As we announced previously, in June of 2017 C2M2 sent out a Call for Proposals (CFP) for our first round of research funding. This CFP was sent out to C2M2 faculty members in relevant fields at all five of the consortium’s universities. The center received 16 proposals covering a wide array of topics, featuring proposed research on V2V and V2I data infrastructure, pedestrian safety, transportation cybersecurity, and ride-sharing, among much else. Many of these proposals feature collaborative research among multiple universities, combining their expertise for a more comprehensive perspective, and several have the backing of the SCDOT and local communities for research support. These proposals were sent out to reviewers in academia and the industry for blind evaluation and 12 of the 16 proposals were selected by our Board of Directors to receive funding, based on their proposed collaboration between universities and the recommendations of the evaluations that each project received. Principal Investigators of the selected projects were informed in September, and projects began in October.
Adaptive Signal Control Algorithms for Connected Vehicles
Lead Principal Investigator – Gurcan Comert, Benedict College
Co-Principal Investigator(s) – Mashrur Chowdhury, Clemson University
October 2017 – March 2020
Project Status – Ended
Funding Amount – UTC $8904.54, BC $4452.27
Sponsoring Organization – OST-R, Benedict College, Clemson University
Description: The proposed research will explore deployment of connected vehicles (CVs) data for traffic signal control. It will aim at communication protocols both connectivity, type of data, data security, and detection. Parameter estimation: Models will depend on market penetration, coverage, and inference based parameter estimation (e.g., delay, queues). Computation Complexity: For computational efficiency using event based data will be investigated. Different information types will be researched for reliable and robust control. What other redundant system is necessary for short to mid-term deployment will be investigated. Reliability of the vehicle information will be answered. What type of coverage is needed, that is, cost of purchasing or maintaining thus efficient allocation. Accommodation of other modes such as trucks, transit, pedestrians, bikes, emergency vehicles will be checked.
Intellectual Merit: This project proposes to develop signal control algorithms from connected roadway users (motorized and nonmotorized) considering the information shared in two way communications (i.e., V2I, I2V, I2I, and V2V).
Broader Impacts: This research aims to develop implementation ready signal control algorithms from connected vehicles, develop algorithms with data defined under current architecture, address computational efficiency and different modes, test algorithms using microscopic simulations, set up experiments on the testbed, test algorithms, and report results and findings.
Impact of Transportation on Air Quality at Elementary and Middle Schools in South Carolina
Lead Principal Investigator – Gurcan Comert, Benedict College
Co-Principal Investigator(s) – Samuel Darko, Benedict College; Nathan Huynh, USC
October 2017 – November 2019
Project Status – Ended
Funding Amount – UTC $18868.50, BC $9434.25
Sponsoring Organization – OST-R, Benedict College, USC
Description: It is believed that there exists a direct correlation between transportation volume and the amount of air pollutants emitted into the environment. A combination of connected vehicles and environmental sensors to collect and monitor traffic flow and air pollutants at strategically selected schools in and around the state of South Carolina. Using connected systems (e.g., CVs, drones) fitted with sensors, periodic real-time traffic volume information and their respective emitted pollutants will be collected. Partial observations from connected vehicles will be utilized for real time emissions for better vehicle management at schools (less hazardous), particularly during drop-off and pickup times. Moreover, the data will be used for model development that can be used as forecasting tool. Different traffic parameters and their impact on emissions at school zones can be used for the optimization of land use and planning purposes. For instance, planning a school with a certain number of students would generate a volume of traffic in a zone that would result an amount of emissions based on vehicle types/compositions and locational specifications. This function can easily be minimized based some constraints and serve as decision-making tool.
Intellectual Merit: This research proposes to model and monitor real-time vehicle emissions, and develop better control schemes in the context of “low emission zones” at school zones.
Broader Impacts: Real-time data from connected systems can as well utilized to improve the user decision making for environmentally friendly modes as well as fuel efficiency and better mobility with unnecessary stops. Impact and quantification of changes (due to daily, weekly or seasonal variations in commuter, freight, inclement weather, and incidents) in emissions will be addressed using rural roadways such as Orangeburg or I-85 near Clemson, SC congestion.
