ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features...

Research ArticleST-LSTM A Deep Learning ApproachCombined Spatio-Temporal Features for Short-TermForecast in Rail Transit

Qicheng Tang Mengning Yang and Ying Yang

School of Big Data amp Soware Engineering Chongqing University Chongqing China

Correspondence should be addressed to Mengning Yang mnyangcqueducn

Received 7 November 2018 Revised 29 December 2018 Accepted 21 January 2019 Published 6 February 2019

Guest Editor Helena Ramalhinho

Copyright copy 2019 Qicheng Tang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The short-term forecast of rail transit is one of the most essential issues in urban intelligent transportation system (ITS) Accurateforecast result can provide support for the forewarning of flow outburst and enables passengers to make an appropriate travel planTherefore it is significant to develop a more accurate forecast model Long short-term memory (LSTM) network has been provedto be effective on data with temporal features However it cannot process the correlation between time and space in rail transit As aresult a novel forecast model combining spatio-temporal features based on LSTM network (ST-LSTM) is proposed Different fromother forecast methods ST-LSTM network uses a new method to extract spatio-temporal features from the data and combinesthem together as the input Compared with other conventional models ST-LSTM network can achieve a better performance inexperiments

1 Introduction

With the development of urban scale the short-term trafficforecast has become a core issue of ITS Accurate short-term traffic forecast can provide technical support for thesurveillance and the forewarning of passenger flow There-fore over the past few decades many data analysis modelshave been proposed to promote the forecast accuracy Amongthese models LSTM network is widely recognized as themost suitable model to deal with traffic forecast LSTM unithas three gates namely input gate forget gate and outputgate which can adjust the state of unit dynamically so LSTMnetwork is able to capture the features on longer time spanTherefore LSTM network can provide a higher accuracyin traffic forecast because traffic data is usually collectedaccording to time series

In recent years researchers pay more attention to thespatial features of traffic flow It is widely acknowledged thattraffic forecast is a problem with spatio-temporal complex-ity ie the problem of spatial transportation in temporaldimension In [1] Zheng Zhao et al establish a network byconnecting several LSTM units which aimed to imitate the

structure of urban traffic However it failed to imitate thestructure of large urban scale Xiaobo Chen et al proposed anewmethod to process spatial features by using sparse hybridgenetic algorithm [2] Liu Qingchao et al proposed a modelbased on manifold similarity to capture the spatial regularityfrom freeway data [3] These two approaches are sensitive tothe spatial features but compared with LSTM network theycannot process temporal features well

In this paper the object of study is the short-termforecast of rail transit In the research we find that dif-fering from other transportation rail transit has stationswith fixed position vehicles with uniform speed and reg-ular schedule Because of these characteristics the spa-tial correlation between stations can be transformed intothe time cost Based on this analysis this paper pro-poses a new method to capture spatio-temporal featuresfrom rail transit data and input the features into a newmodel named spatio-temporal long short-term network (ST-LSTM) which is based on LSTM network Compared withmost existing methods the proposed model has a betterperformance on accuracy and meets the real-time require-ment

HindawiJournal of Advanced TransportationVolume 2019 Article ID 8392592 8 pageshttpsdoiorg10115520198392592

2 Journal of Advanced Transportation

2 Related Work

There are manymethods that have been proposed to improvetraffic forecast including historical average and smoothing[4 5] dynamic linear methods [6 7] traffic theory-basedmethods [8 9] and machine learning methods [10 11]These forecast approaches can be divided into two cat-egories namely parametric approaches and nonparamet-ric approaches Autoregressive integrated moving average(ARIMA) model is widely recognized as a classic methodin parametric approaches As early as the 1970s Levin andTsao found that ARIMA model was the most statisticallysignificant in traffic forecast [12] Parametric approaches havefavorable properties and capture regular variations very wellHowever traffic data usually shows irregular variations Tosolve this problem researchers also paid attention to non-parametric approaches such as nonparametric regressionmodels [13] support vector machine (SVM) [14 15] andrecurrent neural network [16 17] Afterwards recurrent neu-ral network [18] was proposed to process temporal featuressuch as evolutionary neural network (ENN) [19] dynamicneural network (DNN) [20] and nonlinear autoregressivemodels with exogenous inputs (NARX) [21] Among themRNN is widely recognized as a suitable method to capturethe temporal features of passenger flow However previousstudies proved that RNNs failed to capture the long-termfeatures because of vanishing gradient and exploding gradi-ent To solve these problems long short-termmemory neuralnetwork (LSTM NN) [22] was applied in the traffic forecastIn recent years some approaches have been proposed to dealwith the spatio-temporal complexity of traffic data which arementioned in Section 1

Different from these methods this paper proposes anew method to capture spatio-temporal features and a newnetwork based on LSTM to forecast the exit passenger flowof rail transit The remainder of this paper is as followsSection 3 introduces the architecture of ST-LSTM networkExperiments based on the data of Chongqing rail transit areshown in Section 4 Section 5 is composed of the analysis ofexperiment result and future work is at the end of this paper

