Original: Forecasting Taxi Demands with Fully Convolutional Networks and Temporal Guided Embedding
Revised: Forecasting Taxi Demands with Fully Convolutional Networks and Temporal Guided Embedding

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AfﬁliationAddress
Revised: AfﬁliationAddress

Original: Abstract
Revised: Abstract

Original: Learning complex spatiotemporal patterns is a key to predict future taxi demandvolumes.
Revised: Learning complex spatiotemporal patterns is a key to predict future taxi demandvolumes.

Original: We propose temporal guided networks (TGNet), which is an efﬁcientmodel architecture with fully convolutional networks and temporal guided embedding, to capture spatiotemporal patterns.
Revised: We propose temporal guided networks (TGNet), which is an efﬁcientmodel architecture with fully convolutional networks and temporal guided embedding, to capture spatiotemporal patterns.

Original: Existing approaches use complexarchitectures, historical demands (day/week/month ago) to capture the recurringpatterns, and external data sources such as meteorological, trafﬁc ﬂow, or texturedata.
Revised: Existing approaches use complexarchitectures, historical demands (day/week/month ago) to capture the recurringpatterns and external data sources such as meteorological, trafﬁc ﬂow or texture data.

Original: However, TGNet only uses fully convolutional networks and temporal guidedembedding without those external data sources.
Revised: However, TGNet only uses fully convolutional networks and temporalguided embedding without those external data sources.

Original: In this study, only pick-up anddrop-off volumes of NYC-taxi dataset are used to utilize the full potential  in theirhistorical patterns.
Revised: In this study, only pick-upand drop-off volumes of NYC-taxi dataset are used to utilize the full potential ofthe hidden patterns in the historical data points.

Original: We show that TGNet provides notable performance gains ona real-world benchmark, NYC-taxi dataset, over previous state-of-the-art models.
Revised: We show that TGNet providesnotable performance gains on a real-world benchmark, NYC-taxi dataset, overprevious state-of-the-art models.

Original: Finally we explain how to extend our architecture to incorporate external datasources.
Revised: Finally we explain how to extend our architectureto incorporate external data sources.

Original:  Introduction and Related Works
Revised:  Introduction and Related Works

Original: On-demand ride hailing platforms, such as Uber, Didi, and Lyft, fulﬁll more than millions ride hailingdemands per day and become essential part of  urban life style.
Revised: On-demand ride hailing platforms, such as Uber, Didi, and Lyft, fulﬁll more than millions ride hailingdemands per day and become essential part of the urban life style.

Original: Short-term demand forecasting isessential in those platforms since dynamic pricing relies on real-time demand forecast and dispatchsystem can relocate drivers to future high demand areas.
Revised: Short-term demand forecasting isessential in those platforms since dynamic pricing relies on real-time demand forecast and dispatchsystem can relocate drivers to predicted high demand areas.

Original: For example, by adjusting price in a highdemand region, a platform can control the potential short term supply and demand imbalances.
Revised: For example, by adjusting price in a highdemand region, a platform can control the potential short term supply and demand imbalances.

Original: To predict future demand volumes in each region, a predictive model must learn complex spatiotemproal correlations, because a demand volume of each region is highly correlated with a previousdemand volume (temporal pattern) and demand volumes of connected region are also highly correlated(spatial pattern)
Revised: To predict future demand volumes in each region, a predictive model must learn complex spatiotemporal correlations, because a demand volume of each region is highly correlated with a previous demandvolume (temporal pattern) and demand volumes of connected region are also highly correlated (spatialpattern) [4, 9].

Original: To learn a temporal pattern, a statistical approach, such as autoregressive integrated moving average(ARIMA), can be used in each regions, but this approach may fail to capture spatial correlations.
Revised: To learn a temporal pattern, a statistical approach, such as auto-regressive integratedmoving average (ARIMA), can be used in each region, but this approach may fail to capture thespatial correlations.

Original: Vector autoregressive (VAR) model may be used to learn spatial patterns, but this model is known tohave limitation on the high dimensional data [1].
Revised: Vector auto-regressive (VAR) model may be used to learn spatial patterns, butthis model is known to have limitation on the high dimensional data [1].

