Qingyi Ai

Observation-Based Study of Intersection Dilemma Zone Natures

Yellow phase dilemma zone is dynamically distributed at high-speed signalized intersections because of varying driving behaviors in response to yellow indications. This article presents an observation-based study of the natures of dynamic dilemma zones. A case study was conducted at a high-speed intersection in Fairfield, Ohio. Time-based yellow-onset trajectories were obtained using the video-capture-based technique and then they were used to analyze three types of dynamic dilemma zone models: that is, Type I and II dilemma zones, and option zone. The results reveal that the contributing factors of Type I dilemma zone and option zone are not constant but dynamic at different speeds. Vehicle type has been proved a factor that significantly affects drivers’ stopping behavior. Drivers of trucks are more likely to make pass decisions than drivers of passenger vehicles. Cars, sport utility vehicles, vans, and light trucks have similar downstream boundaries of Type II dilemma zone whereas heavy trucks have the furthest upstream boundary of Type II dilemma zone. Finally, the comparison between the option zone and the Type II dilemma zone is analyzed using the sample data.

Empirical Innovation of Computational Dual-Loop Models for Identifying Vehicle Classifications against Varied Traffic Conditions

Loop data gained from freeways have been increasingly applied to generate traffic information for various traffic analysis applications. The clarifying of traffic flow phases is essential for applying length-based vehicle classifications with dual-loop data under various traffic conditions. A challenge lies in identifying traffic phases using variables that could be directly calculated from the dual-loop data. An innovative approach and associated algorithm for identifying traffic phases through a hybrid method that incorporates level of service method and K-means clustering method are presented in this paper. The “phase representative variables” are identified to represent traffic characteristics in the traffic flow phase identification algorithm. The traffic factors influencing the vehicle classification accuracy under non-free traffic conditions are successfully identified using video-based vehicular trajectory data, and the innovative length-based vehicle classification models are then developed. The result of the concept-of-evidence test with use of sample data indicates that compared with the existing model, the accuracy of the estimated vehicle lengths is increased from 42% to 92% under synchronized and stop-and-go conditions. The results presented in this paper provide an understanding of the traffic stream characteristics and the associated theories to lay out a good foundation for further development of relevant microscopic simulation models with other sensing traffic data sources. The capability of measuring vehicle lengths makes dual-loop detectors a potential real-tie source for vehicle classification.

Estimating Emission Impact of Traffic Flow Operation with Dual-loop Data

While many regional level studies have been reported, project level studies are limited because of lacking microscopic traffic parameters revealing transportation emission changes under varied real-world traffic operations. This paper presents a Vehicle Specific Power (VSP) based model for estimating the emission impact of traffic flow over dual-loop monitoring stations in highways. An innovative algorithm is developed to generate traffic volume, vehicle composition and operating mode distribution from dual-loop data. Those parameters are contributing variables in the VSP based emission model and their accuracy is tested using video-based ground-truth data. A case study using the presented model on an I-71 expressway section shows that traffic flow state is a function of operating mode distribution, traffic volume and vehicle composition. These three traffic parameters dominate mobile source emissions along the specific road section studied. The VSP based model provides a linkage between traffic operation and vehicle emission impact analysis. The presented methodology makes it possible for project level mobile source emission impact studies to be performed using the prevailing microscopic real-world traffic data.

Identifying Characteristics of Freeway Traffic Headway by Vehicle Types Using Video Trajectory Data

The significance of vehicle-type-specific headway studies lies in the application of freeway capacity and safety analysis. However, data reliability plays an important role in the ability to understand the characteristics and microscopic modeling of freeway traffic. This study takes advantage of observation-based video data by using data extracting software VEVID to retrieve reliable vehicle trajectory data. The study focused on uniqueness of vehicle-type-specific headway under specific traffic states, as well as mixed headway. Data examination showed significant differences in vehicle-type-specific headways under average traffic conditions, uncongested flow and congested flow. Headways tend to be minimal in uncongested traffic flow, and maximal in congested traffic flow. In addition, the degree of discreteness by vehicle type headway differs significantly under different traffic conditions. Noticeably, in congested traffic, all types of headways tend to be less than reported in literature review results. In particular, Truck-Car and Truck-Truck headways behave considerably differently when traffic shifts between congested and uncongested conditions. The practical applications of these findings including improvement of microscopic car-following models, traffic state identification and vehicle classification.