This project has investigated bus delay and reliability across Greater Manchester, asking where delays occur, how they vary across the network, and how unreliable service may shape everyday mobility. The analysis connects transport accessibility, bus reliability, delay exposure, and uneven mobility into one analytical workflow.
Final Thoughts
A contracting network with uneven losses. Across the three layers of analysis, a consistent picture emerges. Bus accessibility in Greater Manchester has contracted by roughly 20% between 2016 and 2026, with the steepest losses concentrated in peripheral boroughs such as Wigan, Rochdale, and Oldham, precisely the places where rail and Metrolink alternatives are weakest.
A more nuanced delay and deprivation pattern. Delay exposure layered onto this shows a more nuanced pattern than a simple deprivation gradient. Most of Greater Manchester's deprived neighbourhoods sit in central Manchester, where service is frequent and delays are low, and at the regional scale the correlation between delay and deprivation is effectively zero.
Where the burden actually lands. Yet two distinct clusters, five to six LSOAs each in Rochdale and Bolton, combine both high delay and high deprivation, the kind of localised overlap that a city-wide average would obscure entirely. The headline is therefore not that unreliability uniformly punishes the most deprived, but that its burden is geographically concentrated in specific peripheral places, where contracting bus provision and weak alternative modes meet socioeconomic vulnerability. The Bee Network reforms create an opportunity to address these clusters directly, if reliability gains can be targeted rather than averaged across the network.
Why interactive visualisation matters here. This kind of finding is precisely why interactive visualisation matters. A 129,000-cell accessibility grid, 265 route-direction pairs, and stop-level delay distributions across five time bands together represent the kind of multi-scalar, multi-dimensional dataset that resists a single chart or headline figure, and, as the deprivation analysis shows, a system-wide statistic can flatten exactly the spatial detail that carries the policy signal.
From dataset to public exploration. By letting users toggle between years, switch between bus, rail, and Metrolink components, filter by time of day, and drill from the network down to individual LSOAs and stops, the dashboard turns a dense analytical pipeline into something a general audience can explore on their own terms. The aim is not to deliver a fixed conclusion but to make the underlying complexity legible, so residents, planners, and policymakers can interrogate the network at the spatial and temporal scale that matters to them, and locate the specific neighbourhoods where targeted intervention would do the most good.