Traffic big data assisted V2X communications toward smart transportation

An, Chang; Wu, Celimuge

doi:10.1007/s11276-019-02181-6

Cited by 40 publications

(17 citation statements)

References 36 publications

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“…The availability of mining, power transmission, and large-scale data technology and the corresponding technical processing methods are technical means for massive data collection and processing, as well as a large amount of structured, semistructured, and unstructured data sets by systematically using nontraditional existing hardware and software tools, to obtain survey results and analysis. Big data technology mainly includes data extraction technology, data maintenance technology, calculation and processing technology, data research technology, and result display technology [16,17]. At the same time, in the application of large-scale data technology, it mainly involves data sorting and merging, data storage, data mining, and other multilevel and all-round content.…”

Section: Application Of Big Data In Intelligent Transportationmentioning

confidence: 99%

The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

Yan

Chen

2021

Wireless Communications and Mobile Computing

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The core of smart city is to build intelligent transportation system.. An intelligent transportation system can analyze the traffic data with time and space characteristics in the city and acquire rich and valuable knowledge, and it is of great significance to realize intelligent traffic scheduling and urban planning. This article specifically introduces the extensive application of urban transportation infrastructure data in the construction and development of smart cities. This article first explains the related concepts of big data and intelligent transportation systems and uses big data to illustrate the operation of intelligent transportation systems in the construction of smart cities. Based on the machine learning and deep learning method, this paper is aimed at the passenger flow and traffic flow in the smart city transportation system. This paper deeply excavates the time, space, and other hidden features. In this paper, the traffic volume of the random sections in the city is predicted by using the graph convolutional neural network (GCNN) model, and the data are compared with the other five models (VAR, FNN, GCGRU, STGCN, and DGCNN). The experimental results show that compared with the other 4 models, the GCNN model has an increase of 8% to 10% accuracy and 15% fault tolerance. In forecasting morning and evening peak traffic flow, the accuracy of the GCNN model is higher than that of other models, and its trend is basically consistent with the actual traffic volume, the predicted results can reflect the actual traffic flow data well. Aimed at the application of intelligent transportation in an intelligent city, this paper proposes a machine learning prediction model based on big data, and this is of great significance for studying the mechanical learning of such problems. Therefore, the research of this paper has a good implementation prospect and academic value.

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Section: Application Of Big Data In Intelligent Transportationmentioning

confidence: 99%

The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

Yan

Chen

2021

Wireless Communications and Mobile Computing

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“…However, as a shortcoming, RSUs cannot reach the location information in some areas such as tunnels. Also, RSUs collect information on the location of other vehicles, which results in communication overhead [22].…”

Section: Geocast Routing Protocols Without Network Codingmentioning

confidence: 99%

An OLSR-based Geocast Routing Protocol for Vehicular Ad Hoc Networks

Kachooei

Hendessi

Ghahfarokhi

et al. 2021

Peer-to-Peer Netw. Appl.

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In some Vehicular Ad Hoc Networks (VANETs) applications, the geocast routing protocol is used for data transmission from a source vehicle to a group of vehicles located in a common region. Efficient data transmission to the destination region is one of the critical challenges of geocast routing protocols. In this research, the geocast routings are considered that exploit rateless coding to improve the reliability, and so packet delivery ratio. Some of these geocast routing methods use flooding schemes to deliver the messages to the destination region. However, in order to cut high overheads caused by flooding schemes, the routing protocols that use unicast routes for data delivery have been taken into account. In this way, recent geocast routing protocols exploit on-demand unicast routing methods such as Ad-hoc On-Demand Distance Vector (AODV) to deliver the packets to the destination region and then broadcast them in that area. However, the packet delivery ratio and the delay of those methods are respectively lower and higher than flooding-based methods. This paper proposes to exploit the tabledriven Optimized Link State Routing (OLSR) protocol to deliver the messages to the destination region. To customize the OLSR protocol for geocasting, we propose a number of modifications to message flows and data exchanges. Compared to on-demand geocast protocols, OLSR imposes lower message delay and delivers more messages to the destination region at a higher overhead expense. To overcome this overhead, we also propose algorithms to adjust the control message intervals of the OLSR protocol in each node. Simulation results show that our OLSR-based protocol demonstrates better performance in terms of delay and packet delivery ratio than those of the traditional AODV-based method and CALAR-DD protocol regarding various vehicles' densities, vehicles' velocities, message sizes, and destination region sizes. Compared to the traditional OLSR, using the tuned OLSR-based method has also significantly reduced the signaling overhead costs.

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“…In the city, the sensors of the autonomous refuse truck detect all movements in the vicinity of the environment and reacts automatically to any potential incidents, making sure citizens are safe and preventing accidents before they happen. In Reference 19 the authors used deep learning, data analytics, communication technologies to link people, roads and vehicles to solve various traffic‐related issues. This work focuses on various initiatives aimed at creating a vehicle centered, safe environment and comfortable transport.…”

Section: Deep Learning For Smart Mobility and Transportationmentioning

confidence: 99%

A review on deep learning for future smart cities

Bhattacharya

Somayaji

Gadekallu

et al. 2020

Internet Technology Letters

104

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The advancements in Information and Communication Technologies (ICT) made the concept of Smart Cities into reality. In a smart city several Internet of Things (IoT) sensors are deployed across several locations to collect the data about traffic, drainage, mobility of citizens etc. and the insights gained from these data are used to manage resources, assets etc. effectively. Deep Learning has been used extensively on the data generated by IoT sensors in a smart city by several researchers. In this article an attempt is made to survey several state-of-the art on usage of Deep Learning on Smart City data. Several future research directions are suggested at the end of the article.

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Traffic big data assisted V2X communications toward smart transportation

Cited by 40 publications

References 36 publications

The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

An OLSR-based Geocast Routing Protocol for Vehicular Ad Hoc Networks

A review on deep learning for future smart cities

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