railway station design case study pdf

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• Published: 27 September 2022

Node, place, ridership, and time model for rail-transit stations: a case study

• Ahad Amini Pishro 1 ,
• Qihong Yang 2 ,
• Shiquan Zhang 2 ,
• Mojdeh Amini Pishro 3 ,
• Zhengrui Zhang 1 ,
• Yana Zhao 1 ,
• Victor Postel 4 ,
• Dengshi Huang 3 &
• WeiYu Li 3  

Scientific Reports volume  12 , Article number:  16120 ( 2022 ) Cite this article

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• Applied mathematics
• Civil engineering
• Computational science
• Scientific data
• Sustainability

Nowadays, Transit-Oriented Development (TOD) plays a vital role for public transport planners in developing potential city facilities. Knowing the necessity of this concept indicates that TOD effective parameters such as network accessibility (node value) and station-area land use (place value) should be considered in city development projects. To manage the coordination between these two factors, we need to consider ridership and peak and off-peak hours as essential enablers in our investigations. To aim this, we conducted our research on Chengdu rail-transit stations as a case study to propose our Node-Place-Ridership-Time (NPRT) model. We applied the Multiple Linear Regression (MLR) to examine the impacts of node value and place value on ridership. Finally, K-Means and Cube Methods were used to classify the stations based on the NPRT model results. This research indicates that our NPRT model could provide accurate results compared with the previous models to evaluate rail-transit stations.

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Introduction

Public transit operations have now become a logical substitution for private transportation to eliminate the drawbacks such as air pollution and traffic congestion. Transport planners benefit from high-speed trains, subways, and BRTs to implement their cities' Transit-Oriented Development (TOD) concepts. Policymakers, governments, and municipal mayors look forward to providing better access to public transport systems in high-density cities. Thus, comprehensive models for transport planners sound essential. In the past, researchers investigated the potential approaches to match the rail-transit supply and demand. Network accessibility and land use have been considered stem factors to provide the Node-Place (NP) model 1 , 2 . Researchers define node value based on transport access, network design and structure, and other related network variables. In contrast, they determine place value by assessing the number, diversity, and interaction of urban economic, social, or cultural activities.

The NP model is a regional scaled model concentrating on the rail-transit networks and stations to classify TOD typologies. One of the fundamental ideas of this model is providing the accessibility and conditions for the location to develop the transportation provision. In turn, increasing the demand for transport leads to enhancing the location growth and transport system. The relationship between node value and place value was mentioned in past research 3 . However, it seems there are two more dimensions as necessary as node and place values, which have not been considered yet. Ridership refers to the possibility of using transit centers and infrastructures by public transport takers, which relates to land use and time. To obtain a comprehensive model, we need to collect the data at peak and off-peak hours since the mentioned parameters and ridership are functionally linked with time. Analyzing the coordination between node-place-ridership-time (NPRT) values can better understand this model for transport planners.

As an Asian city and a developing area in China, Chengdu benefits from multiple subway lines, high-speed trains, BRTs, and mono-rails. This brings us an idea to select Chengdu city as our case study to propose a comprehensive model for city planners and municipal government. It sounds beneficial for policymakers to apply an extensive model and know the interaction between NPRT values to provide strategic transit plans. Thus, we aimed to add ridership and time values as third and fourth dimensions to the previous node-place model and proposed a new model to evaluate transit stations.

We structured the remainder of this research to derive our proposed NPRT model and check the accuracy of existing models. In the next section, the previous node-place model and also current research works are reviewed. Section “ Methodology and data ” covers the methodological approaches and data acquisition. Section “ Results and discussion ” presents the results, model evaluation, and discussion. Finally, we provide the central conclusion of this research in “ Conclusion ”.

Literature review

Peter Calthorpe introduced Transit-Oriented Development (TOD) concept in his book "The Next American Metropolis" 4 . TOD refers to mixed-use and walkable neighborhoods that provide easy access to public transit for people 5 . TOD neighborhoods include transit stations, public centers, high-density residential and commercial buildings, and walkable streets. Classifying station areas based on their similar functional characteristics and set of morphological is the meaning of TOD classification. Distinguishing the types of TOD is a significant concern described in Calthorpe's book 4 . He defined neighborhood TOD and urban TOD according to the spatial orientation of the area functions. Knowing the importance of accessible stations in TOD neighborhoods, many researchers researched how stations can be efficient and reachable.

A Node-Place (NP) model was proposed to categorize and evaluate public transit stations in 1999 using node value and place value 1 . As we mentioned before, balancing land use with transportation is the principal aim of the NP model. This model was conveyed in a two-dimensional diagram, as shown in Fig.  1 . In this diagram, the station-area land use corresponds to \(x\) -axis (Place). Place content of an area indicates how human interaction is affected by the diversity of urban activities. Besides, \(y\) -axis (Node) belongs to the accessibility of the node, which refers to the relationship between people and their interaction. Based on this diagram, five possible situations can be found.

The Node-place model and five ideal–typical situations for a location 1 .

The middle diagonal line area indicates the "Balance" area (1), which means if the node value and place value are similar and equally strong, that station is considered as an accessible or balanced station. In the Balance zone, infra-systems and land use match each other without any stress to maintain the environment and the system. The "Stress" area (2) shows that the diversity of transportation and activities is over-configurated, and the vital node has maximal physical human interaction, making a substantial place value. A station located in the Stress zone has numerous and realized potential facilities to provide a more efficient land use. The "Dependence" area (3) represents stations where the node and place values are matched but under-configuration. In this zone, the demand for public transportation is deficient. There are enough free spaces, but due to the low demand for public transit, there's no reasonable need for infra-system developments. A station is an "Unbalanced node" (4) if transportation facilities are more available than urban activities. In this area, the land use facilities are relatively lower than the public transit flow supply, leading to jammed traffic, massive transit lines, and environmental degradation. A station is considered an "Unbalanced place" (5) if the opposite situation is actual. Land use activities are more available compared to public transportation systems' supply.

A review of the previous research shows that a two-dimensional (node-place) model cannot cover all of the analysis aspects of a station. According to the node-place model, increasing or decreasing the node and/or place value(s) would bring an unbalanced station in the balance area 6 , 7 . There should be more values to have a comprehensive model since a station with balanced node and place values cannot be efficient or advantageous. In contrast, it does not have a good ridership value. Moreover, the coordination between node, place, and ridership value might not remain steadily constructive in peak and off-peak hours. Therefore, the relationship between node, place, and ridership values should be defined following the time to consider a comprehensive function, including practical values. Engineers and transport planners should design structures and networks concerning the critical situation. Thus, time is as essential as node, place, and ridership values. This research considered all four mentioned values (node, place, ridership, and time) to create our NPRT model to evaluate the efficiency of rail-transit stations.

The node value of a station in the node-place model proposed by Bertolini 1 is defined as the station's network accessibility, including daily service frequency, the number of stations located in the area within 45 min of traveling, and the number of accessible directions at the station. Other researchers added some indexes to measure the station's node value. Proximity to CBD area by Chorus and Bertolini 8 and congestion index by Olaru et al. 9 were added to the node value. At a station, network accessibility includes two significant factors: the accessible opportunities by a station and the transport possibilities to access the opportunities 10 , 11 , 12 , 13 . Zhejing Cao et al. recently added accessible opportunities and network centrality into the node value 14 .

