Daily Trajectory Prediction Using Temporal Frequent Pattern Tree

Cai, Mingwei; Yan, Runze; Doryab, Afsaneh

doi:10.1007/978-981-16-2380-6_29

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…In order to verify the accuracy and efficiency of the method proposed in this paper, real-vehicle datasets and the bicycle check-in datasets publicly available on the Capital Bikeshare's website [24] were selected as the experiment datasets. RNN [13], Transformer [14] and T-pattern algorithms [17][18][19][20][21][22] were chosen as the baseline methods.…”

Section: Resultsmentioning

confidence: 99%

“…Comparative experiments were conducted with such algorithms to analyze the applicability of deep learning in VEC. T-pattern algorithms [17][18][19][20][21][22] belong to class of algorithms based on the vehicle frequent pattern. Comparative experiments were conducted to analyze the accuracy and efficiency of the TPPT algorithm proposed in this paper.…”

Section: Resultsmentioning

confidence: 99%

“…Dong et al [18] proposed RTMatch, an improved real-time trajectory prediction method based on a real-time pattern tree (RTPT) and a hash table (HT). Comito [21] constructed a frequent-pattern tree, which is similar to a T-pattern tree.…”

Section: Trajectory Prediction Researchmentioning

confidence: 99%

“…where the constraints are such that there is no localized out-of-delay. Finally, we propose a search algorithm to address the optimization problem according to Equation (21). The search algorithm is to minimize the energy consumption in Equation ( 20) with the time consumption in Equation (19).…”

Section: Task Offloading Strategies Energy Consumption and Search Alg...mentioning

confidence: 99%

“…These methods also have high accuracy or efficiency. For example, vehicle trajectories can be predicted in real time by constructing T-pattern tree [18][19][20][21], but they have low accuracy. The frequent pattern prediction method proposed in [22] has a higher accuracy rate, but the method is not efficient enough to predict the vehicle trajectory in real time.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Vehicle Trajectory Prediction Method for Task Offloading in Vehicular Edge Computing

Yan,

Gu,

Zhang

et al. 2023

Sensors

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Real-time computation tasks in vehicular edge computing (VEC) provide convenience for vehicle users. However, the efficiency of task offloading seriously affects the quality of service (QoS). The predictive-mode task offloading is limited by computation resources, storage resources and the timeliness of vehicle trajectory data. Meanwhile, machine learning is difficult to deploy on edge servers. In this paper, we propose a vehicle trajectory prediction method based on the vehicle frequent pattern for task offloading in VEC. First, in the initialization stage, a T-pattern prediction tree (TPPT) is constructed based on the historical vehicle trajectory data. Secondly, when predicting the vehicle trajectory, the vehicle frequent itemset with the largest vehicle trajectory support is found in the vehicle frequent itemset of the TPPT. Finally, in the update stage, the TPPT is updated in real time with the predicted vehicle trajectory results. Meanwhile, based on the proposed prediction method, the strategies of task offloading and optimization algorithm are designed to minimize energy consumption with time constraints. The experiments are carried out on real-vehicle datasets and the Capital Bikeshare datasets. The results show that compared with the baseline T-pattern method, the accuracy of the prediction method is improved by more than 10% and the prediction efficiency is improved by more than 6.5 times. The vehicle trajectory prediction method based on the vehicle frequent pattern has high accuracy and prediction efficiency, which can solve the problem of vehicle trajectory prediction for task offloading.

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