Time-Ants: An innovative temporal and spatial ant-based vehicular Routing Mechanism

Doolan, Ronan; Muntean, Gabriel‐Miro

doi:10.1109/ivs.2014.6856444

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…Several approaches used swarm algorithms [20]- [22]. Reference [20] and [21] introduced ant-colony based swarm algorithms, whereas [22] proposed an intelligent water drop algorithm.…”

Section: B Different Routing Techniquesmentioning

confidence: 99%

“…Doolan et al [20] developed Time-Ants, an algorithm which uses an ant-colony optimization approach in both time and spatial domains in order to send vehicles along certain routes when these paths are non-congested. However this approach requires access to historical traffic data, which may not be available in some scenarios.…”

Section: B Different Routing Techniquesmentioning

confidence: 99%

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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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“…Several approaches used swarm algorithms [20]- [22]. Reference [20] and [21] introduced ant-colony based swarm algorithms, whereas [22] proposed an intelligent water drop algorithm.…”

Section: B Different Routing Techniquesmentioning

confidence: 99%

Section: B Different Routing Techniquesmentioning

confidence: 99%

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

Doolan

Muntean

2017

IEEE Trans. Intell. Transport. Syst.

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“…data-driven modeling: Past studies on medium-and-long term traffic prediction generally fall into two categories: dynamical modeling [24] and data-driven modeling [29]. The former makes use of sophisticated simulation paradigms such as agent-based modeling and ant-colony optimization to resemble crowd-dynamics from a microscopic perspective [5]. Due to the complexity, instability and interference of traffic conditions, these models tend to depart considerably from real-life scenarios.…”

Section: Introductionmentioning

confidence: 99%

GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series

Zhang,

Zheng,

et al. 2021

Preprint

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Traffic forecasting is an integral part of intelligent transportation systems (ITS). Achieving a high prediction accuracy is a challenging task due to a high level of dynamics and complex spatial-temporal dependency of road networks. For this task, we propose Graph Attention-Convolution-Attention Networks (GACAN). The model uses a novel Att-Conv-Att (ACA) block which contains two graph attention layers and one spectralbased GCN layer sandwiched in between. The graph attention layers are meant to capture temporal features while the spectralbased GCN layer is meant to capture spatial features. The main novelty of the model is the integration of time series of four different time granularities: the original time series, together with hourly, daily, and weekly time series. Unlike previous work that used multi-granularity time series by handling every time series separately, GACAN combines the outcome of processing all time series after each graph attention layer. Thus, the effects of different time granularities are integrated throughout the model. We perform a series of experiments on three real-world datasets. The experimental results verify the advantage of using multigranularity time series and that the proposed GACAN model outperforms the state-of-the-art baselines.

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Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities

Vlahogianni

2015

Computational Methods in Applied Sciences

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Time-Ants: An innovative temporal and spatial ant-based vehicular Routing Mechanism

Cited by 10 publications

References 13 publications

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

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

GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series

Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities

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