3 Methodology

Short-term forecast for rail transit is a problem with spatio-temporal complexity Suppose the exit passenger flow of sta-tion 119895 is needed to be predictedThe temporal features are thecorrelation between historical data and current data ie theprevious exit passenger flow of station 119895 These features canbe extracted directly because the rail transit data is collectedaccording to the temporal dimensionThe spatial features arethe transportation of passenger flow on geographic positionie the summation of estimated passenger flows from theother stations For every two stations the spatial featuresinclude the volume and the cost of transportation betweenthem The volume of transportation is reflected by thepassenger flow between two stations In the proposed modelspatial correlation matrix (SCM) is integrated to calculatethe volume of transportation The cost of transportation isreflected by several factors such as time cost economic cost

and distance Among them time cost is the most suitablefactor to reflect the spatial correlation which is mentionedin Section 1Therefore time cost matrix (TCM) is introducedto calculate the cost of transportationThe proposedmodel isbased on the technologies including passenger informationsystem (PIS) features extraction method and ST-LSTMnetwork The detail of the technologies will be explained inthis section

31 Passenger Information System Sufficient data is the basisof accurate forecast and PIS can provide us with comprehen-sive data PIS is a huge and complex network Various railtransit data can be collected in real time through the gatesystem ticketing system and vehicle scheduling systemWiththe development of data acquisition technology PIS is ableto provide sufficient support for short-term forecast Basedon the card records the entrance and exit passenger flow ofstations are calculated with a frequency of 10 min which canbe denoted by119894 = 119888119886119903119889119894119889 119900119905119894119898119890 119900119904119905119886119905119894119900119899 119889119905119894119898119890 119889119904119905119886119905119894119900119899 119889119886119905119890 (1)119909 119894119899119895119905 = sum

119894isin119872

1 | 119894119900119904119905119886119905119894119900119899 = 119895 119894119900119905119894119898119890 = 119905 (2)119909 119900119906119905119895119905 = sum119894isin119872

1 | 119894119889119904119905119886119905119894119900119899 = 119895 119894119889119905119894119898119890 = 119905 (3)

where 119894 is a card record 119888119886119903119889119894119889 119900119905119894119898119890 119900119904119905119886119905119894119900119899 119889119905119894119898119890119889119904119905119886119905119894119900119899 and 119889119886119905119890 are the attributes of 119894 which representcard identification origin time origin station destinationtime destination station and date respectively 119872 is thedatabase of card records 119909 119894119899119895119905 is the entrance passengerflow of station 119895 in time 119905 and 119909 119900119906119905119895119905 is the exit passengerflow of station 119895 in time 11990532 Feature Extraction Method The extraction of spatio-temporal features is one of the core problems of the proposedmodel The proposed model extracts temporal features andspatial features respectively and then put them togetherinto the ST-LSTM network The temporal features can beextracted directly because rail transit data is recorded accord-ing to the temporal dimension To extract the spatial featuresTCMmatrix and SCMmatrix are integrated into themethod

321 Time Cost Matrix Time cost is the most suitable factorto reflect the spatial correlation between stations so the timecost between all stations constitutes the TCMmatrix Due tothe changes of schedule and passenger flow TCM matrix isdynamic with time going on Suppose there are119898 stations inthe rail transit system then the size of TCMmatrix is119898times119898which can be denoted by

119879119862119872119905 = (11987911 11987912 119879111989811987921 11987922 1198792119898 119879119896119895 1198791198981 1198791198982 119879119898119898) (4)

Journal of Advanced Transportation 3

StationNojhellip Station

Noj-1StationNoj+1 hellipStation

No1StationNom

InFlow

InFlow

InFlow

OutFlow

ST-Characteristics

t - ΔT1j

t - ΔTj

t - ΔTjminus1j

t - 1

t - ΔTj+1j

t - ΔTj

t - ΔTmj

P1j middot x_in1tminusΔT1Pj middot x_intminusΔT

Pjminus1j middot x_injminus1tminusΔTminus1 x_outjtminus1 Pj+1j middot x_inj+1tminusΔT+1Pj middot x_intminusΔT

Pmj middot x_inmtminusΔT

F_Sjt F_Tjt

InFlow

InFlow

InFlow

Figure 1 Design of the extraction of spatio-temporal features

119879119896119895 = 119905119894119905119894 = 119894119889119905119894119898119890 minus 119894119900119905119894119898119890(119894 isin 119872 119894119889119905119894119898119890 isin 119879lowast 119894119900119904119905119886119905119894119900119899 = 119896 119894119889119904119905119886119905119894119900119899 = 119895) (5)

119879lowast = 119905 + 119902119908 119902 = 0 1 2 n (6)where119879119862119872119905 is the TCMmatrix in time 119905119879119896119895 is the averageof time cost between 119896 and 119895 in historical time series where119896 is origin station and 119895 is destination station 119905119894 is thetime cost between two stations in record 119894 119894 is a card recordin database 119872 which has been defined in Eq(1) 119879lowast is thehistorical time series of time 119905119908 is a week and 119902 is the numberof weeks

In the analysis it is found that passenger flow variesby peoplersquos routine cycle ie from Monday to Sunday Forexample the passenger flow in Thursday is similar to theone in last Thursday not yesterday Therefore to promote theextraction 119879119896119895 in time 119905 represents the average time cost inhistorical time series of time 119905 In Eq (6) 119879lowast is the historicaltime series which consists of time 119905 and the same periodin several weeks ago of it This method is also used in thecalculation of spatial correlation matrix

