Automatic Incident Detection in Intelligent Transportation Systems Using Aggregation of Traffic Parameters Collected Through V2I Communications

Popescu, Otilia; Sha-Mohammad, Sarwar Aziz; Abdel-Wahab, H.; Popescu, Dimitrie C.; El-Tawab, Samy

doi:10.1109/mits.2017.2666578

Cited by 67 publications

(26 citation statements)

References 30 publications

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“…In terms of this, the V21‐based wireless communication like the COC‐based V21 type of architecture in Choi et al seems to be more attractive since it can overcome such issues. In Popescu et al, on the basis of V2I communication, a probabilistic approach was proposed to collect traffic data, in which two new techniques for automatically detecting traffic accidents in highway scenario are utilized. The proposed approach assumes that a roadside unit (RSU) is available at regular intervals (only one vehicle on the given channel communicates with the RSU) on highways.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure‐based vehicular congestion detection scheme for V2I

Ahmad

Chen

Khan

et al. 2018

Int J Communication

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With the increasing number of vehicles, traffic jam becomes one of the major problems of the fast-growing world. Intelligent transportation system (ITS) communicates perilous warnings and information on forthcoming traffic jams to all vehicles within its coverage region. Real-time traffic information is the prerequisite for ITS applications development. In this paper, on the basis of the vehicle-to-infrastructure (V2I) communication, a novel infrastructurebased vehicular congestion detection (IVCD) scheme is proposed to support vehicular congestion detection and speed estimation. The proposed IVCD derives the safety time (time headway) between vehicles by using iterative content-oriented communication (COC) contents. Meanwhile, the roadside sensor (RSS) provides an infrastructure framework to integrate macroscopic traffic properties into the estimation of both the traffic congestion and vehicle safety speed. The main responsibilities of RSS in IVCD are to preserve privacy, aggregate data, store information, broadcast routing table, estimate safety speed, detect traffic jam, and generate session ID (S-ID) for vehicles. Monte Carlo simulations in four typical Chinese highway settings are presented to show the advantage of the proposed IVCD scheme over the existing Greenshield's and Greenberg's macroscopic congestion detection schemes in terms of the realized congestion detection performance. Real road traces generated by Simulation of Urban Mobility (SUMO) over NS-3.29 are utilized to demonstrate that the proposed IVCD scheme is capable of effectively controlling congestion in both single and multilane roads in terms of density and speed health with previous schemes in this field.

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Section: Related Workmentioning

confidence: 99%

Infrastructure‐based vehicular congestion detection scheme for V2I

Ahmad

Chen

Khan

et al. 2018

Int J Communication

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“…Smart mobility solutions have been developed with aim at reducing congestion and fostering faster, greener and cheaper transportation options. Among these solutions, we find systems that use advances in wireless networking and sensor technologies for preventing or automatically detecting incidents [10,11]. The huge amount of data about movement of vehicles provided by GPS and RFID can also be used for creating interactive visual analytics dashboard [8], for monitoring road safety [6], for increasing the understanding of road accidents [4], and also for predicting road accidents based on historical data [7].…”

Section: Related Workmentioning

confidence: 99%

“…The metrics of the ontology created are shown in Figure 6. Currently, the ACI 5-stars dataset is stored locally 11 , we are working to use OpenLink Virtuoso 12 as a triple store and SPARQL query service.…”

Section: Ontology Creationmentioning

confidence: 99%

Managing road safety through the use of linked data and heat maps

Colacino

2017

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics

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Road traffic injuries are a critical public health challenge that requires valuable efforts for effective and sustainable prevention. Worldwide, an estimated 1.2 million people are killed in road crashes each year and as many as 50 million are injured. An analysis of data provided by authoritative sources can be a valuable source for understanding which are the most critical points on the road network. The aim of this paper is to discover data about road accidents in Italy and to provide useful visualization for improving road safety. Starting from the annual report of road accidents of the Automobile Club of Italy, we transform the original data into an RDF dataset according to the Linked Open Data principles and connect it to external datasets. Then, an integration with Open Street Map allows to display the accident data on a map. Here, the final user is able to identify which road sections are most critical based on the number of deaths, injuries or accidents.

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“…With the advances in the fabrication technologies that integrate the sensing and the wireless communication, a largescale wireless sensor networks (WSNs) is formed in the desired fields by deploying dense tiny sensor nodes. Vehicle tracking in WSNs has several prominent merits: firstly, the sensing unit is close to the vehicle, thus the sensed data will be of a qualitatively good geometric fidelity via vehicle-toinfrastructure (V2I) technology [1]; secondly, the information about the target is simultaneously generated by multiple sensors and thus contains redundancy [2]. However, there still exist some unsolved problems as far as vehicle tracking in WSNs concerned.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

Xing

Zhang

et al. 2020

IEEE Sens. Lett.

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Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc. However, the increased tracking accuracy requires more energy consumption. In this letter, a decentralized vehicle tracking strategy is conceived for improving both tracking accuracy and energy saving, which is based on adjusting the intersection area between the fixed sensing area and the dynamic activation area. Then, two deep reinforcement learning (DRL) aided solutions are proposed relying on the dynamic selection of the activation area radius. Finally, simulation results show the superiority of our DRL aided design.

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Automatic Incident Detection in Intelligent Transportation Systems Using Aggregation of Traffic Parameters Collected Through V2I Communications

Cited by 67 publications

References 30 publications

Infrastructure‐based vehicular congestion detection scheme for V2I

Infrastructure‐based vehicular congestion detection scheme for V2I

Managing road safety through the use of linked data and heat maps

Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

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