### Abstract

In this paper, we propose a new method of a traffic signal control based on the predicted distribution of the traffic jam. First, we built a forecasting model to predict the probability distribution of vehicles for traffic jam during the each period of traffic signal. As the forecasting model, the Dynamic Bayesian Network is used and predicted the probability distribution of the amount of the standing vehicle in traffic jam. According to calculation by the Dynamic Bayesian network, the prediction of probability distribution of the amount of standing vehicles in each time will be obtained, and a control rule to adjust the split and the cycle of the signal to maintain the probability of a lower limit and a ceiling of the standing vehicles is deduced. Through the simulation using the actual traffic data of a city, the effectiveness of our method is shown.

Original language | English |
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Title of host publication | Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09 |

Pages | 55-58 |

Number of pages | 4 |

Publication status | Published - 2009 |

Event | 14th International Symposium on Artificial Life and Robotics, AROB 14th'09 - Oita Duration: 2008 Feb 5 → 2009 Feb 7 |

### Other

Other | 14th International Symposium on Artificial Life and Robotics, AROB 14th'09 |
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City | Oita |

Period | 08/2/5 → 09/2/7 |

### Fingerprint

### Keywords

- Dynamic Bayesian network
- Forecasting model
- Probabilistic distribution
- Traffic jam
- Traffic signal control

### ASJC Scopus subject areas

- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction

### Cite this

*Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09*(pp. 55-58)

**Traffic signal control based on predicted distribution of traffic jam.** / Cheng-You, Cui; Ji-Sun, Shin; Fumihiro, Shoji; Lee, HeeHyol.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09.*pp. 55-58, 14th International Symposium on Artificial Life and Robotics, AROB 14th'09, Oita, 08/2/5.

}

TY - GEN

T1 - Traffic signal control based on predicted distribution of traffic jam

AU - Cheng-You, Cui

AU - Ji-Sun, Shin

AU - Fumihiro, Shoji

AU - Lee, HeeHyol

PY - 2009

Y1 - 2009

N2 - In this paper, we propose a new method of a traffic signal control based on the predicted distribution of the traffic jam. First, we built a forecasting model to predict the probability distribution of vehicles for traffic jam during the each period of traffic signal. As the forecasting model, the Dynamic Bayesian Network is used and predicted the probability distribution of the amount of the standing vehicle in traffic jam. According to calculation by the Dynamic Bayesian network, the prediction of probability distribution of the amount of standing vehicles in each time will be obtained, and a control rule to adjust the split and the cycle of the signal to maintain the probability of a lower limit and a ceiling of the standing vehicles is deduced. Through the simulation using the actual traffic data of a city, the effectiveness of our method is shown.

AB - In this paper, we propose a new method of a traffic signal control based on the predicted distribution of the traffic jam. First, we built a forecasting model to predict the probability distribution of vehicles for traffic jam during the each period of traffic signal. As the forecasting model, the Dynamic Bayesian Network is used and predicted the probability distribution of the amount of the standing vehicle in traffic jam. According to calculation by the Dynamic Bayesian network, the prediction of probability distribution of the amount of standing vehicles in each time will be obtained, and a control rule to adjust the split and the cycle of the signal to maintain the probability of a lower limit and a ceiling of the standing vehicles is deduced. Through the simulation using the actual traffic data of a city, the effectiveness of our method is shown.

KW - Dynamic Bayesian network

KW - Forecasting model

KW - Probabilistic distribution

KW - Traffic jam

KW - Traffic signal control

UR - http://www.scopus.com/inward/record.url?scp=78149333146&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78149333146&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:78149333146

SN - 9784990288037

SP - 55

EP - 58

BT - Proceedings of the 14th International Symposium on Artificial Life and Robotics, AROB 14th'09

ER -