322 Spatial Correlation Matrix To forecast the exit passen-ger flow at station 119895 in time 119905 passengers from station 119896 intime 119905 minus 119879119896119895 have to be considered The entrance passengerflow of station 119896 in time 119905 minus 119879119896119895 (119909 119894119899119896119905minus119879119896119895) is availableHowever time 119905 has not happened so the proportion ofpassengers in 119909 119894119899119896119905minus119879119896119895 which set off to station 119895 isunavailable To solve this contradiction spatial factor isintroduced in this paper Spatial factor 119875119896119895 is the historicalaverage probability of passengers between station 119896 and 119895 inentrance passenger flowWhen forecasting the exit passengerflow at station 119895 in time 119905 the spatial influence from station119896 can be calculated by multiplying the spatial factor 119875119896119895

and entrance passenger flow 119909 119894119899119896119905minus119879119896119895 The spatial factorsbetween all stations constitute the spatial correlation matrixThere is an SCMmatrix in each time because the factors varyaccording to the time Suppose there are119898 stations in the railtransit system then the size of SCM matrix is 119898 times 119898 whichis denoted by

119878119862119872119905 = (11987511 11987512 119875111989811987521 11987522 1198752119898 119875119896119895 1198751198981 1198751198982 119875119898119898) (7)

119888119896119895119905minus119879119896119895 = sum119894isin119872

1 | 119894119900119905119894119898119890 = 119905 minus 119879119896119895 119894119900119904119905119886119905119894119900119899= 119896 119894119889119904119905119886119905119894119900119899 = 119895 (8)

119875119896119895 = ( 119888119896119895119905minus119879119896119895119909 119894119899119896119905minus119879119896119895 ) 119905 isin 119879lowast (9)

119879lowast = 119905 + 119902119908 119902 = 0 1 2 n (10)

where 119878119862119872119905 is the SCM matrix of time 119905 119875119896119895 is the spa-tial factor where 119896 is origin station and 119895 is destinationstation 119879119896119895 is the time cost from 119896 to 119895 119888119896119895119905minus119879119896119895 is thenumber of passengers from 119896 to 119895 whose origin time is 119905 minus119879119896119895 119909 119894119899119896119905minus119879119896119895 is the entrance passenger flow of station 119896in time 119905 minus 119879119896119895 119879lowast 119902 and 119908 have been defined in Eq (6)

323 Extraction of Spatio-Temporal Features The structureof extractionmethod is shown in Figure 1 To forecast the exitpassenger flow at station 119895 in time 119905 the temporal features isthe exit passenger flow at station 119895 in time 119905 minus 1 The spatialfeatures are gathered by calculating the number of passengers


ST-Characteristics

Fully Connected Layer

Input Layer

Output Layer

X

+Hidden Layer X

X

Ft

Xt

Otminus1 Stminus1it

ft

st

ot

OtOtSt

Ht

tanh

tanh

F_Sjt F_Tjt

Figure 2 Structure of ST-LSTM network

who will arrive at station 119895 in time 119905 and depart from otherstations The function of extraction can be set as119865 119879119895119905 = 119909 119900119906119905119895119905minus1 (11)119865 119878119895119905 = sum

119896isin119873

119875119896119895∙119909 119894119899119896119905minus119879119896119895 (12)

where 119865 119879119895119905 is the temporal features of station 119895 in time119905 119909 119900119906119905119895119905minus1 is the exit passenger flow of station 119895 in time119905 minus 1 119865 119878119895119905 is the spatial features of station 119895 in time 119905 119873is the set of stations and 119896 is a station in it 119875119896119895 is the spatialfactor which has been defined in Eq (9) 119909 119894119899119896119905minus119879119896119895 is theentrance passenger flow of station 119896 in time 119905 minus 11987911989611989533 Structure of ST-LSTM Network Based on LSTM net-work a fully connected layer is added to combine temporalfeatures and spatial features in ST-LSTMnetworkThemodelwill acquire the best mode of combination through thetraining

The structure of ST-LSTM network is shown in Figure 2The input of the model is spatio-temporal features 119865 119879119895119905and 119865 119878119895119905 and the output is the forecast result 119867119905 There arefour layers in this model namely fully connected layer inputlayer hidden layer and output layerThe fully connected layercombines the features at first and conveys the result 119865119905 to theinput layerThe input of hidden layer119883119905 is calculated throughthe input layerThehidden layer has three gates namely inputgate 119894119905 forget gate 119891119905 and output gate 119900119905 Moreover the stateof the hidden layer is indicated by 119878119905 The inputs of every gateare119883119905 and the previous state 119878119905minus1 The blue points in Figure 2are confluences which stand for multiplications and dashedlines are the transmitting of the previous state Based on the

information flow the structure of ST-LSTM network can besummarized as 119865119905 = 119882119891119904119878119895119905 + 119882119891119905119879119895119905 + 119887119891 (13)119883119905 = 119882ℎ119909119865119905 + 119882ℎℎ119874119905minus1 + 119887ℎ (14)119894119905 = 120590 (119882(119894)119883119905 + 119880(119894)119878119905minus1) (15)119891119905 = 120590 (119882(119891)119883119905 + 119880(119891)119878119905minus1) (16)119900119905 = 120590 (119882(119900)119883119905 + 119880(119900)119878119905minus1) (17)119904119905 = tanh (119882(119888)119883119905 + 119880(119888)119878119905minus1) (18)119878119905 = 119891119905 ∘ 119878119905minus1 + 119894119905 ∘ 119904119905 (19)119874119905 = 119900119905 ∘ tanh (119878119905) (20)119867119905 = 119882119900ℎ119874119905 + 119887119910 (21)

where 119865119905 119883119905 119874119905 and 119867119905 are the output of different layers 119894119905119891119905 119900119905 and 119904119905 are the intermediate variables of the hidden layer119878119905 is the state of the hidden layer 119882119891119904 119882119891119905 119882ℎ119909 119882ℎℎ 119882119900ℎ119882(119894) 119882(119891) 119882(119900) 119882(119888) 119880(119894) 119880(119891) 119880(119900) and 119880(119888) are weightmatrices 119887119891 119887ℎ and 119887119910 are bias vectors and 120590 is sigmoidfunction

