Dwell Time Estimation Using Real-Time Train Operation and Smart Card-Based Passenger Data: a Case Study in Seoul, South Korea

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Article Dwell Time Estimation Using Real-Time Train Operation and Smart Card-Based Passenger Data: A Case Study in Seoul, South Korea

Yoonseok Oh 1 , Young-Ji Byon 2 , Ji Young Song 3, Ho-Chan Kwak 3,* and Seungmo Kang 1,* 1 School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea; [email protected] 2 Department of Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, UAE; [email protected] 3 Railroad Policy Research Team, Future Transport Policy Research Division, Korea Railroad Research Institute, Uiwang 16105, Korea; [email protected] * Correspondence: [email protected] (H.-C.K.); [email protected] (S.K.); Tel.: +82-31-460-5495 (H.-C.K.); +82-2-3290-4862 (S.K.) Received: 4 December 2019; Accepted: 7 January 2020; Published: 9 January 2020

Abstract: Dwell time is a critical factor in constructing and adjusting railway timetables for efficient and accurate operation of railways. This paper develops dwell time estimation models for a Shinbundang line (S line) in Seoul, South Korea using support vector regression (SVR), multiple linear regression (MLR), and random forest (RF) techniques utilizing archived real-time metro operation data along with smart card-based passenger information. In the first phase of this research, the collected data are processed to extract boarding and alighting passenger counts and observed dwell times of each train at all stations of the S line under the current operational environment. In the second phase, we develop SVR, MLR, and RF-based dwell time estimation models. It is found that the SVR-based model successfully estimates the dwell times within 10 s of differences for 84.4% of observed data. The results of this paper are especially beneficial for autonomous railway operations that need constructing and maintaining dynamic railway timetables that require reliable dwell time predictions in real-time.

Keywords: smart card; railway operation data; transit ridership; dwell time estimation; metro timetable; artiﬁcial intelligence

1. Introduction The relatively reliable schedule adherence of railway systems is one of the most attractive merits of the mode for the railway passengers [1]. However, it is still a challenge to operate on railways with perceived reliability. Railway schedules are constructed based on passenger demand, although the arrival patterns of passengers at stations are not uniform or deterministic in real-life. If a particular train’s arrival at a station is delayed, an additional accumulation of arriving passengers in the meantime will result in extended boarding and alighting times when the train dwells at the station, and this event will further propagate delays with respect to subsequent trains upstream. Railway operators and authorities have considered diﬀerent strategies when determining train schedules with respect to stations with expected passenger demands. They employ a “running time supplement” [2,3], a buﬀering time that can help to recover from delays if they occur. To minimize the delay, larger railway operators often control the passenger demand with a more direct approach of employing dedicated personnel on the platforms at stations, who can restrict passengers from boarding trains when they are delayed. There have been various studies in predicting delays and analyzing

