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Suppose $A$ and $B$ are two stations within the mass rapid transit network ofa city. Both stations see approximately the same average daily number ofpassengers entering and exiting their gates. However, passengers are evenlydistributed at $A$, whereas activity is concentrated mainly during peak hoursat $B$. Although the daily travel demand is the same for both stations, $B$requires more resources since the number of vehicles, station dimensions andstaffing level must be tailored to meet the demands of peak hours. Thishypothetical scenario underscores the need to quantify the concentration oftravel demand for optimising resource allocation and planning efficiency in anurban transport network. To this end, we introduce a novel metric for assessingthe temporal concentration of travel demand at different locations in a generictransport network. Our approach is validated using granular data sourced fromsmart travel cards, encompassing 272 London Underground (LU) stations.Additionally, we present a methodological framework based on Random Forests toidentify attributes of the locations of interest within the transport networkthat contribute to varying levels of temporal concentration of travel demand.Our case study unveils that LU stations located in areas characterised by lowresidential, retail, and employment density, predominantly situated in outerLondon, exhibit the most pronounced temporal concentration of travel demand.Conversely, within inner London, stations servicing high-density employmentzones, especially around the City of London, experience a greater temporalconcentration of travel demand compared to those catering to commercial andresidential districts, typically situated in West London.

The temporal concentration of travel demand in an urban transport network