Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

sustainability

Article Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

Wei Yu 1 , Hua Bai 2, Jun Chen 3,* and Xingchen Yan 1 1 College of Automobile and Traﬃc Engineering, Nanjing Forestry University, Nanjing 210037, China; [email protected] (W.Y.); [email protected] (X.Y.) 2 China Design Group Co., Ltd., Nanjing 210014, China; [email protected] 3 School of Transportation, Southeast University, Nanjing 210096, China * Correspondence: [email protected]

Received: 7 August 2019; Accepted: 9 September 2019; Published: 12 September 2019

Abstract: The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an important part of the urban comprehensive transportation system, Nanjing metro has more than 1 million inbound and outbound records of traffic smart cards used by residents every day. How to process these traffic data and present them visually is an urgent problem in modern traffic management. In this study, five working days with normal weather conditions in Nanjing were selected, and the swiping records of the smart cards were extracted, and the space–time characteristics were analyzed. In terms of time analysis, this research analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow on different metro lines, passenger flow intensity on different metro lines and passenger flow comparison at different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. The analysis results and visualized images directly reflect the area where Nanjing metro congestion is located, and also shows the commuting characteristics of residents. It can solve the problem of urban congestion, carry out the rational layout of urban functional areas, and promote the sustainable development of people and cities.

Keywords: metro; passenger ﬂow; space–time variation; commuting characteristics

1. Introduction With the continuous expansion of urban scale, the integrated transportation system is becoming more and more complex to meet the emergence needs of different residents. The construction of traffic informationization has brought about the development of intelligent transportation, and also put forward new requirements for traffic management. The traffic big data analysis emerges as the times require. Traffic big data technology needs to obtain a large number of effective data from daily traffic operations and use mathematical methods to analyze and predict the data. A traffic smart card is the basic tool for urban residents to use various means of transport, recording passengers’ daily travel records. For the urban metro, the automatic ticket-selling and checking system at the metro station will record the card swiping information of each passenger entering and leaving the station. Even temporary passengers have to buy reusable smart cards to take the subway. The large data of urban metro passenger flow mainly comes from the information of passenger card swiping. The passenger flow of urban metro varies with different time and space, including working days, holidays, seasons, residential areas, business centers, workplaces, and other factors, including climate,

Sustainability 2019, 11, 4989; doi:10.3390/su11184989 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 4989 2 of 19 other modes of transportation connecting with the metro, and future planning of the city. The big data of urban metro reflects residents’ commuting habits, and how space–time variables affect these travel habits. Despite the imbalance and randomness of the metro passenger flow data, the big data technology can find the law of equilibrium and certainty behind the big data. In the past, the analysis methods of metro passenger flow often took the form of questionnaires, which is difficult to make accurate quantitative analysis by using the survey data to evaluate and predict. Each city’s public transport system has its own unique structure, which reflects the travel characteristics of urban residents and the spatial and economic structure of the city. Smart cards can record passengers using different means of transport and show a complete route. The number of smart cards provides a large number of accurate passenger travel data, from which a large number of effective information can be excavated. Now, the rapid development of big data mining and deep learning technology provides a technical possibility for the analysis of large traffic data. Some researchers applied various models to study the prediction of subway passenger flow. Wei Y et al. proposed a prediction method combining empirical mode decomposition and back propagation neural network to predict the short-term passenger flow in the metro system. The prediction effect was good [1]. Liu S et al. used the improved particle swarm optimization algorithm to optimize the parameters and put forward a new evaluation index. Taking the passenger flow data of Guangzhou metro station in China during the national day as an example, the effectiveness of this method was verified [2]. Li L et al. proposed a hybrid model for capturing complex patterns. The performance of the hybrid model was compared with ARIMA model and BP neural network using the actual data of Xi’an metro line 1. The results show that the hybrid model is superior to the other two models [3]. Controlling subway passenger flow can effectively alleviate the congestion. Jiang M et al. established a passenger source station selection model based on utility theory, corrected the distribution of incoming passenger flow between stations, and validated it by taking Beijing metro as an example [4]. Li S et al. can effectively calculate the optimal control law by using quadratic programming algorithm for solving the problem of optimal joint dispatch of trains and passenger flow control strategy [5]. Yang J et al. put forward a composite strategy of passenger flow control and bus bridge service, which can effectively reduce the number of stranded passengers and relieve the pressure of congestion [6]. Shi J et al. proposed an effective method for collaborative optimization of train timetable and accurate passenger flow control strategy on supersaturated metro lines. The performance and validity of the proposed method was verified by the operation data of Beijing metro system [7]. Li S et al. proposed a train traffic model for metro line operation based on the uncertain hybrid switching system, which guaranteed the stability of the metro line system under disturbance [8]. In the research of metro big data, some researchers use the smart card data of automatic checking ticket systems to study the travel law of passenger flow. Lee Y K et al. proposed a clustering analysis method for metro passenger travel patterns. Using the real data set in the Seoul metro network, the passenger movement patterns were visualized [9]. Lee K et al. developed a time distance algorithm based on T-card transaction database to measure the time distance between each pair of transmissions and analyzed the spatial structure of accessibility between two time points in the Seoul metropolitan area [10]. Sun L et al. proposed a comprehensive Bayesian statistical reasoning framework to describe the passenger flow distribution model in the complex metro network. The analysis of Singapore’s metro network shows that travel time reliability is of great significance to metro operation [11]. Noh K tried to use Seoul’s urban public data to explore ways to improve the congestion situation of Seoul’s subway, and put forward a policy to establish a new bus route to connect with the subway station [12]. Kim J used multivariate regression analysis and big data processing technology to monitor the congestion degree in real time through the existing congestion situation of the subway, and to analyze the information of its departure and arrival stations [13]. Based on taxi and subway passenger data, Zhu Y explored the post-hurricane recovery model of New York’s road and subway systems. The results show that the road network has a higher elasticity than the subway [14]. Hong L et al. proposed a metro passenger flow assignment method based on automatic ticket sales and checking Sustainability 2019, 11, 4989 3 of 19 data. The preliminary application of OD pair classification for Shanghai metro system shows that the method is feasible [15]. Visualization of big traffic data can facilitate observers to have an intuitive understanding of big data. Wibisono A and others used a fast incremental model to analyze and predict very large traffic data sets, and visually predict traffic flow in sensor points generated in actual map simulation [16]. Zhang J et al. used a spatial interpolation method to discretize bus passenger flow into continuous regional distribution on the basis of big data of Beijing bus IC card and analyzed the spatial and temporal variation trend of all-day passenger flow in Beijing [17]. Hwang U took Yunzhou airport as the research object and carried out big data analysis and visualization analysis to determine the needs of airport users. The results show that airport users are interested in the terminals of railways, highways, and express buses [18]. Nanjing metro network is evolving, and passenger flow has also changed accordingly. Based on the smart card data of the Nanjing metro system in China, Fu X et al. studied the changes of passenger flow and travel time after the new metro line 4 opened in 2017 [19]. Li J et al. established a passenger flow fluctuation model to evaluate the impact of weather conditions on the fluctuation of single-line passenger flow of Nanjing metro in China [20]. Wei Y et al. established the supernetwork model of Nanjing metro network, proposed new parameters to describe the model, and compared it with the traditional space L and space P models [21]. Yu W et al. used the complex network method to analyze the space–time evolution of Nanjing metro network according to the opening sequence of 10 metro lines in Nanjing [22]. Zhao J et al. used the multiple regression model and multiplication model, respectively. Taking Nanjing metro system as an example, the paper discussed the influencing factors of passenger flow between stations and stations [23]. Nanjing metro, together with other modes, constitutes an integrated transportation system. Yang M and others surveyed commuters who entered the subway using public bicycles and analyzed users’ personal characteristics and their commuting experiences [24]. Wu J et al. used the survey data in Nanjing and applied mixed logic to analyze the influencing factors of urban metro commuters’ entry and exit mode selection, and summarized the transfer needs of different groups of commuters in segments [25]. Li Y et al. collected data from Mobiles’ shared bicycle system and applied cluster analysis to analyze the activity pattern of the system near Nanjing metro station [26]. In this study, the smart card data of Nanjing metro were used to select the representative passenger inbound and outbound records of five working days and the space–time characteristics of the passenger flow in the metro were analyzed. This study mainly analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow of different metro lines, passenger flow intensity of different metro lines, and passenger flow comparison of different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. These visual figures provide a useful attempt to understand traffic data intuitively. By analyzing the big data of metro passenger flow, the research can grasp the space–time variation of metro passenger flow, predict the future passenger flow, and lay the foundation for traffic management and traffic planning. Furthermore, traffic planning and urban commercial planning can be combined to achieve the dynamic balance of residents’ residence and work, improve the level of urban public services, and promote the sustainable development of the city.