The cost function is activated after the forecast throughthe training The proposed model is improved by reducingthe output of cost function which can be set as119897119900119904119904 = 10038171003817100381710038171003817119909119900119906119905119895119905 minus 1199091199001199061199051198951199051003817100381710038171003817100381722 (22)

where 119909119900119906119905119895119905 is the forecast of station 119895 in time 119905 and 119909119900119906119905119895119905 isthe actual output

34 Training Algorithm The training algorithm contains twoaspects One is the extraction of spatio-temporal featuresand the other is the training of ST-LSTM network The keypoint of training is minimizing the output of cost function byadjusting the weight matrices and bias vectors The trainingprocedure can be stated as follows

Step 1 Obtaining the Inputs and Labels Capture the temporalfeatures and the spatial features in each time 119905 which are theinput of model Collect the exit passenger flow in each time119905 + 1 as the labelsStep 2 Initialization of the ST-LSTM Network Initialize theweight matrices and bias vectors including 119882119891119904 119882119891119905 119882ℎ119909119882ℎℎ 119882119900ℎ 119882(119894) 119882(119891) 119882(119900) 119882(119888) 119880(119894) 119880(119891) 119880(119900)119880(119888)119887119891 119887ℎand 119887119910Step 3 Fine-tuning the Whole Network Fine-tune the wholenetwork by adjusting the weight matrices and bias vectors inorder to minimize the output of cost function The processwill be stopped until the output meets the qualification or thetime of training reaches the limit


Table 1 Performance of four models

Model ME MAE RMSE MRE Operation timeSARIMA 36070 5133 8237 2963 75 minPSO-SVR 15345 1889 2748 2146 8 minLSTM 18359 2282 3229 2645 37 minST-LSTM 14572 1842 2589 2077 38 min

4 Experiment

Based on the data of Chongqing rail transit four modelsare contained in the experiment namely Seasonal ARIMA(SARIMA) [23] Support Vector RegressionModel combinedwith Particle Swarm Optimization (PSO-SVR) [24] LSTMnetwork [25] and the proposed ST-LSTMnetworkThe targetof forecast is exit passenger flow with a frequency of 10 minThe four models will be trained and tested on 100 stationsThe details of each model are as follows(1) SARIMA The seasonal period lsquoSrsquo is 100 due to theoperating time being from 620 am to 2300 pm (100times10min) After the processing method in [23] ARIMA (210) times(011)100 is finally used(2) PSO-SVR The time period is 100 (100times10 min perday) and the limit of parameter combination of SVR is from[10 008] to [500 03] The final parameter combination willbe selected by PSO in the training(3) LSTMnetworkThe number of units is 10 and the timestep is 100 (100times10 min per day)(4) ST-LSTM network The number of units is 10 and thetime step is 100 (100times10 min per day)

41 Data Description The data of card records are providedby Chongqing City Transportation Development amp Invest-ment Group Co Ltd Compared with other targets such asOrigin-Destination (OD) volume exit passenger flow ismoreaccurate and has less missing data Therefore we calculatethe exit passenger flow from 01 March 2017 to 31 March 2017based on the dataset There are more than 46 million cardrecords After processing 600 thousand data are calculated

42 Evaluation We use several criteria to compare theperformance of fourmodelsMaximumerror (ME) andmeanabsolute error (MAE) are used to measure the accuracy ofmodels Root mean square error (RMSE) is sensitive to thestability of models Mean relative error (MRE) is the mostsuitable to compare the performance of four models Thedefinitions of criteria are

ME = max1le119894le119899

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (23)

MAE = 1119899 119899sum119894=1

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (24)

RMSE = radic 1119899 119899sum119894=1

(120593119894 minus 120593119894)2 (25)

MRE = 1119899 119899sum119894=1

10038161003816100381610038161003816100381610038161003816120593119894 minus 120593119894120593119894 10038161003816100381610038161003816100381610038161003816 (26)

where 120593119894 is the forecast data while 120593119894 is the measured data

43 Training and Testing 5-fold cross validation is used toevaluate the models In 5-fold cross validation the data isdivided into 5 subsets Each subset is a testing set andthe rest of data is the training set The experiments arerepeated 5 times for each station After the experiments theperformance of four models was collected The experimentsare conducted under a desktop computer with Intel i7 320GHZ CPU and 16 GB memory

44 Experiment Result The experiment results of differentalgorithms are shown in Table 1 and the operation time isaveraged on all stations Compared with SARIMA PSO-SVR and LSTM network the proposed ST-LSTM networkachieved a better performance From the view of ME andMAE ST-LSTM network is more accurate than the othermodels Moreover from the view of RMSE ST-LSTM net-work has a better stability Therefore the proposed ST-LSTMnetwork is more suitable for the short-term forecast of railtransit

5 Analysis of Result

When the models have been tested on 100 stations ofChongqing rail transit we find that ST-LSTM networkachieves a higher accuracy than the other models Howeverthe performance of ST-LSTM network fluctuates on differentstations which are shown in Table 2 Therefore we analyzethe experimental results based on the field investigationThe stations in Table 2 are sorted in descending order bypassenger volume Due to the lack of space Table 2 justexhibits the performance on stations of top-10 and bottom-10 passenger volume

