Context-specific volume-delay curves by combining crowd-sourced traffic data with Automated Traffic Counters (ATC): a case study for London

TL;DR

This study develops context-specific volume-delay curves for London by integrating crowd-sourced traffic speed data from Google Maps API with automated traffic counters, enabling more accurate traffic modeling considering local road characteristics.

Contribution

It introduces a novel method combining GPS-based crowd-sourced speed data with traffic volume counts to create localized volume-delay functions for different road types.

Findings

Successful generation of context-specific volume-delay functions in London.

Highlighting the importance of local factors like parking and weather.

Demonstrating the potential of combining crowd-sourced and sensor data for traffic modeling.

Abstract

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.