2. Nanjing Metro Line and Population Situation

2.1. Opening Lines of Nanjing Metro Figure1 shows the route map of Nanjing metro in 2017. As of 17 January 2017, Nanjing metro has opened six lines in the order of 1, 2, 10, S1, S8, and 3. Figure1 shows di fferent lines in different colors. The name of stations of the metro lines are indicated in the figure. From the subway map, we can see that the main lines are line 1, line 2, and line 3, which run through the main urban area Sustainability 2019, 11, 4989 4 of 20

2. Nanjing Metro Line and Population Situation

2.1. Opening Lines of Nanjing Metro Figure 1 shows the route map of Nanjing metro in 2017. As of 17 January 2017, Nanjing metro Sustainabilityhas opened2019 six, 11 lines, 4989 in the order of 1, 2, 10, S1, S8, and 3. Figure 1 shows different lines in different4 of 19 colors. The name of stations of the metro lines are indicated in the figure. From the subway map, we can see that the main lines are line 1, line 2, and line 3, which run through the main urban area of ofNanjing. Nanjing. Line Line 10, 10, line line S1, S1, and and line line S8 S8 are are extension extension lines lines connecti connectingng remote remote suburbs suburbs of of Nanjing. Nanjing. XinjiekouXinjiekou Station,Station, thethe intersectionintersection ofof lineline 1 and line 2, is the geographical and and commercial commercial center center of of Nanjing.Nanjing. Line Line 1 1 and and line line 3 3 form form a a circular circular structure, structure, which which depicts depicts the the outline outline of of the the main main urban urban area area of Nanjing.of Nanjing. The The two two intersections intersections are are Nanjingzhan Nanjingzhan of theof the old old railway railway station station and and Nanjingnanzhan Nanjingnanzhan of theof newthe new railway railway station. station. They They are alsoare also important important transportation transportation hubs. hubs.

FigureFigure 1.1. Route map of Nanjing metro in in 2017. 2017.

TableTable1 shows1 shows the the opening opening sequence sequence and and performance performance of Nanjingof Nanjing metro metro lines lines in 2017. in 2017. From From 2005 to2005 2017, to Nanjing2017, Nanjing metro openedmetro opened six metro six lines.metro Line lines. 1 andLine line 1 and 2 were line opened 2 were atopened a relatively at a relatively long-time interval,long-time and interval, then entered and then a high-frequency entered a high-frequency period of opening, period especially of opening, in 2014,especially when in line 2014, 10, linewhen S1, andline line 10, S8line were S1, openedand line together. S8 were Lineopened 1 was together. opened Line in 2005, 1 was from opened Maigaoqiao in 2005, to from Aotizhonxin, Maigaoqiao a total to ofAotizhonxin, 16 stations. Later,a total line of 16 1 wasstations. opened Later, with line the 1 southwas opened extension with line the in south 2010, andextension some stationsline in 2010, have changedand some names. stations These have 27 changed stations names. were later These collectively 27 stations referred were later to ascollectively line 1. Line referred S1 was to openedas line 1. at eightLine stationsS1 was opened in 2014. at Someeight stations site names in 2014. have Some changed. site names Lines have 1, 2, andchanged. 3 are Lines the main 1, 2, lines,and 3 andare the the numbermain lines, of stations and the of thesenumber lines of is stations obviously of largerthese thanlines theis otherobviously three larger extension than lines. the other The number three ofextension stations onlines. the The extension number line of stations 10, S1, and on S8theis extens small,ion but line the 10, overall S1, and distance S8 is small, is not but short, the whichoverall is duedistance to the is large not short, distance which between is due stations to the larg in remotee distance areas. between stations in remote areas. Sustainability 2019, 11, 4989 5 of 19

Table 1. Sequence and performance table of Nanjing metro lines in 2017.

Opening Sequence Number of Stations Length (km) Opening Time 1 27(16) 38.9 2005 2 26 37.95 2010 10 14 21.6 2014 S1 8 37.3 2014 S8 17 45.2 2014 3 29 44.9 2015

2.2. Nanjing Population Situation According to the Nanjing national economic development and statistical bulletin of 2016, issued by Nanjing statistical bureau, by the end of 2016, Nanjing’s urban and county data population had been growing steadily at a low speed. By the end of 2016, the city’s permanent population was 8.27 million, an increase of 34.1 thousand people, or 0.41 percent, over the end of last year. Among them, 678.14 million people live in cities and towns, accounting for 82% of the total population, an increase of 0.6 percentage points over the end of last year. The population of Nanjing has a direct impact on the subway passenger ﬂow. With the opening of more metro lines, there will be further land planning and opening around the metro stations, which has a clustering eﬀect on residents’ living. More people will choose the convenient public transport mode of metro.

3. Weather and Workday Selection In order to analyze the big data of Nanjing metro, we choose the nearest 2017 data, which is from January 9 to January 13, that is, the working day from Monday to Friday. The data come from weather statistics of meteorological departments, which reflect the real weather situation for post-event statistics rather than pre-forecast. The weather is normal, not extreme weather, so as not to affect the normal passenger flow analysis. Table2 shows the date and weather of the five working days. On 18 January 2017, Nanjing metro line 4 opened. Before that time, the situation of Nanjing metro lines was the same as in 2016.

Table 2. Of ﬁve working days.

Data Week Weather Temperature Wind speed

9 January 2017 Monday Cloudy/Cloudy 8 ◦C/2 ◦C North wind level 3–4/Northeast wind level 3–4 10 January 2017 Tuesday Cloudy/Cloudy 8 C/4 C Northeast wind level 3–4/East Wind level 3 ◦ ◦ ≤ 11 January 2017 Wednesday Cloudy/Light rain 8 C/4 C Northeast wind level 3/North wind level 3 ◦ ◦ ≤ ≤ 12 January 2017 Thursday Cloudy/Cloudy 10 ◦C/0 ◦C West wind level 3–4/West wind level 3–4 13 January 2017 Friday Sunny/Sunny 11 C/ 1 C Southwest wind level 3–4/West wind level 3–4 ◦ − ◦

4. Time Analysis of Metro Passenger Flow in Nanjing

4.1. Big Data Sources and Analysis Procedure Nanjing metro passenger flow data comes from smart card swiping data collected by relevant management departments, including card number, card type, station, inbound and outbound time data, etc. Time data is accurate to seconds. In the following analysis, in order to simplify data processing, the daily data are counted to 24 h, 0–23 o’clock. The passenger flow released by the official metro management department is generally the sum of passenger flow and transfer times. The passenger flow in this study does not consider the number of transfers, that is, the number of inbound or outbound records between any two stations. According to the corresponding relationship, the number of passengers entering and leaving the station is the same. For individual abnormal data, deletion processing was carried out. Sustainability 2019, 11, 4989 6 of 20

relationship, the number of passengers entering and leaving the station is the same. For individual Sustainability 2019, 11, 4989 6 of 19 abnormal data, deletion processing was carried out.