Real – Time and Secure Analysis of Pedestrian Data for Connected Vehicles CVs
Lead Principal Investigator – Amy Apon, Clemson University
Co-Principal Investigator(s) – Gurcan Comert, Benedict College; Mashrur Chowdhury, Clemson University
October 2017 – June 2020
Project Status – Ended
Funding Amount – UTC – $56,360.00; Clemson – $22,500.00;
Benedict – $5,680.00
Sponsoring Organization – OST-R, Benedict College, Clemson
University
Description: As
the sources of regional connected vehicle data become more diverse and plentiful, infrastructure to support these data must scale to very large data; must support data of many types, including sensor data, text, and images; must securely support real-time processing for many applications; and must be trustworthy, and privacy-preserving. Public cloud infrastructure has advanced so that it now offers promising technologies for addressing these requirements. Our target applications include the real-time processing of connected vehicle data and traffic prediction, which is a critical component for many Intelligent Transportation Systems (ITS) applications, such as Advanced Traveler Information Systems, real-time route guidance, and emergency response systems
planning.
Intellectual Merit: This project has twofold aims that include: 1) the investigation of the effectiveness of public cloud infrastructure for the secure real-time processing of streaming data from connected vehicles, and 2) the design and implementation of two key applications of connected vehicle systems using a streaming data model.
Broader Impacts: The outcomes of this research will include developed infrastructure along with best practice recommendations for several connected vehicle applications and lays the computing and technology groundwork for significant collaboration on Smart City, Intelligent Transportation Systems, and Smart Factory, along with connected vehicle applications, across the region.
Uncertainty Quantification of Cyber Attacks on Connected Vehicles and Infrastructure
Lead Principal Investigator – Jim Martin, Clemson University
Co-Principal Investigator(s) – Gurcan Comert, Benedict College
October 2017 – March 2020
Project Status – Ended
Funding Amount – UTC – $62,900; Clemson – $31,450.00; Benedict – $8,950.00
Sponsoring Organization – OST-R, Benedict College, Clemson University
Description: The primary goal for the project is to develop, and validate detection models for system control failures involving connected vehicle applications. The secondary goal is to establish long-term collaborative research between the C2M2 partners and Benedict College to provide re-search and career opportunities for underrepresented minority students. To achieve these goals, the following objectives are identified: We anticipate the proposed research will lead to significant levels of collaborative research, including large multi-institutional grants that will be needed to help the state of South Carolina become the ‘silicon valley’ of the South East in vehicular and manufacturing technology.
Intellectual Merit: (1) Develop an appropriate system model representation, including a characterization of a set of anomalies; (2) Develop predictive methods and training sets such that anomalies can be classified quickly by vehicles or edge nodes; and (3) Create a working group of interested faculty and students at participating universities to engage undergraduate students in educational activities that are synergistic and supportive of the proposed projects goals and objectives.
Broader Impacts: The models developed in this proposed research can be extended to autonomous vehicles, different traffic systems as well as complexity of these systems. Although signalized networks are focused in this project, any other CV application can be analyzed similarly: vulnerabilities can be identified and detection tools/sensors can be deployed for a reliable application.
Assessing the Experience of Providers and Users of Transportation Network Company Ridersharing Services
Lead Principal Investigator – Eric Morris, Clemson University
Co-Principal Investigator(s) – Mashrur Chowdhury, Clemson University; Sakib Khan, Clemson University; Judith Mwakalonge, SCSU
October 2017 – June 2019
Project Status – Ended
Funding Amount – UTC – $56,287.00; Clemson – $42,556.00; SCSU – $6,952.00
Sponsoring Organization – OST-R, Clemson University, SCSU
Description: Many foresee a future of shared mobility, where transportation network companies (TNCs) match passengers with similar origins and destinations on the fly so they can rideshare. If feasible, shared mobility has the potential to reduce VMT, helping the environment, reducing congestion, and reducing crash damage and injuries in the Southeast, nation and world. Shared mobility may also provide lower-cost transportation that will particularly benefit those with low incomes. While the potential benefits of shared mobility would be felt worldwide, this development may be particularly beneficial in the Southeast due to the region’s general auto dependence and sprawling land use, which renders transit service difficult to utilize and may make shared mobility a particularly attractive option in this area. However, it is unclear whether consumers will accept shared mobility for reasons of convenience and comfort. Further, many Uber riders and drivers have expressed strong dissatisfaction with UberPool. Drivers, for example, object to perceived low compensation and high stress. This research will examine in depth how UberPool is serving customers and passengers.
