scholarly journals BIM-GIS INTEGRATION FOR TRAFFIC SAFETY IN CITIES

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 161-166
Author(s):  
M. El Mekawy ◽  
M. Issa ◽  
E. Perjons
Keyword(s):  
Traffic Safety ◽  
Moving Objects ◽  
Smart Cities ◽  
Real Life ◽  
Indoor Navigation ◽  
Scientific Impact ◽  
New Methods ◽  
Car Accidents ◽  
Gis Integration ◽  
City Models

Abstract. This paper reports on the results and further extensions of a concept project that was financially supported by VINNOVA (Sweden’s innovation agency). The project aims, by integrating BIM and GIS, to support traffic safety and contribute towards decreasing the probability of car accidents in general and car-bicycle in more specific. The main objective of the project was to investigate different technologies that support the out- and indoor navigation of moving objects on building and geospatial city models based on BIM and GIS, and to present real life objects which help traffic users in taking better decisions. The concept study had a consortium of six partners (academic and industrial). The project resulted in a proposed solution to be implemented in the ongoing extension of the same project. The proposed solution is argued to be comprehensive that utilises BIM-GIS integration, their capabilities and Real Time Positioning Services (RTPS) for smart cities’ applications. Beside its scientific impact, it can be strongly argued that the proposed solution has a high potential for social-economic impact in creating the awareness and framework for automotive, light-weight vehicles manufacturers and automotive appliances suppliers to collaborate in facing this type of rising traffic problem. In addition to that, the open-source nature of the project will encourage different industrial parties to participate and re-utilize the project’s deliverables in new methods and ideas.

scholarly journals S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 148-176
Author(s):  
Maan Khedr ◽  
Naser El-Sheimy
Keyword(s):  
Smart Cities ◽  
Dead Reckoning ◽  
Human Motion ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Navigation Systems ◽  
Short Term ◽  
Navigation Solution ◽  
Correction Technique ◽  
Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

scholarly journals Drunk Drivers’ Willingness to Use and to Pay for Designated Drivers

2021 ◽  
Vol 13 (10) ◽  
pp. 5362
Author(s):  
Rong-Chang Jou ◽  
Li-Wun Syu
Keyword(s):  
Traffic Safety ◽  
Family Background ◽  
Drunk Driving ◽  
Motor Vehicles ◽  
Drinking Habits ◽  
Car Accidents ◽  
Drunk Drivers ◽  
Double Hurdle ◽  
The Government ◽  
Willingness To Use

While drunk driving accidents, which are a serious problem in Taiwan, have decreased in recent years, cases of drunk driving continue to emerge endlessly, and are a source of traffic risks even when the accidents cause no injuries. In order to prevent drunk driving and reduce car accidents, the government has made laws stricter, and has vigorously promoted “designated drivers”. As the concept of designated drivers is not common in Taiwan, this study mainly explores drunk drivers’ understanding of designated drivers in Nantou County and Taichung City, and investigates the willingness of drunk drivers to use and to pay for designated driving services. This study conducted a questionnaire survey on the drunk drivers of the drunk driving and traffic safety training course held at the Motor Vehicles Office. Double-hurdle and tobit models were applied to investigate the issues mentioned above. According to the test results, the tobit model was superior to the double-hurdle model. The estimation results indicated that distance, age, income, family conditions, and drinking habits influence the willingness to use and to pay for designated drivers. Gender, age, family background, and experience in designated driving cause differences in the willingness to use designated drivers in the two regions. It is expected that the conclusion of this study could provide a direction and reference for the future improvement of designated driving services.

scholarly journals Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

Computation ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 35
Author(s):  
Hind R. Mohammed ◽  
Zahir M. Hussain
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Speed ◽  
Moving Objects ◽  
Real Life ◽  
Fuzzy Rules ◽  
High Rate ◽  
Moving Images ◽  
Animal Images

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.

scholarly journals FIRST STEPS TOWARDS AN INTEGRATED CITYGML-BASED 3D MODEL OF VIENNA

2016 ◽  
Vol III-4 ◽  
pp. 139-146 ◽  
Author(s):  
G. Agugiaro
Keyword(s):  
Energy Use ◽  
Smart Cities ◽  
Three Dimensional ◽  
3D City Model ◽  
First Results ◽  
Sustainable Energy Systems ◽  
City Model ◽  
Set Up ◽  
The City ◽  
City Models

