Planning of National Transport Infrastructure
In the Islamic Countries
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2.7. Data Collection Method, including Statistics and Surveys
Data is necessary to make sound choices in NTI planning. Data are the eyes of the driver.
However, data collection is very expensive. It is one of the largest expenses of statistical
programs. Issues related to data collection are the identification of:
 key data related to NTI;
 Key Performance Indicators related to NTI;
 ways to apply new technologies for data collection;
 ways to disseminate data in order to improve decision making both by public and
private decision makers.
There are several reasons to collect data. Aspects of the planning and operations process that
make use of data are among others:
 design and dimensioning of infrastructure (Who uses it, and for what purpose?);
 cash revenues and non-cash benefits (What are the benefits of the existing
infrastructure?);
 maintenance and operations (What are the costs of the existing infrastructure?);
 asset management (What is the state of the stock of infra assets?);
 adequateness of the infrastructure (What is the need for additional infrastructure?
Is there a policy to influence efficient use of infrastructure?);
 competitiveness (How would this affect the costs of transportation and
international competitiveness?).
There are several methods to collect data:
 Automated data collection, via cameras, sensors, registering devices, transponders
in or aside the infrastructure, mobile telephone data;
 Household and industry surveys, zoning, census information;
 Forecasting (data science and data analytics), big data, modelling.
Data collection can be institutionalized with a role for statistics bureaus or done on a project
basis by other organizations. An advantage of structured and institutionalized data collection is
that definitions, unit of account, techniques of collection, sample size, and reporting periods are
harmonized. In data collection professionalism reigns. The advantage of a constant collection is
that it results in longitudinal data sets.
Other aspects to pay attention to when collecting data are:
 access and ownership of data (public data versus private data);
 data sources;
 data storage.