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.
Pedestrian safety at intersections remains a critical issue. With the dramatic increasing of urban traffic flow, the major threat to pedestrians comes from frequent interactions with turning vehicles at crosswalk. Though crosswalks are operated to give pedestrians prioritized right of way over vehicles, still around 30% of the total traffic accident fatalities in China are pedestrians according to the accident statistics from the Ministry of Public Security of China [
So far the reactive strategies for the purpose of improving pedestrian safety have been primarily based on identifying sites with high crash rates. It is subject to less crash records or validity losing due to changes of road system and operation. On the other hand, traffic conflict technique (TCT) represents an efficient approach to enable a preventive strategy development. Surrogate safety measures (SSMs) serve as near-crash indicators to measure spatial and temporal proximity of road users. In the context of safety assessment and improvement of urban intersections, the conflict between pedestrians and turning vehicles needs special attention. However, there are still limited applications of SSM on pedestrian-vehicle conflict assessment [
Field surveys of pedestrian-vehicle conflict are costly to conduct and suffer from inter- and intraobserver variability for the repeatability and consistency of results [
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. UAVs can be launched and deployed within minutes and exchange with the control center in real time. While in the last decade UAVs have been frequently employed in the military, civilian applications of UAVs still face several technical and institutional barriers, for example, strict airspace and route restrictions. In recent years, an increasing number of countries such as China and US have begun to consider and evaluate flexible air traffic control rules. For instance, the China Air Traffic Control Center promised to open up the low attitude space (lower than 1000 m) management in the following years. Such emerging trend presents a great opportunity for the transportation departments to fully explore the potential of UAVs in road traffic network surveillance. The equipped sensors on the UAVs such as high-resolution camera, radar, and infrared camera can provide bird’s eye view over an intersection or a large area. The entire images and video can be further processed to monitor traffic flow interaction and evaluate traffic state evolution. Thus, UAVs can be an effective aerial traffic information gathering platform. This study will investigate the potential of applying UAV videos for surrogate safety analysis of pedestrian-vehicle conflicts in an accurate and cost-effective way. To the best of the authors’ knowledge, it will be the first attempt to employ UAVs for detailed safety assessment at intersections.
The remainder of the paper is organized as follows. A thorough literature review on UAV applications in transportation engineering and operation as well as SSMs for pedestrian-vehicle conflict assessment is presented first. Then the process of data acquisition using UAVs is introduced and the procedures of trajectory extraction are elaborated. Next, postextracted SSMs at one urban intersection in Beijing, China, are investigated in detail by referring to intersection geometry, traffic volume, and signal control strategy. Last, conclusions are drawn and recommendations are provided for future consideration.
UAV, as an aerial traffic information gathering platform, has been becoming more prominent in transportation engineering and operation. For instance, by utilizing aerial images captured from UAVs, the Washington State Department of Transportation evaluated the use of a UAV as an avalanche control tool on mountain slopes above state highways [
Perhaps the most important role that UAVs could fill is providing a rapid response to incidents [
The spatial perspectives offered by UAVs from the air demonstrate to be more promising than presently available ground-based views for traffic management and monitoring. Useful information can be derived from UAV video for both offline planning and real-time management. To this end, vision-based detection and frame-to-frame matching to track road users are important. However, in practice accurate detection and tracking from the UAV platform is a challenging task due to platform motion, image instability, the relatively small size of the objects, varied appearance, and so forth. Such technical issues may impose limitations to transportation professionals in a variety of intensive research and applies uses. Recently, by using UAV images, Xu et al. [