4.2.4.2. ComparisonComparison ofof PassengerPassenger FlowFlow inin FiveFive WorkingWorking DaysDays FigureFigure2 2 shows shows thethe statisticsstatistics of daily passenger passenger flow flow of of Nanjing Nanjing metro metro in in five five working working days. days. As Ascan can be beseen seen from from the the figure, figure, the the passenger passenger flow flow from from Monday Monday to to Friday Friday ranges from 1.20 millionmillion toto 1.321.32 million.million. PassengerPassenger flowflow fromfrom MondayMonday toto FridayFriday isis increasingincreasing gradually,gradually, butbut thethe increaseincrease isis notnot large,large, aboutabout 0.120.12 million.million. AtAt thethe endend ofof 2016,2016, thethe permanentpermanent populationpopulation ofof NanjingNanjing waswas 8.278.27 million,million, whichwhich can can be be used used to to calculate calculate the the proportion proportion of pedestriansof pedestrians to theto the permanent perman populationent population in about in about one seventh.one seventh. Considering Considering the commuting the commuting needs needs of the of same the resident,same resident, the actual the numberactual number of people of takingpeople thetaking subway the subway is much is smaller. much smaller.

FigureFigure 2.2. StatisticsStatistics ofof dailydaily passengerpassenger ﬂowflow ofof NanjingNanjing metrometro inin ﬁvefive workingworking days.days.

FigureFigure3 3shows shows the the 24-h 24-h passenger passenger flow flow fluctuation fluctuatio ofn of the the metro metro in fivein five working working days. days. The The time time is fromis from 0:00 0:00 to 23:00. to 23:00. The positiveThe positive number number of passenger of passenger flow indicates flow indicates the number the of number inbound of passengers inbound andpassengers the negative and numberthe negative indicates number the number indicates of outboundthe number passengers. of outbound The five-daypassengers. inbound The five-day curve is basicallyinbound thecurve same is asbasically the five-day the same outbound as the curve,five-day because outbound the daily curve, passenger because flow the gapdaily is passenger not large. Ifflow the passengergap is not flow large. disperses If the passenger to 24 h, the flow gap willdisperses become to smaller. 24 h, the The gap diff erencewill become is that thesmaller. morning The peakdifference of the is station that the is amorning blunt angle, peak with of the two station peaks is of a 7:00blunt and angle, 8:00, with while two the peaks morning of 7:00 peak and of 8:00, the stationwhile the is a morning sharp angle, peak with of the an 8:00station peak. is a Inbound sharp angle, and outboundwith an 8:00 evening peak. peaksInbound are sharpand outbound corners, withevening a peak peaks of 18:00. are sharp Before corners, 6:00 in with the morning,a peak of almost18:00. Before no one 6:00 took in the the subway. morning, By almost 18:00 p.m.,no one it wastook athe small subway. peak, andBy then18:00 the p.m., number it was of passengersa small peak, began and to decrease.then the Bynumber 23:00 p.m.,of passengers almost no began one took to thedecrease. subway. By 23:00 p.m., almost no one took the subway. DuringDuring thethe peak peak hours hours at at 7:00 7:00 and and 8:00 8:00 a.m., a.m., the numberthe number of people of people entering entering the station the station was about was 170,000about 170,000 per hour. per During hour. theDuring morning the morning peak at 8:00, peak the at number8:00, the of number people leavingof people the leaving station wasthe station about 220,000was about per hour.220,000 This per is hour. because This the is normal because working the normal time inworking Nanjing time is from in Nanjing 9:00 to 17:00. is from Residents 9:00 to have17:00. to Residents go to work have before to go 9:00. to Consideringwork before the9:00 distance,. Considering the number the distance, of outbound the number passengers of outbound is more concentratedpassengers is than more that concentrated of inbound than passengers. that of Duringinbound the passengers. evening peak, During the the number evening of arrivals peak, the at 17:00number is about of arrivals the same at 17:00 as that is about at 18:00, the about same 150,000.as that at 18:00, about 150,000. Sustainability 2019, 11, 4989 7 of 19 Sustainability 2019, 11, 4989 7 of 20

FigureFigure 3.3. 24-h24-h passenger flow flow fluctuat fluctuationion in in five five working working days. days. 4.3. Peak Analysis of Passenger Flow 4.3. Peak Analysis of Passenger Flow In order to facilitate data analysis and take into account the similarity of passenger flow data In order to facilitate data analysis and take into account the similarity of passenger flow data in infive five working working days, days, the the average average passenger passenger flow flow data data in infive five working working days days wastaken, wastaken, that that is, is,daily daily averageaverage passenger passenger flow. flow. The The peak peak hour hour coe coefficientfficient of of daily daily average average passenger passenger flow flow is is used usedto toreflect reflectthe crowdingthe crowding degree degree of passenger of passenger flow at flow the time at the point. time Peak point. hour Peak coe ffihourcient coefficient refers to the refers proportion to the of passengerproportion flow of concentratedpassenger flow in oneconcentrated hour in a dayin one to thehour total in passengera day to the flow. total This passenger index is flow. mainly This used toindex reflect is the mainly unbalanced used to travelreflect timethe unbalanced of passengers. travel In time practice, of passengers. the design In scale practice, of many the design transportation scale facilitiesof many is transportation based on this. facilities is based on this. Ci ===Fi/Ft(i = 0, 1, 2, 23) (1) CFFiiit/( 0,1,2,23)··· (1) In Equation (1), Ci is peak hour coefficient, Fi is one hour passenger at i o’clock, Ft is passenger flow ofIn one Equation day, and (1),i is C thei is time,peak fromhour 0:00 coefficient, to 23:00. Fi is one hour passenger at i o’clock, Ft is passengerFigure4 flow shows of one the peakday, and hour i coe is thefficient time, of from daily 0:00 average to 23:00. passenger flow. Figure4 corresponds to Figure3Figure to some 4 shows extent, the but peak the hour peak coefficient hour coe offfi cientdaily canaverage be expressed passenger by flow. histogram Figure 4 and corresponds proportion, whichto Figure can visually 3 to some show extent, the crowding but the peak degree hour of passenger coefficient flow can atbe time expressed points. by During histogram the morning and peakproportion, period, which the number can visually of arrivals show the concentrated crowding de atgree 7:00 of andpassenger 8:00, accountingflow at time forpoints. 12% During and 13% respectively,the morning while peak theperiod, number the number of departures of arrivals concentrated concentrated at 8:00,at 7:00 accounting and 8:00, accounting for about 17%. for 12% These dataand reflect 13% respectively, that some subway while the passengers number of in departur Nanjinges use concentrated the subway at as 8:00, a commuting accountingtool, for about starting 17%. These data reflect that some subway passengers in Nanjing use the subway as a commuting work at 9:00 in the morning. Considering the distance of different routes, many passengers choose to tool, starting work at 9:00 in the morning. Considering the distance of different routes, many leave at 7:00 or 8:00. passengers choose to leave at 7:00 or 8:00. Then the number of arrivals and departures began to decline, reaching a low of less than 5% at 11:00, which is also lunchtime. After noon, the number of inbound and outbound passengers gradually increased and reached a new height at the evening peak. The passengers are mainly commuters, and there are not many other passengers who use the Nanjing metro for leisure or tourism. Sustainability 2019, 11, 4989 8 of 19 Sustainability 2019, 11, 4989 8 of 20

FigureFigure 4. 4.Peak Peak hour hour coecoefficientﬃcient of averageaverage daily daily passenger passenger flow. ﬂow.