Appl. Sci. 2020, 10, 476; doi:10.3390/app10020476 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 476 2 of 12 their intrinsic nature [2,3], timetable optimization [4–7], developing appropriate performance indices, and utilizing them for timetable construction [8]. For railway-related research, it is essential to have access to real-life data, including the operation of trains and passenger flows, while a larger amount of data will further benefit the reliability of the analysis. Automatic passenger counters (APCs) and smart card-based fare payment systems have been utilized for such purposes. Um et al. [9] utilized big data based on the smart card system in Seoul, South Korea for assessing performance measures of the public transit services including schedule adherence and crowdedness of passengers. Using smart card-based passenger fare and rail operation data, Hong et al. [10] developed models for predicting actually taken passenger-routes, the number of boarding and alighting passengers at stations, and the number of passengers on board between stations. Berbey et al. [11] used data from Line 1 of the Panama Metro and passengers’ preferences of train ticket classes in order to estimate dwell times at each station. Lam et al. [12] developed a regression model to investigate the relationship between dwell time and the number of boarding and alighting passengers, and tested it using Monte-Carlo simulations. Lee et al. [13] developed a timetable adjustment model for the Hong Kong metro, MTR, by analyzing the departure and arrival information of trains, boarding and alighting passenger counts at stations, and historical data of past causes of delays. Markovic et al. [14] developed a machine learning model based on a support vector regression method for predicting delays at railway stations operated with different classes of trains. Wang and Work [15] proposed a regression-based estimation model for future train delays using only the operation records of trains. Despite numerous efforts in research works regarding train delays, there have not been any significant research works for modeling dwell/delay time of trains utilizing boarding and alighting passenger counts, or onboard crowdedness (load factor), for all trains at all train stations of a metro line. Jiang et al. [16] conducted a similar line of research for Line 8 of the Shanghai Metro in China, yet with limited data and simulated values for load factor, boarding, and alighting passenger counts. Adachi et al. [17] adopted a Gompertz curve to overcome a limited data set of boarding and alighting passenger counts collected at 30-min intervals. A smart card-based transit fare system has been in use in Seoul, South Korea since 2004 and currently 99% of all passengers use the smart card, which provides an excellent basis to collect real-life data for passenger flows. The smart card-based database includes each passenger’s origin and destination stops or stations, associated times, transit transfer locations, etc. This paper utilizes data from real-life operations of trains in the Seoul Metro, and the smart card-based passenger information, for estimating dwell times for each station of a metro line. Section2 describes the data structures of train operations and smart card-based passengers’ information, and how the two databases are processed for further analysis. Section3 develops a methodology based on extracting observed dwell times for each station using real-time data of train operations. Section4 uses the observed dwell times, along with boarding and alighting passenger counts, onboard loading factors, to estimate dwell times. Section5 concludes the paper.

2. Matching the Automatic Fare Collection Data with the Real-Time Train Operation Data This paper develops a dwell time estimation model using boarding and alighting passenger counts, and onboard passenger counts between stations for each train. The initial dataset for analysis was prepared according to the method suggested by Hong et al. [10] based on the smart card passenger information and archived real-time train operation data. This dataset includes the number of trains operating and their current status with respect to their nearest station as one of 3 stages—“approaching,” “arrival,” and “departure”—along with timestamps for when the current stage was acquired. The approaching status is gained as soon as the arriving train passes a balise located at 1000 m upstream of an associated station. The arrival status is achieved as soon as the train passes a balise located 400 m upstream. The status changes to departure when the train passes a balise at 200 m downstream. The smart card-based passenger information includes the serial number of the card, boarding station, boarding time, alighting station, alighting time, transfer station, and transfer Appl. Sci. 2020, 10, 476 3 of 12 time. It is noted that the time associated with boarding, alighting, and transfer refers to the time when the passenger checks in or out with the gates at stations. In this paper, both archived real-time train operation and smart card-based passenger data from the Shinbundang line (S Line) on 31st October 2017 was used. The S line has concentrated commuting passenger demands during both morning and afternoon peak periods, with dominating directions of the majority of passengers: towards the CBD in the morning and towards Bundang in the afternoon (See Figure1). In this study, to show the morning peak clearly with maximum crowdedness, the S line’s Central Business District (CBD)-bound direction towards Gangnam station was selected for the analysis. The S line has a total length of 31.29 km, consisting of 12 stations, of which 4 stations allow inter-line transfers. The fleet size is 20 trains, and they run 327 cycles and 271 cycles in total on weekdays and weekend days, respectively; mostly throughout the entire route, except for the first and last trains each day. There is only 1 class of tickets, and daily ridership on the S line is roughly 247,000,Appl. Sci. 2020 as of, 10 2017., x FOR For PEER train REVIEW operation data, arrival and departure times were identified for each3 train of 12 and each station. However, in cases where departure data were missing, average dwell times based onIt is visual noted observations that the time withassociated video with cameras boarding, wereadded alighting, to the and arrival transfer times refers to estimateto the time the when missing the departurepassenger times.checks in or out with the gates at stations.

