scholarly journals Review of Usage of Real-World Connected Vehicle Data

2020 ◽  
Vol 2674 (10) ◽  
pp. 939-950
Author(s):  
Yun Zhou ◽  
Raj Bridgelall
Keyword(s):  
Real World ◽  
Large Scale ◽  
The United States ◽  
Connected Vehicles ◽  
United States Department ◽  
Connected Vehicle ◽  
Pilot Model ◽  
Vehicle Data ◽  
World Information ◽  
Vehicle Technologies

GPS loggers and cameras aboard connected vehicles can produce vast amounts of data. Analysts can mine such data to decipher patterns in vehicle trajectories and driver–vehicle interactions. Ability to process such large-scale data in real time can inform strategies to reduce crashes, improve traffic flow, enhance system operational efficiencies, and reduce environmental impacts. However, connected vehicle technologies are in the very early phases of deployment. Therefore, related datasets are extremely scarce, and the utility of such emerging datasets is largely unknown. This paper provides a comprehensive review of studies that used large-scale connected vehicle data from the United States Department of Transportation Connected Vehicle Safety Pilot Model Deployment program. It is the first and only such dataset available to the public. The data contains real-world information about the operation of connected vehicles that organizations are testing. The paper provides a summary of the available datasets and their organization, and the overall structure and other characteristics of the data captured during pilot deployments. Usage of the data is then classified into three categories: driving pattern identification, development of surrogate safety measures, and improvements in the operation of signalized intersections. Finally, some limitations experienced with the existing datasets are identified.

scholarly journals Microscopic Estimation of Freeway Vehicle Positions from the Behavior of Connected Vehicles

2020 ◽  
Author(s):  
Noah J. Goodall ◽  
Brian L. Smith ◽  
Byungkyu Brian Park
Keyword(s):  
Traffic Signals ◽  
The United States ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Travel Time Prediction ◽  
High Penetration ◽  
Initial Penetration ◽  
Car Following Model ◽  
Status Data

Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicle's acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates.

scholarly journals Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Raj Bridgelall ◽  
Pan Lu ◽  
Denver D. Tolliver ◽  
Tai Xu
Keyword(s):  
Large Scale ◽  
Statistical Tests ◽  
Poor Quality ◽  
Connected Vehicle ◽  
Cleaning Method ◽  
Mobility Data ◽  
Automated Method ◽  
Vehicle Data ◽  
Chi Squared ◽  
Trip Production

On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.

scholarly journals Proton Pump Inhibitors and Risk of Dementia: A Hypothesis Generated but Not Adequately Tested

2021 ◽  
Vol 36 ◽  
pp. 153331752110624
Author(s):  
Mishah Azhar ◽  
Lawrence Fiedler ◽  
Patricio S. Espinosa ◽  
Charles H. Hennekens
Keyword(s):  
Proton Pump Inhibitors ◽  
Proton Pump ◽  
Real World ◽  
Large Scale ◽  
Basic Research ◽  
The United States ◽  
Epidemiological Studies ◽  
Real World Data ◽  
Health Authorities ◽  
Public Health Authorities

We reviewed the evidence on proton pump inhibitors (PPIs) and dementia. PPIs are among the most widely utilized drugs in the world. Dementia affects roughly 5% of the population of the United States (US) and world aged 60 years and older. With respect to PPIs and dementia, basic research has suggested plausible mechanisms but descriptive and analytic epidemiological studies are not inconsistent. In addition, a single large-scale randomized trial showed no association. When the evidence is incomplete, it is appropriate for clinicians and researchers to remain uncertain. Regulatory or public health authorities sometimes need to make real-world decisions based on real-world data. When the evidence is complete, then the most rational judgments for individual patients the health of the general public are possible At present, the evidence on PPIs and dementia suggests more reassurance than alarm. Further large-scale randomized evidence is necessary to do so.

scholarly journals Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Xiao Li ◽  
Haowen Xu ◽  
Xiao Huang ◽  
Chenxiao Guo ◽  
Yuhao Kang ◽  
...  
Keyword(s):  
Data Privacy ◽  
Large Scale ◽  
Human Mobility ◽  
Data Sources ◽  
Data Uncertainty ◽  
Connected Vehicle ◽  
Research Experience ◽  
Mobility Data ◽  
Vehicle Data ◽  
Spatial Coverage

AbstractEffectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors’ opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source’s main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors’ research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.

scholarly journals Real-world longitudinal data collected from the SleepHealth mobile app study

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sean Deering ◽  
Abhishek Pratap ◽  
Christine Suver ◽  
A. Joseph Borelli ◽  
Adam Amdur ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Digital Health ◽  
The United States ◽  
Mobile App ◽  
Environmental Data ◽  
Sleep Habits ◽  
Real World Data ◽  
Disease Manifestation ◽  
Novel Approach

AbstractConducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants’ daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, and examining user retention patterns and data-sharing preferences. Here, we provide a description of data collected from 7,250 participants living in the United States who chose to share their data broadly with the study team and qualified researchers worldwide.

