### Abstract

Traffic counts are key data generated by traffic surveillance systems. In predicting traffic flows, it is commonplace to assume that traffic at a given location repeats itself from day to day and the change in traffic happens gradually rather than abruptly. Consequently, many existing models for short-term traffic flow forecasting use historical traffic information, real-time traffic counts, or both. This paper proposes a new model based on the Gaussian maximum likelihood method, which explicitly makes use of both historical information and real-time information in an integrated way. The model considers flows and flow increments jointly and treats them as two random variables represented by two normal distribution functions. Each assumption made in the model is verified against the field data. The physical structure of the model is easy to interpret. Computationally, the model is simple to implement and little effort is required for model calibration. The performance of the proposed model is compared with four other models using field data. The proposed model consistently yields predictions with the smallest absolute deviance and the smallest mean square error.

Original language | English (US) |
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Pages | 150-155 |

Number of pages | 6 |

State | Published - 2001 |

Event | 2001 IEEE Intelligent Transportation Systems Proceedings - Oakland, CA, United States Duration: Aug 25 2001 → Aug 29 2001 |

### Other

Other | 2001 IEEE Intelligent Transportation Systems Proceedings |
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Country | United States |

City | Oakland, CA |

Period | 8/25/01 → 8/29/01 |

### Keywords

- Statistical analysis
- Traffic flow
- Traffic forecasting

### ASJC Scopus subject areas

- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications

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## Cite this

*A Gaussian maximum likelihood formulation for short-term forecasting of traffic flow*. 150-155. Paper presented at 2001 IEEE Intelligent Transportation Systems Proceedings, Oakland, CA, United States.