Intellectual Merit: It will use a review of existing literature, an analysis of commentary on social media sites where drivers congregate, Twitter mining, and a survey to determine customer and provider satisfaction, identifying ways in which the service is both succeeding and failing in providing a successful travel experience.
Broader Impacts: The results will help inform us about the potential for ridesharing services like UberPool to serve a large share of travel in the region, nation, and world, and will produce ideas about ways in which such services could be improved to better the experience of drivers and riders.
Assessment of Safety Benefits of Technologies to Reduce Pedestrian Crossing Fatalities at Midblock Locations
Lead Principal Investigator – Jennifer Ogle, Clemson University
Co-Principal Investigator(s) – Mashrur Chowdhury, Clemson University; Dimitra Michalaka, The Citadel; Kweku Brown, The Citadel; Judith Mwakalonge, SCSU
October 2017 – March 2020
Project Status – Ended
Funding Amount – UTC – $58,604.00; Clemson – $22,500.00; SCSU – $13,336.00; The Citadel – $6,802.00
Sponsoring Organization – OST-R, Clemson University, SCSU, The Citadel
Description: Using data from the pedestrian crash characterization, researchers will deploy image detection technology on corridors with more frequent pedestrian crash patterns to determine the extent of the crossing maneuvers. One issue that is faced in all pedestrian studies is the lack of exposure data for pedestrians. This study will seek to provide this data to allow for safety prediction model development to support cost/benefit analysis for short term solutions. Simultaneous to the exposure data collection, researchers will conduct a thorough assessment of existing vehicle based detection technologies to determine the efficacy of these systems for particular types of vehicle and pedestrian interactions. Similar assessments will also cover pedestrian to vehicle technologies. The final step will be to conduct simulations to determine the safety gains possible from various levels of penetration of the pedestrian sensing technologies based on their efficacy for different types of crashes. Throughout the project, researchers will continually assess consequences and advantages of these systems for individuals with physical and cognitive disabilities. Subsequently, this research will identify gaps in current sensing technologies for various pedestrian crash factors and future research needs statements will be developed.
Intellectual Merit: This research will assess safety benefits and shortcomings of new sensing technologies to reduce pedestrian crossing fatalities at midblock locations where they are most vulnerable to injury and death from motor vehicle crashes, and provide recommendations for cost-effective short term infrastructure and technology adoption.
Broader Impacts: The outcomes of this analysis will help to identify where the problem areas are around the state, what roadway design features are most common at crash sites, and which population demographics are most at risk.
Active Traffic Monitoring through Camera Networks with Automatic Camera Calibration for Pan – Tilt – Zoom Cameras
Lead Principal Investigator – Wayne Sarasua, Clemson University
Co-Principal Investigator(s) – Kweku Brown, The Citadel; William J. Davis, The Citadel; Dimitra Michalaka, The Citadel
October 2017 – January 2020
Project Status – Ended
Funding Amount – UTC – $35,186.00; Clemson – $22,500.00; The Citadel- $17,593.00
Sponsoring Organization – OST-R, Clemson University, The Citadel
Description: We propose a network of distributed pan-tilt-zoom (PTZ) cameras acting as active vehicle sensors that will map the location of vehicles throughout a network in real time. Precise mapping of vehicles combined with vehicle-to camera communication will allow one to one correspondence where by the active camera will be able to connect with a vehicle and monitor its movement throughout a camera network. Once the connection is established, camera hand-off will be possible allowing a vehicle to be tracked over several miles. The individual vehicle map-ping approach will take advantage of existing PTZ infrastructure while providing a robust set of traffic parameters for use by a variety of applications including real-time traffic prediction, erratic maneuver/ dangerous driver identification, incident management, transportation network security, as well as the potential to provide new insights into driver behavior. With one to one correspondence between cameras and connected vehicles, system feedback can be catered to individual vehicles. This feedback will be based on tracked trajectories rather than from discrete points.
Intellectual Merit: The investigators propose to research and develop an active computer vision based traffic monitoring system that has the potential to lead to a shift in the paradigm of collecting traffic data in the near-term while supporting connected vehicle applications in the long-term.
Broader Impacts: The ability to identify and track vehicles throughout a network of cameras will take incident detection to a new level while increasing the robustness of data being collected. Vehicles will be able to be tracked for longer distances across distributed cameras, which will allow new possibilities in traffic monitoring, management, and prediction.