Bertolini measured the place value in his proposed node-place model by the station-area land use and the number of residents and employees in economic areas 1 . After Bertolini, other researchers added more indicators to the place value, such as population density, land prices, unemployment rate, number of flats, and core urban area 15 , 16 . Although density and diversity of activities are primary factors in place value measurement, it seems necessary to consider other essential indicators such as parking areas, fed buses at stations, and walking areas. The built environment features were also included in the place value by Zhejing Cao et al. 14 . Moreover, they studied and considered ridership as the third dimension of node-place value and created the node-place-ridership model.

A comprehensive study on the subway stations and CBDs in Chengdu showed that applying the previous node-place and node-place-ridership models couldn't provide a fair and balanced class for stations. In most case study locations, during weekdays, many people need to change the line, go to work, or come back from their working areas. For example, the subway stations called "South Railway Station" and "Chunxi Road" face a lack of trains and enough space for the riders in the mornings from 6:00 to 9:00 and evenings from 17:00 to 20:00. It's due to the high-frequency trips, in the morning and evening, to and from the working destinations which can be reached via this subway station. Moreover, Chunxi Road is one of the CBDs in Chengdu. There are many shopping malls, offices, consulates, visa centers, and training schools at Chunxi Road station. Let's consider the previous models of node-place and node-place-ridership, in which Time was not considered a leading dimension. It's not possible to justify the reason for these unbalanced stations. These subway stations were designed and categorized without considering time as a significant factor. It can be seen that a station classification might change from balanced to unbalanced several times during the day. A fair comparison and investigation of the existing models proved that it's vital to provide a new and more accurate model for city planners, traffic policymakers, and governments to apply a constructive model to classify the stations based on the needs and demands of the society.

This research work's main contribution and novelty present the Node-Place-Ridership-Time (NPRT) method and the Cube model with 27 classes to provide accurate classifications for rail-transit stations during different time-spans. The NPRT model provides a new contribution to the TOD concept, leading to a more progressive and beneficial policy for cities. To obtain the NPRT model, we added a fourth dimension of Time into the node-place-ridership model to evaluate and classify the transit stations. The coordination between ridership and time influences stations' classification to know which stations are balanced and unbalanced. Without a comprehensive model, we would not establish the relative position of a transit station in the urban regional network. This would assist the city planners and governments in updating their applied policies.

Methodology and data

We consider all of Chengdu city as our case study. First, the Chengdu transit system and the study area are presented in this research. Next, we provide a list of node, place, and ridership indicators. To make our research more accurate, we divide the time into four classes to determine the effect of time on ridership at peak, peak-off, weekend, and other regular hours. Different data resources were used to collect the information for our target variables. We apply the Min–Max Normalization method to normalize our data. Afterward, we apply Information Entropy Weighting (IEW) to combine all indicators and create composite nodes and places. Then, to investigate the relationship between four facets of node, place, ridership, and time, we apply the Multiple Linear Regression (MLR) method. This method investigates the relations between different parameters and factors in scientific research works 17 , 18 . Afterward, we propose a comprehensive Node-Place-Ridership-Time (NPRT) model. We apply the NP and NPR models proposed by other previous researchers and our proposed NPRT model to evaluate our research work. In this comparison, we use the same database and check all models' accuracy.

K-Means method

K-Means clustering is one of the most popular unsupervised machine learning algorithms. It is an extensively used technique for data cluster analysis. The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable \(K\) . The algorithm works iteratively to assign each data point to one of \(K\) groups based on the provided features. Data points are clustered based on feature similarity. The steps of K-Means are as follows:

Step 1: Give the parameter \(K,\) which means the number of groups we want the points to be assigned to.

Step 2: Randomly select \(K\) points as the initial cluster centers \({c}_{1}, {c}_{2},\cdots ,{c}_{K}\) .

Step 3: Calculate the distance between each point and each cluster center, then assign it to its nearest center, based on the squared Euclidean distance.

where \(x\) is the point that needs to be assigned to one group.

Step 4: After assigning all the points to the groups, recompute the coordinates of the cluster center, which means replacing the cluster center with the new cluster center.

where \({G}_{i}\) means the \(i-th\) group, \(|{G}_{i}|\) means the number of points in \({G}_{i}\) and \({x}_{i}\) means the \(i-th\) point in \({G}_{i}\) .

Step 5: Repeat Step3 and Step4 until a stopping criterion is met (i.e., no data points change clusters, the sum of the distances is minimized, or some maximum number of iterations is reached).

\(K\) value indicates the number of clusters and is a pre-defined value. In this research, we used the Elbow method to select \(K\) for the K-Means algorithm 19 . Based on Fig.  2 , we can find the value of \(K\) where the Sum of Squared Errors \((SSE)\) decreases sharply \((K=5)\) .

Elbow method and parameter \(K\) for the K-Means algorithm.

Cube method

To classify our stations, we also applied the Cube method, which is made of 3 dimensions: node value, place value, and ridership regarding the time. According to the Cube method, there are three main layers on each node, place, and ridership measurement value, which are Low Balanced (LB), Balanced (B), and High Balanced (HB), as shown in Fig.  3 . The combination of layers on the node, place, and ridership values, leads to 27 classes. Class 1 denotes LB stations in all three values, while class 27 represents HB stations. Cluster 14 means the station is balanced in all three dimensions during the defined time-span. This 3-Dimension illustration provides more understandable coordination between mentioned values and layers compared to the previous models.

Low Balanced (LB), Balanced (B), and High Balanced (HB) classes of the Cube Method.

Moreover, the Cube method also shows the density of stations in or around critical classes. Therefore, policymakers and city planners can easily understand if their plans need to be revised to improve the efficiency of LB and HB stations. An accurate classification result from 1 to 27 would prove the efficiency of this method, as shown in Appendixes B and C .

To understand the relationship between node, place, ridership, and time values, we applied the K-Means method 19 using the "sklearn.cluster.KMeans" measure of Python and also the Cube method on NP, NPR, and our proposed NPRT models to classify Chengdu rail-transit stations, shown in Appendixes B to F .

As can be extracted from Fig.  4 , our research has four main steps. We apply unique methods to prepare, compose, and analyze our data to approach our NPRT model in each step. Table 1 summarizes the application of all the methods used in this research work.

Research structure and applied methods.

The case study area and Chengdu rail transit network

The Chengdu Metro system is considered the rapid rail-transit network of the capital city of Sichuan province, China, with a daily passenger flow of 5,906,123 rides. The system includes twelve subway lines and one light rail line, operated by Chengdu Rail Transit Group Company. Table 2 presents brief information about Chengdu subway lines. Figure  5 presents the Chengdu rail-transit stations.

Chengdu rail-transit network and stations.

Node, place, ridership, and time indicators

Node indicators.

We measure a station's node value by four facets: station facility, accessible transits, accessible destinations, and network centrality. Eight node indicators under these four facets are presented in Table 3 .