Original: To overcome the aforementioned challenges and learna spatiotemporal patterns, recent studies usedeep neural networks to predict future demand volumes and show notable performance gains over thetraditional approaches.
Revised: To overcome the aforementioned challenges and learn spatiotemporal patterns, recent studies usedeep neural networks to predict future demand volumes and show notable performance gains over thetraditional approaches.

Original: To learn a spatial patterns, [12, 11] formed a grid over the region of interests,and aggregated the data of interest, e.g. , crowd ﬂows, as pixel values.
Revised: To learn a spatial patterns, [12, 11] formed a grid over the region of interests,and aggregated the data of interest, e.g. , crowd ﬂows, as pixel values.

Original: After this transformation, wecan think a demand pattern as an image, and convolutional networks can be employed to model localspatial correlations.
Revised: After this transformation, wecan think a demand pattern as an image, and convolutional networks can be employed to model localspatial correlations.

Original: DMVST-Net [9] tried to model spatial, temporal, and semantic contexts, combining local CNN, LSTM, and graph convolutional networks (GCN)  together.
Revised: DMVST-Net [9] modeled spatial, temporal, and semantic contexts by combining local CNN, LSTM,and graph convolutional networks (GCN) all together.

Original: STDN [8], which consists of local 
Revised: STDN [8], which consists of local CNN,

Original: CNN, LSTM, and  temporal attention, predicted future volumes of taxi demand and used externaldata sources such as trafﬁc ﬂow and meteorological datas.
Revised: LSTM, and the temporal attention, predicted future volumes of taxi demand and used external data sources such as trafﬁc ﬂow and meteorological data.

Original: STDN also showed state-of-the-artperformances on NYC-taxi dataset.
Revised: STDN also showed state-of-the-art performanceson NYC-taxi dataset.

Original: In addition, previous approaches [11, 9, 8] used periodic/seasonalhistories (days/weeks ago) to capture recurrent patterns in the time series data.
Revised: In addition, previous approaches [11, 9, 8] used periodic/seasonal histories(days/weeks ago) to capture recurrent patterns in the time series data.

Original: Incorporating external data sources can also improve the forecasting quality.
Revised: Incorporating external data sources can also improve the forecasting quality.

Original: In [12, 11, 9, 8],meteorological data is commonly used.
Revised: In [12, 11, 9, 8],meteorological data is commonly used.

Original: [6] used temporal, spatial, meteorological, event, and theircombination data to predict future taxi demands.
Revised: [6] used temporal, spatial, meteorological, event, and theircombination data to predict future taxi demands.

Original: Trafﬁc in/out ﬂow [8] and textual data from websites [5] are also used to forecast demands.
Revised: Trafﬁc in/out ﬂow [8] and textual data from websites [5] are also used to forecast demands.

Original: In this paper, we propose an efﬁcient model framework (TGNet), fully convolutional networks withtemporal guided embedding.
Revised: In this paper, we propose an efﬁcient model framework (TGNet), fully convolutional networks withtemporal guided embedding.

Original: Our proposed architecture does not use complex model and does notrequire external data sources to achieve the state-of-the-art results, in contrast to the previous stateof-the-art models.
Revised: Our proposed architecture does not use complex model and does notrequire external data sources to achieve the state-of-the-art results, in contrast to the previous stateof-the-art models.

Original: We show that our new architecture is enough to model complex spatiotemporalcorrelation within a dataset to achieve the best performance.
Revised: We show that our new architecture is enough to model complex spatiotemporalcorrelation within a dataset to achieve the best performance.

Original: We believe that our proposed architecturecan providea competitive baseline model on  spatiotemporal domain, including mobility area.
Revised: We believe that our proposed architecturecan provide competitive baseline results on many spatiotemporal datasets.

Original: The main contributions in this paper is as follows.
Revised: The main contributions in this paper is as follows.

Original: First of all, TGNet has a simple architecture tolearn complex spatiotemporal patterns in taxi demands unlike previous approaches that combinecomplex models and external data sources.
Revised: First of all, TGNet has a simple architecture tolearn complex spatiotemporal patterns in taxi demands unlike previous approaches that combinecomplex models and external data sources.

