  Traffic congestion across the world has reached chronic levels. Despite many
technological disruptions, one of the most fundamental and widely used
functions within traffic modelling, the volume delay function, has seen little
in the way of change since it was developed in the 1960's. Traditionally
macroscopic methods have been employed to relate traffic volume to vehicular
journey time. The general nature of these functions enables their ease of use
and gives widespread applicability. However, they lack the ability to consider
individual road characteristics (i.e. geometry, presence of traffic furniture,
road quality and surrounding environment). This research investigates the
feasibility to reconstruct the model using two different data sources, namely
the traffic speed from Google Maps' Directions Application Programming
Interface (API) and traffic volume data from automated traffic counters (ATC).
Google's traffic speed data are crowd-sourced from the smartphone Global
Positioning System (GPS) of road users, able to reflect real-time,
context-specific traffic condition of a road. On the other hand, the ATCs
enable the harvesting of the vehicle volume data over equally fine temporal
resolutions (hourly or less). By combining them for different road types in
London, new context-specific volume-delay functions can be generated. This
method shows promise in selected locations with the generation of robust
functions. In other locations it highlights the need to better understand other
influencing factors, such as the presence of on road parking or weather events.