The station facility is measured by the number of entrances and exits (N1) in each metro station. The accessible transits are measured by the number of metro stations (N2) that one station can reach within 20 min, the number of the station to CBD (Chunxi Road) (N3), and the number of stations to CBD (3rd Tianfu Street) (N4). It is well known that there are 2 CBDs in Chengdu: Chunxi Road and 3rd Tianfu Street. Therefore, we calculate the number of stations and the distance to Chunxi Road and 3rd Tianfu Street. The distance measures the accessible destinations to the CBDs Chunxi Road and 3rd Tianfu Street indicated by (N5) and (N6), respectively. The network centrality consists of degree centrality (N7) and closeness centrality (N8).

Based on the graph modeling, we applied the network centrality to capture the impedance of a station in the transit network 20 . To translate the Chengdu rail-transit network into a graph \(G = \left( {V,E} \right),\) we assign \(V\) of vertices to indicate our stations, and the set \(E\) of edges is for the station linkages. The transit traveling distance is used to weigh the \(E\) 21 . We measure Chengdu network degree centrality (N7) of a transit station \(v \in V\) by the number of links connected to station \(v\) in Eq. ( 3 ), wherein \(L_{vt}\) represents the linkage between station \(v\) and station \(t \in V\) , and \(K\) shows the number of all stations in set \(V\) 22 :

Closeness centrality reflects the node's proximity and reachability within the network component. We measure the closeness centrality (N8) of station \(v\) by the inverse of the sum of shortest transit distances from station \(v\) to all other stations in set \(V\) in Eq. ( 4 ), wherein \(d_{vt}\) denotes the shortest transit distance between station \(v\) and station \(t \in V\) :

Table 4 presents the node indicators values of some stations.

Place indicators

We use a 500-m and 1000-m radius to define the transit catchment area in Chengdu, considering the low-density context of some areas. We measure the station's place value by three facets: design, density, and diversity. Nine place indicators under three facets are presented in Table 3 . Table 5 provides the place indicators values of some stations.

The design is measured by the average price of office land inside the 1000 m-radius catchment area (P1), the average price of commercial land inside the 1000 m-radius catchment area (P3), the average price of residential land inside the 1000 m-radius catchment area (P5), the number of parking lots inside the 500 m-radius catchment area (P8) and the number of buses stops inside the 500 m-radius catchment area (P9). The design is measured by the number of offices within 1000 m (P2), the number of shops within 1000 m (P4), and the number of residences within 1000 m (P6). The diversity consists of public facilities (parks, cultural facilities, schools, hospitals) inside the 1000 m-radius catchment area (P7).

Ridership and time indicators

Because the NPR model's limitation does not consider the implication of time and ignores the difference in ridership about departure and coming, we record the tapped-in and tapped-out arrival trips and construct an NPRT model by considering different conditions.

As we mentioned before, ridership has a direct relationship with time. Therefore, we categorized the passenger traffic into two groups: inbound traffic (I) and the second group for outbound traffic (O). We also divided the time into peak hours, off-peak hours, regular hours, and weekends (T1 to T4). Therefore, we get eight different conditions. IT1 means inbound traffic during working hours, IT2 means inbound traffic during off-hours, IT3 means inbound traffic during the rest of the working day, and IT4 means inbound traffic on two weekend days. OT1 means the ridership of passengers leaving the station during working hours, OT2 means the ridership of passengers leaving the station during off-hours, OT3 means the ridership of passengers leaving the station during the rest of the working day, and OT4 means the ridership of passengers leaving the station on two days of the weekend.

The definition of each class and time-spans from IT1 to OT4 is written in Table 6 .

Table 7 shows the ridership values of some stations during eight time-spans mentioned above.

As for data sources and processing, the number of entrances and exits, offices, shops, residences, parking lots, and bus stops was acquired from Amap ( https://www.amap.com/ ) and SOSO ( https://map.qq.com/ ). The number of stations that one station can reach within 20 min and stations to CBDs ( https://www.chengdurail.com/index_en.html ), the distance to CBDs, and closeness centrality could be required and calculated via the API of Chengdu Metro Website ( https://www.chengdurail.com/index_en.html ). The degree of centrality was acquired from a map of Chengdu Metro Station in 2021 in Fig.  5 . The average price of office, commercial, and residential land was acquired from Anjuke ( https://chengdu.anjuke.com/ ) and Fang ( https://cd.newhouse.fang.com/ ). We collected ridership of all stations from Chengdu Metro. Each station counts both tapped-in departure trips and tapped-out arrival trips for the station's ridership statistics. All the data was acquired in March 2021.

Information entropy weighting (IEW)

To practice our data analysis and compose the indicators, we applied Information Entropy Weighting (IEW) 23 to provide a composite node or place value index. We use the IEW method to integrate \(N1 - N8\) into one Node value and \(P1 - P9\) into one Place value.

First, the decision matrix should be constructed, shown in Eq. ( 5 ). \(m\) stations and \(n\) node value indicators have consisted in \(X\) . Moreover, \(X_{pq}\) indicates the value of indicator \(q\) at station \(p\) . We apply Eq. ( 6 ) to normalize the decision matrix:

Then, \(R_{pq}^{^{\prime}}\) computes the proportion of station \(p\) for indicator \(q\) :

We can calculate the entropy value \(e_{q}\) of indicator \(q\) in Eq. ( 8 ), knowing that if \(R_{pq}^{^{\prime}} = 0\) , then \(\ln R_{pq}^{^{\prime}} = 0.\)

In the next step, we need to calculate the imbalance coefficient using Eq. ( 9 ):

\(W_{q}\) is the weight of indicator \(q\) , which can be extracted from Eq. ( 10 ). Then, to compose the node value index \(N_{p}\) for station \(p\) , we can apply Eq. ( 11 ):

Afterward, we need to normalize the node value index between 0 and 1. In Eq. ( 12 ), \(N\) indicates the array of node value index, \(m\) is the number of stations, and \(p\) is the target station:

Results and discussion

We obtain the equations through Multiple Linear Regression (MLR). Table. 8 provides a list of constants and variables coefficients of our equations. The results of our MLR models are presented in Table 9 .

The general format of our MLR equations is as follows:

where \(\alpha\) is the equation constant, and \(\beta\) and \(\gamma\) are the coefficient of node value and place value, respectively.

To better understand how a station's node value and place value impact its ridership at different times, we must analyze our eight MLR models below. Concerning the parameters of eight MLR models, we can know that the number of entrances and exits, the number of stations to CBD (3rd Tianfu Street), the distance to CBD (Chunxi Road), closeness centrality, and the number of residences within 1000 m are negatively associated with ridership in all facets of time. The number of stations to CBD (Chunxi Road), the distance to CBD (3rd Tianfu Street), degree of centrality, the average price of commercial land, the number of shops within 1000 m, and the number of parking lots and bus stops inside the 500 m-radius catchment area are positively associated with ridership in all facets of time.

The number of stations that one station can reach within 20 min is positively associated with ridership of stations in off-peak hours, is positively associated with ridership of getting outstations in other hours on working days, and is negatively associated with ridership in other times. The average price of office land and the number of public facilities are positively associated with ridership of getting in stations in peak hours, are positively associated with ridership of getting outstations in off-peak hours, and are negatively associated with ridership in other times. The number of offices within 1000 m and the average price of residential land are negatively associated with ridership of getting in stations in peak hours, are negatively associated with ridership of getting outstations in off-peak hours, and are positively associated with ridership in other times.