Original: Secondly, we show notable performance gains usingtemporal guided embedding, which helps the model extract hidden representations conditioned ontemporal context.
Revised: Secondly, we show notable performance gains usingtemporal guided embedding, which helps the model extract hidden representations conditioned ontemporal context over the traditional approaches.

Original: Thirdly, we show competitive results to state-of-the-art performances on NYC-taxidataset without  external data sources.
Revised: Thirdly, we show competitive performance on NYC-taxi dataset without utilizing external data sources compared to the current state-of-the-artresults that uses the external data sources.

Original: Lastly, we also show how to extend TGNet to incorporateexternal data sources.
Revised: Lastly, we also show how to extend TGNet to incorporateexternal data sources.

Original:  Dataset and Methods
Revised:  Dataset and Methods

Original: 2.1 Dataset and Preprocessing
Revised: 2.1 Dataset and Preprocessing

Original: We uses NYC-Taxi dataset, which contains taxi trip records of NYC in 2015, from 01/01/2015 to03/01/2015.
Revised: We uses NYC-Taxi dataset, which contains taxi trip records of NYC in 2015, from 01/01/2015 to03/01/2015.

Original: Taxi pick-up and drop-off volumes in NYC are recorded at every 30 minutes interval.
Revised: Taxi pick-up and drop-off volumes in NYC are recorded at every 30 minutes interval.

Original: Inthis study, the records in ﬁrst 40 days data are trained and the remaining records are tested.
Revised: Inthis study, the records in ﬁrst 40 days data are trained and the remaining records are tested.

Original: In this paper, we split the area of NYC into I × J grid map.
Revised: In this paper, we split the area of NYC into I × J grid map.

Original: We deﬁne the set of non-overlappedregion L = { L 11 , ..., L IJ } .
Revised: We deﬁne the set of non-overlappedregion L = { L 11 , ..., L IJ } .

Original: Also, the set of non-overlapped time intervals is deﬁned by I
Revised: Also, the set of non-overlapped time intervals is deﬁned by I

Original: ={ I 1 , ..., I p } , whose interval is 30 minutes.
Revised: ={ I 1 , ..., I p } , whose interval is 30 minutes.

Original: A taxi demand r has time stamp and request location( r.t, r.l ) .
Revised: A taxi demand r has time stamp and request location( r.t, r.l ) .

Original: Then, the taxi demands at time interval I t are deﬁned by X ( t ) =
Revised: Then, the taxi demands at time interval I t are deﬁned by X ( t ) =

Original: { X ( t ) ij : L ij ∈ L } , where
Revised: { X ( t ) ij : L ij ∈ L } , where

Original: X ( t ) ij = |{ r : r.t ∈
Revised: X ( t ) ij = |{ r : r.t ∈

Original: I t ∧ r.l ∈ L ij }| .
Revised: I t ∧ r.l ∈ L ij }| .

Original: In here, |·| denotes the cardinality of the set.
Revised: In here, |·| denotes the cardinality of the set.

Original: In this study, we convert the data into X ( t : t − T )
Revised: In this study, we convert the data into X ( t : t − T )

Original: ∈ R 3 T , which is an image with ( T + 1) channels.
Revised: ∈ R 3 T , which is an image with ( T + 1) channels.

Original: Here, the colon, : , means channel wise concatenation, that is X ( t : t − 1) =
Revised: Here, the colon, : , means channel wise concatenation, that is X ( t : t − 1) =

Original: (cid:8) X ( t ) ; X ( t − 1) (cid:9) .
Revised: (cid:8) X ( t ) ; X ( t − 1) (cid:9) .

Original: 2.2 Forecasting Problem Formulation
Revised: 2.2 Forecasting Problem Formulation

Original: We predict future demand volumes at time interval t + 1 , given data until t .
Revised: We predict future demand volumes at time interval t + 1 , given data until t .

Original: Nonstationary time series data tends to have periodic and seasonal recurrent patterns.
Revised: Non-stationarytime series data tends to have periodic and seasonal recurrent patterns.

Original: Then,ˆ X ( t +1) =
Revised: Then, ˆ X ( t +1) =

Original: f ( X ( t ) , ..., X ( t − T ) , X ( t − p ) , ..., X ( t − Tp ) , X ( t − s ) , ..., X ( t − Ts ) ) .
Revised: f ( X ( t ) , ..., X ( t − T ) , X ( t − p ) , ..., X ( t − Tp ) , X ( t − s ) , ..., X ( t − Ts ) ) .