Figure 1. Route map and the station ID of Shinbundang S line. Figure 1. Route map and the station ID of Shinbundang S line. Figure2 shows the process of matching the smart card passenger information with the archived real-timeIn this train paper, operation both archived data. The real-time process train starts operation with identifying and smart a card-based passenger passenger who has alighted data from at athe station. Shinbundang Then, the line train (S Line) that on arrived 31st October at the station 2017 was most used. recently The S isline assumed has concentrated to be the onecommuting that the passenger gotdemands oﬀ from. during The departureboth morning time and of the afternoon train that peak the passengerperiods, with had do boardedminating earlier directions is found. of Ifthe the majority boarding of timepassengers: is earlier towards than the the departure CBD in the time morning of the train, and thetowards train numberBundang is in assigned the afternoon to that passenger,(See Figure assuming 1). In this that study, the to passenger show the has mo boardedrning peak and clearly will alight with from maximum that train. crowdedness, If the matching the S processline’s Central does not Business succeed, District the data (CBD)-bound from the smart direction card istowards not used, Gangnam since there station may was be logicalselected errors for the in theanalysis. data. The This S is line reasonable, has a total because length there of 31.29 is no km, way consisting to reduce of the 12 travelstations, time of bywhich overtaking 4 stations between allow trains,inter-line or transferringtransfers. The through fleet size the otheris 20 trains, lines since and thethey S linerun has327 acycles single and class 271 of train.cycles The in total process on stopsweekdays when and the numberweekend of days, the smart-card respectively; data mostly that have throughout been processed the entire is equal route, to theexcept total for number the first of theand smart-card last trains data,each day. N. There is only 1 class of tickets, and daily ridership on the S line is roughly 247,000,Figure as of3 shows 2017. For smart train card-based operation passenger data, arrival data and and departure train operation times were data identified from the S07 for stationeach train to theand S11 each station station. in aHowever, passenger in entry-exit cases where map departu suggestedre data by Hongwere missing, et al. [10 ].average The x-axis dwell represents times based the timeon visual at the observations S07 station, andwith the videoy-axis cameras represents were the ad timeded atto the the S11 arrival station. times Circles, to estimate triangles, the andmissing plus signsdeparture represent times. groups of individual passenger’s smart card data who have completed their trips from S07 toFigure S11 with 2 shows associated the process boarding of matching and alighting the smart times card at thosepassenger stations. information Train number with the 477 archived arrives atreal-time the S07 train at 7:25:56 operation and S11data. at The 7:38:02. process Any starts potential with passengersidentifying whoa passenger were on who train has 477 alighted during at the a station. Then, the train that arrived at the station most recently is assumed to be the one that the passenger got off from. The departure time of the train that the passenger had boarded earlier is found. If the boarding time is earlier than the departure time of the train, the train number is assigned to that passenger, assuming that the passenger has boarded and will alight from that train. If the matching process does not succeed, the data from the smart card is not used, since there may be logical errors in the data. This is reasonable, because there is no way to reduce the travel time by overtaking between trains, or transferring through the other lines since the S line has a single class of train. The process stops when the number of the smart-card data that have been processed is equal to the total number of the smart-card data, N. Figure 3 shows smart card-based passenger data and train operation data from the S07 station to the S11 station in a passenger entry-exit map suggested by Hong et al. [10]. The x-axis represents the time at the S07 station, and the y-axis represents the time at the S11 station. Circles, triangles, and plus signs represent groups of individual passenger’s smart card data who have completed their trips from S07 to S11 with associated boarding and alighting times at those stations. Train number 477 arrives at the S07 at 7:25:56 and S11 at 7:38:02. Any potential passengers who were on train 477 during the trip must be on the 2nd quadrant of the horizontal and vertical lines intersecting at train 477. If Appl. Sci. 2020, 10, 476 4 of 12 Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 12 Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 12 tripthe passengers must be on used the 2nd train quadrant 477, they of must the horizontalpass the boarding and vertical gate at lines S07 intersectingstation before at train train 477 477. arrives If the passengerstheat S07 passengers station, used usedand train musttrain 477, they477,also they mustalight must pass after pass the the boarding the train boarding arrives gate atgate S07at S11at station S07 station. station before Therefore, before train 477 train arrivesthe 477 group arrives at S07 of station,atpassengers S07 station, and surrounded must and also must alight by also the after alightdotted the after trainlines arrivesthe roughly train at reprarrives S11esents station. at S11all Therefore, ofstation. the potential Therefore, the group users ofthe of passengers grouptrain 477 of surroundedpassengersfrom S07 to surrounded byS11 the station. dotted Itby linesis the noted roughlydotted that lines representsthere roughly were allsome repr of the passengersesents potential all of who users the boardedpotential of train 477and users from alighted of S07 train toat S11477the station.fromsame S07stations, It to is S11 noted and station. thatthese thereIt were is noted were excluded that some there from passengers we there analysis. some who passengers boarded and who alighted boarded at and the alighted same stations, at the andsame these stations, were and excluded these were from excluded the analysis. from the analysis.

