2. Discussion of urban railway demand change The railway traffic demand for urban area is analyzed in the population decline society in the study. In

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1 The traffic demand analysis for urban railway networks in population declining society Yoshiyuki Yasuda a, Hiroaki Inokuchi b, Takamasa Akiyama c a Division of Science and Engineering, Graduate school of Kansai University, ,Yamate-cho,Suita, Osaka, , Japan b,c Department of civil, Environmental and Applied System Engineering, ,Yamate-cho,Suita, Osaka, , Japan a k618325@kansai-u.ac.jp b hiroaki@inokuchi.jp c akiyama@kansai-u.ac.jp Abstract: Since Japan is facing a population declining society, the future rapid increase of traffic demand of urban railway network cannot be expected. In particular, this tendency with reducing the population might be obviously observed in Keihanshin area as western side of Japan. The study aims at pointing the direction of urban railway service in terms of urban public transport system. The change of recent railway traffic demand would be classified according to the urban activities and level of railway services. The essential factors would be summarized to discuss the sustainable development of the area. Furthermore, the estimation model is represented by the variables concerning with urban activities and level of railway service. Finally, the maintaining railway traffic demand would be discussed in the population declining society. Keywords: Railway traffic demand, population declining society, railway passenger, decision tree 1. INTRODUCTION The number of railway passenger tends to decline according to the population declining society in Japan. The sustainable development of urban railway networks would be recommended in terms of public transport systems. The railway traffic demand is changed corresponding to the urban activity as well as railway transport service level. Therefore, the essential factors for railway traffic should be different among the urban area. In the study, the railway traffic demand is counted as number of passengers getting on and off the train for each station in Osaka. The increase and decrease of railway traffic demand are summarized from the observation of database for urban railway stations in 2000 and The pattern of railway traffic demand would be classified with knowledge from the observations. The description of decision tree is introduced to determine the relationship between railway traffic demand and urban activities. The different promotion mechanism of railway traffic demand is summarized referring to the area such as central area, middle area and surrounding area. The estimation model of urban railway traffic demand can be created according to the above characteristic variables. Therefore, the direction of urban development in population declining society can be shown from the result of analysis. In previous research, there is a study on urbanization and railway demand (Tokuoka, 2006). The analysis is used the cross section. Also, The study using the model to determine the pricing policy (Mizutani, 2014) and is analyzed by using smart card date (Kii, Terao, Nakamura, 2015) are reported. 1163

2 2. Discussion of urban railway demand change The railway traffic demand for urban area is analyzed in the population decline society in the study. In particular, the tendency of reducing the population should be discussed. The impact of reduction of population to urban railway traffic demand would be summarized. 2.1 The time series change of urban railway demand In the study, time series change of the railway traffic demand is discussed. In particular, the railway traffic demand of Osaka prefecture is investigated to summarize the mechanism traffic demand change. As a background for the study, change of population of Osaka prefecture is illustrated in Figure 1 (Statistics Bureau Japan, 2016). Figure1. Population change of Osaka prefecture Total number of population of Osaka prefecture has been uniform for recent 10 years. However, the percentage of elderly people is becoming larger. On the other hand, working-age population is becoming smaller. It might be support that these situations reflect on decrease of traffic demand. In Figure 2, the trip production unit for each generation is estimated from the person trip survey in Figure2. The trip production unit for each generation As the generation becomes older the number of trip production unit decrease. In particular, the trip production unit is about 1.3 for the generation over age 74. The fact shows 1164