Infrastructure and Policy Needs for Personal Electric Mobility Devices in a Connected Vehicle World
Lead Principal Investigator – Judith Mwakalonge, South Carolina State University
Co-Principal Investigator(s) – Mashrur Chowdhury, Clemson University; Jae Dong Hong, SCSU
October 2017 – November 2019
Project Status – Ended
Funding Amount – UTC – $27,777.00; SCSU – $13,888.00
Sponsoring Organization – OST-R, Clemson University, SCSU
Description: The research team will conduct a comprehensive search of the literature of current PEMDs in the market, how PEMDs operate in different countries and rules governing their operation. The findings from the literature review are crucial in revealing the safety hazards and operation benefits associated with PEMDs use and effective countermeasures for safe inclusion of PEMDs into the existing infrastructure. Further, the research will document the safety incidences in the past 10 years by utilizing the National Electronic Injury Surveillance System (NEISS). The research team plans to conduct two field experiments, one under traditional (current) operating conditions and two, under non-traditional, the connected environment. The field experiments will identify any potential conflicts between PEMDs users and the general traveling public. They will also help us learn how the PEMDs interact with pedestrians in the various pedestrian areas, how safe the PEMDs are in urban areas; the effects of the operating environment including crossing intersections, various lighting conditions (day/night), various weather conditions (wind, rain, cold), on the use of PEMDs.
Intellectual Merit: This research will document the different types of personal electric mobility devices (PEMDs) currently used by the public on public transportation systems.
Broader Impacts: The research findings will help transport planners and public officials to decide how to manage non-motorized facilities (walkways, sidewalks, paths and trails) to maximize PEMDs benefits while minimizing any negative effects. Further, the research results will shed light into infrastructure needs as we evolve into the connected transportation environment.
Development of a Tool to Assess Effectiveness of Intermodal Facility Locations and Designs
Lead Principal Investigator – Nathan Huynh, University of South Carolina
Co-Principal Investigator(s) – William Ferrell, Clemson University
October 2017 – May 2019
Project Status – Ended
Funding Amount – UTC – $95,000.00; USC – $25,387.00; Clemson – $22,500.00
Sponsoring Organization – OST-R, USC, Clemson University
Description: Current logistics systems in the U.S. are inefficient and the results of this inefficiency is wasted fuel, increased costs, and escalating congestion along roads and within urban centers. This project will explore fundamental elements of both innovative infrastructure that is part of a connected logistics system and functional features of this system that will be required to support a future paradigm based on on-demand logistics. Our execution plan begins with a literature review focusing especially on collaborative logistics and multimodal facilities that use connectivity in any form (e.g., autonomous material handling device to move freight). Then, we will use South Carolina as a case study to investigate location and functional requirement on freight transfer terminals as well as exploring the effectiveness of on-demand logistics. To accomplish this, real data will be used to quantify and estimate future freight flows. Then, several types of freight transfer facilities will be identified that support short and long-term improvements. Models will be developed that capture key parameters of freight flow and provides strategic insight into possible locations for multimodal freight terminals and the functional requirements for these terminals.
Intellectual Merit: A practical and user-oriented tool will be developed based on insights from the models to assist decision makers assess the effectiveness of different intermodal facility locations and designs with respect to different freight movement strategies.
Broader Impacts: This project will identify and provide the means to enhance synchronization between transportation modes and create synergies with vehicle and infrastructure automation in order to provide intermodal network flexibility, reliability, resiliency, and demand-responsive customization.
Real Time Classification of Vehicle Types and Modes using Image Analysis and Data Fusion
Lead Principal Investigator – Robert Mullen, University of South Carolina
Co-Principal Investigator(s) – Nathan Huynh, University of South Carolina
October 2017 – April 2019
Project Status – Ended
Funding Amount – UTC – $50,000.00; USC – $25,085.00
Sponsoring Organization – OST-R, USC, Clemson University
Description: The goal of this project is to conduct a feasibility study on the development of software and selection of hardware that will measure multiple transportation modes and classify vehicles by their Federal Highway Administration (FHWA) classification. The research team will install several combined computer/camera systems to monitor the multi-modal traffic in the proximity of the University of South Carolina campus. This area has multiple transportation users, including pedestrians, mopeds, bicycles, motorcycles, passenger cars, trucks, trains and buses. Along with the video data, additional traffic collection sources such as pneumatic tubes and Bluetooth will be used. Multiple cameras will allow three dimensional data of the environment to be constructed in the software. The video data will be combined with other data using statistical updating methods (Bayesian) to produce final multi-modal traffic information. We will also explore counting traffic in non-typical locations, such as counting the number of pedestrians in/outside of cross walks in the roadway or pedestrians crossing stopped trains.