The distance to CBD (Chunxi Road) is significantly negatively associated with ridership of getting in stations in peak hours and ridership of getting outstations in off-peak hours. The number of offices within 1000 m is significantly positively associated with ridership of getting in stations in off-peak hours and ridership of getting outstations on other working days. The number of shops is incredibly positively associated with the ridership of getting in peak hours and the ridership of getting outstations in off-peak hours.

Using Table 8 in the MLR Eq. ( 13 ), we have eight equations from \({\text{IT}}1\) to \({\text{OT}}4.\) For instance, the equation of Inbound traffic during working hours from 6:00 a.m. to 9:00 a.m. would be as follows:

All variables have been 0–1 normalized by Min–Max Normalization for the model input, shown in Appendix A . The variance inflation factor (VIF) is approximately equal to 10, indicating no severe multicollinearity. The adjusted R 2 and R 2 are more extensive than 0.25, showing that the results are promising in model fitting. When using 0.05 as a significance level threshold, F-test shows that our MLR models are significant. The T-Test shows the number of stations to CBD (3 rd Tianfu Street), the distance to CBD (Chunxi Road), degree of centrality, the number of offices within 1000 m, the number of shops within 1000 m, the average price of residential land and the number of bus stops are significant with equation IT1. The distance to CBD (Chunxi Road), degree of centrality, the number of offices, and the number of residences are significant with equation IT2. The number of entrances and exits, degree of centrality, the number of offices, and the number of shops are significant with equations IT3, IT4, and OT1. The number of stations to CBD (3 rd Tianfu Street), the distance to CBD (Chunxi Road), degree centrality, and the number of shops are significant with equation OT2. The distance to CBD (3 rd Tianfu Street), the number of offices, and the number of residences are significant with equation OT3. The number of entrances and exits, degree of centrality, the number of offices, and the number of shops are significant with equation OT4.

Methods and classification results

The coordination between ridership and time influences stations' classification to know which stations are balanced and unbalanced.

Regarding the node value, place value, and ridership extracted in four time-spans, five classes resulted from the K-Means method. Figure  6 summarizes the classification results extracted from the K-Means method for our proposed NPRT model.

Number of stations in K-Means classification method for NPRT model.

In each model from IT1 to OT4, shown in Appendix B , F , and Fig.  6 , based on the NPRT values, the results show that some stations can be balanced or unbalanced; low, medium, high, or extremely high ridership; stress or dependent.

For example, in the model IT1, Xipu station with the result of [0.4523, 0.2703, 1.0] for the node value, place value, and ridership is categorized in class 4, with high ridership and balanced, while Chunxi Road station with the values of [0.5185, 0.8886, 0.1691], is in class 5, low ridership and unbalanced place. Compared to the IT4 model, on weekends, Xipu station is medium ridership and balanced class 4, with the NPR values of [0.4523, 0.2703, 0.4807]. Chunxi Road station for the same model indicates the results of [0.5185, 0.8886, 1.0], falling into class 5, extremely high ridership, and an unbalanced place. Therefore, it can be seen that although the node and place values are essential factors in our classifications model, the ridership at different time-spans can significantly change the results.

As already mentioned, in both K-Means and Cube methods, the concept of ridership is influenced by time. The relationship between ridership and time can also be proved by analyzing the results of the Cube Method. The number of stations in each class of the Cube method is presented in Fig.  7 . Based on Fig.  3 , class 1 has a low node, place, and ridership values, while class 27 comprises high node, place, and ridership values.

Number of stations in Cube classification method for NPRT model.

Regarding Appendix C , F , and Fig.  7 , class 2 includes 52.8% to 58.04% of Chengdu rail-transit stations. In contrast, some other classes, such as class 1, have 0% of the stations. This difference is not only because of the low or high node and place value, but the time as a significant factor in ridership caused the differences as mentioned. Some stations have good status on node and place values. If we only consider the ridership value as a constant value during our investigation, the results would be different from the reality. Stations can be balanced at 1 h but unbalanced at another hour. As an instance, in the OT1 model, Chunxi Road, for NPR values of [0.5185, 0.8886, 1.0] is classified in cluster 27, high node, place, and ridership value, whereas this station in the IT1 model is in class 9, [0.5185, 0.8886, 0.1691], high node and place value, but low ridership. Chunxi Road is one of the CBDs of Chengdu. It is surrounded by many shopping malls, companies, institutions, consulates and visa centers, and headquarters. During the time-span OT1, 6:00–9:00 a.m. on weekdays (Table 6 ), the number of passengers going to work in the Chunxi Road area is considerably higher than the number of people traveling from this location to the other part of the city (IT1 model). Therefore, we can find the influence of time on the ridership and, consequently, on the classification of a station.

To compare our proposed NPRT model with the NP model by Bertolini 1 and the NPR model by Zhejing Cao 14 , we also applied our case study area and rail-transit stations to the NP and NPR models, presented in Appendixes D to F .

Regarding many subway stations in Chengdu and the massive number of classification results, we provided the results of four stations in Table 10 as a sample. Table 10 shows the station classifications resulting from the K-Means method and Cube model, using NP, NPR, and NPRT. The results prove that Chunxi Road was classified as an unbalanced station with high NPR values over different time-spans, while the Financial City is a balanced station during OT3 (class 14 of the Cube model). According to our previous discussion about Fig.  2 , there are five classes in the K-Means method (K = 5). In comparison, the Cube model provides 27 classes, leading to more accurate classifications. For instance, in Table 10 , the K-Means method for the Chunxi Road station has the same result (class 5) during the time-spans IT1 and IT2, whereas the Cube method puts this station at class 9 during IT1 and class 27 over IT2. The results of the Xipu station experience the same situation for the time-spans IT1, IT2, OT3, and OT4.

Moreover, the results show that the classification result to check the station efficiency would not be accurate without considering the relationship between ridership value and time. For example, comparing Chunxi Road station in three different models, we can see the node value, place value, and the ridership in NP, NPR, and NPRT IT1 models are [0.5185, 0.8886, –-], [0.5185, 0.8886, 1.0], and [0.5185, 0.8886, 0.1691], respectively. This station's ridership for the NPR model is extremely high, although it is low for the NPRT IT1 model. This situation is true for some other stations, such as North Railway Station, Wenshu Monastery, Tianfu Square, Sichuan Gymnasium, Hi-Tech Zone, Financial City, and Century City. Therefore, as one of the most important factors in policymaking, the ridership should be considered regarding the critical time-spans, from IT1 to OT4.

The periods IT1 to OT4, NPRT method, and Cube model can assist the policymakers and city planners update their applied policies. We can consider the Chunxi Road station as an example. The NPR values at the time-span OT1, [0.5185, 0.8886, 1.0], and its class 27 would let the municipal government know this location needs some charter trains at the time-span OT1. The charter trains would travel directly between the high frequently-demanded stations to the Chunxi Road. The Chengdu Metro Co. can calculate the frequency and number of required charter trains by knowing the number of riders during the critical time-span.

Moreover, since the Chunxi Road station is located at the junction of lines 2 and 3, the Chengdu Metro Co. would be able to find the Low Balanced (LB) stations on lines 2 and 3 at the time-spans T1. Therefore, every second train can stop at the LB stations during the time-span T1. Southwest Jiaotong University is almost a steady station (class 6). Since this station is located in the Jinniu district, the Jinniu municipal government would be able to apply the results of this study in their potential plans to enhance the classification of this station toward cluster 14, which creates a fully balanced station.