3 the percentage of elderly people affects reduction of traffic demand. The urban railway networks are developed in Osaka area in Japan. There are large cities such as Osaka, Kobe and Kyoto in Keihansin region. Figure 3 shows the outline of urban railway network. Figure 3. The urban railway networks in Keihanshin region (Akiyama, Okushima, Kitamura, 2008) In the study, 192 stations located in Osaka prefecture are selected. According to the railway management organization, six private companies and one public section to OMTB are shown in the area. Table1 summarizes the stations for each railway company. For example, Hanshin line consists of two stations in Osaka prefecture. Table 1. The railway stations in Osaka prefecture Station Limited express station Total railway length Kintetsu km Nankai km Keihan km Hankyu km Hansin km West Japan km OMTB km Total km On the other hand, OMTB line only consists of the stations of underground. Referring to the number of passengers of stations, the railway traffic demand is analyzed. The number of stations which limited express stops is indicated as well. The length of railways corresponds to overall operation route of each company. Railway length of JR West covers the area outside of Keihanshin. The private railway companies manages the railway system in Keihanshin area. The underground system is managed by Osaka Municipal Transportation Bureau (OMTB). The 1165

4 urban railway network is determined within the Keihansin area as shown in the figure. The number of stations are 1276 and 1327 in 2000 and 2010 respectively. The urban railway networks are managed by major railway companies. The railway stations located in Osaka prefecture are observed in the study. The number of passengers in two time sections of 2000 and 2010 are investigated to analyze the mechanism of railway demand changes. Time series change of passengers in each railway company is indicated in Figure 4 Figure 4. The annual number of passengers in each company 2.2 The basic analysis of railway traffic demand The database of urban railway stations (DURS) in Keihanshin area has been created since 2007 in the former study (Akiyama et.al, 2007). The database can describe socioeconomic condition of the area surrounding the railway station and travel behaviours based on the station. The data set for the analysis of the study has been taken from the database. Figure5 illustrates the specific area between the railway station and surrounding area. The area consists of four mesh zones corresponding to national survey with mesh data statistics (500m 500m). Figure 5. The area of mesh data and the station The socioeconomic data is accumulated in each mesh zone. Therefore, the socioeconomic statistics of the area can be determined corresponding to the sum of four zones. In the study, the stations in Osaka prefecture are taken from DURS for the integrated database. There are originally 330 stations recorded in DURS (Akiyama, Tanaka, Okushima, 2007, 1166

5 Akiyama, Okushima, Kitamura, 2008). JR west was originally national railway company. Therefore, they own the urban railway network as well as intercity network. An urban railway network system is separated to discuss the urban area in JR west. The network is called Urban Network. Therefore, number of passenger of some urban central station include the number of passenger of long distance trip. In other private railway companies, they have own classifications railway train such as limited express, express, semi-express, local and so on. The definition of trains is almost similar each other. Figure 6 demonstrates the proportion of passengers in 2010 comparing to passengers in 2000 for each station (Osaka Prefectural Government,2001,2010). The reduction of passengers is observed for many stations by ten to twenty percent. Figure 6. The distribution of railway traffic demand change (2000/2010) It is observed from the diagram that the number of passengers is reduced in 137 stations. As the average value of proportion is The overall railway passengers tend to be reduced in Osaka prefecture. The several reasons for the change of railway traffic demand should be found out in the study. 3. The pattern analysis of railway traffic demand change Even though the railway traffic demand has been reduced in the particular station, the reason for the change cannot be determined instantly. According to the database of the railway stations, the pattern analysis of railway traffic demand change should be mentioned. 3.1 The database of railway traffic demand change It is assumed that the railway traffic demand can be analyzed from the view of socio-economic activity in the station surrounding area and the level of railway service of the station. It reflects on the land use problem as well as transport condition for railway passengers. The definition of change of socio-economic variables are determined in Table 2. Three categories of change can be defined such as increase, decrease and no change for each numerical variable. 1167