Original: where autoregressivemodel f , terms of period p , and season s .
Revised: where auto-regressive model f ,terms of period p , and season s .

Original: When p = s = 0 , in the TGNet setting, the forecasting model only uses adjacent past volumes ofrequests until t − T as features.
Revised: When p = s = 0 , in the TGNet setting, the forecasting model only uses adjacent past volumes ofrequests until t − T as features.

Original: In our setting, the model F forecasts t + 1 future volumes only with
Revised: In our setting, the model F forecasts t + 1 future volumes only with

Original: X ( t : t − T ) and E ( t :) , where E ( t :) is external data available at t .
Revised: X ( t : t − T ) and E ( t :) , where E ( t :) is external data available at t .

Original: Then, ˆ X ( t +1)
Revised: Then, ˆ X ( t +1)

Original: =
Revised: =

Original: F ( X ( t : t − T ) , E ( t :) )
Revised: F ( X ( t : t − T ) , E ( t :) )

Original:  TGNet: Fully Convolutional Networks with Temporal Guided Embedding3.1 Baseline Model Architecture
Revised:  TGNet: Fully Convolutional Networks with Temporal Guided Embedding3.1 Baseline Model Architecture

Original: Figure 1(a) shows fully convolutional networks to forecast taxi demands.
Revised: Figure 1(a) shows fully convolutional networks to forecast taxi demands.

Original: TGNet has symmetricconvoluion and deconvolution operations with skip connections.
Revised: TGNet has symmetricconvolution and deconvolution operations with skip connections.

Original: Skip connections prevent highlevel features of deconvolution from losing precise positional information of each pixel after a stack of convolutional and pooling layers.
Revised: Skip connections prevent highlevel features of deconvolution from losing precise positional information of each pixel after a stack of convolutional and pooling layers.

Original: Average pooling is used to aggregate the feature values ofreceptive area and each subsample represents the characteristic of the area.
Revised: Average pooling is used to aggregate the feature values ofreceptive area and each subsample represents the characteristic of the area.

Original: Dense block containsthree convolutional layers with dense skip connections in [2] to help model learn after pooling.
Revised: Dense block containsthree convolutional layers with dense skip connections in [2] to help model learn after pooling.

Original: Eachsubsample of average pooling represents the semantic features of its receptive area and the denseblock is expected to learn semantic context between large regions.1 × 1 convolutional layers are located at the end of model and operate as position-wise feature extractorand regression.
Revised: Eachsubsample of average pooling represents the semantic features of its receptive area and the denseblock is expected to learn semantic context between large regions.1 × 1 convolutional layers are located at the end of model and operate as position-wise feature extractorand regression.

Original: They integrate various feature sources to predict future demand volume of wholetarget regions.
Revised: They integrate various feature sources to predict future demand volume of wholetarget regions.

Original: It does not consider spatial correlation anymore, but focus on forecast requests withprecise localization.
Revised: It does not consider spatial correlation anymore, but focus on forecast requests withprecise localization.

Original: [Figure]
Revised: [Figure]

Original: Temporal Guided Embedding
Revised: Temporal Guided Embedding

Original: [Figure]
Revised: [Figure]

Original: Trafﬁc data, such as taxi demand, is known to have temporally recurrent patterns, such as periodicityand seasonality.
Revised: Trafﬁc data, such as taxi demand, is known to have temporally recurrent patterns, such as periodicityand seasonality.

Original: The patterns are different for time-of-day, day-of-week, and holiday or not.
Revised: The patterns are different for time-of-day, day-of-week, and holiday or not.

Original: To learnthese patterns, existing researches use corresponding historical inputs such as demand volumes of aday (week) ago at the same time of day, and show promising results [12, 11, 8].
Revised: To learnthese patterns, existing researches use corresponding historical inputs such as demand volumes of aday (week) ago at the same time of day, and show promising results [12, 11, 8].

Original: However, we postulate that this may not be the optimal way to digest such a temporal patterns.
Revised: However, we postulate that this may not be the optimal way to digest such a temporal patterns.

