Parallel Transportation Management and Control System and Its Applications in Building Smart Cities

Zhu, Fenghua; Li, Zhenjiang; Chen, Songhang; Xiong, Gang

doi:10.1109/tits.2015.2506156

Cited by 112 publications

(38 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it has been described before, M(x, y, t) matrix is used for segmentation of MHI. Here, variable M refers to the calculated mask M(x, y, t) presented in Equation (2). The parallelization process of this module was the same as the method described in Fig.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…As it has been described before, M(x, y, t) matrix is used for segmentation of MHI. Variable m_mat refers to M(x, y, t) matrix presented in Equation (2), thus if the value of MHI was bigger than zero, the value of M would be 1 and otherwise, it stores as zero. Pseudocodes of these two implementations are shown in below.…”

Section: Appendix II -Sequential and Parallel Implementations Of M(xmentioning

confidence: 99%

“…For several years, providing safe and secure transportation circumstances have been considered as the basic requirement for the development of industries and increasing social welfare level in developed countries [1]. Nowadays, transportation issues such as environmental pollutions, reduction of energy resources, corporeal and financial damages caused by car accidents and the rapid growth trend of transportation demands -especially during the peak hours of road traffics -have become an unbreakable challenge in all cities around the world [2]. In this regard, Intelligent Transportation Systems (ITS) are defined as the means for collection of tools, facilities, and specializations, such as traffic management and telecommunications technologies in the form of coordinated instruments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

Afshany¹,

Tourani²,

Shahbahrami³

et al. 2019

IJISA

View full text Add to dashboard Cite

Nowadays, Intelligent Transportation Systems (ITS) are known as powerful solutions for handling traffic-related issues. ITS are used in various applications such as traffic signal control, vehicle counting, and automatic license plate detection. In the special case, video cameras are applied in ITS which can provide useful information after processing their outputs, known as Video-based Intelligent Transportation Systems (V-ITS). Among various applications of V-ITS, automatic vehicle speed measurement is a fast-growing field due to its numerous benefits. In this regard, visual appearancebased methods are common types of video-based speed measurement approaches which suffer from a computationally intensive performance. These methods repeatedly search for special visual features of vehicles, like the license plate, in consecutive frames. In this paper, a parallelized version of an appearance-based speed measurement method is presented which is real-time and requires lower computational costs. To acquire this, datalevel parallelism was applied on three computationally intensive modules of the method with low dependencies using NVidia's CUDA platform. The parallelization process was performed by the distribution of the method's constituent modules on multiple processing elements, which resulted in better throughputs and massively parallelism. Experimental results have shown that the CUDA-enabled implementation runs about 1.81 times faster than the main sequential approach to calculate each vehicle's speed. In addition, the parallelized kernels of the mentioned modules provide 21.28, 408.71 and 188.87 speed-up in singularly execution. The reason for performing these experiments was to clarify the vital role of computational cost in developing video-based speed measurement systems for real-time applications.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Section: Appendix II -Sequential and Parallel Implementations Of M(xmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

Afshany¹,

Tourani²,

Shahbahrami³

et al. 2019

IJISA

View full text Add to dashboard Cite

show abstract

“…ITS applications have the advantage of integrating cutting-edge technologies and communication tools with the facilities of transportation. This benefit makes them strong enough to be used in monitoring traffic flow, violation detection and mobility enhancement purposes [4][5]. Among different approaches of ITS, the Video-Based Intelligent Transportation System (VB-ITS) which executes based on the output of video cameras are more suitable and have the advantages of easy maintenance and high flexibility in traffic monitoring [6].…”

Section: Introductionmentioning

confidence: 99%

Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Tourani¹,

Shahbahrami²,

Akoushideh³

et al. 2019

IJIGSP

View full text Add to dashboard Cite

Video-based vehicle speed measurement systems are known as effective applications for Intelligent Transportation Systems (ITS) due to their great development capabilities and low costs. These systems utilize camera outputs to apply video processing techniques and extract the desired information. This paper presents a new vehicle speed measurement approach based on motion detection. Contrary to featurebased methods that need visual features of the vehicles like license-plate or windshield, the proposed method is able to estimate vehicle's speed by analyzing its motion parameters inside a pre-defined Region of Interest (ROI) with specified dimensions. This capability provides realtime computing and performs better than feature-based approaches. The proposed method consists of three primary modules including vehicle detection, tracking, and speed measurement. Each moving object is detected as it enters the ROI by the means of Mixture-of-Gaussian background subtraction method. Then by applying morphology transforms, the distinct parts of these objects turn into unified filled shapes and some defined filtration functions leave behind only the objects with the highest possibility of being a vehicle. Detected vehicles are then tracked using blob tracking algorithm and their displacement among sequential frames are calculated for final speed measurement module. The outputs of the system include the vehicle's image, its corresponding speed, and detection time. Experimental results show that the proposed approach has an acceptable accuracy in comparison with current speed measurement systems.

show abstract

“…The assets of smart cities contains smart transportation systems [2], smart grids [3], smart hospitals [4], smart factories [5] and etc. All these realisations require the universal connectivity of humans and machines.…”

Section: Introductionmentioning

confidence: 99%

Throughput Maximization and Fairness Assurance in Data and Energy Integrated Communication Networks

et al. 2018

Data and Energy Integrated Communication Networks

View full text Add to dashboard Cite

Abstract-A typical data and energy integrated network (DEIN) conceives a conventional base station (BS), which is capable of simultaneously transmitting the data and energy to user equipments (UEs) during the downlink (DL) transmissions by invoking the time-division-multiple-access (TDMA) protocol in the medium access control (MAC) layer. Several UEs operating in this DEIN are capable of harvesting the energy from the DL transmissions by adopting the power splitting (PS) technique and they are also capable of exploiting the harvested energy for powering their uplink (UL) data transmissions by invoking the TDMA protocol in the MAC layer. Both of the UL sumthroughput and the UL fair-throughput of the DEIN is maximised by deciding the duration of each time-slot during the DL/UL transmissions and by determining the optimal PS factor for each UE. Both of these optimization problems are finally solved by the classic method of Lagrange multipliers in close-form. An interesting observation shows that supporting low-throughput data services during the DL transmissions does not degrade the wireless energy transfer and hence does not reduce the throughput of the UL transmissions.

show abstract

Parallel Transportation Management and Control System and Its Applications in Building Smart Cities

Cited by 112 publications

References 28 publications

Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways

Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Throughput Maximization and Fairness Assurance in Data and Energy Integrated Communication Networks

Contact Info

Product

Resources

About