Smooth traffic flow with a cooperative car navigation system

Yamashita, Tomohisa; Izumi, Kiyoshi; Kurumatani, Koichi; Nakashima, Hideharu

doi:10.1145/1082473.1082546

Cited by 59 publications

(46 citation statements)

References 7 publications

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“…Yamashita et al [32] simulated different compliance rates for the algorithm developed. The average travel time of all users decreased as the compliance rate increased.…”

Section: Compliance Ratementioning

confidence: 99%

EcoTrec—A Novel VANET-Based Approach to Reducing Vehicle Emissions

Doolan

Muntean

2017

IEEE Trans. Intell. Transport. Syst.

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Abstract-There are interdependent increases in vehicle numbers, vehicular traffic congestion, and carbon emissions that cause major problems worldwide. These problems include direct negative influences on people's health, adverse economic effects, negative social impacts, local environmental damage, and risk of catastrophic global climate change. There is a drastic need to develop ways to reduce these emissions and EcoTrec, presented in this paper, is one of these innovative approaches. EcoTrec is a vehicular ad hoc network-based vehicle routing solution designed to reduce vehicle carbon emissions without significantly affecting the travel times of vehicles. The vehicles exchange messages related to traffic and road conditions, such as average speed on the road, road gradient, and surface condition. This information is used to build a fuel efficiency model of the routes, based on which the vehicles are recommended to take more efficient routes. By routing vehicles more efficiently, the greenhouse emissions are reduced while also maintaining low traffic congestion levels. This paper presents results of extensive simulations, which show how EcoTrec outperforms other state-of-the-art solutions with different number of vehicles, vehicle penetration, and compliance rates, and when considering different real world road maps from Dublin and Koln.

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“…Yamashita et al [32] simulated different compliance rates for the algorithm developed. The average travel time of all users decreased as the compliance rate increased.…”

Section: Compliance Ratementioning

confidence: 99%

EcoTrec—A Novel VANET-Based Approach to Reducing Vehicle Emissions

Doolan

Muntean

2017

IEEE Trans. Intell. Transport. Syst.

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“…The navigation based on the communication can help the vehicle master enough path information to make a better driving route [4]. To analyze microcosmic behaviors of vehicles, the feature of self-driving, cooperating cars such as convoy and navigation are extracted as rules in the CA model to describe the mixed traffic flow.…”

Section: The Analysis Of Self-driving Cooperating Carsmentioning

confidence: 99%

The Assessment of Traffic with Self-driving, Cooperating Cars

Wang¹,

Huang²,

Sha³

2017

Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)

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Abstract. Based on the NaSch traffic model, we propose a mixing traffic flow model for a multilane system. In this model, the characteristic of Self-driving, Cooperating Cars are abstracted as rules, to make the traffic model can be applied to the traffic with Self-driving, Cooperating Cars. Through the simulation in the model, we found that the Self-driving, Cooperating Cars have a great influence on the capabilities of expressway, and have a decisive action on waiting time before traffic light.

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“…For example, one navigation tool was especially designed by combining sophisticated mobile-source energy and emission models with route minimization algorithms to minimize fuel consumption and vehicle emissions (Barth et al (2007)). Cooperative car navigation system with route information sharing (RIS) is proposed by (Yamashita et al (2005)). In the RIS system, each vehicle transmits route information (current position, destination, and route to the destination) to a route information server, which estimates future traffic congestion using this information and feeds its estimate back to each vehicle.…”

Section: Vehicle Traffic Routing Systemsmentioning

confidence: 99%