These are some examples of potential revised policies based on this study's innovation in developing the station classifications. Regarding the 27 classes from the Cube model and the NPRT method, governments can access the accurate classification results of the stations during critical time-spans T1 to T4 to implement appropriate policies and enhance the rail-transit network efficiency.

In this research, we conducted a case study on Chengdu rail-transit stations to present the relationship between node, place, and ridership. Since the number of riders during the daytime and over the week is not constant, we divided our investigation into four time-spans. It was proved that ridership has a direct relationship with time. So, we included this factor in our study. After collecting the data and providing all the influential parameters, Multiple Linear Regression (MLR) was applied to create our Node-Place-Ridership-Time (NPRT) equations. MLR is a constructive method to model the coordination and relationship between the effective parameters on the NPRT model. We developed our classifications using k-Means and Cube methods and analyzed the results. Stations with exemplary node and place values can not be necessarily balanced or efficient since the ridership and time-span play essential roles on the other side. The policymakers, city planners, and governments need to apply NPRT models to analyze the efficiency of transit stations. Compared with Node-Place (NP) and Node-Place-Ridership (NPR) models presented by previous researchers, our proposed NPRT model provides more accurate results.

Possible directions for future studies

This research investigated the impact of node, place, and time values on ridership to present the NPRT model for classifying rail-transit stations. However, the effect of ridership on node and place values which leads to the bi-directional relationship between the dependent and independent variables would be an open discussion for future studies. Moreover, the effect of the economy, ecology, and sociodemographic characteristics (such as transit mode share, household going-out rate, and age composition) on the NPRT model would be essential for future studies.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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The authors would like to acknowledge the Sichuan University of Science and Engineering, Southwest Jiaotong University, Sichuan University, and Université Paris Nanterre.

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A.A.P. designed the research plan, analyzed the data, and wrote the main manuscript text. He is the first author. Y.Q. conceived the mathematical model. Z.S. and H.D. supervised the numerical parts of this paper. M.A.P., Z.Z., Z.Y., and V.P. collected the data and prepared Appendix A . L.W. prepared Appendix F . All authors reviewed the manuscript. The authors would like to acknowledge the Sichuan University of Science and Engineering, Southwest Jiaotong University, Sichuan University, and Université Paris Nanterre.

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Amini Pishro, A., Yang, Q., Zhang, S. et al. Node, place, ridership, and time model for rail-transit stations: a case study. Sci Rep 12 , 16120 (2022). https://doi.org/10.1038/s41598-022-20209-4

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Tokyo Station City: The railway station as urban place

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Railway stations in Japan and elsewhere are undergoing redevelopment to accommodate new spaces of consumption and leisure. Tokyo Station redevelopment is a representative case illustrating the experiment of integrating new facilities into an existing spatial system. The station's image is being recast as an important urban centre in Tokyo with a particular mix of prestige business, shopping and unique entertainment venues. The walking network is being reconfigured in a larger space with a complex set of new land uses, leading to new spatial configurations and patterns of behaviour. These transformations support a new role for the station. The station redevelopment, along with related investments in the surrounding space represent a distinctly Japanese approach to transit-oriented development. This article examines the urban design strategy underlying these transformations.

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Introduction

Nowhere in the world can one find railway station complexes as large as in Japan. The railways themselves play a central role in urban transportation in Japanese cities, connecting suburb to centre, and city to city. The urbanization that Japan experienced in the twentieth century was accompanied by rapid development of the railway network within urban regions. At the same time, Japanese city centres were deeply affected by railway station development and redevelopment ( Onishi, 1994 ).

Transit-oriented development (TOD) in Japan is a fundamental characteristic of all central city urban development, and is almost exclusively rail-based and specifically not intermodal. TOD refers to the land use characteristics of areas where transit is being promoted ( Dittmar and Ohland, 2004 ; Lund et al, 2004 ). Higher density development and mixed land uses have been used in North America and elsewhere to promote public transit use. TODs in North America typically combine road-based transportation and one or more forms of public transit. After the Second World War, the railway became one of the most important tools for development in Japan, particularly in the context of a weak planning system ( Sorensen, 2002 ). As a result of suburbanization with relatively undeveloped road infrastructure, commuting by train and subway became the most effective way to travel for most people who live in the suburbs and work in city centres. This urban spatial development created unprecedented demand for railway services, which were then met by the railway companies. In the late 1990s, the train line density was 1.01 km of line for every square kilometre with 86 per cent of all travel in Tokyo by rail. The comparable figures were respectively 0.74 km/km 2 and 65 per cent in London and Paris, and 0.41 km/km 2 and 61 per cent in New York ( Focas, 1998 ). In spite of the large part of the travel market occupied by the railway and the intensity of the operations, the companies are involved in much more than rail operations. Since 2000, railway station redevelopment has become one of the most significant new urban regeneration programmes underway in major Japanese cities. The stations and adjacent railway properties are undergoing physical transformation to accommodate new urban functions and to enhance the passengers’ travel experience. The surrounding neighbourhoods are also involved due to their multi-layered connection with the stations as well as their close proximity. Railway stations are consequently performing a different role in the city than that of transport hub, becoming cultural symbols, social communication hubs and business centres.

These projects parallel similar investment programmes in Europe, but with characteristics that are particular to Japan. The layouts, designs and activities of these redesigned stations reflect the operational structure of the railway companies, the location and physical condition of the stations, the close relationship with the surrounding urban space and building complexes, as well as the importance of rail-based travel in Japanese cities. Railway stations in Europe seldom attract high-end business and retailing activity although serious efforts are being taken to correct this historical problem ( Bertolini and Spit, 1998 ). The trend in Japan is to combine commerce, leisure, media, fashion, information, as well as other advanced industries into a new ‘city’ within the city, so as to make the stations important spaces for creation and innovation. Such a focus on creative industry is hardly associated with TOD as practised in North America or Australia, for example. While continuing to serve the needs of travel within the city and beyond, the railway stations are becoming significant places in their own right and at or near the top of the hierarchy of such urban places.

The development of stations is in keeping with the TOD idea of attracting people from the nearby areas on foot. Major pedestrian facilities are then required to support the heavy flows of people through these local areas. In Japanese cities, as in European and North American examples, there is an immediately adjacent area which is in close relationship with the station. But uniquely in the Japanese case, the station building complexes are an accumulation of all kinds of functions, and not merely a point of distribution. Some private railway companies built station-based department store complexes before the Second World War. The Japan National Railways built many ‘station department stores’ after 1950. The pattern of new centres after 2000 is therefore in some continuity with earlier development practices, but at a higher level in quality, service and volume of clientele.

In recent years, the design and functions of the new underground station facilities in Japan are increasingly the object of research (for example, Tanimoto et al, 2004 ). Visitor preferences in facility design, lighting and social ambiance are receiving increasing attention. How people find their way and understand such underground spaces is also an important topic of investigation ( Moriyama, 2009 ), for reasons of evacuation safety but also for the efficient functioning of commercial space. Scholars argue that rising rental rates and commercial benefits have encouraged projects to improve daily services for the white collar working population in the area ( Yoshida, 2007 ). However, the special features of Japanese station redevelopments have not received enough attention. For example, there are few studies touching on the organizational mechanism underlying the redevelopment of railway stations. Nor is there enough attention given to the features of the Japanese approach to TOD, which has had such powerful effects on urban structure and has achieved very high ridership for rail-based travel. It is apparent that this country-wide investment programme is worthy of investigation in its own right because it is at an early stage of innovation and experimentation. What is learned from the Japanese case will also be of interest to planners working on railway station developments in Europe, China and North America.

