scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

scholarly journals Sensitivity of street network capacity under the rain impact: case study of Belgrade

Transport ◽  
2017 ◽  
Vol 33 (2) ◽  
pp. 470-477 ◽  
Author(s):  
Ivan Ivanović ◽  
Jadranka Jović
Keyword(s):  
Urban Transportation ◽  
Weather Conditions ◽  
Network Capacity ◽  
Research Area ◽  
Transportation System ◽  
Transportation Systems ◽  
Street Network ◽  
Adverse Weather ◽  
Demand Sensitivity ◽  
The Impact

It is generally know that adverse weather conditions cause changes in urban transportation system. Research of weather impact on the urban transportation system was additionally intensified by actualisation of climate changes problem. In urban area, precipitation may reduce the efficiency of transportation systems, since it often results in larger travel times and higher congestion levels in street networks. Therefore, it is important to examine the impact of precipitation on the urban street capacity. In accordance with climate characteristics of research area, the focus of this paper was on the rain impact. Impact of rain was analysed only in the context of transport supply, and not of transport demand. Sensitivity of the street network capacity was chosen to represent transportation system supply. It was analysed through the changes in saturation flow rate under the rain. Results of the research have shown significant sensitivity of street network capacity on the rain impact. Moreover, the rain impact was quantified by the capacity sensitivity coefficients, which were implemented in procedure of capacity calculation.

scholarly journals Routing and Mobility Management in the Internet of Things

2020 ◽  
Author(s):  
Bruno Pereira Santos ◽  
Luiz Filipe Menezes Vieira ◽  
Antonio Alfredo Ferreira Loureiro
Keyword(s):  
Internet Of Things ◽  
Mobility Management ◽  
Intelligent Transportation Systems ◽  
Computer Network ◽  
Smart Cities ◽  
Transportation Systems ◽  
The Internet ◽  
Research Areas ◽  
New Research ◽  
The Impact

This Ph.D. Thesis proposes new techniques for routing and mobility management for Internet of Things (IoT). In the future IoT, everyday mobile objects will probably be connected to the Internet. Currently, static IoT's devices have already been connected, but handle mobile devices suitably still being an open issue in IoT context. Then, solutions for routing mobility detection, handover, and mobility management are proposed through an algorithm that integrates Machine Learning (ML) and mobility metrics to figure out devices' mobility events, which we named Dribble. Also, an IPv6 hierarchical routing protocol named Mobile Matrix to boost efficient (memory and fault tolerance) end-to-end connectivity over mobility scenarios. The Thesis contributions are supported by numerous peer-reviewed publications in national and international conferences and journals included in ISI-JCR. Also, the applicability of this Thesis is evident by showing that our results overcome state-of-the-art in static and mobile scenarios, as well as, the impact of the proposed solutions is a step forward in at least two new research areas so-called Internet of Mobile Things (IoMT) and Social IoT, where devices move around and do social ties respectively. Moreover, during the Ph.D. degree, the author has contributed to different computer network fields rather than routing by publishing in areas like social networks, smart cities, intelligent transportation systems, software-defined networks, and parallel computing.

scholarly journals Vehicles Detection System at Different Weather Conditions

2021 ◽  
pp. 2040-2052
Author(s):  
Mustafa Najm Abdullah ◽  
Yousra Hussein Ali
Keyword(s):  
Intelligent Transportation Systems ◽  
High Efficiency ◽  
Detection System ◽  
Weather Condition ◽  
Weather Conditions ◽  
Histogram Equalization ◽  
Transportation Systems ◽  
Support Vector ◽  
Detection Accuracy ◽  
Gaussian Blur