ThenDuring the numberthe evening of arrivals peak period, and departuresthe number beganof arrivals to decline, reached reaching the highest a low at 17:00, of less about than 12%. 5% at 11:00,The which number is alsoof people lunchtime. leaving After the station noon, the reached number the ofhighest inbound at 18:00, and outbound about 12%. passengers It also shows gradually that increasedthere is a and lag reachedrelationship a new between height inbound at the and evening outbound peak. peak, The which passengers is concentrated are mainly around commuters, 18:00. andAfter there 19:00, are notthe manynumber other of inbound passengers and outbound who use the passengers Nanjing began metro to for decrease leisure gradually, or tourism. less than 5%.During Some passengers the evening choose peak period,the subway the numberas their commuting of arrivals reachedtool. The the off-du highestty time at 17:00,is concentrated about 12%. Theat number 17:00, while of people the commuting leaving the time station is not reachedtoo long, the basically highest concentrated at 18:00, about in two 12%. hours. It also shows that there isFigure a lag relationship 5 shows the between 24-h distribution inbound andof the outbound daily average peak, whichpassenger is concentrated flow of six metro around lines. 18:00. AfterHere, 19:00, the thetraffic number of all stations of inbound on each and subway outbound line passengers is counted beganon each to line. decrease As can gradually, be seen from less the than 5%.figure, Some the passengers order of passenger choose the flow subway is line as 1, their 2, 3, commuting10, S8, and S1. tool. The The passenger off-duty flow time of is trunk concentrated line 1, 2, at 17:00,and while 3 is much the commutinglarger than that time of is branch not too line long, 10, basicallyS1, and S8. concentrated in two hours. Figure5 shows the 24-h distribution of the daily average passenger flow of six metro lines. Here, the traffic of all stations on each subway line is counted on each line. As can be seen from the figure, the order of passenger flow is line 1, 2, 3, 10, S8, and S1. The passenger flow of trunk line 1, 2, and 3 is much larger than that of branch line 10, S1, and S8. During the morning peak period, the wave peaks of inbound and outbound stations of line 1 and line 2 are concentrated at 8:00, which is about 60,000. The wave peaks of other lines are concentrated at 7:00. The wave peaks of line 3 are slightly less than 50,000, while those of line 10, S1, and S8 are all less than 10,000. This shows that there are fewer people and more rest time on the main lines 1 and 2 before 7:00. Residents on other routes travel earlier and may need to make longer-distance transfers through the trunk lines. During the evening peak hour, all the inbound and outbound peaks are concentrated at 17:00. The number of inbound and outbound passengers of line 1 and line 2 is basically the same, similar to the morning peak, about 50,000. However, the number of inbound and outbound passengers on line 3 is lower than the peak value during the morning peak period, which is about 35,000. After 19:00, the number of inbound and outbound stations decreased from peak to sub-peak and remained stable until 21:00. After 21:00, the number of inbound and outbound passengers began to decrease sharply, which indicated that the whole city had entered a rest time. Sustainability 2019, 11, 4989 9 of 19

Passenger flow intensity of metro line is the ratio of passenger flow and mileage, which reflects the operation efficiency and compression capacity of the metro line.

S = F /L (j = 1, 2, 3, 6) (2) j j j ···

In Equation (2), Sj is passenger ﬂow intensity, Fj is passenger ﬂow of the line j, Lj is the length of Sustainabilitythe line j, and2019, j11is, 4989 the line number of Nanjing metro lines. 9 of 20

Figure 5. 24-h distribution of daily average passenger flow on six metro lines. Figure 5. 24-h distribution of daily average passenger flow on six metro lines. Figure6 shows the daily average passenger flow intensity of six metro lines. The daily average passengerDuring flow the ofmorning five working peak period, days was the used wave to peaks calculate of inbound the passenger and outbound flow here. stations Passenger of line flow 1 andintensity line unit2 are is concentrated per person/kilometer. at 8:00, Aswhich can beis seenabout from 60,000. the figure,The wave the intensity peaks of of other passenger lines floware concentratedin and out of at the 7:00. same The line wave is notpeaks very of dilinefferent. 3 are slightly For the less inbound than 50,000, passenger while flow, those line of 1 line and 10, line S1, 2 andhave S8 the are strongest all less than passenger 10,000. flow,This shows which that are 13,471there are and fewer 11,664, people respectively. and more Thisrest time shows on that the main line 1 linesand line1 and 2 have 2 before higher 7:00. operational Residents efficiency on other and playroutes a roletravel of theearlier main and line. may The passengerneed to make flow longer-distanceintensity of line 3transfers is only 7465,through which the is trunk much lines. lower than that of line 1 and line 2. The reason is that the extensionDuring direction the evening of line peak 3 is roughlyhour, all the the same inbound as that and of lineoutbound 1. The passengerpeaks are concentrated flow of line 3 isat shared17:00. Theby line number 1. The of passengerinbound and flow outbound intensity passengers of line 10 is of 3591, line 1 which and line is much 2 is basically lower than the thatsame, of similar the main to theline. morning However, peak, the about passenger 50,000. flow However, intensity the of linenumb S1er and of lineinbound S8 is and lower, outbound 1154 and passengers 1021, respectively, on line 3because is lower the than two the antenna peak value paths during extend toothe farmorning into the peak region. period, However, which withis about the gradual35,000. After maturity 19:00, of thereal number estate and of commercialinbound and development outbound stations around metrodecreased stations, from the peak number to sub-peak of resident and populations remained stablewill gradually until21:00. increase, After 21:00, andthe the passenger number of flow inbound intensity and ofoutbound metro extension passengers line began will also to decrease increase, sharply,playing anwhich evacuation indicated role that for the the whole resident city population had entered in urbana rest time. centers. Passenger flow intensity of metro line is the ratio of passenger flow and mileage, which reflects the operation efficiency and compression capacity of the metro line. == SFLjjjj/( 1,2,3,6) (2)

In Equation (2), Sj is passenger flow intensity, Fj is passenger flow of the line j , Lj is the length of the line j , and j is the line number of Nanjing metro lines. Figure 6 shows the daily average passenger flow intensity of six metro lines. The daily average passenger flow of five working days was used to calculate the passenger flow here. Passenger flow intensity unit is per person/kilometer. As can be seen from the figure, the intensity of passenger flow in and out of the same line is not very different. For the inbound passenger flow, line 1 and line 2 have the strongest passenger flow, which are 13,471 and 11,664, respectively. This shows that line 1 and line 2 have higher operational efficiency and play a role of the main line. The passenger flow intensity of line 3 is only 7465, which is much lower than that of line 1 and line 2. The reason is that Sustainability 2019, 11, 4989 10 of 20

the extension direction of line 3 is roughly the same as that of line 1. The passenger flow of line 3 is shared by line 1. The passenger flow intensity of line 10 is 3591, which is much lower than that of the main line. However, the passenger flow intensity of line S1 and line S8 is lower, 1154 and 1021, respectively, because the two antenna paths extend too far into the region. However, with the gradual maturity of real estate and commercial development around metro stations, the number of resident populations will gradually increase, and the passenger flow intensity of metro extension Sustainability 2019, 11, 4989 10 of 19 line will also increase, playing an evacuation role for the resident population in urban centers.

