Trip Generation Study of a Shuttle Service System in Southville 7

Paringit, Maria Cecilia R.; Catolos, Reil Dominic; Custodio, Angelo Josh; Layug, Jacob; Uy, Jonathan; Galupino, Joenel; Lucero, Paolo Ian

doi:10.1016/j.trpro.2020.08.234

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Therefore, a grid cell is in the range of 222.4m*170.4m. This rasterization is similar to Liu [23] and Yue [24]. Each grid cell inherits some of the characteristics from the plot to which it belongs in space, and the rest of the characteristics are generated from the location of the grid cell itself, the surrounding POI and so on.…”

Section: A Study Area and Rasterizationmentioning

confidence: 99%

“…The parking space data for residential quarters is taken into account to measure the impact of car ownership on travel rates. According to Paringit's research [23], car ownership will greatly affect the number of family trips. In general, the weighted sum of the number of families and the number of parking spaces is used as the predicted value of trip generation.…”

Section: Output Data Processing and Validationmentioning

confidence: 99%

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Trip Generation Prediction Based on the Convolutional Neural Network-Multidimensional Long-Short Term Memory Neural Network Model at Grid Cell Scale

Xiao

Sun

et al. 2021

IEEE Access

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The prediction of trip generation is an essential problem for effective traffic engineering and urban management. Traditional methods are on the large spatial scale (e.g. Traffic analysis Zone, TAZ), based on the single source and fewer types data. It is difficult to carry out refined research on smaller spatial units, due to the high aggregation of personal trip survey data. In addition, the experience-based models cannot easily capture complex non-linear relationship, which leads to lower accuracy. Multi-sources data provides the possibility to improve the prediction accuracy of trip generation. Based on the point of interest data (POI), more disaggregate spatial unit are subdivided, and grid-scale spatial correlations are taken into consideration. This paper proposes a Convolutional Neural Network-Multidimensional Long-short term memory neural network (CNN-MDLSTM) model to analyze the spatial correlation between trip generation and land use features, capture prominent features in a spatial range through the convolution structure and describe the spatial interaction using the sequence transfer structure of Long-short term memory neural network (LSTM). The deformed Multi-Dimensional Long-Short Term Memory neural network (MDLSTM) is used to adapt to the two-dimensional spatial relationship. Through case analysis and comparative analysis between models, it is shown that CNN-MDLSTM characterizes the quantitative law of trip generation and land use features better than the other neural network models. In addition, this study also discusses the output prediction accuracy of different land use grid cells and the impact of different land use characteristics on the prediction accuracy.

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Section: A Study Area and Rasterizationmentioning

confidence: 99%

Section: Output Data Processing and Validationmentioning

confidence: 99%