Surrogate Safety Analysis of Pedestrian-Vehicle Conflict at Intersections Using Unmanned Aerial Vehicle Videos

Abstract

Conflict analysis using surrogate safety measures (SSMs) has become an efficient approach to investigate safety issues. The state-of-the-art studies largely resort to video images taken from high buildings. However, it suffers from heavy labor work, high cost of maintenance, and even security restrictions. Data collection and processing remains a common challenge to traffic conflict analysis. Unmanned Aerial Systems (UASs) or Unmanned Aerial Vehicles (UAVs), known for easy maneuvering, outstanding flexibility, and low costs, are considered to be a novel aerial sensor. By taking full advantage of the bird’s eye view offered by UAV, this study, as a pioneer work, applied UAV videos for surrogate safety analysis of pedestrian-vehicle conflicts at one urban intersection in Beijing, China. Aerial video sequences for a period of one hour were analyzed. The detection and tracking systems for vehicle and pedestrian trajectory data extraction were developed, respectively. Two SSMs, that is, Postencroachment Time (PET) and Relative Time to Collision (RTTC), were employed to represent how spatially and temporally close the pedestrian-vehicle conflict is to a collision. The results of analysis showed a high exposure of pedestrians to traffic conflict both inside and outside the crosswalk and relatively risking behavior of right-turn vehicles around the corner. The findings demonstrate that UAV can support intersection safety analysis in an accurate and cost-effective way.

Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures

“…PET, defined as the time difference between when the leading vehicle occupies a location and when the trailing vehicle arrives at this location, is usually used to identify conflicts in combination with TTC [35]. Figure 3 illustrates situations for PET calculations [34].…”

Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

“…The GHM model has two limitations: (a) as a kinematic and deterministic model, it is not capable of assessing the randomness inherent in driving behavior, and (b) it assumes a one-step decision process that only accounts for behavior parameters at the onset of a yellow signal. For these reasons, several stochastic models such as the probit model, the logit model, and the fuzzy logic model have been developed to explain the randomness and uncertainty of stop/pass decision behavior [5][6][7][15][16][17][18][19][20][21][22][23].…”

Simulation of pedestrian evacuation route choice using social force model in large-scale public space: Comparison of five evacuation strategies

“…The pedestrian database of simulation software used to simulate pedestrian flow is based on the SFM [24]. The SFM can simulate important phenomena of escape panic [1,25,[28][29][30][32][33][34][35][36][37][38], such as "clogging", "faster is slower", and "mass behavior".…”

“…Porter et al [27] presented an integrated modeling framework to capture pedestrian walking behavior in congested and uncongested conditions, which the framework was built using a combination of concepts from the social force model. In addition, the robust model was also applied to provide valuable insights into simulate the dynamical features of escape panic [1,21,22], abnormal behaviors localization [28], exit-selecting behaviors analysis [29], detour behavior description [30], exit assignment strategy [31], pedestrian behavior analysis [32][33][34][35][36][37], and pedestrian traffic [38]. The main contribution of this study is the development of a pedestrian evacuation route choice model based on social force theory, which enables to consider the characteristics of pedestrian behavior in the large-scale public space (LPS).…”