51 Base Volume In our research we find that base volume isone of the influence factors of the forecast The performanceof two stations is chosen to shown in Figure 3 whichare station No321 and station No 334 of Chongqing railtransit Both of them are located in the residential districtof Chongqing However station No 334 only attains 5 ofthe base volume of station No321 monthly In the test theMRE on station No321 is 1352 while the MRE on stationNo334 is 2658We use these two different performances assamples to show the influence of base volume The researchsuggests that the stations with higher base volume usuallyhavemore prominent regional features As a result passengerflows of these stations have stronger regularity and are moreinsensitive to the emergent factors Therefore short-termforecast on stations with low base volume is one of thedifficulties in rail transit forecast


Table 2 Forecast performance of ST-LSTM network on different stations

Station ME MAE RMSE MRE321 36696 7108 9912 1352315 36572 5245 7313 1690318 28070 3245 4731 1113114 27084 4466 6063 1376322 21552 3041 4112 1210110 25281 3709 5087 1290606 23899 3344 4846 1197212 20432 3995 5262 1278108 21570 3276 4710 1394123 22377 6118 8189 3658 335 6981 658 1010 2934221 4385 683 907 2839305 5825 955 1293 3225620 6757 646 959 2707621 4515 669 891 3047224 3158 599 798 3293219 3612 540 740 3186619 3596 477 656 2932118 2363 441 572 2988625 4900 547 804 3559

Out

flow

Out

flow

Time (10 min)

Outflow of station No321Base volume 2435531

MRE 01352

2500

2000

1500

1000

500

0

0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500

Outflow of station No334 Base volume 112907MRE 02658

2001751501251007550250

labelprediction

labelprediction

Figure 3 Influence of base volume in forecast

52 Randomness Except for the base volume we discoverthat randomness is another influence factor of the forecastAs shown in Figure 4 the base volume of station No 323and station No123 are both around 900 thousand monthlyHowever the performance of forecast on two stations is quitedifferent In the test the MRE on station No323 is 1673while theMREon stationNo123 is 3837This phenomenonoccurs on a few special stations such as stationNo123 whichis located in the university town of Chongqing Comparedwith the commuters the undergraduates have more choiceon the travel time So the passenger flow of stations whichnext to universities has stronger randomness than othersSimilarly the passenger flowof stations which next to railwaystations or airports is related to the flight scheduleThereforethe randomness from the environment cannot be neglectedon several stations Short-term forecast on these stations isone of the difficulties in rail transit forecast

6 Conclusion and Future Work

Short-term forecast for rail transit is an essential issue inITS We propose the ST-LSTM network which combinesthe temporal features and spatial features To extract spatialfeatures TCMmatrix and SCMmatrix are integrated into themethod Compared with other models the proposed modelis more suitable for rail transit forecast

This study researches on prediction of exit passenger flowbut a model which also includes entrance passenger flow ismore significant for the management In addition except therail transit ITS also contains bus system and taxi systemThe

Journal of Advanced Transportation 7O

utflo

wO

utflo

w

Time (10 min)0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


MRE 016731750

1500

1250

1000

750

500

250

0


MRE 038371000

800

600

400

200

0

labelprediction

labelprediction

Figure 4 Influence of randomness in forecast

correlation between different public transportation is worthconsideration In the future we will try to forecast othertargets of rail transit and then consider the relation amongdifferent transportation Finally a comprehensive system forrail transit will be built to output a more accurate result ofshort-term forecast

Data Availability

Thedata used in the experiments are freely available at httpsdrivegooglecomopenid=1RuH080U 9PHdh9B9VoOjNur-ODWnHAYaf and more data of Chongqing rail transit areavailable by contacting the corresponding author

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this article

Acknowledgments

This work is supported by the Central Universities underGrant 106112017CDJXY090001 andChongqingMajor Scienceand Technology Projects of Artificial Intelligence underGrant CSTC2017RGZN-ZDYF0150

References

[1] Z Zhao W Chen X Wu P C Y Chen and J Liu ldquoLSTMnetwork A deep learning approach for Short-term trafficforecastrdquo IET Intelligent Transport Systems vol 11 no 2 pp 68ndash75 2017

[2] X Chen ZWei X Liu Y Cai Z Li and F Zhao ldquoSpatiotempo-ral variable and parameter selection using sparse hybrid geneticalgorithm for traffic flow forecastingrdquo International Journal ofDistributed Sensor Networks vol 13 no 6 pp 1ndash14 2017

[3] Q Liu Y Cai H Jiang X Chen and J Lu ldquoTraffic state spatial-temporal characteristic analysis and short-term forecastingbased on manifold similarityrdquo IEEE Access vol 6 pp 9690ndash9702 2017

[4] K Farokhi Sadabadi M Hamedi and A Haghani ldquoEvaluatingmoving average techniques in short-term travel time predictionusing an AVI data setrdquo in Transportation Research Board 89thAnnual Meeting vol 2010

[5] B L Smith and M J Demetsky ldquoTraffic flow forecastingcomparison of modeling approachesrdquo Journal of TransportationEngineering vol 123 no 4 pp 261ndash266 1997

[6] W Min and L Wynter ldquoReal-time road traffic prediction withspatio-temporal correlationsrdquo Transportation Research Part CEmerging Technologies vol 19 no 4 pp 606ndash616 2011