Figure 2. Smart card-based passenger information matching process against the real-time train Figure 2. Smart card-based passenger information matching process against the real-time train Figureoperation 2. data.Smart card-based passenger information matching process against the real-time train operation data. operation data.

Figure 3. Entry-exit map of the trip from Jeongja (S07) station to Yangjae (S11)(S11) station.station. Figure 3. Entry-exit map of the trip from Jeongja (S07) station to Yangjae (S11) station. Figure 4 shows the number of passengers boarding, alighting, and arriving onboard at each stationFigure on the 4 shows S line. the Trains number depart of passengersfrom the right boarding, towards alighting, the left, and and arriving each line onboard in each at graph each station on the S line. Trains depart from the right towards the left, and each line in each graph Appl. Sci. 2020, 10, 476 5 of 12

Figure4 shows the number of passengers boarding, alighting, and arriving onboard at each station onAppl. the SSci. line. 2020, Trains10, x FOR depart PEER REVIEW from the right towards the left, and each line in each graph represents5 of 12 a train. Red lines represent trains operated during the morning peak-hours from 6:30 a.m. to 10:30 a.m.,represents and blue a linestrain. representRed lines represent trains run trains during oper theated afternoon-peak during the morning hours peak-hours from 6:00 p.m. from to 6:30 9:00 a.m. p.m. to 10:30 a.m., and blue lines represent trains run during the afternoon-peak hours from 6:00 p.m. to Trains run outside of peak-hours are represented by gray lines. 9:00 p.m. Trains run outside of peak-hours are represented by gray lines.

FigureFigure 4. (a4.) The(a) The number number of boarding of boarding passengers passengers at each at station. each station. (b) The ( numberb) The ofnumber alighting of passengersalighting at eachpassengers station. at ( ceach) The station. number (c) ofThe onboard number passengers of onboard when passengers the train when had the been train arrived had been at each arrived station. at each station. Figure4 shows passenger boarding, alighting and onboard counts. When observed from the right, startingFigure 4 with shows the passenger ﬁrst station boarding, S01, in alighting the morning and peak,onboard the counts. boarding When counts observed from from S01 to the S08 stations,right, starting inclusive, with are the signiﬁcantly first station higher S01, in than the morning the rest ofpeak, stations the boarding combined. counts This from is due S01 to to the S08 fact thatstations, the regions inclusive, covered are bysignificantly stations from higher S01 than to S08 the arerest dedicated of stations residential combined. areas This is (bed due towns) to the offact the Greaterthat the Seoul regions Area covered (GSA), by S09 stations and S10 from are S01 located to S0 in8 are an outskirtsdedicated of residential Seoul, and areas S11 (bed and S12towns) are of located the in theGreater CBD Seoul of Gangnam Area (GSA), area S09 of and Seoul. S10 are During located the in evening an outskirts peak of period, Seoul, and boarding S11 and counts S12 are at located the S08 in the CBD of Gangnam area of Seoul. During the evening peak period, boarding counts at the S08 Appl. Sci. 2020, 10, 476 6 of 12

Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 12 station stands out as there is also a large concentrated commercial area called “Pangyo Techno Valley”. station stands out as there is also a large concentrated commercial area called “Pangyo Techno This area is also related to the high number of alighting at S08 in the morning. Valley”. This area is also related to the high number of alighting at S08 in the morning. DuringDuring the the morning morning peak-hour peak-hour period, period, atat stationsstations from S01 S01 to to S06, S06, inclusive, inclusive, and and S09, S09, there there are are nearlynearly zero zero alighting alighting passengers. passengers. At At S07 S07 andand S08S08 stations,stations, some passengers passengers al alight,ight, while while most most of ofthe the passengerspassengers alight alight at at S11 S11 and and S12 S12 stations stations inin thethe CBD.CBD. There are relatively relatively high high numbers numbers of of alighting alighting passengerspassengers at S07at S07 and and S08 S08 compared compared to otherto other residential residential areas areas because because there there are concentrationsare concentrations of o ﬃof ce buildingsoffice buildings near those near stations. those stations Note that. Note S07 andthat S08S07 alsoand serveS08 also as transferserve asstations transfer connectedstations connected with other metrowith lines. other In metro the case lines. of stationIn the case S09, of there station are onlyS09, there residential are only facilities residential near facilities the station, near hence the station, resulting in veryhence low resulting alighting in counts.very low In alighting the afternoon-peak counts. In andthe non-peakafternoon-peak periods, and most non-peak alighting periods, counts most occur at S11alighting and S12 counts stations. occur at S11 and S12 stations. DueDue to to the the various various characteristics characteristics mentioned mentioned earlier,earlier, the onboard passenger passenger counts, counts, as as shown shown in in FigureFigure4c, 4c, continue continue to to increase increase as as trains trains approach approach the the ﬁnal final stationstation S12S12 in the CBD. CBD. It It is is also also noted noted that that mostmost trains trains in in the the morning-peak morning-peak period period experienceexperience passengerspassengers occupying occupying at at the the full full capacity capacity of ofthe the trainstrains and and even even exceeding exceeding the the capacity capacity with with a a factor factor ofof 22 betweenbetween the the S07 S07 and and S11 S11 stations. stations.

3. Estimating3. Estimating Observed Observed Dwell Dwell Time Time of of Train Train When When DepartureDeparture Information Is Is Missing Missing ThisThis paper paper utilizes utilizes the the real-time real-time operation operation datadata ofof thethe metro trains. The The collected collected data data have have relativelyrelatively well-recorded well-recorded arrival arrival times times of of trains,trains, whilewhile often missing the the departure departure times. times. Therefore, Therefore, in casesin cases where where the the departure departure times times are are missing, missing, thethe observedobserved dwell time time for for each each train train at at each each station station was estimated by using typical travel times between stations. was estimated by using typical travel times between stations. As illustrated in Figure 5, the train arrival times at station S and S + 1 is easily determined from As illustrated in Figure5, the train arrival times at station S and S + 1 is easily determined from the train operation data. The arrival time refers to the time when a train passes 400 m upstream of the train operation data. The arrival time refers to the time when a train passes 400 m upstream of an an associated station. The time difference between the 2 stations consists of the braking time at station associated station. The time difference between the 2 stations consists of the braking time at station S (BS), dwell time at station S (DWS), and the travel time between the station S and 400 m upstream S (BlocationS), dwell of timestation at S station + 1. If Swe (DW assumeS), and the the braking travel times time are between similar the for stationall stations, S and the 400 braking m upstream time location of station S + 1. If we assume the braking times are similar for all stations, the braking time at station S (BS) can be subtracted from the time difference between the two stations S and S + 1 in at stationorder to S estimate (BS) can the be actual subtracted dwell fromtime. theThe timeS line, di fromfference which between the data the were two collected, stations is S specifically and S + 1 in ordersuitable to estimate for such the assumptions, actual dwell because time. Theit is Sautonomously line, from which operated the data and were has constant collected, braking is specifically times suitableat stations for such with assumptions, a large headway because of 4 min, it is autonomouslyand has a relatively operated low chance and has of constantcongestion braking occurring times at stationsdue to the with influence a large of headway trains downstream. of 4 min, and The has assumption a relatively of constant low chance braking of congestion times was occurringverified duevisually to the influencewith video of cameras trains downstream. installed on all The trai assumptionns. Figure of6 shows constant the braking time difference times was between verified visuallyarrivals with of trains video at cameras all stations installed (solid on black all trains.and red Figure lines),6 andshows average the time travel di timesfference visually between observed arrivals of trainsfrom trains at all equipped stations(solid with video black camera and reds (blue lines), dotted and averageline). The travel difference, times visuallyDWS, between observed the TT fromS trainsand equipped TDS is the with estimated video camerasdwell time (blue for dottedeach train line). at Theeach di stationfference, in DWthe Scase, between of missing the TT departureS and TD S is thedata. estimated It is noted dwell that the time red for lines each represent train at time each di stationfference in between the case arrivals of missing at stations departure of the data. first It5 is notedtrains that in thethe redearly lines morning represent period, time where difference there are between no delays, arrivals while at black stations lines of represent the first 5for trains all other in the earlytrains. morning period, where there are no delays, while black lines represent for all other trains.

