Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network

Fernández, Susel; Hadfi, Rafik; Ito, Takayuki; Marsa-Maestre, Iván; Velasco, Juan R.

doi:10.3390/s16081287

Cited by 40 publications

(16 citation statements)

References 36 publications

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“…Some researchers [12][13][14] suggested estimating the speed and number of vehicles by placing sensors for particular lengths at road sides. However, the proposed sensor systems are not perfect due to the difference between the estimated vehicle speeds and the exact arrival times at intersections.…”

Section: Smart Traffic Light Systemmentioning

confidence: 99%

Smart Traffic Lights over Vehicular Named Data Networking

Al-qutwani

Wang

2019

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The existing traffic light system fails to deal with the increase in vehicular traffic requirements due to fixed time programming. Traffic flow suffers from vehicle delay and congestion. A new networking technology called vehicular ad hoc networking (VANET) offers a novel solution for vehicular traffic management. Nowadays, vehicles communicate with each other (V2V), infrastructure (V2I), or roadside units (V2R) using IP-based networks. Nevertheless, IP-based networks demonstrate low performance with moving nodes as they depend on communication with static nodes. Currently, the research community is studying a new networking architecture based on content name called named data networking (NDN) to implement it in VANET. NDN is suitable for VANET as it sends/receives information based on content name, not content address. In this paper, we present one of VANET’s network applications over NDN, a smart traffic light system. Our system solves the traffic congestion issue as well as reducing the waiting time of vehicles in road intersections. This system replaces the current conventional system with virtual traffic lights (VTLs). Instead of installing traffic lights at every intersection, we utilize a road side unit (RSU) to act as the intersection controller. Instead of a light signal, the RSU collects the orders of vehicles that have arrived or will arrive at the intersection. After processing the orders according to the priority policy, the RSU sends an instant message for every vehicle to pass the intersection or wait for a while. The proposed system mimics a human policeman intersection controlling. This approach is suitable for autonomous vehicles as they only receive signals from the RSU instead of processing many images. We provide a map of future work directions for enhancing this solution to take into account pedestrian and parking issues.

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Section: Smart Traffic Light Systemmentioning

confidence: 99%

Smart Traffic Lights over Vehicular Named Data Networking

Al-qutwani

Wang

2019

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“…In [ 14 ], the authors present an ontology-based architecture to improve the driving environment through a traffic sensor network. The system performs different tasks in an automated way such as the traffic light management that includes the adjustment of the duration and intensity of the light, considering the traffic flow and the weather conditions.…”

Section: Related Workmentioning

confidence: 99%

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

Cruz-Piris

Rivera

Fernández

et al. 2018

Sensors

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One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

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“…e SSN ontology provides sensors, sensor observations, and the knowledge of the environment. It is being quickly adopted by many applications [2,8,13], including tra c-related studies [3,4] and is considered as a standard ontology for Semantic Sensor Networks. We consider a tra c camera as a sensing device, a camera image as sensor output, and tra c as an observation.…”

Section: Semantic Modelling -Imagery-based Tra Ic Sensing Knowledge Gmentioning

confidence: 99%

A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras

Muppalla

Lalithsena

Banerjee

et al. 2017

Proceedings of the 2017 ACM on Web Science Conference

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Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network

Cited by 40 publications

References 36 publications

Smart Traffic Lights over Vehicular Named Data Networking

Smart Traffic Lights over Vehicular Named Data Networking

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras

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