[7] X Fei C C Lu and K Liu ldquoA bayesian dynamic linear modelapproach for real-time short-term freeway travel time predic-tionrdquo Transportation Research Part C Emerging Technologiesvol 19 no 6 pp 1306ndash1318 2011

[8] K Nagel and M Schreckenberg ldquoA cellular automaton modelfor freeway trafficrdquo Journal de Physique I vol 2 no 12 pp 2221ndash2229 1992

[9] L Li X Chen Z Li and L Zhang ldquoFreeway travel-timeestimation based on temporal-spatial queueing modelrdquo IEEETransactions on Intelligent Transportation Systems vol 14 no3 pp 1536ndash1541 2013

[10] X Zhang and J A Rice ldquoShort-term travel time predictionrdquoTransportation Research Part C Emerging Technologies vol 11no 3-4 pp 187ndash210 2003

[11] YWei andM Chen ldquoForecasting the short-termmetro passen-ger flow with empirical mode decomposition and neural net-worksrdquo Transportation Research Part C Emerging Technologiesvol 21 no 1 pp 148ndash162 2012

[12] M Levin and Y D Tsao ldquoOn forecasting freeway occupanciesand volumes (abridgment)rdquo Transportation Research Recordvol 773 pp 47ndash49 1980

[13] A Rosenblad ldquoJ J Faraway Extending the linear model with Rgeneralized linear mixed effects and nonparametric regressionmodelsrdquo Computational Statistics vol 24 no 2 pp 369-3702009

[14] Y Zhang and Y Liu ldquoTraffic forecasting using least squaressupport vector machinesrdquo Transportmetrica vol 5 no 3 pp193ndash213 2009

[15] C-H Wu J-M Ho and D T Lee ldquoTravel-time predictionwith support vector regressionrdquo IEEETransactions on IntelligentTransportation Systems vol 125 no 6 pp 515ndash523 2004

[16] I Okutani and Y J Stephanedes ldquoDynamic prediction oftraffic volume through Kalman filtering theoryrdquo TransportationResearch Part B Methodological vol 18 no 1 pp 1ndash11 1984

[17] H Liu H van Zuylen H van Lint and M Salomons ldquoPredict-ing urban arterial travel time with state-space neural networksand Kalman filtersrdquo Transportation Research Record no 1968pp 99ndash108 2006


[18] P Lingras S Sharma andM Zhong ldquoPrediction of recreationaltravel using genetically designed regression and time-delayneural networkmodelsrdquoTransportationResearchRecord vol 13no 1 pp 435ndash446 2002

[19] E I Vlahogianni M G Karlaftis and J C Golias ldquoOptimizedandmeta-optimized neural networks for short-term traffic flowprediction a genetic approachrdquo Transportation Research Part CEmerging Technologies vol 13 no 3 pp 211ndash234 2005

[20] L Shen Freeway Travel Time Estimation and Prediction usingDynamic Neural Networks (Ph D Dissertation) Florida Inter-national University 2008

[21] X Zeng and Y Zhang ldquoDevelopment of recurrent neural net-work considering temporal-spatial input dynamics for freewaytravel time modelingrdquo Computer-Aided Civil and InfrastructureEngineering vol 28 no 5 pp 359ndash371 2013

[22] X Ma Z Tao Y Wang H Yu and Y Wang ldquoLong short-termmemory neural network for traffic speed prediction usingremotemicrowave sensor datardquoTransportation Research Part CEmerging Technologies vol 54 pp 187ndash197 2015

[23] S V Kumar and L Vanajakshi ldquoShort-term traffic flow pre-diction using seasonal ARIMA model with limited input datardquoEuropean Transport Research Review vol 7 no 3 2015

[24] W Hu L Yan K Liu and H Wang ldquoA short-term trafficflow forecastingmethod based on the hybrid PSO-SVRrdquoNeuralProcessing Letters vol 43 no 1 pp 155ndash172 2016

[25] C Xu J Ji and P Liu ldquoThe station-free sharing bike demandforecasting with a deep learning approach and large-scaledatasetsrdquo Transportation Research Part C Emerging Technolo-gies vol 95 pp 47ndash60 2018

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2 Related Work

There are manymethods that have been proposed to improvetraffic forecast including historical average and smoothing[4 5] dynamic linear methods [6 7] traffic theory-basedmethods [8 9] and machine learning methods [10 11]These forecast approaches can be divided into two cat-egories namely parametric approaches and nonparamet-ric approaches Autoregressive integrated moving average(ARIMA) model is widely recognized as a classic methodin parametric approaches As early as the 1970s Levin andTsao found that ARIMA model was the most statisticallysignificant in traffic forecast [12] Parametric approaches havefavorable properties and capture regular variations very wellHowever traffic data usually shows irregular variations Tosolve this problem researchers also paid attention to non-parametric approaches such as nonparametric regressionmodels [13] support vector machine (SVM) [14 15] andrecurrent neural network [16 17] Afterwards recurrent neu-ral network [18] was proposed to process temporal featuressuch as evolutionary neural network (ENN) [19] dynamicneural network (DNN) [20] and nonlinear autoregressivemodels with exogenous inputs (NARX) [21] Among themRNN is widely recognized as a suitable method to capturethe temporal features of passenger flow However previousstudies proved that RNNs failed to capture the long-termfeatures because of vanishing gradient and exploding gradi-ent To solve these problems long short-termmemory neuralnetwork (LSTM NN) [22] was applied in the traffic forecastIn recent years some approaches have been proposed to dealwith the spatio-temporal complexity of traffic data which arementioned in Section 1

