scholarly journals Online Event Recognition from Moving Vehicles: Application Paper

2019 ◽  
Vol 19 (5-6) ◽  
pp. 841-856
Author(s):  
EFTHIMIS TSILIONIS ◽  
NIKOLAOS KOUTROUMANIS ◽  
PANAGIOTIS NIKITOPOULOS ◽  
CHRISTOS DOULKERIDIS ◽  
ALEXANDER ARTIKIS
Keyword(s):  
Event Recognition ◽  
Weather Data ◽  
External Information ◽  
Commercial Vehicles ◽  
Real World Data ◽  
Mobility Data ◽  
Composite Event ◽  
Points Of Interest ◽  
Moving Vehicles ◽  
Application Paper

AbstractWe present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency.

Dagstuhl Seminar on the Foundations of Composite Event Recognition

2021 ◽  
Vol 49 (4) ◽  
pp. 24-27
Author(s):  
Alexander Artikis ◽  
Thomas Eiter ◽  
Alessandro Margara ◽  
Stijn Vansummeren
Keyword(s):  
Event Recognition ◽  
Event Data ◽  
Composite Event ◽  
Geographically Distributed

Composite event recognition (CER) is concerned with continuously matching patterns in streams of 'event' data over (geographically) distributed sources. This paper reports the results of the Dagstuhl Seminar "Foundations of Composite Event Recognition" held in 2020.

Communication Networks to Connect Moving Vehicles to Transportation Systems to Infrastructure

2015 ◽  
pp. 815-835
Author(s):  
Kira Kastell
Keyword(s):  
Wireless Communication ◽  
Communication Networks ◽  
Data Transfer ◽  
Critical Role ◽  
Transportation System ◽  
Transportation Systems ◽  
Wireless Access Networks ◽  
Mobility Data ◽  
Wireless Communication Network ◽  
Moving Vehicles

Communication in transportation systems not only involves the communication inside a vehicle, train, or airplane but it also includes the transfer of data to and from the transportation system or between devices belonging to that system. This will be done using different types of wireless communication. Therefore in this chapter, first, the fundamentals of mobile communication networks are shortly described. Thereafter, possible candidate networks are discussed. Their suitability for a certain transportation system can be evaluated taking into consideration the system's requirements. Among the most prominent are the influence of speed and mobility, data rate and bit error rate constraints, reliability of the system and on-going connections. As in most of the cases, there will be no single best wireless communication network to fulfil all requirements, and in this chapter also hybrid networks are discussed. These are networks consisting of different (wireless) access networks. The devices may use the best suited network for a given situation but also change to another network while continuing the on-going connection or data transfer. Here the design of the handover or relocation plays a critical role as well as localization.

scholarly journals Social Compliance During High Stringency Periods Efficiently Reduces COVID-19 Incidence: Evidence from Google Mobility Reports

2021 ◽  
Author(s):  
Lasse Hyldig Hansen ◽  
Thomas Lykke Rasmussen ◽  
Palle Villesen
Keyword(s):  
Human Behavior ◽  
Residential Mobility ◽  
Explanatory Power ◽  
Viral Transmission ◽  
Weather Data ◽  
Surveillance Program ◽  
Mobility Data ◽  
Extensive Test ◽  
Current Compliance ◽  
Variance Explained

Abstract It is crucial to understand and learn as much as possible from the current global Sars-CoV-2 pandemic for the sake of future precautions. Apart from strong government restrictions such as complete lockdowns, curfews, and mask mandates, other factors influence viral transmission. Since June 2020, Denmark has had an extensive test and surveillance program and made data publicly available at the municipality level. Here we use these data and integrate publicly available data on government restrictions, weather data, and mobility data to model COVID-19 incidence in 98 Danish municipalities from September 2020 to February 2021. The inclusion of municipality heterogeneity, weather and mobility data increases the amount of variance explained by ~29% compared to a simpler model taking only incidence and restrictions into account. We found a strong and significant effect from temperature which interacts with government restrictions. Our results indicate that higher temperatures limit viral transmission when government restrictions are low, but that the temperature effect diminishes under stronger restrictions. This is most likely due to a change in human behavior rather than a biological effect. Likewise, we found that changes in residential mobility were significant factors that also interacted with restrictions. When restrictions were strong, we found that increased residential mobility resulted in decreased COVID-19 incidence, suggesting residential mobility as a proxy for compliance. Our results show the increased explanatory power of integrating different variables when modeling COVID-19 incidence. The weather seems to predict human behavior in a quite predictable way and mobility data could be used to measure current compliance with government restrictions.

scholarly journals A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas

2020 ◽  
Vol 54 (3) ◽  
pp. 606-630 ◽  
Author(s):  
Giacomo Dalla Chiara ◽  
Lynette Cheah ◽  
Carlos Lima Azevedo ◽  
Moshe E. Ben-Akiva
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Commercial Vehicle ◽  
Choice Model ◽  
Discrete Event ◽  
Real Data ◽  
Behavior Modeling ◽  
Model Parameters ◽  
Commercial Vehicles ◽  
Real World Data

Understanding factors that drive the parking choice of commercial vehicles at delivery stops in cities can enhance logistics operations and the management of freight parking infrastructure, mitigate illegal parking, and ultimately reduce traffic congestion. In this paper, we focus on this decision-making process at large urban freight traffic generators, such as retail malls and transit terminals, that attract a large share of urban commercial vehicle traffic. Existing literature on parking behavior modeling has focused on passenger vehicles. This paper presents a discrete choice model for commercial vehicle parking choice in urban areas. The model parameters were estimated by using detailed, real-world data on commercial vehicle parking choices collected in two commercial urban areas in Singapore. The model analyzes the effect of several variables on the parking behavior of commercial vehicle drivers, including the presence of congestion and queueing, attitudes toward illegal parking, and pricing (parking fees). The model was validated against real data and applied within a discrete-event simulation to test the economic and environmental impacts of several parking measures, including pricing strategies and parking enforcement.