The SRC-II process

1981 ◽  
Vol 300 (1453) ◽  
pp. 129-139 ◽  
Keyword(s):  
Gas Turbines ◽  
Large Scale ◽  
Coal Gasification ◽  
The United States ◽  
Electric Utility ◽  
Department Of Energy ◽  
Fuel Oil ◽  
United States Department ◽  
Selling Price ◽  
Home Heating

Extensive laboratory and pilot plant experimental work on the Solvent Refined Coal process by Gulf Oil Corporation over the past 18 years, sponsored by the Fossil Fuel Division of the United States Department of Energy and its predecessor agencies, has led to the development of an improved version of the process known as SRC-II. This work has shown considerable promise in recent years and plans are now being made to demonstrate the SRC-II process with commercial size equipment in a 6000 ton/day (5440 t/day) plant to be located near Morgantown, West Virginia. On the basis of recent economic studies, the products (both liquid and gas) from a future large-scale commercial plant are expected to have an overall selling price of $4.25-4.75/GJ (first quarter 1980 basis). The major product of the primary process is distillate fuel oil of less than 0.3 % sulphur for use largely as a non-polluting fuel for generating electrical power and steam, especially in the east where utilities and industry are currently using petroleum products. In such applications, SRC-II fuel oil is expected to be competitive with petroleum-derived fuels within the next decade. During this period, SRC-II fuel oil should be economically attractive compared with coal combustion with flue gas desulphurization in electric utility and industrial boilers, particularly in the major metropolitan areas. Naphtha produced by the SRC-II process can be upgraded to a high-octane unleaded gasoline to supplement petroleum-derived supplies. Significant quantities of pipeline gas are also produced at a cost that should be competitive with s.n.g. from direct coal gasification. Light hydrocarbons (ethane, propane) from the process may be effectively converted to ethylene. In addition, certain fractions of the fuel oil might also be used in medium-speed diesel engines and automotive gas turbines. For many of these applications, the fuel oil and other products from the SRC-II process would displace high-quality petroleum fractions, which could then be used for production of diesel fuels, jet fuels, home heating oil and gasoline by conventional refinery processes.

scholarly journals Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Pangwei Wang ◽  
Yunfeng Wang ◽  
Hui Deng ◽  
Mingfang Zhang ◽  
Juan Zhang
Keyword(s):  
Traffic Management ◽  
Trajectory Optimization ◽  
Fuel Efficiency ◽  
Optimization Method ◽  
Connected Vehicles ◽  
Full Potential ◽  
Connected Vehicle ◽  
Vehicle Technologies ◽  
Vehicle Fuel Efficiency ◽  
Trajectory Problem

It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme.

scholarly journals Bridging the Gap between SUMO & Kuksa: Using A Traffic Simulator for Testing Cloud-based Connected Vehicle Services

10.29007/9kkv ◽  
2019 ◽  
Author(s):  
Philipp Heisig ◽  
Sven Erik Jeroschewski ◽  
Johannes Kristan ◽  
Robert Höttger ◽  
Ahmad Banijamali ◽  
...  
Keyword(s):  
Real World ◽  
Business Models ◽  
Cloud Services ◽  
Connected Vehicles ◽  
Data Sets ◽  
Connected Vehicle ◽  
Cloud Platform ◽  
On The Road ◽  
Context Specific ◽  
High Level

The emerging usage of connected vehicles promises new business models and a high level of innovation, but also poses new challenges for the automotive domain and in particular for the connectivity dimension, i. e. the connection between vehicles and cloud environments including the architecture of such systems. Among other challenges, IoT Cloud platforms and their services have to scale with the number of vehicles on the road to provide functionality in a reliable way, especially when dealing with safety-related functions. Testing the scalability, functionality, and availability of IoT Cloud platform architectures for connected vehicles requires data from real world scenarios instead of hypothetical data sets to ensure both the proper functionality of distinct connected vehicle services and that the architecture scales with a varying number of vehicles. However, the closed and proprietary nature of current connected vehicle solutions aggravate the availability of both vehicle data and test environments to evaluate different architectures and cloud solutions. Thus, this paper introduces an approach for connecting the Eclipse SUMO traffic simulation with the open source connected vehicle ecosystem Eclipse Kuksa. More precisely, Eclipse SUMO is used to simulate traffic scenarios including microscopic properties like the position or emission. The generated data of each vehicle is then be sent to the message gateway of the Kuksa IoT Cloud platform and delegated to an according example service that consumes the data. In this way, not only the scalability of connected vehicle IoT architectures can be tested based on real world scenarios, but also the functionality of cloud services can be ensured by providing context-specific automotive data that goes beyond rudimentary or fake data-sets.

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