Can mixed-methods help us better understand congestion on Low Traffic Neighbourhood boundary roads?

Abstract

Low Traffic Neighbourhoods (LTNs) aim to improve conditions for walking, wheeling and cycling by restricting motor vehicle movements on residential streets while maintaining access to all addresses. Despite generally positive evidence, LTNs faced backlash, often linked to concerns that motor traffic from inside LTNs is displaced onto surrounding ‘boundary roads’. In this paper, we bring together large-scale sensor data and spatially-transcribed interview data from a case-study LTN to discuss how mixed methods analysis can help to ease the LTN controversy by revealing the multiple ways in which the ‘problem’ of congestion is understood. By integrating quantitative evidence of changes in congestion associated with LTN implementation with residents' perceptions and experiences of the same scheme, we discuss how and why these diverge, revealing the complexity of capturing what congestion is. We argue that concerns about congestion are influenced not only by changes in traffic volumes, but also by how these changes are framed in public discourse. We consider dissonances between what ‘counts’ for residents and what is counted in quantitative data, and how what is (in)visible to residents affects their perceptions of congestion. We also highlight the limitations of each method and the importance of integrating multiple forms of evidence. The paper helps nuancing perspectives on congestion and its role in LTN debates, while also providing guidance on mixed methods approaches to evaluating transport policies. We recommend that these should combine attention to localised impacts with a broader evaluation framework that reflects the long-term public health and climate goals of LTNs.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 9 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.