6 Table 2. The variables for demand estimation of railway passengers Variables 1 Number of passengers (NPS) 2 Number of train type(ntt) 3 Daytime population (DPO) 4 Residential Population (RPO) Proportion of elderly people 5 (PEP) Annual product sales amount 6 (PSA) 7 Number of students(nos) 8 Number of households(noh) 9 Number of employees(noe) 10 Number of offices(noo) 11 Number of workers(now) Number of departure trains 12 (NPT) 13 Number of destnations (NOD) Indicator decrease: ΔNPS< 5 %, no change: -5 % < ΔNPS <5 %, increase:δnps> 5% decrease: ΔNTT=<- 1, no change: ΔNTT =0, increase:δntt>=1 decrease: ΔDPO< -5 %, no change: -5 % <ΔDPO <5 %, increase:δdpo> 5% decrease: ΔRPO <- 5 %, no change: -5 % < ΔRPO <5 %, increase:δrpo> 5% no: ΔPEP <5 %, yes:δpep> 5% decrease: ΔPSA < -5 %, no change: -5 % < ΔPSA <5 %, increase:δpsa> 5% decrease: ΔNOS < -5 %, no change: -5 % < ΔNOS <5 %, increase:δnos> 5% decrease: ΔPSA < -5 %, no change: -5 % < ΔPSA <5 %, increase:δpsa> 5% decrease: ΔPSA < -5 %, no change: -5 % < ΔPSA <5 %, increase:δpsa> 5% decrease: ΔPSA < -5 %, no change: -5 % < ΔPSA <5 %, increase:δpsa> 5% decrease: ΔPSA < -5 %, no change: -5 % < ΔPSA <5 %, increase:δpsa> 5% decrease: ΔNTT=<- 1, no change: ΔNTT =0, increase:δfntt>=1 decrease: ΔNOP =<- 1, no change: ΔNOP =0, increase:δnop>=1 Δ:Change rate Zone of the station corresponds to the base of the area surrounding the station. The regional type for zone with the station is categorized into urban central, intermediate zone and suburb zone. It reflects on the different function of the railway station to the area. Number of passengers (NPS) represents the number of people departing from the station and the people arriving at the station. Number of train type (NTT) represents the count of different of train types. For example, the train types stopping at JR Takatsuki station are special rapid, rapid, local. Therefore, the value of NTT for Takatsuki station is counted as three. Daytime population (DPO) represents the daytime population density corresponding to surrounding area (1km 2 mesh). Residential population (RPO) represents the residential population density corresponding to surrounding area (1km 2 mesh). Proportion of elderly people (PEP) represents the percentage of elderly people aged over 64 living in the surrounding area (1km 2 mesh). Annual product sales amount (PSA) represents commercial sales account in the surrounding area (1km 2 mesh). Number of students (NOS) represents commuting students into the surrounding area (1km 2 mesh). Number of households (NOH) represents the household density corresponding to surrounding area (1km 2 mesh). Number of employees (NOE) represents commuting employees into the surrounding area (1km 2 mesh). Number of offices (NOO) represents the office density corresponding to surrounding area (1km 2 mesh). Number of workers (NOW) represents the percentage of workers living in the surrounding area (1km 2 mesh). Number of departure trains (NOW) represents daily total number of trains departing from the station. Number of destinations (NOD) represents how many destinations are determined for different direction from the stations. The type of the station might be categorized by the variable. Concerning with the population, several variables such as DPO, RPO, and PEP are determined. Basically, independent factors are assumed to choose. The correlation analysis between A few factors have relatively strong correlations. For example, high correlation between residential population and number of employees is observed. Similarly, high correlation between daytime population and number of workers is observed as well. On the centrally, all other factors do not have high correlation. 3.2 The mechanism of railway traffic demand change The change of railway traffic demand can be analyzed with cross section data in 2000 and 1168

