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)

Original: 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 demand volumes.

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 efﬁcient model 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 texture data.
Revised: Existing approaches use complex architectures, historical demands (day/week/month ago) to capture the recurring patterns, and external data sources such as meteorological, trafﬁc ﬂow, or texture data.

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

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

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

Original: Finally we explain how to extend our architectureto incorporate external data sources.
Revised: Finally we explain how to extend our architecture to 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 the urban life style.
Revised: On-demand ride hailing platforms, such as Uber, Didi, Lyft and Kakao T, fulﬁll more than millions ride hailing demands 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 topredicted high demand areas.
Revised: Short-term demand forecasting is essential in those platforms since dynamic pricing relies on real-time demand forecast and dispatch system can relocate drivers to 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 high demand region, the potential short term supply and demand imbalances can be controlled.

Original: 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].
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 demand volume (temporal pattern) and demand volumes of connected region are also highly correlated (spatial pattern) [4, 9].

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

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

Original: 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.
Revised: To overcome the aforementioned challenges and learn spatiotemporal patterns, recent studies use deep neural networks to predict future demand volumes and show notable performance gains over the traditional 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, we can think a demand pattern as an image, and convolutional networks can be employed to model local spatial correlations.

Original: DMVST-Net [9] modeled spatial, temporal, and semantic contexts by combining local CNN, LSTM,and graph convolutional networks (GCN) all 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 CNN,
Revised: STDN [8], which consists of local CNN,

Original: LSTM, and the temporal attention, predicted future volumes of taxi demand and used external data sources such as trafﬁc ﬂow and meteorological data.
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-art performanceson NYC-taxi dataset.
Revised: STDN also showed state-of-the-art performances on NYC-taxi dataset.

Original: In addition, previous approaches [11, 9, 8] used periodic/seasonal histories(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 their combination 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 web sites [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 with temporal 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 not require 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 spatiotemporal correlation within a dataset to achieve the best performance.

Original: We believe that our proposed architecturecan provide competitive baseline results on many spatiotemporal datasets.
Revised: We believe that our proposed architecture can 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 to learn complex spatiotemporal patterns in taxi demands unlike previous approaches that combine complex 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 over the traditional approaches.
Revised: Secondly, we show notable performance gains using temporal guided embedding, which helps the model extract hidden representations conditioned on temporal context over the traditional approaches.

Original: Thirdly, we show competitive performance on
Revised: Thirdly, we show competitive performance on

Original: NYC-taxi dataset without utilizing external data sources compared to the current state-of-the-artresults that uses the external data sources.
Revised: NYC-taxi dataset without utilizing external data sources compared to the current state-of-the-art results 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 incorporate external data sources.

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

Original: 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 to 03/01/2015.

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: In this 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-overlapped region 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 ) ∈ R 3T , which is an image with ( T + 1) channels.
Revised: In this study, we convert the data into X ( t : t − T ) ∈ R 3 , 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 futuredemand volumes at timeinterval t+ 1 , given data until t .
Revised: To forecast t + 1 future volumes at time t , we use adjacent past volumes of taxi requests until t − T as features.

Original: Non-stationarytime series data tends to have periodic and seasonal  recurrent patterns. Then, ˆ X ( t +1)
Revised: Periodic and seasonal inputs are not used to capture recurrent patterns in taxi demands, but temporal guided embedding replaces them.

Original: = f ( X ( t ) , ..., X ( t − T ) , X ( t − p ) , ..., X ( t − Tp ) , X ( t − s ) , ..., X ( t − Ts ) ) . where auto-regressive model f ,terms of period p , and season s . When p = s = 0 , in the TGNet setting, the forecasting model only uses adjacent past volumes ofrequests until t − T as features.
Revised: We introduce temporal guided embedding in the next section.

Original: In our setting, the model F forecasts t + 1 future volumes only with X ( t : t − T ) and E ( t :) , where E ( t :) is external data available at t .
Revised: The model F forecasts t + 1 future volumes only with 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 Embedding 3.1 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 symmetricconvolution and deconvolution operations with skip connections.
Revised: TGNet has symmetric convolution 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 high level 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 of receptive 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 contains three 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: Each subsample of average pooling represents the semantic features of its receptive area and the dense block 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 extractor and 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 whole target regions.

Original: It does not consider spatial correlation anymore, but focus on forecast requests withprecise localization.[Figure]
Revised: It does not consider spatial correlation anymore, but focus on forecast requests with precise localization.

Original:  Temporal Guided Embedding
Revised: 3.2 Temporal Guided Embedding

Original: [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 periodicity and 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 learn these patterns, existing researches use corresponding historical inputs such as demand volumes of a day (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: For example, when a person is asked to forecast a taxi demand volume, he or she may want to know temporal information, such as time-of-day, day-of-week, and holiday information. If not, it is just pattern matching with input data, without understanding of temporal contexts in taxi demands.

Original: This indicates that usingmeta temporal information can be mucheffective to inference the recurring patterns.
Revised: This indicates that using temporal information can be much effective 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 categorical variables: time-of-day (48 dimensional 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 ontaxi demand inputs astemporal guidance for TGNet.
Revised: TGNet learns embedding of temporal information and it is concatenated on demand inputs as temporal guidance.

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 of complex 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 expect that temporal guided embedding is enough to capture temporal contexts than usage of periodic and seasonal inputs.

Revised: Note that ﬁguring out the right period and seasonality by looking at partial ACF is often time consuming, which shows the temporal guided embedding’s another advantage over the existing approaches.

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 by deconvolutional 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 of heterogeneous 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 dataset and concatenate on position-wise layer.

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

Original: [Table]
Revised: [Table]

Original:  Experiment Results 
Revised:  Experiment Results 4.1 Implementation

Original: 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) and drop-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 convolutional layers 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 are used.

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

Original: In table 1, temporal guided embedding  shows notable performance gains of MAPE and RMSE.
Revised: In table 1, (a), (b), and (c) notate the components in Figure 1. Temporal guided embedding (b) shows notable performance gains of MAPE and RMSE, capturing recurrent patterns in taxi demands without previous periodic and seasonal inputs.

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 with temporal closeness/periodic/seasonal inputs and incorporates external data, such as trafﬁc ﬂow and meteorological 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 embedding and uses drop-off data.

Original: The number of trainable parameters is also 8 times smaller than previousstate-of-the-art model, STDN.
Revised: The number of trainable parameters is also 8 times smaller than previous state-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 spatiotemporal patterns 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 with temporal 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, temporal guided embedding learns and helps FCN to extract spatiotemporal features conditioned on temporal contexts.

Original: Indeed, temporal guided embedding provides notable performancesgains on forecasting taxi demands on real-world dataset.
Revised: Indeed, it provides notable performances gains 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 simple architecture 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 and predict 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 calibration of both data and model can be important issue of interpretability when abrupt changes or atypical events 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 in general.