scholarly journals Composite Event Recognition for Maritime Monitoring

2019 ◽  
Author(s):  
Manolis Pitsikalis ◽  
Alexander Artikis ◽  
Richard Dreo ◽  
Cyril Ray ◽  
Elena Camossi ◽  
...  
Keyword(s):  
Event Recognition ◽  
Composite Event

scholarly journals Little Botany: A Mobile Game Utilizing Data Integration to Enhance Plant Science Education

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Suphanut Jamonnak ◽  
En Cheng
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Real World ◽  
Social Life ◽  
Educational Games ◽  
Daily Basis ◽  
Educational Game ◽  
Weather Data ◽  
Plant Structure ◽  
Real World Data

Mobile devices are rapidly becoming the new medium of educational and social life for young people, and hence mobile educational games have become an important mechanism for learning. To help school-aged children learn about the fascinating world of plants, we present a mobile educational game called Little Botany, where players can create their own virtual gardens in any location on earth. One unique feature of Little Botany is that the game is built upon real-world data by leveraging data integration mechanism. The gardens created in Little Botany are augmented with real-world location data and real-time weather data. More specifically, Little Botany is using real-time weather data for the garden location to simulate how the weather affects plants growth. Little Botany players can learn to select what crops to plant, maintain their own garden, watch crops to grow, tend the crops on a daily basis, and harvest them. With this game, users can also learn plant structure and three chemical reactions.

Roll Damping Analysis of In-Field FPSO Roll Response

2017 ◽  
Author(s):  
Arjan Voogt ◽  
Harish Pillai ◽  
Robert Seah
Keyword(s):  
Real World ◽  
Weather Conditions ◽  
Fatigue Loading ◽  
Nonlinear Damping ◽  
Complex Problem ◽  
Weather Data ◽  
Empirical Estimation ◽  
Real World Data ◽  
Roll Damping ◽  
Novel Approach

Due to the resonance behavior of roll motions, roll damping is an important consideration for vessel motions and associated extreme and fatigue loading on the hull, topsides and risers of an FPSO. In many cases radiation damping is limited and passive damping devices such as bilge keels are installed to spur viscous eddies and hence limit the roll motions. This contributes nonlinear damping to an already complex problem. Designers often rely on model tests to assess this damping. Based on test results, empirical and semi-empirical estimation models have been developed for different ship types and are available in current literature, but examples of benchmark validation with real world data are limited. These benchmarks are often hindered by uncertainty in the observed weather conditions, vessel loading conditions and vessel heading with respect to the waves. This paper discusses these challenges and introduces a novel approach used to characterize the actual roll damping for an FPSO under real world conditions. The assumptions, methodology and results will be discussed in this paper. In this study, 5 years of hindcast weather data is examined along with FPSO heading and roll motion measurements. The roll damping characteristics of this FPSO was expected to change over the course of the measurements and the study documents the actual variation of roll damping under various conditions over this period.

scholarly journals Multi-Group Encoder-Decoder Networks to Fuse Heterogeneous Data for Next-Day Air Quality Prediction

2019 ◽  
Author(s):  
Yawen Zhang ◽  
Qin Lv ◽  
Duanfeng Gao ◽  
Si Shen ◽  
Robert Dick ◽  
...  
Keyword(s):  
Air Quality ◽  
Heterogeneous Data ◽  
Air Pollutant ◽  
Weather Data ◽  
Quality Prediction ◽  
Prevention Measures ◽  
Convolution Operators ◽  
Real World Data ◽  
Air Quality Prediction ◽  
Cross Domain

Accurate next-day air quality prediction is essential to enable warning and prevention measures for cities and individuals to cope with potential air pollution, such as vehicle restriction, factory shutdown, and limiting outdoor activities. The problem is challenging because air quality is affected by a diverse set of complex factors. There has been prior work on short-term (e.g., next 6 hours) prediction, however, there is limited research on modeling local weather influences or fusing heterogeneous data for next-day air quality prediction. This paper tackles this problem through three key contributions: (1) we leverage multi-source data, especially high-frequency grid-based weather data, to model air pollutant dynamics at station-level; (2) we add convolution operators on grid weather data to capture the impacts of various weather parameters on air pollutant variations; and (3) we automatically group (cross-domain) features based on their correlations, and propose multi-group Encoder-Decoder networks (MGED-Net) to effectively fuse multiple feature groups for next-day air quality prediction. The experiments with real-world data demonstrate the improved prediction performance of MGED-Net over state-of-the-art solutions (4.2% to 9.6% improvement in MAE and 9.2% to 16.4% improvement in RMSE).

scholarly journals Modeling Spatio-Temporal Evolution of Urban Crowd Flows

2019 ◽  
Vol 8 (12) ◽  
pp. 570 ◽  
Author(s):  
Kun Qin ◽  
Yuanquan Xu ◽  
Chaogui Kang ◽  
Stanislav Sobolevsky ◽  
Mei-Po Kwan
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Moving Objects ◽  
Morphological Changes ◽  
Morphological Evolution ◽  
Analytical Framework ◽  
Real World Data ◽  
Evolutionary Patterns ◽  
Mobility Data ◽  
Spatio Temporal

Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestion, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, yet it is very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix-computation-based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps: (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes, and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification, and correlation into a unified and efficient analytical framework. We validated the proposed methodology under both synthetic and real-world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers, and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity, and spatial constraint are also discussed.

Sign in / Sign up

Export Citation Format

Share Document