Indicator study

Theory described that station area characteristics can be roughly divided in on the one hand intensity and diversity of activities and on the other as accessibility of the station area and the railway network. As outcome variable of these station area characteristics station use is also described. The choice of indicators used in the descriptive model to measure these station area characteristics has a large influence on the results. It also highly depends on the structural availability of the required data over a longer period of time. Therefore a simultaneously studies are done on which indicators can be used and what data is actually available to quantify the characteristics that these indicators represent.

5.1.1

Indicator selection

Previous studies yield a list of potential indicators to cover the TOD factors (Bertolini, 1999; Chorus, 2012; Reusser et al., 2008). The indicators used for this study were extracted from the literature discussed in the theoretical framework. No additional research was done to obtain a broader set of indicators, as it was assumed that they are sufficient to describe station area characteristics.

Due to the longitudinal nature of this study, the most important data requirement is its continuity over the entire study period. Furthermore, for practical and time consuming reasons a balance was found between the number of indicators and the increased model accuracy. The two data resources that qualified best for use in this research are general census and timetable data. Additionally, in order to describe general longitudinal trends, an economic indicator is necessary.

Based on the list of possible indicators and the type of data source, we used 10 indicators and a single outcome variable in this research. In Table 4 the list of included indicators is displayed and which type of data is used to quantify these indicators. In Table 5, the excluded indicators are displayed with a short description of the reason of exclusion.

Table 4 List of chosen indicators

Indicator Data source

Activity

Population Population census data (GIS)

The number of jobs per economic sector Population census data (GIS)

Degree of functional mix Population census data (GIS)

Accessibility

Directions served by train Timetable data

Frequency of train services Timetable data

Number of stations or activities within a certain travel time or distance

Timetable data/ Population census data (GIS)

Type of train services Timetable data

Station use and traffic flow

Passenger numbers Transportation census data

Gross Domestic Product IMF economic census data

5.1.2

Indicator exclusion

To measure the accessibility of station area access and egress modes several indicators were proposed by previous research, but none were adopted in this study. In the Tokyo Metropolitan Area, walking is the most important mode of access and egress for station areas. However, conduciveness of the walking environment of station area surroundings is excluded from this study. This is due to the complexity of the methods objectively measuring these characteristics and the limited availability of the associated data.

Proposed indicators for the use of bicycle were disregard based on a lack of historical data on the availability of bicycle lanes and bicycle parking capacity. Car accessibility did not play a significant role in this research because its share is negligible as access or egress mode. It must be stated that in the hilly suburbs the car is more often used as access mode for dropping-off and picking-up passengers. This function cannot be described by the indicators proposed in previous research such as distance to the closest highway exit and parking capacity near the station area.

The number of directions of other public transport (bus & tram) has been used as an indicator in previous research, but was not applied here. One reason to not use this indicator was that there is no single source which can supply data on the number of bus lines/ directions. In the catchment area of the Den-En Toshi line, many different private and public bus operators are active, collecting the appropriate data does not fit within this research framework. The second reason was that many bus lines have a service frequency below once per hour. This has to do with a certain policy strategy of some of the private bus companies (Personal correspondence with Tokyu Corporation). Counting just the number of bus lines could therefore give a distorted image. For example, the more elaborate service frequency should be included in such an analysis as well.

Since the suggested data sources lack the ability to describe the station area accessibility, data of the Person Trip Survey (PTS, a survey on the travel behaviour of the citizens of the TMA that is held every 5 years) was considered. From the PTS data the number of trips and modes used to and from station areas can be derived. However, describing the station access and egress characteristics in this indirect way was disregarded because it does not show why it is conducive to this particular access or egress mode but only shows the trip characteristics. Furthermore, it cannot describe any change over time as only the PTS for 2008 was made available.

Table 5: List of excluded indicators

Indicator Reason of exclusion

Activity

Conference rooms and educational facilities No historical data available Distance from the station to the town centre Not relevant for this study

Commercial services Sufficiently explained by employee data

Urbanity of station surroundings No clear operationalization available

Accessibility

Conduciveness of station area for walking No clear operationalization available Number of directions of other public

transport (bus)

Difficult to obtain, and expected to give distorted image

Daily frequency of other public transport Difficult to obtain

Distance to the closest motorway access No important access or egress mode

Car parking capacity No important access or egress mode

Bicycle access (bicycle lanes) No historical data available

Bicycle parking capacity No historical data available

Passenger frequency Used as a outcome variable in this study

Direction of commuters Used as a outcome variable in this study

Staffing All Stations identical

Quality of intermodal change No clear operationalization available Composition of station area users No data available

Age and history of railway station No clear operationalization available Type of railway station (network function) No clear operationalization available

Ticket availability All stations identical

In addition, some indicators that have not been described in the indicator list can be extracted from the timetable data. Such as first and last train (total hours of operation), total number of trains on one day, weekend services and off-peak frequencies, both during day and evening times. These indicators are disregarded because they are predictor variables for a differentiation that was not done in this study. As will be described in the section on modelling passenger flow, no distinction was made between different days of the week or time of day.

In general, most of the indicators proposed in previous research were disregarded solely because of their lack of proper historically continuous data. Others were disregarded because of the unclear operationalization and the kind of data required, describing the proposed indicator. The remaining indicators cover three of the five TOD factors, leaving out “distance to transit” and “design”. Therefore, a partial set of predictor variables for the station accessibility was used in the model building, which has to be taken into account when interpreting the results.