A Simulation Study of Connected Vehicle Systems Using Named Data Networking

Jiang, Tao; Xu, Xiaowei; Lu, Pu; Hu, Yu; Qiu, Zhijun

doi:10.1007/978-3-319-05506-0_4

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The WMD model for evening peak hours was calibrated in previous studies 26,27 and applied in others. 24,28 Briefly, the model was calibrated in two stages: (i) system calibration, which was executed using a TFlowFuzzy procedure in VISUM 29,30 ; this process was repeated until the resulting origin–destination (OD) matrix approached the real OD matrix (i.e., until the calibration criteria is met); and (ii) operational calibration, which fine-tuned the driving behavior parameters in VISSIM through trial and error until calibration criteria were met. 26,31 The driver behavior parameters included link behavior parameters (Safety Distance Reduction Factor – SDRF), the headway time parameter (CC1), and the visibility parameters (i.e., emergency stop and lane change) at each link connector.…”

Section: Methodsmentioning

confidence: 99%

Network-level comparison of various Forward Collision Warning algorithms

Tawfeek

El-Basyouny

2018

SIMULATION

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Rear-end collisions represent a quarter to one-third of the total number of collisions occurring on North American roads. Consequently, Forward Collision Warning (FCW) algorithms have been developed to mitigate this type of critical collision by warning drivers about an impending rear-end event. The algorithms are typically tested to ensure their effectiveness in reducing specific events, such as rear-end conflicts and/or collisions, or by assessing the change in the frequency and severity of braking maneuvers. Such assessments are usually microscopic in nature and deal with isolated (independent) situations. This paper aims at assessing six FCW algorithms at a network level with varying market penetration rates using a calibrated micro-simulation model. The algorithms were assessed in terms of their safety (rear-end conflicts frequency), mobility (travel times), and environmental impacts (emissions and fuel consumption). Based on the results of this study, most of the FCW algorithms did not have a significant effect on mobility nor environmental impacts at various market penetration rates. On the contrary, all the algorithms showed significant safety improvements, in terms of reducing rear-end conflicts, as the market penetration rates increased. The only exception was a single algorithm that tends to be more conservative in terms of braking distance. The results showed that situational improvements (on a driver level) caused by using FCW systems will generally translate into systematic improvements (on a network level). This is important due to the anticipated gradual increase in intelligent vehicles, which are expected to be equipped with FCW systems, on our roads soon.

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Section: Methodsmentioning

confidence: 99%

Network-level comparison of various Forward Collision Warning algorithms

Tawfeek

El-Basyouny

2018

SIMULATION

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“…NDN can also be employed in roadside backbone infrastructures to enhance the data communication to/from vehicles, i.e., vehicle-to-infrastructure communication. Jiang et al [21] proved that NDN can effectively reduce the delay of delivering data to vehicles through a federated simulation platform. The work in [22] exploits NDN to improve the efficiency of data retrieval from vehicles at mobility.…”

Section: A Employ Ndn To Support Vehiclesmentioning

confidence: 99%

Towards Efficient NDN Framework for Connected Vehicle Applications

2020

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Named Data Networking (NDN) recently arises as a promising technology to support connected vehicle (CV) applications due to the match between their characteristics. However, the fast mobility and the vast number of vehicles raise great challenges in designing a scalable and efficient NDN network for CV applications. Therefore, in this paper, we develop an NDN based CV application framework that handles the challenge through innovations in two aspects. First, we propose a hierarchical hyperbolic NDN backbone architecture (H2NDN). H2NDN exploits the location dependency of CV applications to develop a hierarchical router topology and a hierarchical data/interest namespace. As a result, efficient and scalable data retrieval can be achieved by only configuring static forwarding information base (FIB) on NDN routers. To avoid overloading high-level routers, H2NDN integrates hyperbolic routing into the hierarchical architecture through carefully designed hyperbolic planes. Second, on top of the H2NDN architecture, we further model the optimal data caching problem. Based on the modeling, we propose a distributed adaptive caching strategy that can greatly improve the efficiency of the H2NDN backbone in supporting CV applications. Extensive ndnSIM based experiments with real traffic data in a city prove the efficiency and scalability of the proposed NDN application framework. INDEX TERMS Connected vehicle, named data networking, connected vehicle applications.

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A flexible control study of variable speed limit in connected vehicle systems

et al. 2015

IJES

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A Simulation Study of Connected Vehicle Systems Using Named Data Networking

Cited by 3 publications

References 6 publications

Network-level comparison of various Forward Collision Warning algorithms

Network-level comparison of various Forward Collision Warning algorithms

Towards Efficient NDN Framework for Connected Vehicle Applications

A flexible control study of variable speed limit in connected vehicle systems

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