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.

As an indispensable necessity in daily routine of citizens, hazardous materials (Hazmat) not only plays an increasingly important role, but also brings a series of transportation uncertainty phenomena, the most prominent of which is a safety problem. When it attempts to find the best vehicle route scheme that can possess the lowest risk attribute in a fuzzy random environment for a single warehouse, the influence of cost should also be taken into account. In this study, a new mathematical theory was conducted in the modeling process. To take a full consideration of uncertainty, vehicle travel distance and population density along the road segment were assumed to be fuzzy variables. Meanwhile, accident probability and vehicle speed were set to be stochastic. Furthermore, based on the assumptions, authors established three chance constrained programming models according to the uncertain theory. Model I was used to seek the achievement of minimum risk of the vehicle route scheme, using traditional risk model; the goal of Model II was to obtain the lowest total cost, including the green cost, and the main purpose of Model III was to establish a balance between cost and risk. To settle the above models, a hybrid intelligent algorithm was designed, which was a combination of genetic algorithm and fuzzy random simulation algorithm, which simultaneously proved its convergence. At last, two experiments were designed to illustrate the feasibility of the proposed models and algorithms.

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Because of the convenience and the quickness, electric bikes gradually become the main travel mode of urban resident in China, and electric bike-car mixed flow becomes the new characteristics of urban traffic flow. But their role in mixed traffic flow and their effects on cars have become important problems for transportation designers. Mixed traffic flow simulation model provides the designers an effective tool to acquire the relation between flow and density in a new design scheme, achieve solutions for optimal design features before implementation, and help to make the design more rational. Based on the electric bike-car mixed flow characteristics and social force model, this article proposes a microscopic model which can represent the behavior of electric bike-car mixed flow in road segment. First, the inner and outer forces determining the movement mechanism of electric bikes and cars were analyzed, respectively; second, we developed a modified social force model to describe the electric bikes and cars behaviors; third, the social force model was modified and extended for mixed traffic flow by considering the interactions between electric bikes and cars, lanechanging behavior, and conflict avoidance behavior. The interaction parameters of the social force-based model for electric bike-car mixed flow are estimated using empirical data. The established microscopic model can be used to estimate critical flow of separating vehicle and electric bike and determine proper electric bike lane width according to different electric bike requirements by simulating.