FigureFigure 6.6. Passenger strength strength of of metro metro lines. lines. 5. Spatial Analysis of Metro Passenger Flow 5. Spatial Analysis of Metro Passenger Flow 5.1. Passenger Flow Comparison of Stations 5.1. Passenger Flow Comparison of Stations ByBy the the end end of 2016,of 2016, Nanjing Nanjing had had opened opened six metrosix me linestro lines with with 113 stations.113 stations. There There are someare some similar intersectionssimilar intersections between thesebetween lines, these which lines, play which the role play of transportationthe role of transportation hub and transfer. hub and Table transfer.3 shows theTable code 3 of shows Nanjing the metro code stations.of Nanjing The metro codes stations of these. stationsThe codes are markedof these bystations automatic are faremarked collection by system,automatic which fare is usedcollection to record system, passenger’s which is origin–destinationused to record passenger’s data of swiping origin–destination smart cards. data of swiping smart cards. Table 3. Code of Nanjing metro stations. Table 3. Code of Nanjing metro stations. Station Code (1–57) Station Name Station Code (58–113) Station Name Station Code Station Code 1 aotizhongxinStation Name 58Station jiangxinzhou Name (1–57)2 yuantong 59(58–113) linjiang 31 zhongshengaotizhongxin 60 58 pukouwanhuichengjiangxinzhou 42 xiaoxingyuantong 61 59 nanjinggongyedaxuelinjiang 53 andemenzhongsheng 62 60 pukouwanhuicheng longhualu 64 zhonghuamenxiaoxing 63 61 nanjinggongyedaxue wendelu 75 sanshanjieandemen 64 62 longhualu yushanlu 86 zhangfuyuanzhonghuamen 65 63 lukoujichangwendelu 97 xinjiekousanshanjie 66 64 xiangyulunanyushanlu 108 zhujiangluzhangfuyuan 67 65 lukoujichang xiangyulubei 119 gulouxinjiekou 68 66 zhengfangzhongluxiangyulunan 1210 xuanwumenzhujianglu 69 67 xiangyulubei jiyindadao 1311 xinmofanmalugulou 70 68 hehaidaxuefochengxiluzhengfangzhonglu 1412 nanjingzhanxuanwumen 71 69 jiyindadao cuipingshan 15 hongshandongwuyuan 72 taishanxincun 16 maigaoqiao 73 taifenglu 17 youfangqiao 74 gaoxinkaifaqu 18 yurundajie 75 xinxigongchengdaxue 19 aotidong 76 xiejiadian 20 xinglongdajie 77 dachang Sustainability 2019, 11, 4989 11 of 19

Table 3. Cont.

Station Code (1–57) Station Name Station Code (58–113) Station Name 21 jiqingmendajie 78 getang 22 yunjinlu 79 changlu 23 mochouhu 80 huagongyuan 24 hanzhongmen 81 liuhekaifaqu 25 shanghailu 82 longchi 26 daxinggong 83 xiongzhou 27 xianmen 84 fenghuangshangongyuan 28 minggugong 85 fangzhouguangchang 29 muxuyuan 86 shenqiao 30 xiamafang 87 babaiqiao 31 xiaolingwei 88 jinniuhu 32 zhonglingjie 89 linchang 33 maqun 90 xinghuolu 34 jinmalu 91 dongdachengxianxueyuan 35 xianhemen 92 tianruncheng 36 xuezelu 93 liuzhoudonglu 37 xianlinzhongxin 94 shangyuanmen 38 yangshangongyuan 95 wutangguangchang 39 nandaxianlinxiaoqu 96 xiaoshi 40 jingtianlu 97 nanjinglinyedaxue.xinzhuang 41 tianlongsi 98 jimingsi 42 ruanjiandadao 99 fuqiao 43 huashenmiao 100 changfujie 44 nanjingnanzhan 101 fuzimiao 45 shuanglongdadao 102 wudingmen 46 hedingqiao 103 yuhuamen 47 shengtailu 104 qiazimen 48 baijiahu 105 daminglu 49 xiaolongwan 106 mingfaguangchang 50 zhushanlu 107 hongyundadao 51 tianyindadao 108 shengtaixilu 52 longmiandadao 109 tianyuanxilu 53 nanyida.jiangsujingmaoxueyuan 110 jiulonghu 54 nanjingjiaoyuan 111 chengxindadao 55 zhongguoyaokedaxue 112 dongdajiulonghuxiaoqu 56 mengdudajie 113 mozhoudonglu 57 lvboyuan

Figure7 shows a comparison of the average daily passenger flow at 113 metro stations. From the figure, we can see that there is a certain mapping relationship between inbound and outbound passenger flow, and the basic figure is consistent. The station with the largest passenger flow is very obvious, reaching about 90,000, which is Xinjiekou. Xinjiekou is the intersection of metro line 1 and line 2. It is also the geographical and commercial center of Nanjing. Although the city scale of Nanjing is expanding and the new commercial center is also dispersing passenger flow, Xinjiekou still occupies an important commercial position after transformation and upgrading. In addition to Xinjiekou, some stations with large passenger traffic remain around 30,000–40,000. There are also many stations with passenger flow of less than 3000, which indicates that these stations are in relatively remote areas, so passenger flow is very small, and further commercial development is needed to drive passenger flow. Because the codes of Nanjing metro stations are related to the sequence of line opening, the earlier the station coding number on the subway line opening, the smaller the number of station coding. The codes of stations on each metro line are basically continuous. From Figure7, it can be seen that different metro lines have different carrying capacities for passenger flow. Most of the passenger flow is carried by the early main lines, and the passenger carrying capacity of some remote lines has not Sustainability 2019, 11, 4989 12 of 20 Sustainability 2019, 11, 4989 12 of 19 still occupies an important commercial position after transformation and upgrading. In addition to Xinjiekou, some stations with large passenger traffic remain around 30,000–40,000. There are also beenmany brought stations into fullwith play. passenger The passenger flow of less carrying than 3000, capacity which of indicates different that stations these onstations the same are in line is also direlativelyfferent. remote Generally, areas, there so passenger is a central flow station. is very Severalsmall, and other further stations commercial around development the central stationis formneeded a small to central drive passenger area, which flow. carries most of the passenger flow on the metro line.

Figure 7. Distribution of daily average passenger ﬂow at metro stations. Figure 7. Distribution of daily average passenger flow at metro stations.

