Data from connected probe vehicles can be critical in estimating road traffic conditions.
Unfortunately, current available data is usually sparse due to the low reporting frequency …

Trajectory data collected from probe vehicles become increasingly important for urban traffic
operation and management. However, current data tend to be sparse in time and space due …

In probe-based traffic monitoring systems, traffic conditions can be inferred based on the
position data of a set of periodically polled probe vehicles. In such systems, the two …

Travel time estimation and prediction on urban arterials is an important component of Active
Traffic and Demand Management Systems (ATDMS). This paper aims in using the …

A characteristic of low frequency probe vehicle data is that vehicles traverse multiple
network components (eg, links) between consecutive position samplings, creating …

Vehicle trajectories can provide a clear picture of the traffic flow that plays a pivotal role in
traffic management and control. Two types of traffic sensors, ie, fixed and mobile sensors …

Floating car data are beneficial in estimating traffic conditions in wide areas and are playing
an increasing role in traffic surveillance. However, widespread application is limited by low …

The paper presents a statistical model for urban road network travel time estimation using
vehicle trajectories obtained from low frequency GPS probes as observations, where the …

Estimating and predicting traffic conditions in arterial networks using probe data has proven
to be a substantial challenge. Sparse probe data represent the vast majority of the data …

Probe vehicles that measure position and speed have emerged as a promising tool for traffic
data collection and performance measurement, but the sampling rates of most probe vehicle …