Criteria for Temporal Aggregation of the Traffic Data from a Heterogeneous Traffic Stream

Suvin, P.V.; Karmakar, Ankita R.; Islam, Sabina Y.; Mallikarjuna, C.

doi:10.1016/j.trpro.2020.08.114

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…When the data aggregation size is too small, the collected data contains noise with randomness, which reduces the accuracy of forecast results. However, when the data aggregation size is too large, traffic information will be lost in the aggregation process and the timeliness of forecasting will also be affected due to the increase in forecasting interval [24,25]. UTCS has high requirements for the accuracy and timeliness of traffic forecasting, so the optimization of data aggregation size is crucial to improving the efficiency of UTCS.…”

Section: Introductionmentioning

confidence: 99%

Optimal Aggregate Size of Traffic Sequence Data Based on Fuzzy Entropy and Mutual Information

Guan

2022

Sustainability

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Before traffic forecasting, it is usually necessary to aggregate the information by a certain length of time. An aggregation size that is too short will make the data unstable and cause the forecast result to be too biased. On the other hand, if the aggregation size is too large, the data information will be lost, resulting in the forecast results tending towards an average or slow response. With the development of intelligent transportation systems, especially the development of urban traffic control systems, high requirements are placed on the real-time accuracy of traffic forecasting. Therefore, it is an essential topic of traffic forecasting research to determine aggregation sizes. In this paper, the mutual information between the forecast input information and the forecast result and the sequence complexity of the forecast result measured by approximate entropy, sample entropy, and fuzzy entropy are considered; then, the optimal data aggregation size is given. To verify the proposed method, the validated data obtained from the simulation is aggregated and calculated with different aggregation sizes, then used for forecasting. By comparing the prediction performance of different aggregate sizes, the optimal aggregate size was found to reduce MSE by 14–30%. The results show that the method proposed in this paper is helpful for selecting the optimal data aggregation size in forecasting and can improve the performance of prediction.

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Section: Introductionmentioning

confidence: 99%