Missing data is a problem often found in real-world datasets and it can degrade the
performance of most machine learning models. Several deep learning techniques have …

Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to
provide proactive traffic state information to road network operators. A variety of methods to …

Traffic volume is a critical piece of information in many applications, such as transportation
long-range planning and traffic operation analysis. Effectively capturing traffic volumes on a …

Traditional methods of counting bicyclists are resource-intensive and generate data with
sparse spatial and temporal detail. Previous research suggests big data from crowdsourced …

This study examines determinants of bicycle volume in the built environment with a five-year
bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) …

Missing values are prevalent in spatio-temporal traffic data, undermining the quality of data-
driven analysis. While prior works have demonstrated the promise of tensor completion …

As urban populations grow, there is a growing need for efficient and sustainable modes,
such as bicycling. Unfortunately, the lack of bicycle demand data stands as a barrier to …

Traffic volumes are fundamental for evaluating transportation systems, regardless of travel
mode. A lack of counts for non-motorized modes poses a challenge for practitioners …

Emerging sources of mobile location data such as Strava and other phone-based apps may
provide useful information for assessing bicycle activity on each link of a network. Despite …

Reliable information on daily bicycle traffic provides a fundamental basis for city planners
and scientists. To estimate daily bicycle counts for various German locations with different …