Spatio-Temporal Ensemble Method for Car-Hailing Demand Prediction

Liu, Yang; Lyu, Cheng; Khadka, Anish; Zhang, Wenbo; Liu, Zhiyuan

doi:10.1109/tits.2019.2948790

Cited by 33 publications

(10 citation statements)

References 24 publications

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“…LGBM is an updated version of tree-based gradient boosting developed by Microsoft. Like other ensemble techniques, LGBM combines different weak learners (i.e., decision trees) to form a powerful and robust prediction algorithm [ 68 ]. LGBM is a quick method, and it is highly efficient for large-scale prediction problems.…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children’s TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children’s TMC and detect its determinants since it can present the relative influence of variables on children’s TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of “hyperparameters”. To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and “Hyperopt”. Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children’s TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children’s TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, “walkability” and “bikeability” of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

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Section: Methodsmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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“…The most recent research has also shown that some spatial and temporal patterns influence forecasting more than others. This is the case for some districts [5], conglomerate areas featuring similar functional characteristics (e.g., residential, commercial and industrial areas), that show correlated traffic patterns and explain much of the city's traffic.…”

Section: Introductionmentioning

confidence: 94%

Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem

2022

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In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel autoencoder architecture featuring time-distributed convolutions, cascade hierarchical units and two distinct attention mechanisms (one spatial and one temporal) that effectively captures and exploits complex spatial and temporal patterns in mobility data for short-term flow prediction problem. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task.

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“…Stacking usually uses the entire training set to generate the base model, and then the metamodel is trained using the output of the base model as input. Liu et al [14] proposed a spatiotemporal ensemble framework for predicting parking demand. The prediction results of the base learner for time features are considered channels of images and then ensembled using the features of convolutional neural networks (CNNs) that can capture spatial information.…”

Section: Precision-oriented Approachesmentioning

confidence: 99%

Using dual evolutionary search to construct decision tree based ensemble classifier

Chen

Zhang

Pan

et al. 2022

Complex Intell. Syst.

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A typical ensemble learning process typically uses a forward integration mechanism to construct the ensemble classifier with a large number of base classifiers. Based on this mechanism, it is difficult to adjust the diversity among base classifiers and optimize the structure inside ensemble since the generation process has a certain amount of randomness, which makes the performance of ensemble classifiers heavily dependent on the human design decisions. To address this issue, we proposed an automatic ensemble classifier construction method based on a dual-layer evolutionary search mechanism, which includes a tree coding-based base classifier population and a binary coding-based ensemble classifier population. Through a collaborative searching process between the two populations, the proposed method can be driven by training data to update the base classifier population and optimize the ensemble classifiers globally. To verify the effectiveness of the dual evolutionary ensemble learning method (DEEL), we tested it on 22 classification tasks from 4 data repositories. The results show that the proposed method can generate a diverse decision tree population on the training data while searching and constructing ensemble classifiers from them. Compared with 9 competitor algorithms, the proposed method achieved the best performance on 17 of 22 test tasks and improved the average accuracies by 0.97–7.65% over the second place. In particular, the generated ensemble classifiers show excellent structure, which involve small number and diverse decision trees. That increases the transparency of ensembles and helps to perform interpretability analysis on them.

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Spatio-Temporal Ensemble Method for Car-Hailing Demand Prediction

Cited by 33 publications

References 24 publications

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Listening to the City, Attentively: A Spatio-Temporal Attention-Boosted Autoencoder for the Short-Term Flow Prediction Problem

Using dual evolutionary search to construct decision tree based ensemble classifier

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