This article evaluates the feasibility of two scenarios of phase transition signals, that is, the flashing green together with red-yellow light and the green countdown together with red countdown, at signalized intersections in terms of e-bike rider behavior. An evaluation framework is first proposed. During the phase transition, the stop-go and start-up behavioral parameters are collected at four intersections in Shanghai, China. Sensitivity analysis is then performed to identify the most significant factors that influence the occurrence of traffic conflicts during the phase transition. Based on the above analysis results, case studies were finally done to look into safety performance of the two scenarios of phase transition signals, indicated by the distributions of post encroachment time at the conflict point and the occurring probability of extremely small post encroachment times. Research result shows the transition signal combination of green countdown + red countdown tends to cause traffic accidents more easily and thus less safe compared to the transition signal combination of flashing green + red-yellow. Unlike the conventional method generally based on the deterministic traffic flow theory, the proposed methodology has a wide application. With the aid of it, traffic engineers are capable of designing transition signals in a more scientific manner.
The safety state of road network in the bridge-tunnel groups is an important factor in evaluating road traffic safety. This article describes a case study simulating brittleness behavior of road traffic safety statue in the bridge-tunnel groups. First, the safety state of road network and brittleness behavior characteristics are analyzed; second, the relationship between brittleness behavior and safety entropy is also introduced, and the collapse process of the travel system of road traffic network in the bridge-tunnel groups is simulated, which is based on the adaptive agent digraph theory. Finally, the rear-end accident data from Liu'an mountainous freeway are verified through the reasonability and feasibility of adaptive agent digraph theory. The case study has shown that there is a certain delay in the collapse of whole system; the key of vertex collapse will lead to the collapse of the whole system in a short time; the initial value of each vertex has a significant impact on the collapse of the whole system. The findings in this study provide scientific guidance to potentially improve the current mountainous freeway design and traffic management policy.
Average Annual Daily Traffic is typically estimated by applying seasonal factors (SFs) to short-term counts. SFs are obtained from continuous count sites and assigned to short-term count sites. This assignment procedure is usually empirical and subjective. Some previous studies have attempted to establish relationships between SFs and influential variables to provide an objective and data-driven alternative for SF assignment. However, in rural areas, SFs are difficult to model due to low land use intensity and, sometimes, significant through traffic. This paper presents a study of relationships between monthly SFs and hourly traffic patterns, land use, and other variables, using data from 116 continuous counters in rural areas throughout Florida. It is found that hourly traffic patterns are related to traffic seasonality and can be used to improve the modeling of influential variables that affect SF. The influential variables are then used for seasonal factor assignment and estimation. The proposed method achieved an average error of four percent, with 95 percent of the estimated monthly SFs having an error of no more than ten percent
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