In orderBecause to analyzethe codes the of Nanjing passenger metro flow stations distribution are related law to ofthe Nanjing sequence metro of line station, opening, the the most representativeearlier the morningstation coding and evening number peakson the weresubway selected line opening, to analyze the thesmaller congestion the number situation of station of metro passengercoding. flow. The Thecodes morning of stations peak on each time metro is 8:00 line in theare morningbasically continuous. and the evening From Figure peak time7, it can is 18:00be in the evening.seen that different metro lines have different carrying capacities for passenger flow. Most of the Figurepassenger8 shows flow theis carried comparison by the of early metro main stations lines, ofand inbound the passenger passenger carrying flow atcapacity 8:00 and of outboundsome passengerremote flow lines at has 18:00. not been The brought figure shapes into full of pl 8:00ay. The inbound passenger passenger carrying flow capacity and 18:00of different outbound passengerstations flow on the are same very line similar is also at different. metrostations, Generally, only there individual is a central stationsstation. Several are di ffothererent. stations Inbound passengeraround flow the atcentral some station stations form is slightly a small central larger thanarea, outboundwhich carries passenger most of the flow. passenger The three flow stations on the with metro line. the highest passenger flow at 8:00 are Maigaoqiao, Youfangqiao, and Liuzhoudonglu, which are about In order to analyze the passenger flow distribution law of Nanjing metro station, the most 10,000.representative Other inbound morning passenger and evening flows arepeaks basically were se belowlected to 5000. analyze The the four congestion stations withsituation the highestof outboundmetro passenger passenger flowflow. atThe 18:00 morning are Liuzhoudonglu, peak time is 8:00 Xinjiekou, in the morning Maigaoqiao, and the andevening Youfangqiao, peak time is which are about18:00 7000–8500. in the evening. The outbound passenger flow of other stations is basically below 5000. Among the symmetryFigure comparison 8 shows of the inbound comparison and outbound of metro graphics,stations of the inbound most unusual passenger one flow is Xinjiekou, at 8:00 and where the inboundoutbound passenger passenger flow flow at at 8:00 18:00. is lessThe thanfigure other shapes stations, of 8:00 whileinbound the passenger outbound flow passenger and 18:00 flow at 18:00outbound is more than passenger other stations. flow are Thevery reason similar may at metro be that stations, Xinjiekou, only asindividual a commercial stations center, are different. is gradually decreasingInbound in passenger population, flow so at fewer some peoplestations gois slight out toly worklarger andthanmore outbound people passenger leave work flow. atThe 18:00. three Figurestations9 showswith the the highest comparison passenger of metroflow at stations8:00 are ofMaigaoqiao, outbound Youfangqiao, passenger flow and atLiuzhoudonglu, 8:00 and inbound which are about 10,000. Other inbound passenger flows are basically below 5000. The four stations passenger flow at 18:00. The figure shapes of outbound passenger flow at 8:00 and inbound passenger with the highest outbound passenger flow at 18:00 are Liuzhoudonglu, Xinjiekou, Maigaoqiao, and flow atYoufangqiao, 18:00 of the which subway are about station 7000–8500. are very The similar. outbound The outboundpassenger flow passenger of othe flowr stations at the is basically same station is muchbelow larger 5000. than Among the the inbound symmetry passenger comparison flow. of Theinbound reason and mayoutbound be that graphics, the statutory the most working unusual day in Nanjingone is Xinjiekou, is 9:00, so where the passenger the inbound flow passenger is concentrated flow at 8:00 to is 8:00. less than At 18:00, other Xinjiekou,stations, while the the station withoutbound the highest passenger passenger flow flow,at 18:00 had is more about than 12,000. other stations. Inbound The passenger reason may flow be that of other Xinjiekou, stations as a was basically below 5000. The highest outbound passenger flow at 8:00 was Xinjiekou, which was about 2000. The stations with more passenger flow were Zhujianglu, Jimingsi, and Daxinggong, which had 9000–10000. The passenger flow of other stations was basically below 5000. Sustainability 2019, 11, 4989 13 of 20 commercial center, is gradually decreasing in population, so fewer people go out to work and more people leave work at 18:00.

Sustainability 2019, 11, 4989 13 of 20

commercial center, is gradually decreasing in population, so fewer people go out to work and more Sustainability 2019, 11, 4989 13 of 19 people leave work at 18:00.

Figure 8. Comparison of metro stations of inbound passenger flow at 8:00 and outbound passenger flow at 18:00.

Figure 9 shows the comparison of metro stations of outbound passenger flow at 8:00 and inbound passenger flow at 18:00. The figure shapes of outbound passenger flow at 8:00 and inbound passenger flow at 18:00 of the subway station are very similar. The outbound passenger flow at the same station is much larger than the inbound passenger flow. The reason may be that the statutory working day in Nanjing is 9:00, so the passenger flow is concentrated to 8:00. At 18:00, Xinjiekou, the station with the highest passenger flow, had about 12,000. Inbound passenger flow of other stations was basically below 5000. The highest outbound passenger flow at 8:00 was Xinjiekou, which was about 2000. The stations with more passenger flow were Zhujianglu, Jimingsi, and Daxinggong, FigureFigure 8. 8.Comparison Comparison of of metro metro stationsstations of inbound passe passengernger flow ﬂow at at 8:00 8:00 and and outbound outbound passenger passenger which had 9000–10000. The passenger flow of other stations was basically below 5000. ﬂowflow at at 18:00. 18:00.

Figure 9 shows the comparison of metro stations of outbound passenger flow at 8:00 and inbound passenger flow at 18:00. The figure shapes of outbound passenger flow at 8:00 and inbound passenger flow at 18:00 of the subway station are very similar. The outbound passenger flow at the same station is much larger than the inbound passenger flow. The reason may be that the statutory working day in Nanjing is 9:00, so the passenger flow is concentrated to 8:00. At 18:00, Xinjiekou, the station with the highest passenger flow, had about 12,000. Inbound passenger flow of other stations was basically below 5000. The highest outbound passenger flow at 8:00 was Xinjiekou, which was about 2000. The stations with more passenger flow were Zhujianglu, Jimingsi, and Daxinggong, which had 9000–10000. The passenger flow of other stations was basically below 5000.

Figure 9. Comparison of metro stations of outbound passenger ﬂow at 8:00 and inbound passenger ﬂow at 18:00.

5.2. Spatial Distribution of Station Flow at Morning and Evening Peaks In order to study the spatial distribution of station traffic during morning peak and evening peak, the time periods of 8:00 during morning peak and 18:00 during evening peak were selected to analyze the passenger traffic of 113 stations. Here, the image of passenger flow thermogram is used to express the passenger flow of the subway station. The thermogram shows the route of passenger flow and the geographical area where passenger flow is located in the form of special highlights. The passenger flow thermogram of metro stations is used to express the crowding degree of metro stations, and different colors are used to express the different passenger flow of the stations. Sustainability 2019, 11, 4989 14 of 19

According to the international general color warning standards, there are four kinds of color. Green represents unimpeded, 0–2999, yellow represents congestion, 3000–5999, orange represents congestion, 6000–8999, red represents very congested, more than 9000. In order to express the warning color conveniently, the color of the subway line waschanged to grey. The circle represents different subway stations, and the color in the circle represents the passenger flow of the subway stations. On the thermodynamic chart, the name of the starting station, the intersection station, and the station divided by the congestion state of the metro line are labeled. Figure 10 shows the inbound passenger flow distribution at 8:00 of morning peak. From the picture, we can see that the red stations are Liuzhoudonglu, Maigaoqiao, and Youfangqiao. Yellow stations are Tianruncheng, Nanjinglinyedaxue.xinzhuang, Maqun, Sanshanjie, Zhonghuamen, Andermen, and Shuanglongdadao. Crowded stations are concentrated on the main lines of the metro. For metro line 1, the most congested stations were Maigaoqiao, Sanshanjie, Zhonghuamen, and Shuanglongdadao. Andermen is an important transfer station because of the large number of residents nearby. For metro line 2, there are more residents near Youfangqiao and Maqun. For line 3, Nanjinglinyedaxue. xinzhuang is an important public transport hub, and close to the railway station, so the passenger flow is large. Liuzhoudonglu and Tianruncheng are special kinds of sites, which are separated from the main urban area by the Yangtze river. In this area, a large number of real estate has been developed and there are many inhabitants. Other residents who need to cross the river and enter the city also need to transfer to the subway here. Because of the congestion of the Yangtze river bridge, the subway is a necessary Sustainabilityand effective 2019 travel, 11, 4989 choice compared with bus and driving. 15 of 20

Figure 10. Distribution of inbound passenger flow ﬂow at 8:00 of morning peak.