Intellectual Merit: (1) Image subtractions from successive images will be used to identify objects in the area of interest. (2) A discriminate function based on the object geometry and image texture will be used to classify objects. (3) The development of the object discriminant function as well as utilization of digital image correlation or other video object motion determination approaches will be the major contribution of this research.
Broader Impacts: Broader Impacts: The collection and analysis of integrated multimodal movement of people and goods will provide transportation planners with better quantitative information about the existing system. Beyond providing raw counts, an integrated video based system could provide information about unsafe practices of pedestrians and moped users that could be used to improve safety for these users.
Railway Right of Way Monitoring and Early Warning System (RailMEWS) Based on Satellite and Aerial Imagery
Lead Principal Investigator – Dimitris Rizos, University of South Carolina
Co-Principal Investigator(s) – Robert Mullen, University of South Carolina
October 2017 – April 2019
Project Status – Ended
Funding Amount – UTC – $50,000.00; USC – $25,045.00
Sponsoring Organization – OST-R, USC, Clemson University
Description: In this Phase-I, one-year project we propose to conduct feasibility studies and provide recommendations for the development of a Railway Right of Way Monitoring and Early Warning System (RailMEWS). The feasibility study will answer the following questions:
- How can we use drones and satellites to monitor the railway infrastructure?
- What infrastructure components can be monitored effectively and what are the potential limitations of a RailMEWS in each case?
- What railway Infrastructure Monitoring Systems (IMS) are available today for integration with satellite and drone data?
- What are the desired functions and design parameters of an Early Warning System? (e.g. connectivity to the signaling system, real-time vs. centralized processing, etc)
- What is the incremental investment needed to develop the RailMEWS system?
Intellectual Merit:We propose to process information obtained from commercially available satellite and aerial imagery and combine with information obtained by conventional monitoring systems to develop maps that show the kinematic behavior of the railway infrastructure at the terrain, sub-structure and track scales
Broader Impacts: The proposed work will set the foundations for the development and implementation of a RailMEWS to the railway network, its expansion to other transportation modes and its implementation to the transportation network statewide and beyond. This research will set the framework for larger research projects with diverse research partners and will be ex-tended in future studies to monitor the roadway network, ports, and inland ports simultaneously and will be integrated with their respective signaling systems.
Improved Resiliency of Transportation Networks through Connected Mobility
Lead Principal Investigator – Paul Ziehl, University of South Carolina
Co-Principal Investigator(s) – Robert Mullen, University of South Carolina; Weichiang Pang, Clemson University
October 2017 – March 2019
Project Status – Ended
Funding Amount – UTC – $ 95,001.00; USC – $25,005.00; Clemson – $23,000.00
Sponsoring Organization – OST-R, USC, Clemson University
Description: Recent events, such as Hurricane Sandy, have shown how cascading failures of infrastructure systems result in catastrophic disaster events. Transportation networks are complex, interdependent, and critical for safety. Embedded intelligence within transportation networks, however, is extraordinarily rare. When an extreme event does occur, it is critical that sub-components (bridges) within the network be evaluated in near real-time for re-routing of traffic and post disaster emergency response purposes. Prior knowledge developed at the University of South Carolina in relation to remote wireless systems for infrastructure assessment at the local (bridge and bridge component) level will be leveraged to complement expertise in assessment of resiliency in transportation networks at Clemson University. The greater Charleston, SC area will be used for study due to its large and diverse population coupled with vulnerability to hurricanes, earthquakes, and man made disasters
Intellectual Merit: The goal of this project is to lay the necessary groundwork for the deployment of intelligent transportation infrastructure, with a focus on extreme events in complex transportation networks. The methodology is the fusing of remotely gathered damage information from critical components (bridges) with rapid and reliable assessment of the resiliency of transportation networks.
Broader Impacts: The outcome of the project will be a framework whereby remotely gathered structural health assessment data will be incorporated into transportation network reliability models. This will enable quantification of the benefits of intelligent systems within transportation networks and will serve as a tool to determine the most critical locations within the network for timely deployment of such systems..