In recognition of the above, this article examines one such railway station redevelopment project, Tokyo station, from the urban design perspective. Firstly, we discuss why the railway companies operating at Tokyo station are making these particular investment decisions. Secondly, the programmes themselves are examined for their contribution to the making of a new urban place in the constellation of such places in the Tokyo metropolitan region. Thirdly, how the spaces and their associated activities are accommodated within the physical constraints of the stations and the surrounding environment is considered. Finally, we evaluate the effects of station restructuring on surrounding urban space, with particular attention to a pre-existing shopping facility adjacent the station.

Redevelopment of Railway Stations in Japan

A new phase of station redevelopment began in the 2000 decade, when railway companies themselves invested in their properties, together with financial partners. Major stations such as Tokyo, Yokohama, Nagoya and Fukuoka saw large-scale redevelopment. The projects included extensions to the pedestrian system, the provision of new commercial facilities and public leisure spaces, atmospheric effects, and high-rise office buildings.

The common goal of Japanese railway station redevelopment projects is to enhance the in-station commercial function among many others, making the station a powerful magnet for visitors. These projects are mostly initiated by the station companies, who need to diversify their operations under the Japanese railway system organization and operations.

There are roughly three types of railway companies: Japan Railways Group, which was founded as a result of the privatization and break-up of the Japanese National Railway in 1987, private companies and city-owned companies. Each company operates one or several lines. Most of the stations are also operated by the railway companies, whose railway lines pass through the station. When several lines of different companies intersect at a certain station, they normally share the intersection space and related facilities. The station is consequently operated collaboratively by several companies. In most cases, one of the companies acts as the chief owner. In order to interconnect with each other, the companies have to work together to achieve a reasonable spatial distribution plan that allows efficient interchange between the various lines. This often makes the station space very complex.

Until the 1970s, the Japan National Railways company operated many rail routes all over the country. They began to face economic difficulties in the late 1960s and had accumulated large debts by the beginning of the 1980s. To help the companies achieve financial health, the government began privatizing the railways, dividing JR into six passenger transportation companies according to their geographic locations. The privatization accompanied a change in regulation which allowed the newly privatized railway companies, like JR East, the major owner-operator of Tokyo station, to accumulate profits through commercial activities in addition to pure transportation uses ( Ieda et al, 2001 ). In exchange, railway companies became responsible for recovering capital investment through their operations and related investment decisions. Although rail-based travel in Japan, as a proportion of all travel, is the highest worldwide at about 36 per cent of all kilometers travelled, many observers consider the railway system in Japan to be underinvested. Railway companies hold territorial monopolies, which discourage them from making investments in railway services. To make such improvements in services, these private companies must either raise fares or derive benefits from other operations. Improving rail services has become increasingly difficult as the cost of providing infrastructure has risen very rapidly. Expected decline in passenger numbers, as a result of demographic decline, is an additional reason companies are reluctant to invest ( Ieda et al, 2001 ). On the other hand, their territorial monopolies encourage the companies to invest in ancillary services, which are also highly profitable, and represent an increasing proportion of the railway companies’ revenues ( Kanemoto and Kiyono, 1995 ). These services enhance the travel experience, do not add to travel cost and exploit the lands under control of the railway companies.

In an early study of redevelopment of inner city areas in Japan, it was observed that the addition of a department store or shopping centre had a significant positive effect on land value. The great majority of such developments that resulted in increased numbers of shoppers in the last two decades were at railway station locations ( Onishi, 1994 ). However, the investments in ancillary services at railway stations are not evenly distributed over stations. One of the reasons for uneven investment is the availability of lands for such development. Another important reason is that commuters making non-work-related stops are most likely to make those stops at the commuting terminal and at the work place zone ( Nishii and Kondo, 1992 ). As a consequence, interchange stations with good accessibility in the railway network attract office development and related commercial and personal services. Among these interchange station areas, a limited number of stations with both good accessibility and connectivity between urban and regional transport infrastructure are developing as important places in the urban region. Examples in Tokyo include Tokyo, Shinjuku, Shibuya and Ueno stations. In the larger metropolitan region, new rail-based centres have sprung up in Yokohama, Chiba and Omiya. As a consequence, once-simple and direct daily travel patterns have become much more complex, multi-directional and multi-purpose.

The land development policies of the Japanese railway companies are not unique to Japan although they have particular characteristics that distinguish them from the parallel activities of the European railway companies. In France, for example, rail passengers were spending 22 per cent of their travel costs on products and services within the railway station in the late 1990s ( Perrin, 1998 ). The investment policies of the French, Dutch and German railway companies emphasize services related to travel and the improvement of the ambience of the station. The European railway station is increasingly an important place in the city, a destination in its own right and a magnet for related investment ( Reusser et al, 2008 ). Commercial space nevertheless remains highly oriented to travel, whereas in Japan the station hosts a comprehensive set of services and products, equivalent to city core shopping districts. The highly integrated development of real estate with railway services in the Japanese cases is in part due to structural differences in the railway companies. The French, Dutch and German railway companies have distinct and relatively autonomous real estate divisions ( Priemus and Konings, 2001 ). The European practice has been to hive off real estate no longer required for the core transport operations of the railway companies. The real estate developments on former railway lands are urban districts in their own right, accessible to the railway station but primarily a component of the city fabric. Developments have been designed to revitalize station-adjacent areas that suffer from poor connections to the rest of the city and a negative image ( Staudacher, 2001 ). In Japan, however, the railway lines are an integral component of the city fabric, as are the stations. As a consequence, the real estate operations are an increasingly important part of railway activity, within the stations, on lands owned by the railway companies and in the immediately adjacent urban space. In this respect, the redevelopment of stations is equivalent to city centre revitalization.

Tokyo Station Redevelopment Projects

The status of tokyo station in japan.

The redevelopment of Tokyo station is among the most prominent projects in the national trend of redevelopment. It is particularly important due to the station's symbolic status, important location, and its role in Tokyo’s transportation system. It has national and global status because of the important districts immediately adjacent the station. Regarded as the entrance to and the face of Tokyo, it is surrounded by such important areas as the Imperial palace, and the Ginza and Nihonbashi commercial areas ( Figure 1 ). On the west side of the station, the Marunouchi entrance leads to the Imperial palace and the office area. On the east, the Yaesu entrance leads to the important office buildings of Yaesu area.

Tokyo Station in the local environment of central Tokyo with other recent developments along the railway corridor.

Tokyo Station is the busiest railway station in Japan in terms of the number of trains. The number of passengers entering the station daily reaches 380 000, ranking fifth among stations where the East Japan Railway Company is an owner. In contrast, income earned by the company within its station premises reaches 260 million yen per day, which places Tokyo station first in terms of benefit, ahead of Shinjuku station, for example, which earns the company some 160 million yen per day ( Yoshida, 2007 ). On the other hand, Shinjuku station has far higher transiting passenger volumes than Tokyo station so that more space in the station is actually devoted to movement. The goods and services that would otherwise invest the station are displaced to neighbouring sites.