7 2010. The passengers in the specific railway stations are observed. The socio-economic statistics are obtained in the mesh data surrounding the station. The mechanism of the railway traffic demand change can be described referring to the socio-economic condition and the service level of railway traffic. The change of railway traffic demand can be analyzed with cross section data in 2000 and The passengers in the specific railway stations are observed. The socio-economic statistics are obtained in the mesh data surrounding the station. The mechanism of the railway traffic demand change can be described referring to the socio-economic condition and the service level of railway traffic. Figure 7 demonstrates the example of the large station in central Osaka. Figure 7. The socio-economic factors for railway station (Yodoyabashi) The passengers in the station reduces from 140,525 to 105,680 in 2000 and Even though the resident population and daytime population are both increased, the number of railway passengers decreases. The increase of resident population in urban central corresponds to the decrease in commuters and private activity people from the surrounding area. Another example case is shown in Figure 8. This example shows the reduction of passengers as well. However, the reason of reduction seems to be different from the previous example. The passenger of Izumisano station in Nankai railway decreases from 23,233 to 21,376, even though the residential population (RPO) and Daytime population (DPO) increase. The location of station is defined as middle city area. Therefore, the mechanism can be described as: IF the station (STN) belongs to the intermediate zone and RPO is increased and DPO is increased THEN DEM is decreased. As there are no reason of population for the reduction of passengers, another reason should be found. According to the additional observation, the large scale shopping center has been opened in 2000, 2007 and The third example is shown in Figure 9. It is the case the passengers are increased from 2000 to 2010 in the large station in the urban central of Osaka. The station of Kitashinchi located at the south zone of Osaka central station area including JR, Hankyu, Hanshin, Keihan and OMBT). 1169

8 Figure 8. The socio-economic factors for railway station (Izumisano) Figure 9. The socio-economic factors for railway station (Kitashinti) Since DPO is reduced and RPO slightly increases, the change of population cannot connect with the increase of passengers. Therefore, the service level of railway is analyzed. The fact is found that the number of trains are increases as the new route as Osaka-higashi line is open (Inokuchi, Akiyama, Okushima, 2016). It can be concluded that the service level of railway transport in the station has been improved. 3.3 The discriminant model for railway traffic demand change According to the observation of passengers of railway station and surroundings, the change patterns of railway traffic demand are classified. The different demand change patterns would be explained separately corresponding to each condition of railway station surrounding area. The complex relationship between the factors can be formulated with rule based description such as production rules. Figure 10 demonstrate the decision tree to summarize the classification rules. 1170

9 Figure 10. Classification rules with decision tree description The decision tree demonstrates the mechanism of distinction for demand increase and decrease with observed factors. The consequence of discriminant process is indicated as,,and for decrease, no change, increase respectively. The path for the consequence is described as IF/THEN rules. In the study, the significant discriminant rules with consequence involving over ten individuals are numbered as 1 to 7. Referring to the path1 for rule 1, the discriminant knowledge as IF (1) Region zone type is urban central, (2) RPO (Residential population) is increased and (2) DPO (daytime population) is increased THEN DRT (demand of railway traffic) is decreased. All conclusion becomes the rule. The rule was 27 rules in total. Among all 192 stations, it was increase 24 Station, 31 changeless stations, decrease 137 Station. Figure 11. Significant discriminant rules 1171

10 The spatial conditions for location of railway station such as urban central, intermediate class area and surrounding area are determined. The urban central corresponds to the central of prefecture as inner city of Osaka city. The intermediate class area corresponds to the regional core city with middle scale population. Another location of the railway station is determined as suburban area. 4. Statistical analysis of urban rail demand change The fundamental approach to determine the significant factors would be introduced. The linear regression analysis should be a primitive approach to determine the essential factors in the relation to railway traffic demand. 4.1 The application of linear regression analysis The discriminant model for change of traffic demand is created in the previous section. The significant factors have been summarized. The linear regression analysis is applied to describe the relation between the independent variable and railway traffic demand. The independent variables are determined corresponding to the indicators in the discriminant model in the previous section. The change of railway traffic demand would be estimated by linear regression model with eight previous variables. The partial regression coefficients are summarized in Table 3. Table 3. Partial regression coefficients Variables Coefficient t statistics Constant Daytime population Residental Population Percentage of elderly people Number of students Product sales amount Number of destinations Number of departure trains Number of train type According to the t-statistics, product sales amount (PSA) and number of destinations (NOD) are significant variables. Economic activity and railway service level seem to be important factors. On the other hand, the coefficient of the percentage of elderly people become plus. The result that interpretation had difficult appeared. It seems to be caused by analyzing it including the station varying in the background. The performance of estimation is confirmed from the statistics in Table 4. Table 4. The statistics for estimation results Recurrence statistics Multiple regression R Multiple decision R Compensation R Standard error The number of the observation

