Vehicle Traffic Congestion Estimation Based on RFID

Al-Naima, Fawzi M.; Hamd, Hassan A.

doi:10.5772/54923

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Real-time event-based parallel transportation management systems with radio frequency identification technology, or RFID, are attracting research interest [12,20,21]. Inadequacies in present models motivated us to propose the cloud model, MOVIE.…”

Section: Related Workmentioning

confidence: 99%

A Cloud Model for Distributed Transport System Integration

Paul

Hamilton

D'Souza

2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

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Public transport systems already encounter vast amounts of data per day, ranging from the GPS information of vehicles to sensor checks on real-time locations. Newer technologies such as social media, sensor networks or passenger counting systems, to name just a few, present new opportunities as well as new challenges to accommodate even larger data volumes, essentially leading to a big data problem in the context of real-time distributed transport systems. We argue that contemporary models for such systems are inadequate or less than adequate in meeting these new challenges. In this paper we present a new cloud-based model for integrated, distributed public transport systems to enable greater flexibility, portability, and interoperability for different controllers and sensors. Our model is called MOVIE, an acronym for "Model Operations View Intelligence Events". The proposed MOVIE model describes an event controlled system which incorporates the cloud and various messaging protocols in order to provide a safe and secure way of communication between the users, sensors and the multimodal transportation network.We describe our new model in some detail and then present a simulation of a passenger counting system to deliver up to the minute information for passengers, to enable them to make more informed choices about their travel options. With the emergence of social media, public transport patrons may connect with transport systems in cities to receive and provide updates of the status of the traffic at that time.

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Section: Related Workmentioning

confidence: 99%

A Cloud Model for Distributed Transport System Integration

Paul

Hamilton

D'Souza

2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

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“…Many congestion estimation and prediction algorithms have been adopted based on the data formalisation using either of the above two technology. The former approach include data acquisition methods using infrastructures such as the Global Positioning System (GPS), (Duc binh, 2018, Kong, 2016, sensor systems such as inductive loop detectors, infrared detectors (Bhaskar, 2015;Al-Naima) and traffic video archives (Reddy, 2018;Ke, 2018;Amudapuram, 2012). And later approach utilises Vehicle to Vehicle (V2V) communications from smart vehicles to gather traffic data (Duc-Binh, 2018; R. Wang, 2019;Badeddine, 2019;Manipriya, 2020).…”

Section: Introductionmentioning

confidence: 99%

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

Sankaranarayanan

Mala

Mathew

2021

International Journal of Intelligent Information Technologies

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Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

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“…The growing number of vehicles and increased population have triggered to the requirement of effective traffic management systems. An Intelligent Transportation System (ITS) is an effective approach to solve traffic problems without building any extra physical infrastructure such as tunnels and bridges [3]. It applies Information and Communication Technologies (ICT) to the online transportation systems to improve performance and help to alleviate traffic congestion and optimize fuel consumption [4].…”

Section: Introductionmentioning

confidence: 99%

“…RFID is shaping up to be an important building block for the IoT. The affordable cost with improved benefits has made RFID a reliable technology with a competitive advantage [3]. RFID is highlighted as one of the converging technologies and main catalyst playing a significant role in this project.…”

Section: Introductionmentioning

confidence: 99%

A Traffic Congestion Framework for Smart Riyadh City based on IoT Services

Al-Majhad¹,

Bramantoro²,

Syamsuddin³

et al. 2018

ijacsa

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Internet of Things has become one of the most challenging issues in many researches to connect physical things through the internet by creating a virtual identity for everything. Traffic congestion in Riyadh city is chosen due to the proliferation in the number of vehicles on Riyadh roads that is resulting in grumbling by residents. Currently, there are few reliable services offered to residents from the traffic department enabling them to access traffic information. A new traffic congestion framework for Riyadh is proposed to help the development of traffic congestion services. This framework aims to benefit from the current Riyadh road infrastructure and apply the Internet of Things paradigm for detecting traffic congestion with Everything as a Service approach. Sensing devices are used to identify the congestion of the traffic flow through providing multiple proposed services such as a vehicle counting, live streaming video and rerouting services. Users are able to access the services by using proposed mobile application connected to the internet, as those services are integrated with public map service. By using the services, the users are able to identify the exact location where congestion occurs and an alternate solution can be provided easily. To achieve this, Business Process Execution Language is embedded as a supporting framework layer. Due to the effectiveness in this layer, executable workflows are designed to combine the proposed services with the legacy Riyadh services as individual model. This approach clearly defines how the services are executed through the proposed models. A quantitative evaluation is provided to support the usability of this research.

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Vehicle Traffic Congestion Estimation Based on RFID

Cited by 11 publications

References 12 publications

A Cloud Model for Distributed Transport System Integration

A Cloud Model for Distributed Transport System Integration

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

A Traffic Congestion Framework for Smart Riyadh City based on IoT Services

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