This course introduces sources of quantitative data used in transport planning as well as the methods and practices used to analyse travel behaviour and transport systems. Throughout the course, we critically assess different types of transport data and we will examine the theory underpinning common statistical methods used in transport. The course also includes practical components where we apply the methods we have discussed to real-world data and consider how to present the results effectively.

The course will be delivered in 3 hourly blocks, once per week, over 9 consecutive weeks.

SPS5062: Quantitative Data Analysis 2

Understanding Transport Choices URBAN5102

There will be two assessments:

■ Part-way through the term, students will submit a portfolio covering exercises set in class (~1,000 words) which will assess their ability to critically assess the suitability of different transport datasets and their ability to analyse data in R and present their results in an appropriate format. [25% of grade]

■ At the end of the term, student will write a report on data analysis undertaken: maximum 4,000 words in length (75%)

In keeping with the National Occupational Standards for Transport Planning, the aims of this course are to introduce design, tools and techniques, and data sources used in transport planning. Using a case-based approach, the course will introduce the student to the major methodological approaches in urban studies and provide experience in presenting results from the analysis of real-world data.

By the end of this course students will be able to:

■ Describe data types commonly used in transport planning and critically evaluate their strengths and limitations;

■ Explain the theory underpinning the key statistical models used in transport studies and critically evaluate their strengths and limitations;

■ Apply these key statistical models to real-world data relating to travel behaviour or transport systems;

■ Design appropriate quantitative data analysis to investigate a transport planning issue using real-world data and methods presented on this course; and

■ Interpret results from models and explain the results in a clear and logical manner.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.