Drone-Based Computer Vision-Enabled Vehicle Dynamic Mobility and Safety Performance Monitoring

This report documents the research activities to develop a drone-based computer vision-enabled vehicle dynamic safety performance monitoring in Rural, Isolated, Tribal, or Indigenous (RITI) communities. The acquisition of traffic system information, especially the vehicle speed and trajectory information, is of great significance to the study of the characteristics and management of the traffic system in RITI communities. The traditional method of relying on video analysis to obtain vehicle number and trajectory information has its application scenarios, but the common video source is often a camera fixed on a roadside device. In the videos obtained in this way, vehicles are likely to occlude each other, which seriously affects the accuracy of vehicle detection and the estimation of speed. Although there are methods to obtain high-view road video by means of aircraft and satellites, the corresponding cost will be high. Therefore, considering that drones can obtain high-definition video at a higher viewing angle, and the cost is relatively low, we decided to use drones to obtain road videos to complete vehicle detection. In order to overcome the shortcomings of traditional object detection methods when facing a large number of targets and complex scenes of RITI communities, our proposed method uses convolutional neural network (CNN) technology. We modified the YOLO v3 network structure and used a vehicle data set captured by drones for transfer learning, and finally trained a network that can detect and classify vehicles in videos captured by drones. A self-calibrated road boundary extraction method based on image sequences was used to extract road boundaries and filter vehicles to improve the detection accuracy of cars on the road. Using the results of neural network detection as input, we use video-based object tracking to complete the extraction of vehicle trajectory information for traffic safety improvements. Finally, the number of vehicles, speed and trajectory information of vehicles were calculated, and the average speed and density of the traffic flow were estimated on this basis. By analyzing the acquiesced data, we can estimate the traffic condition of the monitored area to predict possible crashes on the highways

Associations of personality characteristics with transport behavior and residence location decisions

The objective of this paper is to investigate potential associations between personality and individual travel behavior characteristics. The explorations were based on responses to a mailback household survey from individuals residing in selected Chicago suburbs conducted in spring 1989. Three dimensions of personality were examined: social introversion or extroversion, affinity for suburban living and affinity for material possessions. Personality characteristics tend to correlate well with residence location selection, automobile ownership and travel characteristics. Specifically, socially extroverted people tend to make more trips, more nonwork trips and travel substantially longer distances by automobile for nonwork trips compared with socially introverted people. Materialistic people tend to spend a larger portion of their income for automobile acquisition; they also tend to own more expensive automobiles compared with utilitarian people. More people with an affinity for suburban living tend to reside in outerring, low-density suburbs instead of innerring, high-density suburbs. Thus, personality factors improve the understanding of transport behavior. On the other hand, personality characteristics cannot be affected by policy measures, while values for personality variables are hard to gather and predict. The problem of application of models with personality variables may be solvable for current (i.e. nonforecasting) applications if people can be classified into a small number of personality classes which can be assessed by a manageable number of attitudinal statements. As this study demonstrates, this is feasible.

Extracting Arterial Access Density Impacts on Safety Performance Based on Clustering and Computational Analysis

Access density is defined as the number of accesses per unit length along an arterial. Numerous studies conducted in various regions have indicated that access density has a significant influence on crash occurrences and severities. However, these research findings tend to simplify the relationship between access density and crash attributes and overlook the distinctive local roadway geometric and traffic flow characteristics. This study was conducted to quantitatively understand the impacts of various access densities on the safety performance of major arterials in New Mexico. A cluster analysis and a negative binomial model have been used through computational analysis to investigate the relationship between access density and crash rate. The analysis results demonstrate the piecewise relationship and verify that access density imposes heterogeneous influences on crash rates given different access density ranges, and lower public and commercial access rates are associated with lower crash rates. The impacts of other access features, such as access usage type and median opening type, on crash rates are also investigated. The research findings are helpful to improve safety performance on major arterials in urban metropolitan areas