Emmanouil N. Barmpounakis

Vision-based multivariate statistical modeling for powered two-wheelers maneuverability during overtaking in urban arterials

The present paper employs advanced statistical modeling in order to assess the spatial factors that may influence the maneuverability of Powered Two-Wheelers (PTW) in urban corridors during overtaking. Using trajectory data from video recordings, two types of maneuvers are examined: those that occur with a lane change and those conducted without speed change and/or lane change (on the fly). The modeling approach has two parts: First, the probability to conduct an overtake is statistically modeled. Second, a Multiple Indicators-Multiple Causes (MIMIC) latent variable model is developed to assess the microscopic traffic characteristics and other critical factors that may affect the maneuverability of PTW. Results show that during overtaking, speed difference from the preceding vehicle is the most significant factor. Further results from the MIMIC model indicate that the PTW maneuverability is related to the type of overtake, the distance between the PTW and the vehicle being overtaken, as well as speeds.

How accurate are small drones for measuring microscopic traffic parameters

Abstract Small Unmanned Aerial Vehicles (sUAV or drones) have been one of the latest tools for monitoring transportation infrastructure and operations. Their lower cost compared to current fixed location camera systems or Manned Aerial Vehicles (MAV) and their ability to read just their view area depending on the situation they face, make them a promising tool of collecting both macroscopic and microscopic data. However, although drone technology and computer vision techniques are advancing fast, there is little information on how accurate and reliable they are for collecting microscopic traffic data. In this paper, we examine the potential of using sUAV as part of the ITS infrastructure as a way of extracting naturalistic trajectory data from aerial video footage from a low volume four-way intersection and a pedestrian passage. Moreover, the accuracy of speed data collected from a drone compared to data collected from an On-Board Diagnostics II (OBD-II) device is examined. For this, a controlled experiment where the vehicle was driven in various speeds and the drone flew in ranging altitudes was conducted. Results show that accuracy is highly dependent on the stabilization of the video and the geo-reference procedure. Moreover, the capabilities of such systems are examined in traffic applications and the way they can be part of future transportation infrastructure is discussed.