Original: Forexample, when a person is asked to predict a taxi demand volume, he or she may want to knowthe temporal context, such as time-of-day, day-of-week, and holiday information, not the day orweek ago demand volume itself.
Revised: Forexample, when a person is asked to predict a taxi demand volume, he or she may want to knowthe temporal context, such as time-of-day, day-of-week, and holiday information, not the day orweek ago demand volume itself.

Original: This indicates that using meta temporal information can be mucheffective to inference the recurring patterns.
Revised: This indicates that using meta temporal information can be mucheffective to inference the recurring patterns.

Original: Thus, we use temporal information as categoricalvariables: time-of-day (48 categorical variables for every 30 minutes), day-of-week (7), holiday (1),and previous day of holiday (1).
Revised: Thus, we use temporal information as categoricalvariables: time-of-day (48 categorical variables for every 30 minutes), day-of-week (7), holiday (1),and previous day of holiday (1).

Original: TGNet learns embedding of temporal information and it is concatenated on taxi demand inputs astemporal guidance for TGNet.
Revised: TGNet learns embedding of temporal information and it is concatenated on taxi demand inputs astemporal guidance for TGNet.

Original: The fully convolutional networks extract hidden representations ofcomplex spatiotemporal patterns in taxi demands, conditioned on temporal embedding.
Revised: The fully convolutional networks extract hidden representations ofcomplex spatiotemporal patterns in taxi demands, conditioned on temporal embedding.

Original: We expectthat temporal guided embedding is enough to capture temporal contexts than usage of periodic andseasonal inputs.
Revised: We expectthat temporal guided embedding is enough to capture temporal contexts than usage of periodic andseasonal inputs.

Original: 3.3 Late Fusion with Supplemental Data Sources
Revised: 3.3 Late Fusion with Supplemental Data Sources

Original: In TGNet, features from other data sources can be concatenated to feature maps that are extracted bydeconvolutional layers.
Revised: In TGNet, features from other data sources can be concatenated to feature maps that are extracted bydeconvolutional layers.

Original: This type of late fusion is a common approach to combine intra-modality ofheterogeneous data sources [10].
Revised: This type of late fusion is a common approach to combine intra-modality ofheterogeneous data sources [10].

Original: In this paper, we encode drop-off information in NYC-taxi datasetand concatenate on position-wise layer.
Revised: In this paper, we encode drop-off information in NYC-taxi datasetand concatenate on position-wise layer.

Original: In the future, we expect various types of information can beattached to a same manner.
Revised: In the future, we expect various types of information can beattached in the same manner.

Original: [Table]
Revised: [Table]

Original:  Experiment Results
Revised:  Experiment Results

Original: 4.1 Implementation
Revised: 4.1 Implementation

Original: The area of NYC is divided into 20 × 10 grids.
Revised: The area of NYC is divided into 20 × 10 grids.

Original: Recent historical volumes of taxi demand (4 hours) anddrop-off (8 hours) are used.
Revised: Recent historical volumes of taxi demand (4 hours) anddrop-off (8 hours) are used.

Original: Hyper-parameters of compared models are followed original papers.
Revised: Hyper-parameters of compared models are followed original papers.

Original: In
Revised: In

Original: TGNet, 32, 128, 128, 128, and 128 ﬁlters of 3 × 3 (de)convolutional layers and two 1x1 convolutionallayers with 256 ﬁlters are used.
Revised: TGNet, 32, 128, 128, 128, and 128 ﬁlters of 3 × 3 (de)convolutional layers and two 1x1 convolutionallayers with 256 ﬁlters are used.

Original: Temporal information and drop-off are encoded by 8 and 32 ﬁlters,respectively.
Revised: Temporal information and drop-off are encoded by 8 and 32 ﬁlters,respectively.

Original: Adam optimizer [3] with learning rate 0.01 and decay 0.01 is used.
Revised: Adam optimizer [3] with learning rate 0.01 and decay 0.01 is used.

Original: Batch size is 128and the model is trained using early stopping, validated by 20% of training data.
Revised: Batch size is 128and the model is trained using early stopping, validated by 20% of training data.