Different from these methods this paper proposes anew method to capture spatio-temporal features and a newnetwork based on LSTM to forecast the exit passenger flowof rail transit The remainder of this paper is as followsSection 3 introduces the architecture of ST-LSTM networkExperiments based on the data of Chongqing rail transit areshown in Section 4 Section 5 is composed of the analysis ofexperiment result and future work is at the end of this paper

3 Methodology

Short-term forecast for rail transit is a problem with spatio-temporal complexity Suppose the exit passenger flow of sta-tion 119895 is needed to be predictedThe temporal features are thecorrelation between historical data and current data ie theprevious exit passenger flow of station 119895 These features canbe extracted directly because the rail transit data is collectedaccording to the temporal dimensionThe spatial features arethe transportation of passenger flow on geographic positionie the summation of estimated passenger flows from theother stations For every two stations the spatial featuresinclude the volume and the cost of transportation betweenthem The volume of transportation is reflected by thepassenger flow between two stations In the proposed modelspatial correlation matrix (SCM) is integrated to calculatethe volume of transportation The cost of transportation isreflected by several factors such as time cost economic cost

and distance Among them time cost is the most suitablefactor to reflect the spatial correlation which is mentionedin Section 1Therefore time cost matrix (TCM) is introducedto calculate the cost of transportationThe proposedmodel isbased on the technologies including passenger informationsystem (PIS) features extraction method and ST-LSTMnetwork The detail of the technologies will be explained inthis section

31 Passenger Information System Sufficient data is the basisof accurate forecast and PIS can provide us with comprehen-sive data PIS is a huge and complex network Various railtransit data can be collected in real time through the gatesystem ticketing system and vehicle scheduling systemWiththe development of data acquisition technology PIS is ableto provide sufficient support for short-term forecast Basedon the card records the entrance and exit passenger flow ofstations are calculated with a frequency of 10 min which canbe denoted by119894 = 119888119886119903119889119894119889 119900119905119894119898119890 119900119904119905119886119905119894119900119899 119889119905119894119898119890 119889119904119905119886119905119894119900119899 119889119886119905119890 (1)119909 119894119899119895119905 = sum

119894isin119872

1 | 119894119900119904119905119886119905119894119900119899 = 119895 119894119900119905119894119898119890 = 119905 (2)119909 119900119906119905119895119905 = sum119894isin119872

1 | 119894119889119904119905119886119905119894119900119899 = 119895 119894119889119905119894119898119890 = 119905 (3)

where 119894 is a card record 119888119886119903119889119894119889 119900119905119894119898119890 119900119904119905119886119905119894119900119899 119889119905119894119898119890119889119904119905119886119905119894119900119899 and 119889119886119905119890 are the attributes of 119894 which representcard identification origin time origin station destinationtime destination station and date respectively 119872 is thedatabase of card records 119909 119894119899119895119905 is the entrance passengerflow of station 119895 in time 119905 and 119909 119900119906119905119895119905 is the exit passengerflow of station 119895 in time 11990532 Feature Extraction Method The extraction of spatio-temporal features is one of the core problems of the proposedmodel The proposed model extracts temporal features andspatial features respectively and then put them togetherinto the ST-LSTM network The temporal features can beextracted directly because rail transit data is recorded accord-ing to the temporal dimension To extract the spatial featuresTCMmatrix and SCMmatrix are integrated into themethod

321 Time Cost Matrix Time cost is the most suitable factorto reflect the spatial correlation between stations so the timecost between all stations constitutes the TCMmatrix Due tothe changes of schedule and passenger flow TCM matrix isdynamic with time going on Suppose there are119898 stations inthe rail transit system then the size of TCMmatrix is119898times119898which can be denoted by

119879119862119872119905 = (11987911 11987912 119879111989811987921 11987922 1198792119898 119879119896119895 1198791198981 1198791198982 119879119898119898) (4)




No1StationNom

InFlow

InFlow

InFlow

OutFlow

ST-Characteristics

t - ΔT1j

t - ΔTj

t - ΔTjminus1j

t - 1

t - ΔTj+1j

t - ΔTj

t - ΔTmj




F_Sjt F_Tjt

InFlow

InFlow

InFlow







119878119862119872119905 = (11987511 11987512 119875111989811987521 11987522 1198752119898 119875119896119895 1198751198981 1198751198982 119875119898119898) (7)

119888119896119895119905minus119879119896119895 = sum119894isin119872

1 | 119894119900119905119894119898119890 = 119905 minus 119879119896119895 119894119900119904119905119886119905119894119900119899= 119896 119894119889119904119905119886119905119894119900119899 = 119895 (8)


119879lowast = 119905 + 119902119908 119902 = 0 1 2 n (10)




ST-Characteristics


Input Layer

Output Layer

X

+Hidden Layer X

X

Ft

Xt

Otminus1 Stminus1it

ft

st

ot

OtOtSt

Ht

tanh

tanh

F_Sjt F_Tjt



119896isin119873

119875119896119895∙119909 119894119899119896119905minus119879119896119895 (12)












4 Experiment




ME = max1le119894le119899

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (23)

MAE = 1119899 119899sum119894=1

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (24)

RMSE = radic 1119899 119899sum119894=1

(120593119894 minus 120593119894)2 (25)