The importance of efficient vehicle detection (VD) is increased with the expansion of road networks and the number of vehicles in the Intelligent Transportation Systems (ITS). This paper proposes a system for detecting vehicles at different weather conditions such as sunny, rainy, cloudy and foggy days. The first step to the proposed system implementation is to determine whether the video’s weather condition is normal or abnormal. The Random Forest (RF) weather condition classification was performed in the video while the features were extracted for the first two frames by using the Gray Level Co-occurrence Matrix (GLCM). In this system, the background subtraction was applied by the mixture of Gaussian 2 (MOG 2) then applying a number of pre-processing operations, such as Gaussian blur filter, dilation, erosion, and threshold. The main contribution of this paper is to propose a histogram equalization technique for complex weather conditions instead of a Gaussian blur filter for improving the video clarity, which leads to increase detection accuracy. Based on the previous steps, the system defines each region in the frame expected to contain vehicles. Finally, Support Vector Machine (SVM) classifies the defined regions into a vehicle or not.  As compared to the previous methods, the proposed system showed high efficiency of about 96.4% and ability to detect vehicles at different weather conditions.

scholarly journals Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape

2021 ◽  
Vol 13 (22) ◽  
pp. 12891
Author(s):  
Olasupo O. Ajayi ◽  
Antoine B. Bagula ◽  
Hloniphani C. Maluleke ◽  
Isaac A. Odun-Ayo
Keyword(s):  
Systematic Review ◽  
Information And Communication Technologies ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Western World ◽  
Smart Transportation ◽  
African Cities ◽  
The Impact

Intelligent Transportation Systems (ITS), also known as Smart Transportation, is an infusion of information and communication technologies into transportation. ITS are a key component of smart cities, which have seen rapid global development in the last few decades. This has in turn translated to an increase in the deployment and adoption of ITS, particularly in countries in the Western world. Unfortunately, this is not the case with the developing countries of Africa and Asia, where dilapidated road infrastructure, poorly maintained public/mass transit vehicles and poverty are major concerns. However, the impact of Westernization and “imported technologies” cannot be overlooked; thus, despite the aforementioned challenges, ITS have found their way into African cities. In this paper, a systematic review was performed to determine the state of the art of ITS in Africa. The output of this systematic review was then fed into a hybrid multi-criteria model to analyse the research landscape, identify connections between published works and reveal research gaps and inequalities in African ITS. African peculiarities inhibiting the widespread implementation of ITS were then discussed, followed by the development of a conceptual architecture for an integrated ITS for African cities.

scholarly journals DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5240
Author(s):  
Anis Koubaa ◽  
Adel Ammar ◽  
Mahmoud Alahdab ◽  
Anas Kanhouch ◽  
Ahmad Taher Azar
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Edge Computing ◽  
Aerial Images ◽  
Computation Offloading ◽  
The Internet ◽  
Real Time Processing ◽  
Computing Approach

Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.

scholarly journals Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fen He ◽  
Paria Karami Olia ◽  
Rozita Jamili Oskouei ◽  
Morteza Hosseini ◽  
Zhihao Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Pedestrian Detection ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Main Challenge ◽  
Learning Techniques ◽  
The World ◽  
Self Driving Cars

Intelligent transportation systems have been very well received by car companies, people, and governments around the world. The main challenge in the world of smart and self-driving cars is to identify obstacles, especially pedestrians, and take action to prevent collisions with them. Many studies in this field have been done by various researchers, but there are still many errors in the accurate detection of pedestrians in self-made cars made by different car companies, so in the research in this study, we focused on the use of deep learning techniques to identify pedestrians for the development of intelligent transportation systems and self-driving cars and pedestrian identification in smart cities, and then some of the most common deep learning techniques used by various researchers were reviewed. Finally, in this research, the challenges in each field are discovered, which can be very useful for students who are looking for an idea to do their dissertations and research in the field of smart transportation and smart cities.