11 Even though the multiple correlation coefficient is relatively large, the root mean square error is over 50,000. Therefore, the estimation cannot be sufficient. The relation between the observation and the estimation is shown in Figure 12. Figure 12. The estimation of railway traffic demand change The under estimation is observed in large scale station in the urban central such as JR Osaka, Hankyu Umeda and OMTB Nanba. The linear regression model is not enough to estimate the railway traffic demand of large-scale station. 4.2 The verification of estimation model The future railway traffic demand can be estimated with the parameters determined previously. The linear regression model for 2000 would be applied to estimate the value of The estimation results are summarized in Table 5. Table 5. The statistics for estimation results Recurrence statistics multiple corression coefficient coefficient of determination RMSE The number of the observation 192 It is confirmed that the similar estimation is available with the linear regression model with the same parameters for the observation in The similar statistics are provided in Table 5. The relation between the observation and the estimation is shown in Figure 13. It is confirmed from the analysis that the railway traffic demand estimation model can be constructed referring to the essential factors in the discriminant model to determine decrease and increase of the railway passengers. 1173

12 Figure 13.The application of linear regression analysis 5. Concluding remarks The decrease of railway traffic demand has been observed in Keihanshin area. The railway passengers in the stations have been analyzed in the study. The findings of the study can summarized as follows: 1) It is realized that the railway traffic demand has been decreased on the railway networks in Osaka prefecture. The database for railway station is created to analyze the change of railway traffic demand. The essential factors are summarized to correlate with the railway demand change between 2000 and Several patterns of railway traffic demand can be summarized. 2) The discriminant model for railway traffic demand change is created with regional type, socio-economic factors and service level of railway. The description of decision tree is applied in the model to demonstrate the mechanism of railway traffic demand change. The mechanism of traffic demand change can be described as inference rules. 3) The estimation model of railway traffic demand is developed according to the discriminant rules accumulated previously. The linear regression model can be proposed to estimate the traffic demand change in future. The specific railway transport policy might be evaluate quantitatively. For further study, the advanced technique to describe non-linear relation would be introduced. The empirical analysis to estimate land use change would be introduced as well. Furthermore, the interaction of railway demand change between the closed stations would be investigated. REFERENCES Akiyama, T., Tanaka, N., Okushima, M. (2007) Discussion for Potential of Railway Station in Kansai Urban Region, Infrastructure Planning Review, Vol. 24, pp (in Japanese). Akiyama, T., Okushima, M., Kitamura, R. (2008) The empirical analysis of the railway station and town management concerning with urban activities, The Japanese Journal of 1174

13 Transportation Economics, No. 51, pp (in Japanese). Inokuchi, H., Akiyama, T., Okushima, M. (2016) Influence on Passenger Flow by the Secular Change of Keihanshin Railroad Network, The Japanese Journal of Transportation Economics, No. 59, pp (in Japanese). Osaka Prefectural Government (2001) Osaka Statistic Year Book (in Japanese). Osaka Prefectural Government (2011) Osaka Statistic Year Book (in Japanese). Mizutani, J. (2014) Yardstick Regulation in Japan s Railway Industry, Transport policy studies review, Vol. 17, No. 2 (in Japanese). Tokuoka, K. (2006) Urbanization and Rail Transportation Demand, The Doshisha University Economic Review, Vol.57 No.3 (in Japanese). Kii M, Terao, J, Nakamura, K (2015) Journal of the Eastern Asia Society for Transportation Studies, Vol. 11, pp Statistics Bureau Japan (2016) Population Census, kokusei/index.htm. 1175