MRE = 1119899 119899sum119894=1

10038161003816100381610038161003816100381610038161003816120593119894 minus 120593119894120593119894 10038161003816100381610038161003816100381610038161003816 (26)










Out

flow

Out

flow

Time (10 min)


MRE 01352

2500

2000

1500

1000

500

0

0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


2001751501251007550250

labelprediction

labelprediction







utflo

wO

utflo

w

Time (10 min)0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


MRE 016731750

1500

1250

1000

750

500

250

0


MRE 038371000

800

600

400

200

0

labelprediction

labelprediction



Data Availability




Acknowledgments


References





























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





No1StationNom

InFlow

InFlow

InFlow

OutFlow

ST-Characteristics

t - ΔT1j

t - ΔTj

t - ΔTjminus1j

t - 1

t - ΔTj+1j

t - ΔTj

t - ΔTmj




F_Sjt F_Tjt

InFlow

InFlow

InFlow







119878119862119872119905 = (11987511 11987512 119875111989811987521 11987522 1198752119898 119875119896119895 1198751198981 1198751198982 119875119898119898) (7)

119888119896119895119905minus119879119896119895 = sum119894isin119872

1 | 119894119900119905119894119898119890 = 119905 minus 119879119896119895 119894119900119904119905119886119905119894119900119899= 119896 119894119889119904119905119886119905119894119900119899 = 119895 (8)


119879lowast = 119905 + 119902119908 119902 = 0 1 2 n (10)




ST-Characteristics


Input Layer

Output Layer

X

+Hidden Layer X

X

Ft

Xt

Otminus1 Stminus1it

ft

st

ot

OtOtSt

Ht

tanh

tanh

F_Sjt F_Tjt



119896isin119873

119875119896119895∙119909 119894119899119896119905minus119879119896119895 (12)












4 Experiment




ME = max1le119894le119899

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (23)

MAE = 1119899 119899sum119894=1

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (24)

RMSE = radic 1119899 119899sum119894=1

(120593119894 minus 120593119894)2 (25)

MRE = 1119899 119899sum119894=1

10038161003816100381610038161003816100381610038161003816120593119894 minus 120593119894120593119894 10038161003816100381610038161003816100381610038161003816 (26)










Out

flow

Out

flow

Time (10 min)


MRE 01352

2500

2000

1500

1000

500

0

0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


2001751501251007550250

labelprediction

labelprediction







utflo

wO

utflo

w

Time (10 min)0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


MRE 016731750

1500

1250

1000

750

500

250

0


MRE 038371000

800

600

400

200

0

labelprediction

labelprediction



Data Availability




Acknowledgments


References





























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



ST-Characteristics


Input Layer

Output Layer

X

+Hidden Layer X

X

Ft

Xt

Otminus1 Stminus1it

ft

st

ot

OtOtSt

Ht

tanh

tanh

F_Sjt F_Tjt



119896isin119873

119875119896119895∙119909 119894119899119896119905minus119879119896119895 (12)












4 Experiment




ME = max1le119894le119899

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (23)

MAE = 1119899 119899sum119894=1

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (24)

RMSE = radic 1119899 119899sum119894=1

(120593119894 minus 120593119894)2 (25)

MRE = 1119899 119899sum119894=1

10038161003816100381610038161003816100381610038161003816120593119894 minus 120593119894120593119894 10038161003816100381610038161003816100381610038161003816 (26)










Out

flow

Out

flow

Time (10 min)


MRE 01352

2500

2000

1500

1000

500

0

0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


2001751501251007550250

labelprediction

labelprediction







utflo

wO

utflo

w

Time (10 min)0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


MRE 016731750

1500

1250

1000

750

500

250

0


MRE 038371000

800

600

400

200

0

labelprediction

labelprediction



Data Availability




Acknowledgments


References





























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





4 Experiment




ME = max1le119894le119899

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (23)

MAE = 1119899 119899sum119894=1

1003816100381610038161003816120593119894 minus 1205931198941003816100381610038161003816 (24)

RMSE = radic 1119899 119899sum119894=1

(120593119894 minus 120593119894)2 (25)

MRE = 1119899 119899sum119894=1

10038161003816100381610038161003816100381610038161003816120593119894 minus 120593119894120593119894 10038161003816100381610038161003816100381610038161003816 (26)










Out

flow

Out

flow

Time (10 min)


MRE 01352

2500

2000

1500

1000

500

0

0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


2001751501251007550250

labelprediction

labelprediction







utflo

wO

utflo

w

Time (10 min)0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


MRE 016731750

1500

1250

1000

750

500

250

0


MRE 038371000

800

600

400

200

0

labelprediction

labelprediction



Data Availability




Acknowledgments


References





























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





Out

flow

Out

flow

Time (10 min)


MRE 01352

2500

2000

1500

1000

500

0

0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


2001751501251007550250

labelprediction

labelprediction







utflo

wO

utflo

w

Time (10 min)0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


MRE 016731750

1500

1250

1000

750

500

250

0


MRE 038371000

800

600

400

200

0

labelprediction

labelprediction



Data Availability




Acknowledgments


References





























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



utflo

wO

utflo

w

Time (10 min)0 100 200 300 400 500

Time (10 min)0 100 200 300 400 500


MRE 016731750

1500

1250

1000

750

500

250

0


MRE 038371000

800

600

400

200

0

labelprediction

labelprediction



Data Availability




Acknowledgments


References





























RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia













RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features...

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Transcript of ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features...