Figure 5. Time components of railway operation between stations and deﬁnitions of TTS and TDS. Figure 5. Time components of railway operation between stations and definitions of TTS and TDS. Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 12 Appl. Sci. 2020, 10, 476 7 of 12 Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 12

Figure 6. TTS and TDS at each station and the concept of estimating the DWS at each station.

Figure 6. TTS and TDS at each station and the concept of estimating the DWS at each station. Figure 6. TTS and TDS at each station and the concept of estimating the DWS at each station. Figure 7 shows a relationship between the estimation of observed dwell times for all trains at all stationsFigure versus7 shows the asum relationship of boarding between and alighting the estimation passenger of observed counts. The dwell x-axis times represents for all trains the sum at all of Figure 7 shows a relationship between the estimation of observed dwell times for all trains at all stationsboarding versus and alighting the sum ofpassengers boarding while and alighting the y-axis passenger represents counts. the estimation The x-axis of observed represents dwell the times. sum stations versus the sum of boarding and alighting passenger counts. The x-axis represents the sum of ofEach boarding symbol and represents alighting a passengersstation, as illustrated while the iny-axis Table represents 1. The mini themum estimation observed of dwell observed time dwellis 16 s. boarding and alighting passengers while the y-axis represents the estimation of observed dwell times. times.The variability Each symbol of dwell represents times is a station,found to as be illustrated larger when in Tablethe passenger1. The minimum counts are observed smaller, dwell and smaller time Each symbol represents a station, as illustrated in Table 1. The minimum observed dwell time is 16 s. iswhen 16 s. the The passenger variability counts of dwell are larger. times isIn found addition, to be it largeris found when that thethe passengerminimum dwell counts time are increases smaller, The variability of dwell times is found to be larger when the passenger counts are smaller, and smaller andas the smaller passenger when counts the passenger increase. counts It is interesting are larger. to In note addition, that many it is found observed that dwell the minimum times at dwellcertain when the passenger counts are larger. In addition, it is found that the minimum dwell time increases timestations increases seem asto have the passenger constant dwel countsl times increase. regardless It is interestingof passenger to counts. note that This many is a result observed of enforced dwell as the passenger counts increase. It is interesting to note that many observed dwell times at certain timesdwell at times certain at some stations major seem stations to have being constant assisted dwell by timesdedicated regardless employees of passenger on the platforms, counts. This limiting is a stations seem to have constant dwell times regardless of passenger counts. This is a result of enforced resultthe number of enforced of boarding dwell times passengers. at some major stations being assisted by dedicated employees on the dwell times at some major stations being assisted by dedicated employees on the platforms, limiting platforms, limiting the number of boarding passengers. the number of boarding passengers.

