Effects of Pedestrian Crossing on Minor Road Capacity at Two-Way Stop-Controlled Intersections

Two-way stop-controlled (TWSC) intersections have been used extensively in the United States and other parts of the world when traffic signal control is not warranted. However, it was found that when a major road vehicle yields to crossing pedestrians, minor road traffic could use this extra gap, which tends to improve the capacity of some minor vehicle movements. The current capacity modeling methods did not take into account the effects of the pedestrian crossing on minor road capacity. This paper proposes an analytical model to quantify the increased capacity of minor street movements contributed by minor street pedestrian crossings and validates the model using both field data collected at a three-leg TWSC intersection and through the stochastic simulation method. A sensitivity analysis was performed to reveal the impacts of various factors on minor road capacity. In general, it was found that minor road left-turn capacity at the study intersection was positively correlated to pedestrian crossing volume, yielding rate, and pedestrian crossing time. Besides, modeling results showed that under relatively heavier conflict traffic volume conditions, the effect of the pedestrian crossing on increased capacity on the minor road was more significant.

Capacity Modeling of Permitted Left-Turn Signalized Intersections with Probabilistic Priority

Probabilistic yielding behavior is commonly observed at permitted left-turn signalized intersections in China. Nevertheless, its impact on traffic capacity has not been explored in existing research. The uniqueness of this traffic operation is that neither left-turn traffic nor through traffic holds an absolute priority. Based on queuing theory, this research developed an analytical capacity model that took into account the probabilistic priority phenomenon at permitted left-turn signals. To validate the developed capacity model, stochastic simulations with different combinations of left-turn traffic yielding rates and through-traffic flow rates were performed. It was found that modeling results from the analytical model precisely matched the stochastic simulation results. In comparison with traditional capacity estimation models that assumed through traffic holds an absolute priority, this research revealed that when through-traffic flow rate is around 1000 vehicles per hour (vph), the capacity of left-turn traffic increased from 233 vph to 536 vph when left-turn traffic yielding rates decrease from 1 to 0.2; whereas when left-turn traffic yielding rates decreased from 0.2 to 0, left-turn capacity increased sharply to 1200 vehicles per hour (vph). In this regard, it is critical to take into account the probabilistic left-turn yield behavior when developing permitted left-turn signal warrants.

TRAFFIC CAPACITY ANALYSIS IN A ONE-LANE ROADWORK ZONE

Interventions to road are always required to keep road network in a good condition for this reason road works such as maintenance or reconstruction are carried out regularly. Roadwork zone distorts typical driving conditions and brings many unknown factors to driving process that increase the likelihood of an accident or congestion formation. Roadworks zones in Lithuania are a serious problem due to congestion formation, therefore, before the real construction works, it is necessary to carry out traffic flow and capacity modeling as well as to choose the appropriate traffic organization scheme through roadworks zones. The biggest congestion of vehicles in Lithuania occurs in roadworks zones where there is one lane left and traffic is controlled by traffic light, which do not adapt to real traffic conditions, but operate according to the created cycle and frequency model. The simulation analysis of roadwork zone with temporary traffic lights, in order to select optimal green light cycle and frequency is represented, in this paper.

Advanced Computation Capacity Modeling for Delay-Constrained Placement of IoT Services

A vast range of sensors gather data about our environment, industries and homes. The great profit hidden in this data can only be exploited if it is integrated with relevant services for analysis and usage. A core concept of the Internet of Things targets this business opportunity through various applications. The virtualized and software-controlled 5G networks are expected to achieve the scale and dynamicity of communication networks required by Internet of Things (IoT). As the computation and communication infrastructure rapidly evolves, the corresponding substrate models of service placement algorithms lag behind, failing to appropriately describe resource abstraction and dynamic features. Our paper provides an extension to existing IoT service placement algorithms to enable them to keep up with the latest infrastructure evolution, while maintaining their existing attributes, such as end-to-end delay constraints and the cost minimization objective. We complement our recent work on 5G service placement algorithms by theoretical foundation for resource abstraction, elasticity and delay constraint. We propose efficient solutions for the problems of aggregating computation resource capacities and behavior prediction of dynamic Kubernetes infrastructure in a delay-constrained service embedding framework. Our results are supported by mathematical theorems whose proofs are presented in detail.