Original: After pre-train using
Revised: After pre-train using

Original: L2 loss, the model is optimized by L1 loss function, because it’s robust on noises in trafﬁc data [8].
Revised: L2 loss, the model is optimized by L1 loss function, because it’s robust on noises in trafﬁc data [8].

Original: 4.2 Numerical Results
Revised: 4.2 Numerical Results

Original: In evaluation, we ﬁltered low-value samples less than 11 to obtain a meaningful performance metric[8], since a low demand value is not a concern in a real-world application.
Revised: In evaluation, we ﬁltered low-value samples less than 11 to obtain a meaningful performance metric[8], since a low demand value is not a concern in a real-world application.

Original: Mean absolute percentage error (MAPE) and root of mean squared error (RMSE) are used asperformance measures.
Revised: Mean absolute percentage error (MAPE) and root of mean squared error (RMSE) are used asperformance measures.

Original: In table 1, temporal guided embedding shows notable performance gains of
Revised: In table 1, temporal guided embedding shows notable performance gains of

Original: MAPE and RMSE.
Revised: MAPE and RMSE.

Original: TGNet also shows the best MAPE performance and competitive result of RMSE.
Revised: TGNet also shows the best MAPE performance and competitive result of RMSE.

Original: The results are noteworthy.
Revised: The results are noteworthy.

Original: The state-of-the-art model, STDN, combines local CNN and LSTM withtemporal closeness/periodic/seasonal inputs and incorporates external data, such as trafﬁc ﬂow andmeteorological data.
Revised: The state-of-the-art model, STDN, combines local CNN and LSTM withtemporal closeness/periodic/seasonal inputs and incorporates external data, such as trafﬁc ﬂow andmeteorological data.

Original: However, TGNet only contains FCN structure with temporal guided embeddingand uses drop-off data.
Revised: However, TGNet only contains FCN structure with temporal guided embeddingand uses drop-off data.

Original: The number of trainable parameters is also 8 times smaller than previousstate-ot-the-art model, STDN.
Revised: The number of trainable parameters is also 8 times smaller than previousstate-of-the-art model, STDN.

Original: We conclude that TGNet efﬁciently captures complex spatiotemporalpatterns in taxi demand itself.
Revised: We conclude that TGNet efﬁciently captures complex spatiotemporalpatterns in taxi demand itself.

Original:  Conclusion and Discussion
Revised:  Conclusion and Discussion

Original: In this paper, we propose temporal guided networks (TGNet), fully convolutional networks withtemporal guided embedding to capture spatiotemporal pattern in taxi demands.
Revised: In this paper, we propose temporal guided networks (TGNet), fully convolutional networks withtemporal guided embedding to capture spatiotemporal pattern in taxi demands.

Original: Especially, temporalguided embedding learns and helps fully convolutional networks to extract spatiotemporal featuresconditioned on temporal contexts.
Revised: Especially, temporalguided embedding learns and helps fully convolutional networks to extract spatiotemporal featuresconditioned on temporal contexts.

Original: Indeed, temporal guided embedding provides notable performancesgains on forecasting taxi demands on real-world dataset.
Revised: Indeed, temporal guided embedding provides notable performancesgains on forecasting taxi demands on real-world dataset.

Original: We also show that TGNet has simplearchitecture to add various data sources with late fusion.
Revised: We also show that TGNet has simplearchitecture to add various data sources with late fusion.

Original: In the future we will investigate how to encode other context information, including meteorology,articles, and other trafﬁc information.
Revised: In the future we will investigate how to encode other context information, including meteorology,articles, and other trafﬁc information.

Original: Our study will be extended to analyze atypical events andpredict multi-step ahead future, based on TGNet architecture.
Revised: Our study will be extended to analyze atypical events andpredict multi-step ahead future, based on TGNet architecture.

Original: Especially, uncertainty calibrationof both data and model can be important issue of interpretability when abrupt changes or atypicalevents occur.
Revised: Especially, uncertainty calibrationof both data and model can be important issue of interpretability when abrupt changes or atypicalevents occur.

Original: Finally, we expect that TGNet can be applied to solve other spatiotemporal problems ingeneral.
Revised: Finally, we expect that TGNet can be applied to solve other spatiotemporal problems ingeneral.