The operational system of Tokyo station and its vicinity

Tokyo Station is primarily owned by East Japan Railway Company (JR Higashi Nihon or JR-EAST), the privatized company once part of Japan (National) Railways (JR). The Shinkansen high-speed railway through Tokyo Station, along with space above and below the tracks is owned and operated by the Central Japan Railway Company (JR Tokai).

The Marunouchi side of the station has long been Tokyo's most prestigious office district. From 2000, high-rise towers were added to the low-rise blocks with efforts to preserve the façades of many buildings, including several that predate the Second World War. To compensate for the higher density currently being practiced in Marunouchi, Tokyo Metropolitan Government required an extensive and generously dimensioned underground walking system that connects many of the renewed buildings to Tokyo Station ( Figure 2a ). The major owner of real estate in Marunouchi is the Mitsubishi company whose headquarters is immediately opposite Tokyo Station entrance. This company's extensive real estate holdings facilitated the development of the underground system, since most of the underground corridors connect their own buildings under the streets. The building of the underground system provided the opportunity to redesign the street environment as well. Today the streets of Marunouchi are traffic-calmed and tree-lined, hosting sponsored public events, public art and luxury retailing.

The pedestrian system of Tokyo Station ( a ) and the recorded pedestrian flows of First Avenue and Yaesu shopping centre ( b ).

On the east or Yaesu side of the station, major property owners include the Mitsui real estate company, the Kajima Yaesu development company, the International Tourism company and the Shinnihon Sekiyu (Nippon Oil Corporation, currently JX Nippon Oil and Energy Corporation). The Yaesu side has always been associated with everyday business, entertainment and living. The world-famous Ginza shopping district is within walking distance as is the Tsukiji wholesale fish market, the world's largest.

The configuration of Tokyo station area

Tokyo station is complex in configuration ( Figure 2a ). There are five platforms and 20 lines on the ground level; four platforms and eight lines underground serving urban railways; five platforms and 10 lines on the ground level for the Shinkansen; and finally, one platform and two lines for the subway lines. In addition, there are three station concourses and two layers of pedestrian system. There are three entrances from three main directions, namely Marunouchi at the west, Yaesu at the east, and Nihonbashi at the north-east, with the main pedestrian flow between the west and the east.

The ground level pedestrian system is at grade at its eastern, Yaesu entrance while elevated a few steps at the west exit, because of a slight declination toward the Imperial gardens and palace. The underground pathway system is entirely at the same level, immediately below the street and the three controlled station concourses. The underground level is linked to the ground with regularly spaced stairwells leading directly to uncovered sidewalks.

Despite the fact that the station offers one toll-free corridor that connects the east and the west, the huge volume and the complex space make Tokyo Station a major barrier between east and west, accentuating the different roles and character of Marunouchi and Yaesu. Bars and restaurants proliferate in the nearest reaches of Yaesu, supplying the Marunouchi business district which has little such activity of its own. As the railway companies begin to emphasize place-based activity and consumption, there is a new need to promote pedestrian linkage between the various components of the emerging underground system. Such underground facilities are exceedingly expensive to retrofit to existing station facilities, given the exiguity of the spaces and the abundance of complex underground infrastructure. It is important that the linkages work well and pedestrian flows are sufficient to support the costly commercial space created in this restructuring effort. The last section of this article examines one such linkage effort.

Redevelopment projects at Tokyo station

Considering the important status of Tokyo station in Japan and its role in the city, the key urban design challenge becomes firstly, to heighten the symbolic status of Tokyo station by emphasizing its unique identity; secondly, to make better links between the two sides of the station; thirdly, to make the station an integral part of the city.

There are two projects that show how Tokyo station responds to the above challenges. One is the Tokyo station city project for the whole station area, and the other is the First Avenue project for the underground pedestrian system.

Tokyo Station City project

As the main station company, JR East collaborated with other companies and launched a major re-investment programme known as ‘Tokyo Station City’ ( Figure 3 ). Its ambitious pursuit is to make Tokyo station a leading urban place in Tokyo. With this goal in mind, the redevelopment project has several components. The first is the restoration of the early twentieth-century station, damaged during the Second World War, to its original architectural form. This work accompanies the beautification project consisting of a tree-lined boulevard from the symmetrically arranged station through the Marunouchi district to the Imperial Palace. This vista is symbolically extended across the station to the Yaesu side by demolishing the Tetsudo Kaikan buildings on the Yaesu side, symbolically uniting the two sides of the station ( Figure 4 ).

Tokyo Station City project as proposed by the developer. Source : Gransta: Tokyo Station in Evolution and ‘Tokyo Station City’, Tetsudo Kaikan, 2009.

The proposed vista from the Imperial palace and gardens through Marunouchi to Yaesu, with the restored station façade. Source : Gransta: Tokyo Station in Evolution and ‘Tokyo Station City’, Tetsudo Kaikan, 2009.

High-rise office buildings were constructed around station facilities at the Yaesu side and connected directly with station entrances. By transferring development rights to Yaesu side from the Marunouchi side, the historical station building could be restored in place while promoting new development on the other side of the station site. These new buildings include the Sapia tower, and GranTokyo North and South towers, all connected directly with the station's Marunouchi and Nihonbashi entrances through the GranRoof facility ( Figure 5 ). The Sapia tower is owned by the station company, while the GranTokyo buildings are owned jointly by other companies. Tokyo Station City's office buildings are intended to be the most technically advanced in Japan. ‘Sapia’ derives from the Greek ‘sapience’, meaning knowledge or wisdom. More than merely a commercial venture, such buildings are intended to act as crucibles of research and education, with facilities devoted to university activities, for example. The GranSta facility opened in late 2007 and has become the main commercial space inside the station complex. A three-floor deck is under construction between the GranTokyo North and South towers, which will incorporate more shops and open spaces ( Figure 5 ). The Station Square at the Yaesu side will be renewed by 2013. A great deal of design effort characterizes these projects, intended to give Tokyo station a pre-eminent position in the city and contribute to a favourable national and international image.

The Gran Roof facility connecting the recently completed towers at the Yaesu side of the station. Source : Gransta: Tokyo Station in Evolution and ‘Tokyo Station City’, Tetsudo Kaikan, 2009.

First Avenue project

Along with the large-scale Tokyo Station City project, there are also big changes in the pedestrian system. For example, Tokyo Station Development Company Limited, a subsidiary of station owner Central JR, opened the first phase of its commercial development known as First Avenue in 2008 ( Figure 6a and b ). First Avenue is located parallel to and two levels below the Shinkansen tracks at the underground level, directly connected to the north side passage between the Marunouchi and Yaesu sides of Tokyo station and at the same level as the long-open passage. It is also directly connected to the Yaesu Shopping Centre with one existing and two new connections. Kitchen Street, also developed by Tokyo Station Development Company, is directly connected to the sole pedestrian link between the Marunouchi and Yaesu sides of the station. The ‘free’ passage leads directly to Kitchen Street, First Avenue and the new Daimaru department store. First Avenue runs the length of the station with two new tunnels cut through to the Yaesu shopping centre, to facilitate linkage between First Avenue and the existing shopping centre although the two facilities are owned and operated by different companies. The First Avenue facility currently houses 102 shops, about one-third of which are devoted to food services. Two blocks of First Avenue are known as Tokyo Character Street, where 15 shops sell signature goods related to popular television shows. Another segment of this development, known as Tokyo Ramen Street, will fully open in 2011 and house eight famous noodle restaurants.

