What is it about?
Due to confidentiality and privacy issues, ride-hailing data are sometimes released to the researchers by removing spatial adjacency information of the zones that hinders the detection of spatio-temporal dependencies. We proposed a novel spatio-temporal deep learning architecture, integrating feature importance layer with a spatio-temporal deep learning architecture containing 1D convolutional neural network (1D-CNN) and zone-distributed independently recurrent neural network (IndRNN), to learn spatio-temporal dependencies from ride-hailing data with anonymised spatial adjacency information while forecasting demand and supply-demand gap. Experiments with real-world datasets from Didi Chuxing shows that the proposed approach outperforms conventional time-series models and machine learning models. Additionally, the feature importance layer provides an interpretation of the model by revealing the contribution of the input features to prediction.
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Why is it important?
Our proposed approach detects spatial dependencies from the anonymised zones in a multivariate manner through 1D CNN and temporal dependencies in an independent manner through zone-distributed IndRNN. Furthermore, we integrate feature importance layer with a spatio-temporal deep learning architecture, which determines spatial weighting that adapts the model to accurately forecast both demand and supply-demand gap and also indicates the contribution of the corresponding input features for spatio-temporal forecasting.
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This page is a summary of: Using spatio‐temporal deep learning for forecasting demand and supply‐demand gap in ride‐hailing system with anonymised spatial adjacency information, IET Intelligent Transport Systems, May 2021, the Institution of Engineering and Technology (the IET),
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Ni habari gani hiyo? Kwa sababu ya masuala ya siri na faragha, data ya safari-hailing wakati mwingine hutolewa kwa watafiti kwa kuondoa habari ya jirani ya kijiografia ya maeneo ambayo huzuia ugunduzi wa utegemezi wa nafasi-wakati. "Kama ilivyoelezwa hapo juu, ""mfumo wa kujifunza kina wa nafasi-wakati, unaounganisha safu ya umuhimu wa kipengele na usanifu wa kujifunza kina wa nafasi-wakati unaojumuisha mtandao wa neva wa 1D wa convolutional na mtandao wa neva wa kawaida wa mzunguko wa mzunguko (IndRN), kujifunza utegemezi wa nafasi-wakati kutoka kwa data ya safari na habari ya ukaribu wa nafasi isiyojulikana wakati wa utabiri wa mahitaji na upatikanaji.""" Majaribio na seti za data za ulimwengu halisi kutoka Didi Chuxing zinaonyesha kwamba mbinu iliyopendekezwa inazidi mifano ya kawaida ya mfululizo wa wakati na mifano ya kujifunza mashine. Aidha, kipengele umuhimu safu hutoa tafsiri ya mfano kwa kufunua mchango wa vipengele pembejeo kwa utabiri. Picha na Thought Catalog kwenye Unsplash Kwa nini ni muhimu? Njia yetu ya mapendekezo inagundua utegemezi wa nafasi kutoka kwa maeneo ya kutokujulikana kwa njia ya multivariate kupitia 1D CNN na utegemezi wa muda kwa njia ya kujitegemea kupitia eneo la kusambazwa la IndRNN. Kwa kuongezea, tunaunganisha safu ya umuhimu wa kipengele na usanifu wa kujifunza kina wa nafasi-wakati, ambayo huamua uzito wa nafasi ambayo inabadilisha mfano ili kutabiri kwa usahihi mahitaji na upungufu wa mahitaji ya ugavi na pia inaonyesha mchango wa vipengele vinavyolingana vya pembejeo kwa utabiri wa nafasi-wakati. "Kutumia ""mafunzo ya kina ya nafasi na wakati"" kwa utabiri wa mahitaji na upatikanaji wa mahitaji katika mfumo wa kusafiri na habari ya ukaribu wa nafasi isiyojulikana, IET Intelligent Transport Systems, Mei 2021, Taasisi ya Uhandisi na Teknolojia (IET) ""Uweza kusoma maandishi kamili:"