Pheromone Model: Application to Traffic Congestion Prediction

Ando, Yasushi; Masutani, Osamu; Sasaki, Hiroshi; Iwasaki, Hiromichi; Fukazawa, Yoshiaki; Honiden, Shinichi

doi:10.1007/11734697_14

Cited by 22 publications

(24 citation statements)

References 6 publications

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“…The lightweight agents in delegate MASs have some resemblance to an approach known as polyagents introduced by Brueckner and Parunak [20], [21]. Propagation of information through traffic networks using biologically inspired mechanisms such as pheromones or swarms have extensively been studied by Ando in [22] and by Tatomir in [23].…”

Section: B Propagation Of Informationmentioning

confidence: 99%

“…1) Use of Pheromones: In [22], pheromones are used to aggregate and propagate traffic densities in traffic networks. Vehicles in [22] drop virtual pheromones at their current location.…”

Section: B Propagation Of Informationmentioning

confidence: 99%

“…Vehicles in [22] drop virtual pheromones at their current location. These pheromones will locally spread before evaporating over time.…”

Section: B Propagation Of Informationmentioning

confidence: 99%

“…The use of pheromones in [24] resembles the approach in [25] in that it uses pheromones to represent the current traffic situation in a virtual environment. How the pheromones are applied in [24] differs from [22], but the view that they provide on the traffic network is similar.…”

Section: B Propagation Of Informationmentioning

confidence: 99%

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A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems

Claes

Holvoet

Weyns

2011

IEEE Trans. Intell. Transport. Syst.

195

100

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Abstract-Advanced vehicle guidance systems use real-time traffic information to route traffic and to avoid congestion. Unfortunately, these systems can only react upon the presence of traffic jams and not to prevent the creation of unnecessary congestion. Anticipatory vehicle routing is promising in that respect, because this approach allows directing vehicle routing by accounting for traffic forecast information. This paper presents a decentralized approach for anticipatory vehicle routing that is particularly useful in large-scale dynamic environments. The approach is based on delegate multiagent systems, i.e., an environment-centric coordination mechanism that is, in part, inspired by ant behavior. Antlike agents explore the environment on behalf of vehicles and detect a congestion forecast, allowing vehicles to reroute. The approach is explained in depth and is evaluated by comparison with three alternative routing strategies. The experiments are done in simulation of a real-world traffic environment. The experiments indicate a considerable performance gain compared with the most advanced strategy under test, i.e., a traffic-message-channel-based routing strategy.

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Section: B Propagation Of Informationmentioning

confidence: 99%

“…1) Use of Pheromones: In [22], pheromones are used to aggregate and propagate traffic densities in traffic networks. Vehicles in [22] drop virtual pheromones at their current location.…”

Section: B Propagation Of Informationmentioning

confidence: 99%

“…Vehicles in [22] drop virtual pheromones at their current location. These pheromones will locally spread before evaporating over time.…”

Section: B Propagation Of Informationmentioning

confidence: 99%

Section: B Propagation Of Informationmentioning

confidence: 99%

See 2 more Smart Citations

A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems

Claes

Holvoet

Weyns

2011

IEEE Trans. Intell. Transport. Syst.

195

100

View full text Add to dashboard Cite

show abstract

“…In [2], the authors predict the urban traffic flow through an integrated learning model with seven machine learning classification methods. In the paper [3], Y Ando et al Have proposed a pheromone model, which takes the vehicle as the pheromone released by insects, and predicts the traffic congestion through the pheromone mechanism. In the [4], Pedro Lopez-Garcia, Enrique et al proposed a genetic algorithm based on Onieva and cross entropy algorithm to predict traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

A New Traffic Congestion Forecast Model Based on City Cell

Ren¹,

Yang²,

Zhao³

2017

dtcse

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Abstract. Solving the problem of urban traffic congestion has always been an important issue in urban development. By predicting the congested areas in the city, it can be dealt with more timely and alleviate traffic difficulties. This paper presents a traffic congestion prediction model based on local correlation of urban cells. Reducing the granularity of the analysis to the cell can effectively improve the efficiency of the area, and the local correlation takes into account the effect of regional and interregional traffic loading. The experimental results show that the accuracy and recall rate of the proposed model are good.

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