As an alternative to crash risk estimation based on limited crash data, SSMs serve as near-crash indicators to measure the severity and frequency of traffic conflict events. Numerous SSMs have been suggested for safety evaluation of traffic facilities as shown in Allen at el. [
In the case of pedestrian-vehicle conflict at intersections, turning vehicles typically have to filter through conflicting pedestrian flow at crosswalk during permitted signal phase as implemented in China and US. Under the mixed impact of surrounding environment, crosswalk geometry, signal operation, and pedestrians moving in different directions, turning vehicles might take risky behavior by not yielding to pedestrians or passing through small gaps in pedestrian flow, which poses a threat to pedestrian safety. The most commonly used SSMs for pedestrian conflict assessment include but not limited to the following measures: Time to Collision (TTC), which is defined as the time that remains until a collision between two road users would have occurred if the collision course and speed difference are maintained [ Postencroachment Time (PET), which is defined as the time difference between the moment when an offending road user leaves an area of potential collision and the moment of arrival of a conflicted road user possessing the right of way [ Time to Zebra (TTZ), which is a variation of TTC in order to estimate frequency and severity of critical encounters between crossing pedestrians and vehicles that are approaching the crosswalk [ Deceleration-to-Safety Time (DST), which is the necessary deceleration to reach a nonnegative PET value if the movement of the conflicting road users remains unchanged [ Gap Time (GT), which is defined as the time lapse between the completion time of encroachment by one road user and the arrival time of the interacting road user if they continue with the same speed and path [
In general, Allen et al. [
However, PET has inherent drawbacks in its ability to accurately capture conflict severity [
In order to investigate pedestrian-vehicle conflict, road users should be detected and then tracked frame-to-frame in UAV video. In this study, we extract the trajectories for vehicles and pedestrians, respectively, at intervals of every 0.04 s by using the detection and tracking system developed in our previous studies [
An image processing system for automated vehicle trajectory extraction was developed based on UAV videos [
The workflow of vehicle detection and tracking.
Similar to the work of Beymer et al. [
The configuration of entry and exit regions for turning vehicles.
Left-turn
Right-turn
Pedestrian detection and tracking from the UAV platform is a challenging task due to the small size of the objects and the high-density crowd. A semiautomatic pedestrian detection and tracking system was developed for pedestrian trajectory data extraction from UAV aerial images [
The workflow of pedestrian detection and tracking.
(a) Tracking trajectory visualization and (b) tracking point visualization.
Note that the tracked positions or trajectories might contain measurement errors. Kalman filtering (KF) was used to correct the errors and smooth the raw trajectory data. The KF computes the best estimate of the state vector (i.e., position coordinates) by minimizing the squared error according to the estimation of the past state and the present state. The image coordinates were converted to geographic coordinates by projective transformation.
The available observations are trajectory profiles based on time series. From these data, all relevant quantities of vehicles and pedestrians, such as positions, velocities, and acceleration, can be derived either directly or by applying finite differences. The ordinary differential equations for speed and acceleration can be solved as follows:
Traditional traffic conflict technique usually use PET and TTC to represent the probability of collision or how close the conflict is to a collision [
In the context of vehicle conflict assessment, PET is defined as the time difference between the moment when the first vehicle passed the conflict area and the moment of arrival of the second vehicle subsequently at the same area. In the context of vehicle-pedestrian conflict assessment, PET can be similarly defined as the time difference between the departure of the encroaching pedestrian from the potential collision point and the arrival of the conflicting vehicle at the point, or vice versa. However, as PET only considers the last moment of the interaction, it has limitations in indicating pedestrian safety during the course of vehicle-pedestrian interaction.
