An Energy-Harvesting Railroad Tie for Improving Track\ua0Condition Monitoring and Safety

Researchers at the Railway Technologies Laboratory (RTL) of the Center for Vehicle Systems and Safety (CVeSS) at Virginia Tech have designed and developed an energy harvesting railroad tie, shown in Figure 1, to power trackside electronics and sensors for improving track condition monitoring and safety. As a member of the Rail Transportation Engineering and Advance Maintenance (RailTEAM) consortium led by the University of Nevada Las Vegas (UNLV), RTL is funded by the U.S. Department of Transportation University Transportation Center program. RTL explores technologies that advance railroad sciences and enable the U.S. rail industry to become more efficient and globally competitive. The shortage of electrical power along railroad tracks significantly limits the railroads\u2019 ability to apply intelligent solutions for improving rail safety and connectivity. Much advanced wayside electrical equipment desired by the U.S. railroads cannot be employed readily on tracks due to the absence of electrical power. For example, some railroads use drones as a preferred means of physical inspection of track conditions, but the average maximum battery life for most commercial drones is only 22 to 27 minutes, significantly limit their operational range and length of flight

Evaluation of the HUD Elder Cottage Housing Opportunity (ECHO) program

ECHO housing was introduced in the United States in the 1980s based on a program started in Australia in 1975. An ECHO unit is a small house in which an elderly person resides and which is placed near the home of a host (either relatives or close friends of the elderly person). The purpose of this arrangement is to make it convenient and efficient for the occupants of the host family dwelling to provide assistance to the elderly person residing in the smaller ECHO house. Although ECHO housing provides a means for keeping an elderly resident close to family and friends and may delay or eliminate the necessity of institutional care, administering an ECHO housing program is difficult. Issues surrounding design, quality, maintenance, and oversight vary depending on location and the key groups involved. Problems arise when ECHO units are no longer needed due to death of the resident or other family problems. Relocating units is difficult in terms of where to move them and how to move them without damage. The costs of moving the units add considerably to the overall costs that vary depending on a variety of factors. In addition, zoning is often a barrier that limits ECHO housing to large lots and rural areas

Vulnerable Road User Mobility Assistance Platform

Researchers at the Virginia Tech Transportation Institute (VTTI) and North Carolina A&T State University (NC A&T) are collaborating on an innovative project sponsored by the Center for Advanced Transportation Mobility (CATM) Tier 1 UTC. The goal of the Vulnerable Road User Mobility Assistance Platform (VRU-MAP) project is to improve the safety and efficiency of mobility for people with disabilities who walk and use transit in urban and suburban environments. We are accomplishing this by developing an application platform that will provide highly personalized guidance. As with existing navigation software, this app will allow travelers to use their smartphones to map out routes to destinations and provide turn-by-turn directions. However, this app will provide routes that are custom-tailored to an individual\u2019s unique needs and capabilities. For example, a wheelchair user needs to avoid stairs, while a person who is frail may require a place to sit and rest at regular intervals. The VRU-MAP app will enable users to save personal information about themselves that is relevant to their transportation needs, such as stamina and ability to traverse uneven terrain. It will then combine that personal information with publicly-available information about route nodes, elevation changes, weather, traffic, multimodal transit, etc., along with crowd-sourced information about route impediments (such as construction), facilities, rest opportunities, etc. to provide personalized route guidance for users

Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles towards Uncertainty of Actuated Traffic Signals

69A43551747123This report introduces a robust green light optimal speed advisory (GLOSA) system for fixed and actuated traffic signals which considers a probability distribution. These distributions represent the domain of possible switching times from the signal phasing and timing (SPaT) messages. The system finds the least-cost (minimum fuel consumption) vehicle trajectory using a computationally efficient A* algorithm incorporated within a dynamic programming (DP) procedure to minimize the vehicle\u2019s total fuel consumption. Constraints are introduced to ensure that vehicles do not collide with other vehicles, run red indications, or exceed a maximum vehicular jerk for passenger comfort. Results of simulation scenarios are evaluated against empirical comparable trajectories of uninformed drivers to compute fuel consumption savings. The proposed approach produced significant fuel savings compared to an uninformed driver behavior, amounting to 37% on average for deterministic SPaT and 30% for stochastic SPaT data. A sensitivity analysis was performed to understand how the degree of uncertainty in SPaT predictions affects the optimal trajectory\u2019s fuel consumption. The results present the required levels of confidence in these predictions to achieve savings in fuel consumption. Specifically, the study demonstrates that the proposed system can be within 85% of the maximum savings if the timing error is (\ub13.3 seconds) at a 95% confidence level. Results also emphasize the importance of more reliable SPaT predictions as the time to green decreases relative to the time the vehicle is expected to reach the intersection given its current speed

Cooperative Perception of Connected Vehicles for Safety

69A3551747115/ Project 05-115In cooperative perception, reliably detecting surrounding objects and communicating the information between vehicles is necessary for safety. However, vehicle-to-vehicle transmission of huge datasets or images can be computationally expensive and often not feasible in real time. A robust approach to ensure cooperation involves relative pose estimation between two vehicles sharing a common field of view. Detecting the object and transferring its location information in real time is necessary when the object is not in the ego vehicle\u2019s field of view. In such scenarios, reliable and robust pose recovery of the object at each instant ensures the ego vehicle accurately estimates its trajectory. Once pose recovery is established, the object\u2019s location information can be obtained for future trajectory prediction. Deterministic predictions provide only point estimates of future states which is not trustworthy under dynamic traffic scenarios. Estimating the uncertainty associated with the predicted states with a certain level of confidence can lead to robust path planning. This study proposed quantifying this uncertainty during forecasting using stochastic approximation, which deterministic approaches fail to capture. The current method is simple and applies Bayesian approximation during inference to standard neural network architectures for estimating uncertainty. The predictions between the probabilistic neural network models were compared with the standard deterministic models. The results indicate that the mean predicted path of probabilistic models was closer to the ground truth when compared with the deterministic prediction. The study has been extended to multiple datasets, providing a comprehensive comparison for each model