Figure 11 shows the outbound passenger flowflow distributiondistribution atat 8:008:00 ofof thethe morningmorning peak.peak. From the picture, picture, we we can can see see that that the the red red stations stations are areXinjiekou, Xinjiekou, Daxinggong, Daxinggong, Gulou, Gulou, and Zhujianglu. and Zhujianglu. The Theorange orange sites sites areRuanjiandadao and and Shanghailu. Shanghailu. The yellowThe stationsyellow are stations Nanjingzhan, are Xinmofanmalu,Nanjingzhan, Xuanwumen,Xinmofanmalu, Jimingsi, Xuanwumen, Hanzhongmen, Jimingsi, Xianmen, Hanzhongmen, Sanshanjie, Zhangfuyuan,Xianmen, Sanshanjie, Changfujie, Jiqingmendajie,Zhangfuyuan, Aotidong,Changfujie, Yuantong, Jiqingmendajie, Tianlongsi, Aotidong, and Nanjingnanzhan. Yuantong, Tianlongsi,The crowded and area Nanjingnanzhan. of outbound passenger The crowded flow is areaconcentrated of outbound in the passenger main urban flow area is of concentrated Nanjing, first in in the the main commercial urban centerarea of area Nanjing, with Xinjiekou first in the as commercialthe main part, center and secondlyarea with inXinjiekou the new urbanas the areamain with part, Aotidong and secondly as the in main the part.new urban This shows area with that Aotidong as the main part. This shows that although Nanjing has established some new commercial centers after many years of urban planning, it has not effectively replaced the old central area. Xinjiekou area is still the first choice for residents to leave the station at morning peak. The Aotiarea is close to Xinjiekou area, sharing part of the outbound passenger flow. In the new urban commercial and real estate planning, it is also necessary to disperse the working residents to new areas, so that the subway can play an effective role in dredging. Sustainability 2019, 11, 4989 15 of 19 although Nanjing has established some new commercial centers after many years of urban planning, it has not effectively replaced the old central area. Xinjiekou area is still the first choice for residents to leave the station at morning peak. The Aotiarea is close to Xinjiekou area, sharing part of the outbound passenger flow. In the new urban commercial and real estate planning, it is also necessary to disperse theSustainability working 2019 residents, 11, 4989 to new areas, so that the subway can play an effective role in dredging. 16 of 20

Figure 11. Distribution of outbound passenger flow ﬂow at 8:00 of morning peak.

Figure 12 shows the inboundinbound passengerpassenger flowflow distributiondistribution atat 18:0018:00 ofof eveningevening peak.peak. From the picture, we can see that the red station is Xinjiekou, and the yellow station is Daxinggong, Gulu, Zhujianglu, andand Ruanjiandadao. Ruanjiandadao. Inbound Inbound passenger passenger flow flow base isbase relatively is relatively smooth, smooth, but the Xinjiekou but the areaXinjiekou is still area relatively is still relatively congested, congested, which is which also due is also to going due to off goingwork off of work people of workingpeople working nearby. Thenearby. Ruanjiandadao The Ruanjiandadao is another is another congested congested station. station. Ruanjiandadao Ruanjiandadao is an important is an important trunk roadtrunk in road the softwarein the software valley valley of the of Yuhuatai the Yuhuatai district district of Nanjing. of Nanjing. There There are many are many software software enterprises enterprises along along the road,the road, and and there there are manyare many staff staffthat that leave leave work work at 18:00. at 18:00. Figure 13 shows the outbound passenger flow distribution at the 18:00 of evening peak. From the picture, we can see that the orange sites are Xinjiekou, Yuantong, Magaoqiao, and Liuzhoudonglu. The yellow stations are Maqun, Sanshanjiet, Shuanglongdadao, and Tianruncheng. As a commercial center, Xinjiekou has many people here for leisure and consumption at night, so the outbound passenger flow at night is also relatively large. Yuantong is an important transfer hub. Liuzhoudonglu is the first station for metro line 3 to cross the river. There are many buses transferring here. Sanshanjie, Maqun, Shuanglongdadao, and Tianruncheng are residential centers, which also bring a large number of outbound passengers. Sustainability 2019, 11, 4989 16 of 19 Sustainability 2019, 11, 4989 17 of 20

Sustainability Figure2019, 11Figure, 12.4989 Distribution 12. Distribution of of inbound inbound passengerpassenger flow ﬂow at at18:00 18:00 of evening of evening peak. peak. 18 of 20

Figure 13 shows the outbound passenger flow distribution at the 18:00 of evening peak. From the picture, we can see that the orange sites are Xinjiekou, Yuantong, Magaoqiao, and Liuzhoudonglu. The yellow stations are Maqun, Sanshanjiet, Shuanglongdadao, and Tianruncheng. As a commercial center, Xinjiekou has many people here for leisure and consumption at night, so the outbound passenger flow at night is also relatively large. Yuantong is an important transfer hub. Liuzhoudonglu is the first station for metro line 3 to cross the river. There are many buses transferring here. Sanshanjie, Maqun, Shuanglongdadao, and Tianruncheng are residential centers, which also bring a large number of outbound passengers.

FigureFigure 13. Distribution 13. Distribution of of outbound outbound passenger flow ﬂow at 18:00 at 18:00 of evening of evening peak. peak.

6. Conclusions By 2016, Nanjing had opened six metro lines, namely trunk line 1, 2, 3, and extension line 10, S1, and S8. The rapid development of the metro system to a certain extent connects the main urban and suburban areas, alleviating the traffic pressure of the city, but still faces traffic management problems such as congestion. The automatic ticket-checking system of Nanjing metro records a large number of passengers’ information of swiping cards, more than one million cards per day. These large metro data provide the original basis for analyzing space–time characteristics of residents’ travel. In addition, various images were selected to visualize the subway passenger flow. In this study, the smart card swiping records during five working days with normal weather were selected for statistical analysis of passenger flow. The daily card swiping data were counted to 24 h, from 0:00 to 23:00. Passenger flow from Monday to Friday increased from 1.20 million to 1.32 million. The five-day inbound curve is basically coincidental with the five-day outbound curve. The inbound and outbound curves also have a certain reflection relationship. Passenger flow during morning and evening peak periods can effectively reflect the degree of urban traffic congestion. The peak hour coefficient of daily average passenger flow is used to reflect the crowding degree of passenger flow at the time point. During the morning peak, the number of arrivals and departures concentrated at 7:00 and 8:00, while the number of departures concentrated at 8:00. During the evening peak period, the number of arrivals reached the highest at 17:00 and the number of departures reached the highest at 18:00. During the morning peak period, the peak values of inbound and outbound waves of line 1 and line 2 are concentrated at 8:00, while those of other lines are concentrated at 7:00. This shows that people entering the main line 1 and 2 have more rest time. During the evening peak, all the inbound and outbound peaks are concentrated at 17:00. The passenger flow intensity of metro reflects the passenger flow per unit length of metro. For the inbound passenger flow, line 1 and line 2 have the strongest passenger flow, higher operational efficiency, and play the role of the main lines. The passenger flow intensity of line 3 is slightly higher than that of other branch lines. There is a certain mapping relationship between the inbound and outbound passenger flow of 113 metro stations, and the basic graphics are consistent. Passenger flow at Xinjiekou is obviously larger than that at other stations. Passenger flow at many remote stations is very small. The figure shapes of inbound passenger flow at 8:00 and outbound passenger flow at 18:00 in the subway station are very similar, just like inbound passenger flow at 8:00 and outbound passenger flow at 18:00. In this study, green, yellow, orange, and red warning colors are used to indicate the crowding degree of passenger flow at Nanjing metro stations. For the inbound passenger flow at 8:00 of the morning peak, the crowded stations are concentrated in the main lines of the metro, as well as some nearby stations with a high density of residents. For the distribution of outbound passenger flow at 8:00 of the morning peak, the crowded area is concentrated in the main urban area of Nanjing. For the distribution of inbound passenger flow at 18:00 of the evening peak, the basis of passenger flow is relatively smooth, but the Xinjiekou area is still relatively congested. The Ruanjiandadao is another congested site. For the outbound passenger flow at 18:00 of the evening peak, Xinjiekou area is still relatively crowded, and some transfer hubs and residential centers are also crowded. There is still much space for further research. This research chose five working days of data, although to some extent this reflects the travel law of residents, but follow-up scientific research can be compared with rest days, holidays, and other data, showing the fluctuation law of week, month, season, and year. In addition, factors such as climate and failure can be considered. The classification of passengers can be further determined, which can be combined with urban geography, reflecting the relationship between passenger flow and business, work, and residential areas. In short, the use of metro big data can not only improve the level of traffic management, master residents’ travel rules, provide a basis for future traffic planning, but can also be combined with the commercial planning and development of real estate, so that the metro plays a dual role in transportation and commerce in the city. Sustainability 2019, 11, 4989 18 of 19

