secondary school teachers to develop rigorous, standards-aligned lesson plans. Six students were recruited from across multiple disciplines including the liberal arts, physics, geology, and science education. Results of the training were strongly positive as indicated by post-training anonymous survey results. During the second phase, students developed a total of 19 lesson plans. Examples of such topics include the societal impact of autonomous vehicles, transportation safety, and traffic flow.

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PI's Background: Dr. Maryam Nazari

Project's Start date: 16 June, 2018

Project's End date: 31 December, 2018

Abstract: Application of Tire-Derived Aggregates (TDA) as a green, durable, and economically-efficient material, enhances the sustainability of transportation infrastructure. Throughout the course of this research study, the application of TDA in combination with expanded clay (EC) aggregates will be investigated in concrete slabs used in road pavements and bridge decks serving nonauto traffic, such as bicycle routes, through a set of experimental tests and life-cycle cost analyses. To this end, TDA, which is obtained from recycled tires, and EC, produced in rotary kilns, substitute coarse aggregates in conventional concrete. The final product, also known as lightweight rubberized concrete, is durable and economically-efficient. It also enhances the sustainability of transportation infrastructure by mitigating the necessary maintenance and rehabilitation needs of these slabs. In this report, an experimental study has been undertaken to first estimate mechanical properties of lightweight rubberized concrete using 100% EC, 100% TDA, and a mixture of 20% EC – 80% TDA; the TDA was replaced by the volume of the EC aggregates. Next, a series of static flexural and dynamic impact-fatigue tests were performed on simply-supported beam specimens and slab assemblies, respectively, to measure both modulus of rupture and durability when subjected to the applied loads. The cyclic testing results confirmed a lower flexural strength of the rubberized concrete specimens. However, the specimens exhibited an ability to withstand larger plastic deformations up until the point of failure. Using the results of impact-fatigue tests, a life- cycle cost analysis was also performed, which confirmed long-term benefits of constructing green and durable infrastructure, using TDA and EC, on transportation investments. In conclusion, using these durable materials in infrastructural construction will lessen their maintenance and rehabilitation needs. Further, this application will divert waste tires from landfills.

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PI's Background: Dr. Christian Wandeler

Project's Start date: March 1st, 2019

Project's End date: October 31st, 2019 

Abstract: The complexity of a globalized world, accelerating technological advances, and rapid change challenge educational systems. Around the world the call is to develop 21st century skills with a focus on career readiness, ability for lifelong learning, and collaboration skills. The development of the foundational elements of civic engagement (civic knowledge, skills, and dispositions) of children and youth is also a dominant concern for educators and policymakers. Unfortunately, not all youth have the same opportunities to develop civic self-efficacy. However, the civic empowerment engagement gap can be closed by providing underserved students with interactive and authentic civic experiences.

We strove to create such an authentic civic experience and piloted the Fresno State Transportation Challenge (FSTI) at an elementary school in the Washington Unified School district, Fresno County, California. The research question for this innovation grant was: Can we leverage the expertise and resources of the Fresno State Transportation Institute to bring high quality educational experience to underserved students and help them improve their communities?

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