Context-specific volume–delay curves by combining crowd-sourced traffic data with automated traffic counters: A case study for London

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 modeling, the volume–delay function has seen little in the way of change since it was developed in the 1960s. 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.

Data-Driven Traffic Assignment Through Density-Based Road-Specific Congestion Function Estimation

“…Compared to the capacity values provided by the NTIS model, which tend to group around either 6500 or 8500, there is a wider spread of values. On average the fitted values are 17% lower than the values provided in the NTIS model, which is not as large a difference as the 66% average decrease of fitted values from reference table values in [12].…”

“…Road-specific fitting to overcome this problem has seen the use of uncongested traffic flow data and travel time from automatic detectors [12]. Yet this is only viable for non-congested traffic, where flow and travel time readings increase together.…”

“…Prominent works on hyper-critical fitting or roadspecific functions are not often concerned with quanti-fying the impact of these formulations on TAP solutions [12] [13] and are often limited to a small number of roads or traffic sensors. Furthermore, both [12] and [13] only investigated the suitability of the commonly used Bureau of Public Roads (BPR) formulation for these types of fitting.…”

“…The NTIS-provided values of capacity are used for edges without sufficient congestion data for this estimation. The free-flow travel time t 0 a is obtained by taking the 95 th percentile of the observed speeds [12][21] [35] as the free-flow speed then converting it to the travel time through the edge length.…”