Author Contributions: H.B. undertook the data collection. J.C. provided an interpretation of the results. W.Y. wrote the majority of the paper. X.Y. contributed to the paper review and editing. Funding: This research was funded by Key Project of National Natural Science Foundation of China (No. 51638004), and Basic Research Program of Science and Technology Commission Foundation of Jiangsu Province (No. BK20180775). Acknowledgments: The authors would like to express their sincere thanks to the anonymous reviewers for their constructive comments on an earlier version of this manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Wei, Y.; Chen, M.C. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. Part C Emerg. Technol. 2012, 21, 148–162. [CrossRef] 2. Liu, S.; Yao, E. Holiday Passenger Flow Forecasting Based on the Modified Least-Square Support Vector Machine for the Metro System. J. Transp. Eng. Part A Syst. 2017, 143, 04016005. [CrossRef] 3. Li, L.; Wang, Y.; Zhong, G.; Zhang, J.; Ran, B. Short-to-medium term passenger flow forecasting for metro stations using a hybrid model. KSCE J. Civ. Eng. 2018, 22, 1937–1945. [CrossRef] 4. Jiang, M.; Li, H.Y.; Xu, X.Y.; Xu, S.P.; Miao, J.R. Metro passenger flow control with station-to-station cooperation based on stop-skipping and boarding limiting. J. Cent. South Univ. 2017, 24, 236–244. [CrossRef] 5. Jiang, M.; Li, H.Y.; Xu, X.Y.; Xu, S.P.; Miao, J.R. Joint optimal train regulation and passenger flow control strategy for high-frequency metro lines. Transp. Res. Part B Methodol. 2017, 99, 113–137. 6. Yang, J.; Jin, J.G.; Wu, J.; Jiang, X. Optimizing Passenger Flow Control and Bus-Bridging Service for Commuting Metro Lines. Comput. Aided Civ. Infrastruct. Eng. 2017, 32, 458–473. [CrossRef] 7. Shi, J.; Yang, L.; Yang, J.; Gao, Z. Service-oriented train timetabling with collaborative passenger flow control on an oversaturated metro line: An integer linear optimization approach. Transp. Res. Part B Methodol. 2018, 110, 26–59. [CrossRef] 8. Li, S.; Yang, L.; Gao, Z. Optimal switched control design for automatic train regulation of metro lines with time-varying passengers arrival flow. Transp. Res. Part C Emerg. Technol. 2018, 86, 425–440. [CrossRef] 9. Lee, Y.K. Discovery of travel patterns in seoul metropolitan subway using big data of smart card transaction systems. J. Soc. E Bus. Stud. 2013, 18, 211–222. 10. Lee, K.; Park, J.S. Accessibility changes in the metropolitan Seoul subway system: time-distance algorithms based on the T-card big data and an accessibility measurement model for un-fixed transportation networks. J. Econ. Geogr. Soc. Korea 2014, 17, 98–113. 11. Sun, L.; Lu, Y.; Jin, J.G.; Lee, D.H.; Axhausen, K.W. An integrated Bayesian approach for passenger flow assignment in metro networks. Transp. Res. Part C Emerg. Technol. 2015, 52, 116–131. [CrossRef] 12. Noh, K. An exploratory study on improvement method of the subway congestion based big data convergence. J. Digit. Converg. 2015, 13, 35–42. 13. Kim, J. Subway congestion prediction and recommendation system using big data analysis. J. Digit. Converg. 2016, 14, 289–295. [CrossRef] 14. Zhu, Y.; Ozbay, K.; Xie, K.; Yang, H. Using big data to study resilience of taxi and subway trips for hurricanes sandy and irene. Transp. Res. Rec. 2016, 2599, 70–80. [CrossRef] 15. Hong, L.; Li, W.; Zhu, W. Assigning passenger flows on a metro network based on automatic fare collection data and timetable. Discret. Dyn. Nat. Soc. 2017, 2017, 4373871. [CrossRef] 16. Wibisono, A.; Jatmiko, W.; Wisesa, H.A.; Hardjono, B.; Mursanto, P. Traffic big data prediction and visualization using Fast Incremental Model Trees-Drift Detection (FIMT-DD). Knowl. Based Syst. 2016, 93, 33–46. [CrossRef] 17. Zhang, J.; Chen, Z.; Liu, Y.; Du, M.; Yang, W.; Guo, L. Space–time visualization analysis of bus passenger big data in Beijing. Clust. Comput. 2018, 21, 813–825. [CrossRef] 18. Hwang, U. Visualization analysis of big data on issue of interest to passengers at wonju airport. J. Hotel Resort 2019, 18, 147–163. 19. Fu, X.; Gu, Y. Impact of a new metro line: analysis of metro passenger flow and travel time based on smart card data. J. Adv. Transp. 2018, 2018, 9247102. [CrossRef] Sustainability 2019, 11, 4989 19 of 19

20. Li, J.; Li, X.; Chen, D.; Godding, L. Assessment of metro ridership ﬂuctuation caused by weather conditions in asian context: using archived weather and ridership data in Nanjing. J. Transp. Geogr. 2018, 66, 356–368. [CrossRef] 21. Wei, Y.; Ning, S. Establishment and analysis of the super network model for Nanjing metro transportation system. Complexity 2018, 2018, 4860531. [CrossRef] 22. Yu, W.; Chen, J.; Yan, X. Space-time evolution analysis of the Nanjing metro network based on a complex network. Sustainability 2019, 11, 523. [CrossRef] 23. Zhao, J.; Deng, W.; Song, Y.; Zhu, Y. Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models. Transportation 2014, 41, 133–155. [CrossRef] 24. Yang, M.; Liu, X.; Wang, W. Empirical analysis of a mode shift to using public bicycles to access the suburban metro: Survey of Nanjing, China. J. Urban Plan. Dev. 2016, 142, 05015011. [CrossRef] 25. Wu, J.; Yang, M.; Sun, S. Modeling travel mode choices in connection to metro stations by mixed logit models: a case study in Nanjing, China. Promet Traﬃc Transp. 2018, 30, 549–561. [CrossRef] 26. Li, Y.; Zhu, Z.; Guo, X. Operating characteristics of dockless bike-sharing systems near metro stations: case study in Nanjing city, China. Sustainability 2019, 11, 2256. [CrossRef]

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