A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

Barrachina, Javier; Garrido, Piedad; Fogue, Manuel; Martínez, Francisco J.; Cano, Juan-Carlos; Calafate, Carlos T.; Manzoni, Pietro

doi:10.1007/s11277-015-2392-4

Cited by 36 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…During the accident event, a vehicle interacting with the RSU on the specified channel at regular intervals, which is available on the roadside. An approximation of the instantaneous congestion scheme of the vehicle is also proposed in [8], based on data obtained by the RSU and vehicle. This scheme calculates the congestion level from the RSU messages, and RSU collects the beacon messages from both architecture, such as V2V and V2I.…”

Section: A Scheme For Congestion Mitigation and Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

Adbeb¹,

Wu²

2020

IJACSA

View full text Add to dashboard Cite

In VANETs (Vehicular Ad-hoc Networks), the number of vehicles increased continuously, leading to significant traffic problems like traffic congestion, a feasible path, and associated events like accidents. Though, the Intelligent Transportation System (ITS) providing excellent services, such as safety applications and emergency warnings. However, ITS has limitations regarding traffic management tasks, scalability, and flexibility because of the enormous number of vehicles. Therefore, extending the traditional VANET architecture is indeed a must. Thus, in the recent period, the design of the SD-VANETs (Software-Defined Networking defined VANETs) has gained significant interest and made VANET more intelligent. The SD-VANET architecture can handle the aforesaid VANET challenges. The centralized (logically) SDN architecture is programmable and also has global information about the VANET architecture. Therefore, it can effortlessly handle scalability, traffic management, and traffic congestion issues. The traffic congestion problem leads to longer trip times, decreases the vehicles' speed, and prolong average endto-end delay. Though, somewhere, some routes in the network are available with capacity, which can minimize the congestion problem and its characteristics. Therefore, we proposed heuristic algorithms called Congestion-Free Path (CFP) and Optimize CFP (OCFP), in SD-VANET architecture. The proposed algorithms address the traffic congestion issue and also provide a feasible path (less end-to-end delay) for a vehicle in VANET. We used the NS-3 simulator to evaluate the performance of the proposed algorithms, and for generating a real scenario of VANET traffic; we use the SUMO module. The results show that the proposed algorithms decrease road traffic congestion drastically compared to exiting approaches.

show abstract

Section: A Scheme For Congestion Mitigation and Detectionmentioning

confidence: 99%

“…In VANET, vehicles are like mobile nodes. They collect and disseminate information about their speed, current position, destination [5], [6], [7], [8]. In some emergency conditions, such are health issues, road accidents, and congestion, the VANET architecture (ITS) ensure the driving safety, alternative routes, and timely report.…”

Section: Introductionmentioning

confidence: 99%

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

Adbeb¹,

Wu²

2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…V2I has proved to be effective in monitoring and controlling traffic, which are important means for reducing travel times and fuel consumption. In order to monitor traffic congestions, Barrachina et al [3] developed a V2I-method for estimating traffic densities in a certain area with a 3.04 % accuracy, which is valuable information for traffic control applications. Optimized traffic flow was researched by Cai et al [4], who simulated adaptive traffic signals that operated on travel-time approximations computed from vehicle positions.…”

Section: State-of-the-artmentioning

confidence: 99%

“…V2I is an area of extensive research. Traditionally, research on V2I applications has mainly focused on acquiring statistical data from vehicles [3][4] [5]. These data are beneficial for traffic design, traffic control and road maintenance applications.…”

Section: Introductionmentioning

confidence: 99%

Novel Convolutional Neural Network-Based Roadside Unit for Accurate Pedestrian Localisation

Ojala

Vepsäläinen

Hanhirova

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Hazardous situations may easily be caused by limited visibility at urban traffic intersections due to buildings, fences, flora and other obstacles. Thus, drivers approaching an intersection have limited reaction time when other obscured road users, such as pedestrians and cyclists, appear unexpectedly. Previous research has been conducted on applications warning drivers of approaching out-of-sight vehicles. However, less focus has been on the detection and awareness applications revealing the presence of pedestrians. We propose a novel system that displays the driver real-time locations and types of hidden road users at traffic intersections. A roadside unit is installed in the infrastructure which sends safety-critical object data to the vehicle, supporting the real-time decision-making of the driver. The roadside unit consists of a monovision camera streaming video to a computing unit which performs object detection and distance measurements on the detected objects. This paper validates the capability of the proposed system of localising a pedestrian, and also examines its sensitivity to installation and detection errors. The results show that the accuracy of the proposed system is suitable for the intended application. However, an error in the vertical angle of the roadside unit camera caused an exponential error in the distance approximation in respect to the measured distance. The detection accuracy was noticed to decrease at long distances and in dark surroundings. Moreover, in order to reduce the effect of the presented errors, the camera should be installed as high as possible without hindering its detection capabilities.

show abstract

“…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. An estimate of the vehicle's instantaneous density scheme based on data collected by the vehicle and RSU was presented in Barrachina et al as well. It combines the density estimation by using the aggregate beacon messages received by each RSU for both V2I and V2V system.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure‐based vehicular congestion detection scheme for V2I

Ahmad

Chen

Khan

et al. 2018

Int J Communication

View full text Add to dashboard Cite

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.

show abstract

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

Cited by 36 publications

References 33 publications

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

Novel Convolutional Neural Network-Based Roadside Unit for Accurate Pedestrian Localisation

Infrastructure‐based vehicular congestion detection scheme for V2I

Contact Info

Product

Resources

About