This article combines a value-at-risk model with fuzzy theory and proposes a model using chance measure based on the value-at-risk model named chance-value-at-risk (ChVaR). The proposed model considers two measures, probability measure and credibility measure. The objective of this model is to determine the best route schedule that minimizes the risk at certain probability levels and credibility levels. For the proposed model, the correctness of its founding is proven. A detailed solution procedure is presented and tested to solve the ChVaR model. To verify the applicability of the model, two different scale cases are given: the first case indicates that the model can provide a satisfactory solution within a relatively small error range, and the second case routes the path of hazardous material transportation in Changchun, China. According to different probability levels and credibility levels, the ChVaR model provides different paths and multiple alternative choices for a decision maker. This point is important in practical scenarios.
Pedestrian evacuation dynamics in a classroom is always a complex process influenced by many fuzzy factors. It is very difficult and inappropriate to quantify the impact of these fuzzy factors by using the mathematical formula. Existing microscopic simulation models have made many efforts to use accurate mathematical method to model the fuzzy interaction behaviors between pedestrians under the view-limited condition. This study tries to fill this gap by establishing a microscopic simulation model which can represent the fuzzy behaviors of pedestrians under view-limited condition. The developed fuzzy social force model (FSFM) combines fuzzy logic into conventional social force model (SFM). Different from existing models and applications, FSFM adopts fuzzy sets and membership functions to describe the pedestrian evacuation process. Seven fuzzy sets are defined for this process, such as stop/go, moving direction, desired force, force from obstacles, force from pedestrian, force from indicators, and acceleration. Membership function of each input factor is calibrated based on the observed data. Model performance is verified by comparing speed distribution, velocity-density relationship, and results of simulation and observation evacuation time. Besides, the proposed model is applied to assess the number and space distribution of exit indicators and stickers. By comparing simulation results with existing models, the paper concludes that FSFM is able to well reproduce pedestrian movement dynamics in real world under view-limited condition.
Limited pedestrian microcosmic simulation models focus on the interactions between pedestrians and vehicles at unmarked roadways. Pedestrians tend to head to the destinations directly through the shortest path. So, pedestrians have inclined trajectories pointing destinations. Few simulation models have been established to describe the mechanisms underlying the inclined trajectories when pedestrians cross unmarked roadways. To overcome these shortcomings, achieve solutions for optimal design features before implementation, and help to make the design more rational, the paper establishes a modified social force model for interactions between pedestrians and vehicles at unmarked roadways. To achieve this goal, stop/go decision-making model based on gap acceptance theory and conflict avoidance models were developed to make social force model more appropriate in simulating pedestrian crossing behaviors at unmarked roadways. The extended model enables the understanding and judgment ability of pedestrians about the traffic environment and guides pedestrians to take the best behavior to avoid conflict and keep themselves safe. The comparison results of observed pedestrians' trajectories and simulated pedestrians' trajectories at one unmarked roadway indicate that the proposed model can be used to simulate pedestrian crossing behaviors at unmarked roadways effectively. The proposed model can be used to explore pedestrians' trajectories variation at unmarked roadways and improve pedestrian safety facilities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.