First Avenue phase 1 opened in 2008 ( a ); Phase 2 under development in 2009 ( b ).

The case of First Avenue illustrates the place-based strategies of the railway station owners. The latest trends in products and services are finding their way into the stations, attracting a new clientele of younger people, countering the image of Tokyo Station as a somewhat staid, conservative business location. By specializing segments of the pedestrian walking system the companies have created a sense of place, even if place in this case is underground and connected to other places only with the pedestrian system.

The First Avenue project is a link between the existing pedestrian system and the Yaesu shopping centre. Although a place in its own right, attracting long lines of customers to the new restaurant venues, for example, it is also connected to the Yaesu shopping centre through the two new underground connections. Although under separate ownership, the companies have an interest in connecting with each other and benefitting from each other’s trade. The pedestrian volumes recorded in 2009 in the First Avenue and Yaesu shopping centre show to what extent the visitors are shared among these different facilities ( Figure 2b ). The counts reveal strong flows between the facilities with inputs from the office buildings and from the Yaesu district, but also from the Marunouchi side.

Pedestrian volumes in this part of Tokyo station reach as high as 6000 persons per hour. High peak traffic is no longer restricted to mid day on working days, but is repeated in the late afternoon and early evening as visitors discover the Tokyo Station area as a place for consumption and educational activities. The success of the First Avenue project is leading a re-examination of the Yaesu shopping centre operation. That centre was positioned as a convenient service centre for business people in the vicinity and from the Marunouchi district, but also as a climate-controlled pathway into the station from the east. Traditionally, this centre paid much less attention to its place-based characteristics. The centre has a wide variety of affordable and readily available goods and services, mixed together in the various corridors. Until now, the operators have tried to identify the corridors thematically by colour and symbol, but not by the content of the operations or centre image. This approach seems likely to change as the visitors to Tokyo station gravitate to the newest venues. Already, the centre has begun to introduce new activities and design elements in the corridors nearest First Avenue in an attempt to capture more of the movement.

Overall, the various operators in Tokyo station compete and cooperate. Limited pedestrian access and the restrictions of building underground have forced the various property operators to consider how to use common facilities, including the pedestrian system, to their own advantage. While one of the greatest challenges of building in underground space is creating a viable and sustainable image in the long term, these experiments at Tokyo station illustrate one approach using activity themes concentrated at places with attention given to the connections between such places.

Conclusion and Discussion

Japanese station redevelopment projects show a distinctive approach to TOD, in which the central station complex becomes more multifunctional, and the linkage system is more thematic to satisfy a diversity of needs.

This change is made possible through close cooperation between the land owners. In the Tokyo station case, although JR East, Central JR and Mitsubishi are able to undertake their own redevelopment projects, the social and commercial success of the whole system depends to a considerable extent on cooperation among these companies. Cooperation includes the development of linkage between the various components of Tokyo Station, largely through the further development of the underground pedestrian system.

In these redevelopment projects, the stations are no longer taken as an exclusively transportation-oriented facility, but rather as integrated city space. They begin to represent the cutting edge of the city by including fashion trends and new ideas in an up-to-date physical setting. These developments are transforming the railway stations. From their beginnings as a pure transportation hub, the stations also became commercial operations designed to serve business travellers. These commercial operations expanded to serve a larger segment of the population, adding leisure facilities and reasons to remain in the space for longer periods. The station areas are evolving again into places for the exchange of ideas and the promotion of lifestyle, within a physical framework that incorporates innovations in building and space technology. The railway stations are in effect becoming nerve centres for the so-called ‘intelligent’ city, in which the transportation function plays a supportive role and no longer a central role. Such places have the particular advantage of being exposed to the highest volumes of foot traffic in the city.

To achieve these ends, all the new facilities and spaces are tightly interwoven within the pedestrian system. This makes for a richer pedestrian experience but also one where the whole space is highly accessible. Because of early attention to the connectivity between surrounding areas and the railway stations, achieved through pedestrian facilities on the surface, the station is assured a steady flow of inbound pedestrian traffic ( Figure 7a and b ). The indoor walkable ‘city’ is thus connected to the larger space around the station, which is also undergoing transformation in keeping with the new and expanded role of the station itself.

The Yaesu entrance and the new Daimaru department store ( a ); The Nihonbashi entrance to Tokyo station ( b ).

Western countries can learn from this Japanese redevelopment process in a number of ways. A different organizational system makes the Japanese case difficult to copy directly. Nevertheless, Japanese railway stations show a highly efficient land use model, which makes maximum use of the space under, over and beside the stations. Strapped by limited land resources, the necessary concentration of facilities became one of the most positive features of the redevelopment. In this way, Japanese railway stations have avoided the experience of European railway stations where there persists a zone of lower value and less accessible space in the immediate vicinity of the station. In the European case, this persistent problem has delayed redevelopment of those inner city lands, even as the stations themselves have been upgraded. In the Japanese case, the station is enclosed by new developments and pedestrian facilities that have largely overcome the historical disconnect between the transportation facility and the surrounding environment.

The next phase of development is of particular interest. The evolution of the station as an important centre for creation and exchange provides an interesting template for the development of transportation hubs elsewhere. A place of transit becomes also a place for exchange and communication at the city’s cutting edge.

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John Zacharias

College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China

Tianxin Zhang

Faculty of Environment and Information Studies, Keio University, 5322 Endo, Fujisawa, Kanagawa, 252-0882, Japan

Naoto Nakajima

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Zacharias, J., Zhang, T. & Nakajima, N. Tokyo Station City: The railway station as urban place. Urban Des Int 16 , 242–251 (2011). https://doi.org/10.1057/udi.2011.15

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Published : 25 October 2011

Issue Date : 01 December 2011

DOI : https://doi.org/10.1057/udi.2011.15

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An Architectural Study on the Railway Station Buildings in Malaysia during British Era, 1885-1957.

— This paper attempted to emphasize on the station buildings façade elements. Station buildings were essential part of the transportation that reflected the technology. Comparative analysis on architectural styles will also be made between the railway stations of Malaysia and British. The Malay Peninsula which is strategically situated between the Straits of Malacca and the South China Sea makes it an ideal location for trade. Malacca became an important trading port whereby merchants from around the world stopover to exchange various products. The Portuguese ruled Malacca for 130 years (1511–1641) and for the next century and a half (1641–1824), the Dutch endeavoured to maintain an economic monopoly along the coasts of Malaya. Malacca came permanently under British rule under the Anglo-Dutch Treaty, 1824. Up to Malaysian independence in 1957, Malaya saw a great influx of Chinese and Indian migrants as workers to support its growing industrial needs facilitated by the British. The growing tin ore mining and rubber industry resulted as the reason of the development of the railways as urgency to transport it from one place to another. Railways were the pioneers of modern transportation introduced by the British in 1885 in Malaya. The existence of railway transportation becomes more significant when the city started to bloom and the British started to build grandeur buildings that have different functions; administrative buildings, town and city halls, railway stations, public works department, courts, and post offices.

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