FigureFigure 7. 7.Relationship Relationship betweenbetween estimatedestimated dwelldwell timetime andandthe the number number of of boarding boarding and and alighting alighting passengerspassengers at at each each station station of of S S line. line. Figure 7. Relationship between estimated dwell time and the number of boarding and alighting passengers at each station of S line.

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Sci.Sci. 20202020,, 1010,, 476x FOR PEER REVIEW 99 ofof 1212 Appl. Sci. 2020, 10, x FOR PEER REVIEW 9 of 12 Table 3. The proportion of estimated data depends on the relative difference between actual dwell TableTabletime and 3. 3.The Theestimated proportion proportion dwell of of estimatedtime estimated by the data modeldata depends depends types. on theon the relative relative diff erencedifference between between actual actual dwell dwell time andtime estimated and estimated dwell dwell time bytime the by model the model types. types. Model Type <10% (%) <20% (%) <30% (%) ModelSVRModel Type Type <10%52.8<10% (%) (%) < <20%20%71.4 (%)(%) <30% <30% (%)81.6 (%) SVRRF SVR 42.352.8 52.8 71.469.7 81.681.081.6 MLRRF RF 42.336.5 42.3 69.762.1 81.081.0 73.8 MLRMLR 36.5 36.5 62.1 73.8 73.8

FigureFigure 8.8. Dwell time estimation performance comparisoncomparison amongamong thethe ((aa)) SVR,SVR, ((bb)) RF,RF, ((cc)) MLRMLR models.models. Figure 8. Dwell time estimation performance comparison among the (a) SVR, (b) RF, (c) MLR models.

FigureFigure 9.9. DwellDwell timetime estimationestimation performanceperformance comparisoncomparison byby thethe timetime ofof day.day. ((aa~~cc)) inin thethe morningmorning peak,Figurepeak, ((d d9.~~f f)Dwell) inin thethe time eveningevening estimation peakpeak andand performance ((gg~~ii)) inin thethe comparison normalnormal period.period. by the time of day. (a~c) in the morning peak, (d~f) in the evening peak and (g~i) in the normal period. Appl. Sci. 2020, 10, x FOR PEER REVIEW 10 of 12 Appl. Sci. 2020, 10, 476 10 of 12 To further enhance the SVR model with 3 variables (with boarding, alighting, and onboard passenger counts at independent variables) which was found to be the most accurate model among To further enhance the SVR model with 3 variables (with boarding, alighting, and onboard the 3 models, we developed different SVR models with 3 additional input variables: train arrival time, passenger counts at independent variables) which was found to be the most accurate model among station information, and time difference between arrivals at 2 consecutive stations. As shown in the 3 models, we developed different SVR models with 3 additional input variables: train arrival Tables 4 and 5, it is found that the performances of SVRs with additional input variables were further time, station information, and time difference between arrivals at 2 consecutive stations. As shown in enhanced. In particular, the scenario with less than 5 s of error was improved by 20% in accuracy. Tables4 and5, it is found that the performances of SVRs with additional input variables were further Most notably, the enhanced SVR model performed at 97.2% accuracy for the scenario with less than enhanced. In particular, the scenario with less than 5 s of error was improved by 20% in accuracy. 10 s of error. Figure 10 shows the comparison of estimation performance between the 6-variable Most notably, the enhanced SVR model performed at 97.2% accuracy for the scenario with less than 10 model with the 3-variable model. s of error. Figure 10 shows the comparison of estimation performance between the 6-variable model with the 3-variable model. Table 4. The proportion of estimated data depends on the absolute difference between actual dwell Tabletime 4.andThe estimated proportion dwell of time estimated by the data number depends of variables on the absoluteof SVR models. difference between actual dwell time andModel estimated Type dwell time by the <3 number s (%) of variables of <5 SVR s (%) models. <10 s (%) SVR: 3 variablesModel (A) Type 53.3 <3 s (%) <5 67.2 s (%) <10 s (%) 84.4 SVR: 6 variablesSVR: 3 variables(B) (A) 76.3 53.3 67.2 87.6 84.4 97.2 ImprovementsSVR: (B-A) 6 variables (B)23.0 76.3 87.620.4 97.2 12.8 Improvements (B-A) 23.0 20.4 12.8 Table 5. The proportion of estimated data depends on the relative difference between actual dwell Tabletime 5.andThe estimated proportion dwell of estimated time by the data number depends of variables on the relative of SVR di ffmodels.erence between actual dwell time and estimatedModel dwell Type time by the number <10% of(%) variables of SVR <20% models. (%) <30% (%) SVR: 3 variablesModel (C) Type 52.8< 10% (%) <20% 71.4 (%) <30% (%) 81.6 SVR: 6 variablesSVR: 3 variables(D) (C) 72.1 52.8 71.4 92.9 81.6 96.3 ImprovementsSVR: (D-C) 6 variables (D)19.3 72.1 92.921.5 96.3 14.7 Improvements (D-C) 19.3 21.5 14.7