Alternatively, TTC has been commonly implemented as a measure of conflict severity for the whole interaction process. It was originally defined as the time that remains for the paired vehicles before they collide, if both continue at their present speeds along their respective trajectories. TTC can be easily detected in the rear-end conflict situation because the trajectories of the paired vehicles are assumed to be overlapped. However, it cannot be detected (or does not exist) in most of the interactions if the trajectories of the paired users intersect, for example, the pedestrian-vehicle conflict and the conflict between left-turn and opposing through vehicles. In the rear-end conflict, the following vehicle will definitely collide with the leader vehicle if the speed of the follower is higher. However, for the pedestrian-vehicle conflict, the cases that the pedestrian and the vehicle occupy the trajectory intersection point at the same moment are rare. To overcome this problem, we use the Relative Time to Collision (RTTC) as the indicator to measure the conflict severity. As shown in Figure
RTTC and PET of pedestrian-vehicle conflict identified on the time-space diagram (modified from [
In data processing, both RTTC and PET are obtained as a function of paired vehicle-pedestrian speeds and spacing. A time-space diagram identifying RTTC and PET for a pedestrian-vehicle conflict event is illustrated in Figure
The PET for such a conflict event can be obtained as
Note that RTTC is instant varying and continually calculated between conflicting vehicles and pedestrians. Thus, a set of RTTC values will be obtained for each conflict. The minimum RTTC (
The selected study site is the intersection of Huayuan Road and Beitucheng Road in the Haidian District of Beijing, China. The intersection is located on a key route to the downtown area, characterized by higher vehicle volume and medium-to-high pedestrian demand during peak hours. For signal control, this intersection is fixed-time controlled with a cycle length of approximately 120 s. The yellow time durations are 3 s and the all-red durations are 1 s at all the approaches. The three-phase control plan is presented in Figure
Signal phasing at study site.
Experiments were conducted using aerial videos captured by an optical camera (Gopro Hero Black Edition 3) with a 1920 × 1080 resolution mounted on a quadrotor UAV (model: Phantom 2). Figure
Quadrotor UAV for video collection.
In total, the dataset consists of the trajectories of 1494 pedestrians and 282 right-turn vehicles. The visualization of the extracted trajectories is presented in Figure
Visualization of extracted trajectories.
Pedestrians
Right-turn vehicles
Comparison of extracted right-turn vehicle trajectories at cross-sections.
Based on extracted trajectory data, traffic conflicts between pedestrians and right-turn vehicles were identified and SSMs, that is, PET and RTTC, were calculated accordingly. In terms of SSMs, the conflict analysis aims to identify conflict frequency, severity and location (conflict points).
The spatial distribution of small PETs (which are less than 3 s in this study) is shown in Figure
Spatial distribution of the number of small PETs.
The number of small PETs inside and outside of the crosswalk.
North
South
East
West
Different from PET, RTTC reflects the potential conflict severity during the course of vehicle-pedestrian interaction. Figure
Spatial distribution of
In general, there are two types of vehicle-pedestrian conflict, that is, vehicle yielding to pedestrian, also known as pedestrian passing first (PPF), and pedestrian yielding to vehicle, also known as vehicle passing first (VPF). The PPF and VPF cases are compared because these two types of conflicts can result in different safety performance. Figure
The number of critical RTTC inside and outside of the crosswalk.
North
South
East
West
Despite the prominent advantage of UAVs for emergency and traffic monitoring, there has been no research yet to employ UAVs for detailed safety assessment at intersections. In practice, accurate detection and tracking from UAVs is a challenging task due to platform motion, image instability, the relatively small size of the objects and varied appearance, and so forth. This study, as a pioneer work, investigated the feasibility of applying UAV video for surrogate safety analysis of pedestrian-vehicle conflict at intersections. By taking full advantage of the bird’s eye view offered by UAV, the image processing systems for automated vehicle trajectory extraction and semiautomatic pedestrian trajectory extraction were developed, respectively. Based on the trajectory data collected from one urban intersection in Beijing, China, two SSMs, that is, PET and RTTC, were employed to represent the frequency, severity, and location of pedestrian-vehicle conflict. 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.
Still, there are some limitations of this study. Firstly, due to the limitations of top-down views, the characteristics of pedestrian heterogeneity, for example, gender and age, cannot be identified in the video and thus are not discussed in this study. In pedestrian safety analysis, a recognized key issue [
The authors declare that they have no conflicts of interest.
The authors appreciate the National Natural Science Foundation of China (no. 51308475 and no. U1564212) for funding support of this research. Special thanks go to Mr. Yongzheng Xu and Mr. Yalong Ma for their efforts in the UAV data analysis.