Encryption Techniques for Intelligent Transportation Systems via Deep Learning for IOV in Smart Cities

2021 ◽  
Author(s):  
DEEPAK CHOUDHARY ◽  
Roop Pahuja
Keyword(s):  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Wireless Sensor ◽  
Intrusion Prevention ◽  
Intrusion Detection And Prevention

Abstract Now-a-days, there is exponential growth in the field of Wireless Sensor Networks. A connected car is an ssential element of the Internet of Vehicles(IoV) vision that is a highly attractive application of the Internet of Things (IoT).The underlying technologies include Internet of Everything (IoE), artificial intelligence,machine learning, neural networks, sensor technologies, and cloud/edgecomputing. The connectivity between vehicles is through inter communicationbetween sensors and smart devices inside the vehicles, as well as smart systems inthe environment as part of the Intelligent Transportation Systems (ITS).In WSN’s security is a major concern, since most of communication happen through a wireless media hence probability of attacks increases drastically. Intrusion detection as well as prevention measures should be taken for secure communication, hence observations of intrusion detection and prevention techniques have taken immense precedence in the research field.With the help of intrusion detection and prevention systems, we can categorize the activities of user in two categories namely normal activities and suspicious activities.There is a need to design effective intrusion detection and prevention system by exploring deep learning for wireless sensor networks. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learning. In this, we have proposed a deep learning model using a convolutional Neural Network classifier. The proposed model consists of two stages like intrusion detection and intrusion prevention. The proposed model learn useful feature representations from a large amount of labeled data and then classifies them. In this work, a Convolutional Neural Network is used to prevent intrusion for wireless sensor networks. To evaluate and test the effectiveness of the proposed system, a WSN-DS dataset is used, and experiments are conducted on the dataset. The experimental results show that proposed system achieves 97% accuracy and performs substantially better than the existing system. The proposed work can be used as a future benchmark for deep learning and intrusion prevention research communitiesin the smart cities now a days.

scholarly journals Evaluating the Impact of Drone Signaling in Crosswalk Scenario

2020 ◽  
Vol 11 (1) ◽  
pp. 157
Author(s):  
Sana Bouassida ◽  
Najett Neji ◽  
Lydie Nouvelière ◽  
Jamel Neji
Keyword(s):  
Intelligent Transportation Systems ◽  
Emergency Services ◽  
Data Dissemination ◽  
Smart Cities ◽  
Low Cost ◽  
Well Being ◽  
Transportation Systems ◽  
Traffic Efficiency ◽  
Braking Distance ◽  
The Impact

The characteristic pillars of a city are its economy, its mobility, its environment, its inhabitants, its way of life, and its organization. Since 1980, the concept of smart city generally consists of optimizing costs, organization, and the well-being of inhabitants. The idea is to develop means and solutions capable of meeting the needs of the population, while preserving resources and the environment. Owing to their little size, their flexibility, and their low cost, Unmanned Aerial Vehicles (UAV) are today used in a huge number of daily life applications. UAV use cases can be classified into three categories: data covering (like surveillance and event covering), data relaying (like delivery and emergency services), and data dissemination (like cartography and precise agriculture). In addition, the interest to Cooperative Intelligent Transportation Systems (C-ITS) has risen in these recent years, especially in the context of smart cities. In such systems, both drivers and traffic managers share the information and cooperate to coordinate their actions to ensure safety, traffic efficiency, and environment preservation. In this work, we aimed at introducing a UAV in a use case that is likely to happen in C-ITS. A conflict is considered involving a car and a pedestrian. A UAV observes from the top of the scene and will play the role of the situation controller, the information collector, and the assignment of the instructions to the car driver in case of a harmful situation to avoid car-pedestrian collision. To this end, we highlight interactions between the UAV and the car vehicle (U2V communication), as well as between the UAV and infrastructure (U2I communication). Hence, the benefit of using UAV is emphasized to reduce accident gravity rate, braking distance, energy consumption, and occasional visibility reduction.

Modified fuzzy-based greedy routing protocol for VANETs

2020 ◽  
Vol 39 (6) ◽  
pp. 8357-8364
Author(s):  
Thompson Stephan ◽  
Ananthnarayan Rajappa ◽  
K.S. Sendhil Kumar ◽  
Shivang Gupta ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Routing Protocol ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Research Area ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Greedy Routing ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.

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