This paper presents and discusses the results regarding the initial steps (selection, analysis, preparation and eventual integration of a number of datasets) for the creation of an integrated, semantic, three-dimensional, and CityGML-based virtual model of the city of Vienna. CityGML is an international standard conceived specifically as information and data model for semantic city models at urban and territorial scale. It is being adopted by more and more cities all over the world. <br><br> The work described in this paper is embedded within the European Marie-Curie ITN project “Ci-nergy, Smart cities with sustainable energy systems”, which aims, among the rest, at developing urban decision making and operational optimisation software tools to minimise non-renewable energy use in cities. Given the scope and scale of the project, it is therefore vital to set up a common, unique and spatio-semantically coherent urban model to be used as information hub for all applications being developed. This paper reports about the experiences done so far, it describes the test area and the available data sources, it shows and exemplifies the data integration issues, the strategies developed to solve them in order to obtain the integrated 3D city model. The first results as well as some comments about their quality and limitations are presented, together with the discussion regarding the next steps and some planned improvements.

scholarly journals MODELLING CHANGES, STAKEHOLDERS AND THEIR RELATIONS IN SEMANTIC 3D CITY MODELS

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 137-144
Author(s):  
S. H. Nguyen ◽  
T. H. Kolbe
Keyword(s):  
Change Detection ◽  
Smart Cities ◽  
Deep Understanding ◽  
Structural Level ◽  
Mathematical Methods ◽  
Conceptual Approach ◽  
3D City Models ◽  
Digital Twins ◽  
Virtual City ◽  
City Models

Abstract. Urban digital twins have been increasingly adopted by cities worldwide. Digital twins, especially semantic 3D city models as key components, have quickly become a crucial platform for urban monitoring, planning, analyses and visualization. However, as the massive influx of data collected from cities accumulates quickly over time, one major problem arises as how to handle different temporal versions of a virtual city model. Many current city modelling deployments lack the capability for automatic and efficient change detection and often replace older city models completely with newer ones. Another crucial task is then to make sense of the detected changes to provide a deep understanding of the progresses made in the cities. Therefore, this research aims to provide a conceptual framework to better assist change detection and interpretation in virtual city models. Firstly, a detailed hierarchical model of all potential changes in semantic 3D city models is proposed. This includes appearance, semantic, geometric, topological, structural, Level of Detail (LoD), auxiliary and scoped changes. In addition, a conceptual approach to modelling most relevant stakeholders in smart cities is presented. Then, a model - reality graph is used to represent both the different groups of stakeholders and types of changes based on their relative interest and relevance. Finally, the study introduces two mathematical methods to represent the relevance relations between stakeholders and changes, namely the relevance graph and the relevance matrix.

scholarly journals Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN

2020 ◽  
Vol 12 (12) ◽  
pp. 1908
Author(s):  
Tzu-Yi Chuang ◽  
Jen-Yu Han ◽  
Deng-Jie Jhan ◽  
Ming-Der Yang
Keyword(s):  
Object Recognition ◽  
Moving Objects ◽  
Low Cost ◽  
Three Dimensional ◽  
Real Life ◽  
Moving Object ◽  
Camera Motion ◽  
Ptz Camera ◽  
Real Life Data ◽  
Close Range

Moving object detection and tracking from image sequences has been extensively studied in a variety of fields. Nevertheless, observing geometric attributes and identifying the detected objects for further investigation of moving behavior has drawn less attention. The focus of this study is to determine moving trajectories, object heights, and object recognition using a monocular camera configuration. This paper presents a scheme to conduct moving object recognition with three-dimensional (3D) observation using faster region-based convolutional neural network (Faster R-CNN) with a stationary and rotating Pan Tilt Zoom (PTZ) camera and close-range photogrammetry. The camera motion effects are first eliminated to detect objects that contain actual movement, and a moving object recognition process is employed to recognize the object classes and to facilitate the estimation of their geometric attributes. Thus, this information can further contribute to the investigation of object moving behavior. To evaluate the effectiveness of the proposed scheme quantitatively, first, an experiment with indoor synthetic configuration is conducted, then, outdoor real-life data are used to verify the feasibility based on recall, precision, and F1 index. The experiments have shown promising results and have verified the effectiveness of the proposed method in both laboratory and real environments. The proposed approach calculates the height and speed estimates of the recognized moving objects, including pedestrians and vehicles, and shows promising results with acceptable errors and application potential through existing PTZ camera images at a very low cost.

scholarly journals New methods for traffic safety rating at at-grade intersections

2020 ◽  
Vol 50 ◽  
pp. 528-532
Author(s):  
Anatoly Plotnikov ◽  
Maksim Asaul
Keyword(s):  
Traffic Safety ◽  
New Methods

scholarly journals An urban expressway forwarding scheme for cognitive Internet of vehicles