Promoting Native Roadside Plant Communities and Ensuring Successful Vegetation Establishment Practices

118543The loss of vegetation from roadside activities can lead to erosion and an increased sediment load in stormwater ponds. Current VDOT procedures regarding approved seed blends and establishment practices have led to inconsistent vegetation establishment and greatly rely on introduced species. Growing concerns regarding the threat of introduced, invasive species have increased the promotion of native plants in landscapes. One example is VDOT\u2019s participation in the Candidate Conservation Agreement for monarchs fostering a desire to better understand factors that may improve milkweed abundance. Native seed blends, however, have failed to produce soil stabilization or long-term establishment in the past, presumably because of erroneous species selection, seed dormancy, and competitive displacement by weedy vegetation. This study was conducted to (1) identify and document potential procedural improvements for successful roadside vegetation establishment in Virginia; (2) propose candidate native plants for VDOT see blend consideration based on a statewide plant community assessment on Virginia roadsides; and (3) summarize the literature on availability, cost, and establishment success of candidate native species. A review of VDOT\u2019s vegetation establishment practices indicates that procedural inconsistencies related to the development of Roadside Development Sheets and recent restrictions on fertilizer application may be contributing to vegetation establishment failures. A statewide plant community assessment evaluated 490 sites and identified 616 unique plant species among the 67,330 plants surveyed. The Shannon Diversity Index was calculated for 2,450 10-m transects, indicating that plant biodiversity was higher on low-maintenance distal backslopes compared with high-maintenance road edges, shoulders, and ditches. Plant biodiversity was also higher on secondary roads than on primary roads. The unique introduced species encountered were relatively stable across Virginia\u2019s seven ecoregions, but unique native species were more ecosystem dependent. Unique native species increased from 114 species on the road edge and shoulder to 281 species on the distal backslope. The likelihood of encountering a native plant increases from 1 in 4 on the road edge to 1 in 2 on the distal backslope. Among the native plants that were most frequently encountered, seeds were often unavailable or price prohibitive. Andropogon virginicus, Tridens flavus, Dichanthelium clandestinum, Tripsacum dactyloides, and Sorghastrum nutans have desirable attributes as native roadside grasses and are among the top 20 most commonly encountered native grasses on Virginia roadsides. The average cost of the seed for these grasses was $59 per pound compared with$2.40 per pound for tall fescue. Among grasses that are currently not commercially available, Setaria parviflora, Eragrostis pectinacean, Dichanthelium laxiflorum, and Panicum anceps are among the top 10 most commonly encountered native grasses and have characteristics that would be desirable for roadside vegetation. At least one milkweed species was observed at 37 out of 490 sites statewide (7.6%). The report recommends that VDOT explore opportunities to improve understanding of procedural policy and to implement procedural improvements, including revisions to the roadside development sheet. Additional opportunities for research include testing native plants for establishment and long term dominance

Quantifying the Impact of Cellular Vehicle-to-Everything (C-V2X) on Transportation System Efficiency, Energy and Environment

69A43551747123As communication technology develops at a rapid pace, connected vehicles (CVs) can potentially enhance vehicle safety while reducing energy consumption and emissions via data sharing. Many researchers have attempted to quantify the impacts of such CV applications and cellular vehicle-to-everything (C-V2X) communication. Highly efficient information interchange in a CV environment can provide timely data to enhance the transportation system\u2019s capacity, and it can support applications that improve vehicle safety and minimize negative impacts on the environment. This study summarizes existing literature on the safety, mobility, and environmental impacts of CV applications; gaps in current CV research; and recommended directions for future CV research. The study investigates a C-V2X eco-routing application that considers the performance of the C-V2X communication technology (mainly packet loss). The performance of the C-V2X communication is dependent on the vehicular traffic density, which is affected by traffic mobility patterns and vehicle routing strategies. As a case study of C-V2X applications, we developed an energy-efficient dynamic routing application using C-V2X Vehicle-to-Infrastructure (V2I) communication technology. Specifically, we developed a Connected Energy-Efficient Dynamic Routing (C-EEDR) application and used it in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles

Driver Behavior in Traffic

DTFH61-09-H-00007Existing traffic analysis and management tools do not model the ability of drivers to recognize their environment and respond to it with behaviors that vary according to the encountered driving situation. The small body of literature on characterizing drivers behavior is typically limited to specific locations (i.e., by collecting data on specific intersections or freeway sections) and is very narrow in scope. This report documented the research performed to model driver behavior in traffic under naturalistic driving data. The research resulted in the development of hybrid car-following model. In addition, a neuro-fuzzy reinforcement learning, an agent-based artificial intelligence machine-learning technique, was used to model driving behavior. The naturalistic driving database was used to train and validate driver agents. The proposed methodology simulated events from different drivers and proved behavior heterogeneities. Robust agent activation techniques were also developed using discriminant analysis. The developed agents were implemented in VISSIM simulation platform and were evaluated by comparing the behavior of vehicles with and without agent activation. The results showed very close resemblance of the behavior of agents and driver data. Prototype agents prototype (spreadsheets and codes) were developed. Future research recommendations include training agents using more data to cover a wider region in the Wiedemann regime space, and sensitivity analysis of agent training parameters