FigureFigure 10. 10.Estimation Estimation performance performance of of the the 6-variable 6-variable and and the the 3-variable 3-variable SVR SVR model. model.

Appl. Sci. 2020, 10, 476 11 of 12

5. Conclusions This paper develops a railway dwell time estimation model using SVR, RF, and MLR methods. In the first phase, smart card-based passenger information is matched against the real-time train operation data from the S line of the Seoul Metro in Seoul, South Korea, for extracting boarding, alighting, on-bard passenger counts, and the observed dwell times for all trains at all stations. When the train departure times were missing, an assumption of constant braking time was adopted to estimate actual/observed dwell times, since the S line is autonomously operated with minimal variability in braking times. In the second phase, the paper develops dwell time estimation models utilizing the extracted information from the first phase. The SVR, RF and MLR-based models were developed for dwell time estimations while boarding, alighting, and onboard passenger counts were treated as independent variables and the dwell time was set as a dependent variable. In the comparative scenarios with less than 3, 5, and 10 s of errors between the estimations and the observed values, the SVR model performed the best, with an accuracy of 67.2% in the scenario with less than 5 s of error. Then the SVR model was enhanced by including additional independent variables, including arrival times and time difference of arrivals, at 2 consecutive stations for all trains at all stations. It was found that the enhanced SVR model improved the accuracy by 20% to an accuracy of 87.6% for the scenario of less than 5 s of error. In the case of less than 10 s of error, the improved SVR model performed at 97.2% accuracy. This research is unique in the sense that, firstly, it extracts boarding, alighting, and onboard passenger counts using data from real-life metro operations and smart card-based passenger information for all trains at all stations on an urban metro line. Secondly, this research develops dwell time estimation models with high performance accuracies that are validated by real-life data. The results of this paper are especially beneficial for autonomous railway operation, which requires construction and maintenance of dynamic railway timetables that require reliable dwell time real-time predictions. However, this estimation model may not work well if incidents such as rolling stock failure, or big events (i.e., sports games or exhibitions) occur near metro stations that may increase the demand dramatically and present unseen data to the proposed models. Enhancing the proposed estimation models to cover not only a general commute-based operation environment, but also the situations affected by such special events, are topics of future studies.

Author Contributions: In this study, all of the authors contributed to the writing of the manuscript. Y.O. designed the overall modeling framework and performed the data analysis. Y.-J.B. helped with the data analysis and drafted the initial version of the manuscript. J.Y.S. supported data acquisition and contributed to the results interpretation. H.-C.K. and S.K. advised on the modeling process and coordinated the overall research. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by a grant from R&D Program of the Korea Railroad Research Institute, Republic of Korea. Acknowledgments: The authors thank Seong-Jong Woo and Soo-In Lee for their help in data processing. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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