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091294
Author(s):  
Jing Wang ◽  
Huyin Zhang ◽  
Sheng Hao ◽  
Chuhao Fu
Keyword(s):  
Cognitive Radio ◽  
Traffic Safety ◽  
Smart Cities ◽  
Radio Spectrum ◽  
Base Stations ◽  
Radio Link ◽  
Integer Optimization ◽  
Internet Of Vehicles ◽  
Urban Expressway ◽  
Channel Availability

The Internet of vehicles is an essential component for building smart cities that can improve traffic safety and provide multimedia entertainment services. The cognitive radio–enabled Internet of vehicles was proposed to resolve the conflict between the increasing demand of Internet of vehicles applications and the limited spectrum resources. The multi-hop transmission is one of the most important issues in cognitive radio–enabled Internet of vehicles networks. Nevertheless, most existing forwarding solutions designed for the cognitive radio–enabled Internet of vehicles did not consider the urban expressway scenario, where primary base stations are densely installed with small coverage areas. In this case, it is difficult to ensure that the sender and the receiver of the same cognitive radio link have similar channel availability statistics, which makes cognitive radio links more likely to be interrupted. To address this challenge, we develop a multi-hop forwarding scheme to minimize the end-to-end delay for such networks. We first formulate the delay minimization problem as a non-linear integer optimization problem. Then, we propose an approach to select the relay candidates by jointly considering the high mobility of vehicles and the unique cognitive radio spectrum usage distributions in urban expressway scenarios. Finally, we propose the low-latency forwarding strategies by considering the channel availability and the delay cost of different situations of relay candidates. Simulations show the advantages of our proposed scheme, compared with state-of-art methods.

scholarly journals Depth Prediction without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

2019 ◽  
Vol 33 ◽  
pp. 8001-8008 ◽  
Author(s):  
Vincent Casser ◽  
Soeren Pirk ◽  
Reza Mahjourian ◽  
Anelia Angelova
Keyword(s):  
Unsupervised Learning ◽  
Moving Objects ◽  
Robot Navigation ◽  
Main Idea ◽  
Object Motion ◽  
Indoor Navigation ◽  
Depth Prediction ◽  
Novel Approach ◽  
Indoor Scenes ◽  
Scene Depth

Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics. Previous work in unsupervised image-to-depth learning has established strong baselines in the domain. We propose a novel approach which produces higher quality results, is able to model moving objects and is shown to transfer across data domains, e.g. from outdoors to indoor scenes. The main idea is to introduce geometric structure in the learning process, by modeling the scene and the individual objects; camera ego-motion and object motions are learned from monocular videos as input. Furthermore an online refinement method is introduced to adapt learning on the fly to unknown domains. The proposed approach outperforms all state-of-the-art approaches, including those that handle motion e.g. through learned flow. Our results are comparable in quality to the ones which used stereo as supervision and significantly improve depth prediction on scenes and datasets which contain a lot of object motion. The approach is of practical relevance, as it allows transfer across environments, by transferring models trained on data collected for robot navigation in urban scenes to indoor navigation settings. The code associated with this paper can be found at https://sites.google.com/view/struct2depth.

scholarly journals Two Weighted Fuzzy Goal Programming Methods to Solve Multiobjective Goal Programming Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Mousumi Gupta ◽  
Debasish Bhattacharjee
Keyword(s):  
Goal Programming ◽  
Real Life ◽  
Fuzzy Goal Programming ◽  
Relative Importance ◽  
Objective Functions ◽  
Weighting Method ◽  
Fuzzy Operator ◽  
New Methods ◽  
Aspiration Levels ◽  
Fuzzy Goal

We propose two new methods to find the solution of fuzzy goal programming (FGP) problem by weighting method. Here, the relative weights represent the relative importance of the objective functions. The proposed methods involve one additional goal constraint by introducing only underdeviation variables to the fuzzy operatorλ(resp., 1-λ), which is more efficient than some well-known existing methods such as those proposed by Zimmermann, Hannan, Tiwari, and Mohamed. Mohamed proposed that every fuzzy linear program has an equivalent weighted linear goal program where the weights are restricted as the reciprocals of the admissible violation constants. But the above proposition of Mohamed is not always true. Furthermore, the proposed methods are easy to apply in real-life situations which give better solution in the sense that the objective values are sufficiently closer to their aspiration levels. Finally, for illustration, two real examples are used to demonstrate the correctness and usefulness of the proposed methods.

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