A Framework for Collision Prediction Using Historical Accident Information and Real-time Sensor Data: A Case Study for the City of Ottawa

2019 ◽  
Author(s):  
Enrique Reveron ◽  
Ana-Maria Cretu
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Collision Prediction ◽  
The City ◽  
Historical Accident

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

Innovative real-time sensing of flow dynamics in groundwater and sediments to map contaminant spreading

2021 ◽  
Author(s):  
Goedele Verreydt ◽  
Niels Van Putte ◽  
Timothy De Kleyn ◽  
Joris Cool ◽  
Bino Maiheu
Keyword(s):  
Surface Water ◽  
Groundwater Flow ◽  
Real Time ◽  
Flow Direction ◽  
Flow Dynamics ◽  
Sensor Data ◽  
Time Data ◽  
Mass Fluxes ◽  
First Case

<p>Groundwater dynamics play a crucial role in the spreading of a soil and groundwater contamination. However, there is still a big gap in the understanding of the groundwater flow dynamics. Heterogeneities and dynamics are often underestimated and therefore not taken into account. They are of crucial input for successful management and remediation measures. The bulk of the mass of mass often is transported through only a small layer or section within the aquifer and is in cases of seepage into surface water very dependent to rainfall and occurring tidal effects.</p><p> </p><p>This study contains the use of novel real-time iFLUX sensors to map the groundwater flow dynamics over time. The sensors provide real-time data on groundwater flow rate and flow direction. The sensor probes consist of multiple bidirectional flow sensors that are superimposed. The probes can be installed directly in the subsoil, riverbed or monitoring well. The measurement setup is unique as it can perform measurements every second, ideal to map rapid changing flow conditions. The measurement range is between 0,5 and 500 cm per day.</p><p> </p><p>We will present the measurement principles and technical aspects of the sensor, together with two case studies.</p><p> </p><p>The first case study comprises the installation of iFLUX sensors in 4 different monitoring wells in a chlorinated solvent plume to map on the one hand the flow patterns in the plume, and on the other hand the flow dynamics that are influenced by the nearby popular trees. The foreseen remediation concept here is phytoremediation. The sensors were installed for a period of in total 4 weeks. Measurement frequency was 5 minutes. The flow profiles and time series will be presented together with the determined mass fluxes.</p><p> </p><p>A second case study was performed on behalf of the remediation of a canal riverbed. Due to industrial production of tar and carbon black in the past, the soil and groundwater next to the small canal ‘De Lieve’ in Ghent, Belgium, got contaminated with aliphatic and (poly)aromatic hydrocarbons. The groundwater contaminants migrate to the canal, impact the surface water quality and cause an ecological risk. The seepage flow and mass fluxes of contaminants into the surface water were measured with the novel iFLUX streambed sensors, installed directly in the river sediment. A site conceptual model was drawn and dimensioned based on the sensor data. The remediation concept to tackle the inflowing pollution: a hydraulic conductive reactive mat on the riverbed that makes use of the natural draining function of the waterbody, the adsorption capacity of a natural or secondary adsorbent and a future habitat for micro-organisms that biodegrade contaminants. The reactive mats were successfully installed and based on the mass flux calculations a lifespan of at least 10 years is expected for the adsorption material.  </p>

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Applying a Model for Trip Assignment and Dynamic Routing of Automated Taxis with Congestion: System Performance in the City of Delft, The Netherlands

2018 ◽  
Vol 2672 (8) ◽  
pp. 588-598 ◽  
Author(s):  
Xiao Liang ◽  
Gonçalo Homem de Almeida Correia ◽  
Bart van Arem
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Traffic Congestion ◽  
Programming Model ◽  
Profit Maximization ◽  
The Real ◽  
Real Size ◽  
The Impact ◽  
The City

This paper proposes a method of assigning trips to automated taxis (ATs) and designing the routes of those vehicles in an urban road network, and also considering the traffic congestion caused by this dynamic responsive service. The system is envisioned to provide a seamless door-to-door service within a city area for all passenger origins and destinations. An integer programming model is proposed to define the routing of the vehicles according to a profit maximization function, depending on the dynamic travel times, which varies with the ATs’ flow. This will be especially important when the number of automated vehicles (AVs) circulating on the roads is high enough that their routing will cause delays. This system should be able to serve not only the reserved travel requests, but also some real-time requests. A rolling horizon scheme is used to divide one day into several periods in which both the real-time and the booked demand will be considered together. The model was applied to the real size case study city of Delft, the Netherlands. The results allow assessing of the impact of the ATs movements on traffic congestion and the profitability of the system. From this case-study, it is possible to conclude that taking into account the effect of the vehicle flows on travel time leads to changes in the system profit, the satisfied percentage and the driving distance of the vehicles, which highlights the importance of this type of model in the assessment of the operational effects of ATs in the future.

A Case Study of Embedding Real-time Infrastructure Sensor Data to BIM

2014 ◽  
Author(s):  
Jianli Chen ◽  
Tanyel Bulbul ◽  
John E. Taylor ◽  
Guney Olgun
Keyword(s):  
Real Time ◽  
Sensor Data

A Case Study: São Paulo and Its Real-Time Museum of the City

2016 ◽  
pp. 191-213
Author(s):  
Salvatore Iaconesi ◽  
Oriana Persico
Keyword(s):  
Real Time ◽  
Sao Paulo ◽  
São Paulo ◽  
The City

Use of Real-Time Rig-Sensor Data To Improve Daily Drilling Reporting, Benchmarking, and Planning—A Case Study

2007 ◽  
Vol 22 (03) ◽  
pp. 217-226 ◽  
Author(s):  
Wolfgang Mathis ◽  
Gerhard Thonhauser ◽  
Gerhard Wallnoefer ◽  
Johannes Ettl
Keyword(s):  
Real Time ◽  
Sensor Data

Impacts of Changing from Permissive/Protected Left-Turn to Protected-Only Phasing: Case Study in the City of Tucson, Arizona

2019 ◽  
Vol 2673 (4) ◽  
pp. 616-626
Author(s):  
Xiaofeng Li ◽  
Alexander Weber ◽  
Adrian Cottam ◽  
Yao-Jan Wu
Keyword(s):  
Data Driven ◽  
Sensor Data ◽  
Signal Timing ◽  
Delay Model ◽  
Safety Measures ◽  
Trade Off ◽  
Near Misses ◽  
Left Turn ◽  
The City

Recent research has focused on the safety or mobility impacts of signal timing. Several studies have compared the choice between a protected-only left turn (PO) and a protected-permissive left turn (PPLT). However, few have compared both the safety and mobility impacts, and their tradeoffs. This study proposed data-driven methods to conduct a pilot study at an intersection in Tucson, Arizona. This study evaluated the impacts on vehicular mobility and multi-modal safety when changing from a PPLT to a PO. First, the daily and annual delay for the through and left-turn movements for the intersection was evaluated using a calibrated delay model and year-long 15-min traffic sensor data. Then, real-world near misses between cyclists, pedestrians, and vehicles were manually collected and analyzed using 48 h of videos. Last, both mobility and safety measures were converted into an annual cost to determine the trade-off between the before (PPLT) and the after (PO) situations. The results of this study demonstrate the feasibility of the proposed methods, providing practitioners with different options to evaluate left-turn phasing strategies effectively and efficiently.

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