Cellular automata model for traffic flow at intersections in internet of vehicles

Zhao, Hongfu; Liu, Xinru; Chen, Xiaoxu; Jian-cheng, LU

doi:10.1016/j.physa.2017.11.152

Cited by 49 publications

(24 citation statements)

References 29 publications

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“…For instance, Tonguz et al build an urban traffic mobility model in [30], where they also focus on the intersection control mechanisms. Other solutions such as [31] include in their model parameters like the vehicle speed, the braking lights, and the possible conflicts between traffic flows.…”

Section: Related Workmentioning

confidence: 99%

Automated Optimization of Intersections Using a Genetic Algorithm

2019

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Traffic jams in large cities, in addition to having a very high economic cost, cause an increase in emissions generated by vehicles over the same route being driven under normal conditions. In recent years, there has been a rapid evolution in the technologies applied to the field of autonomous vehicles. There are currently commercial solutions for assisted driving and semi-autonomous driving systems, with very favorable forecasts for reaching a completely autonomous vehicle scenario in the coming decades. This new environment generates opportunities and challenges to reduce congestion in scenarios with autonomous or semi-autonomous vehicles. This paper focuses on the automatic optimization of the passage of vehicles through intersections. The intersections are one of the most conflict-generating elements in a traffic network. This type of conflicts arises because the intersections must manage multiple traffic flows with different priorities and preferences, often leading to traffic jams. The problem has been addressed by proposing three mechanisms to model any type of intersection, to calculate the roads with fewer points of conflict between their inputs and outputs, and to optimize the arrival rate of vehicles using a Genetic Algorithm to achieve the maximum performance of the intersection. To validate this solution, a cellular automata simulator has been developed, which can be adapted to both autonomous and conventional vehicle scenarios and can provide realistic results when certain conditions are met. The results obtained have been compared with other traditional solutions (priority and traffic lights) using microscopic traffic simulations, and with those obtained in other studies showing the advantages of the proposed system. The proposed systems achieve a throughput improvement between 9.21% and 36.98% compared with the traditional solutions.

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Section: Related Workmentioning

confidence: 99%

Automated Optimization of Intersections Using a Genetic Algorithm

2019

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“…When the red light I red turns to the green light I green for the s1th to s2th seconds, the vehicle closer to the intersection will have the situation of accelerating through the traffic light. Therefore, considering the aforementioned various situations, we can get the driving rules of intersection vehicles (1)When the traffic light status is I red , the driving rule of the vehicle is

v_{f, n}^{m, j} ((), t + 1) = ({, \begin{array}{c} \max (() v_{f, n}^{m, j} ((), t) - a_{cross}, 1) italicif d - {safe}_{f, n}^{m, j} ((), t) \neq 0 italicand c_d \leq italicdis 1 \\ 0 italicif d - {safe}_{f, n}^{m, j} ((), t) = 0 . \end{array})

Where c_d is the distance between the vehicle and the zebra crossing, dis 1 is a fixed value, taking dis 1 = 100.…”

Section: Modeling Of Mixed Traffic Flow In Cellular Automata At Singlmentioning

confidence: 99%

“…Therefore, considering the aforementioned various situations, we can get the driving rules of intersection vehicles. 48,49 (1) When the traffic light status is I red , the driving rule of the vehicle is…”

Section: Traffic Rules At Intersectionsmentioning

confidence: 99%

Construction of extended ant colony labor division model for traffic signal timing and its application in mixed traffic flow model of single intersection

Jiang

Chen

et al. 2019

Concurrency and Computation

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Summary The ant colony labor division model can be used to solve the dynamic and variable traffic signal timing problem because of its adaptive and self‐adjusting characteristics. Based on the basic model, this paper proposes a new extended ant colony labor division model for traffic signal timing. This model is combined with the vehicle characteristics to modify the calculation method of environmental stimulus values. Using the vehicle delay in the unit period, the original fixed response threshold is modified to the dynamic response threshold, and the state transition equation is reconstructed. Based on the mixed traffic flow model of two‐phase single‐point intersection cellular automata, through comparative experiments and discussion and analysis, it is found that the extended ant colony labor division model can effectively improve road traffic capacity according to road conditions and reasonable traffic signal timing.

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“…Uma mensagem de emergência é acionada quando um evento imprevisto como um acidente, condições terríveis da estrada, engarrafamento ou um veículo anormal aparece, e assim, os motoristas podem ajustar a velocidade do veículo ou alterar as rotas de acordo com as informações recebidas (ZHU et al, 2017). Zhao et al (2018), por meio da IoV a velocidade de um veículo é aumentada, ao passo que a distância em relação ao carro seguinte é reduzida; conflitos nos cruzamentos são mitigados mais rapidamente; o controle de sinal é mais flexível; a taxa de utilização de luz verde aumenta; e a zona do dilema (área onde o veículo não pode passar pela linha de parada, nem parar com segurança dentro da linha de parada durante a luz amarela) desaparece.…”

Section: Benefícios Da Iovunclassified