The route choice of pedestrians during evacuation under conditions of both good and zero visibility is investigated using a group of experiments conducted in a classroom, and a microscopic pedestrian model with discrete space representation. Observation of the video recordings made during the experiments reveals several typical forms of behavior related to preference for destination, effect of capacity, interaction between pedestrians, following behavior and evacuation efficiency. Based on these forms of behavior, a microscopic pedestrian model with discrete space representation is developed. In the model, two algorithms are proposed to describe the movement of pedestrians to a destination under conditions of both good and zero visibility, respectively. Through numerical simulations, the ability of the model to reproduce the behavior observed in the experiments is verified. The study is helpful for devising evacuation schemes and in the design of internal layouts and exit arrangements in buildings that are similar to the classroom.

Route choice in pedestrian evacuation under conditions of good and zero visibility: Experimental and simulation results

Measuring regional transport sustainability using super-efficiency SBM-DEA with weighting preference

Safety impacts of bicycle infrastructure: A critical review.

A simulation of bi-direction pedestrian flow based on cellular automata (CA) will be presented from two aspects: direction split and pedestrians’ walking habit in this paper The simulation uses Dynamic Parameters Model (DPM) to simplify tactically the decision-making process of pedestrians in their movements A new parameter right-hand parameter is introduced to describe the pedestrians’ walking preference The relationships of velocity–density and flow–density will be studied and analyzed It is found that there are phase transitions at the critical density point, and the pedestrian flow shows distinctive characteristics at different phases with different relationships of velocity–density and flow–density It is also found that direction split and pedestrians’ walking habit affect the value of critical density point and the figures of velocity–density and volume–density curves In conclusion, the simulation can reflect and describe some pedestrian flow self-organization phenomena and transition trend of empirical pedestrian flow curves

Study on bi-direction pedestrian flow using cellular automata simulation

Optimal Number and Location Planning of Evacuation Signage in Public Space

Simulation of pedestrian evacuation flow with affected visual field using cellular automata

Pedestrian facility planning and pedestrian flow management and control depend on the theory of pedestrian flow. Simulation of pedestrian evacuation flow is presented based on cellular automata （CA） in this paper. Two dynamic parameters are formulated to simplify tactically the decision-making process of pedestrians, which can reflect the pedestrian judgment on the surrounding conditions and decide the pedestrian’s choice of action. The relationships of evacuation time against pedestrian density and exit width for different sizes of systems are studied. Moreover, the effect of exits arrangement on evacuation time is analyzed. It is observed that there is a linear relationship between evacuation time and pedestrian density, however, there is a negative exponential one between evacuation time and exit width. And exits arrangement affects pedestrian evacuation time.

Pedestrian evacuation flow simulation based on cellular automata

The whole society has paid more and more attention to the road traffic safety in China. In order to scientifically and accurately predict the future road traffic safety situation, evaluation indexes and main influence factors of road traffic safety were analyzed. Then traffic accident death toll was selected as the evolution index and three factors were taken as parameters, including motor vehicle amount, highway mileage and per capital gdp. The two prediction models of BP neural network and hybrid algorithm based on neural network and genetic algorithm were built respectively. The neural models used the traffic accident data from 1978 to 1998 as training samples. And the data from 1999 to 2004 were used to check the models. Forecasting result shows that neural network model based on genetic algorithms has higher prediction accuracy and more network generalization than those of BP neural network; the forecasting values of accident death toll in 2010 and 2020 are 139 thousand people and 167 thousand people respectively.

Macro Prediction Model of Road Traffic Accident Based on Neural Network and Genetic Algorithm

According to the content and characteristics of highway transportation sustainable development,authors construct the evaluation indices from three aspects: the development level, development ability and development harmonization;present the calculation method and dimensionless problem of the evaluation index attribute value; applying the fuzzy identification theory and AHP method, according to the realized degree of objects of highway transportation sustainable development, put forward a comprehensive evaluation model of multi-step fuzzy identification for highway transportationThis evaluation method and model are applied in evaluating the highway transportation of Tianjin city, and the results indicate that the evaluation method is easy to calculate and is accord with the practice condition

Evaluation indices and model of highway transportation sustainable development

The stochastic properties of signalized intersection capacities under mixed traffic conditions were analyzed, and capacity reliability of signalized intersection was defined. VISSIM simulations were employed to provide capacity data for probability distribution calibrations. Typical percentile capacities of the lanes were calculated. An evaluation method for reliability of capacity was proposed when arriving volume was considered as a random variable. The sensitivity of reliability of capacity to the mean value of the conflicting pedestrian and bicycle volumes was analyzed. The results show that the impedance effects of pedestrians and bicycles result in great randomness of the exclusive turning lane capacities, but result in relatively little changes of random fluctuations of the shared lane capacities. Normal distribution is applicable to model the probability distribution of the capacities during 10 minutes or longer intervals. With a higher arriving vehicle volume, the reliability of capacity is more sensitive to the mean values of the pedestrian and bicycle volumes.

Shao Chun-fu

Papers

Simulation of pedestrian evacuation flow with affected visual field using cellular automata

Pedestrian evacuation flow simulation based on cellular automata

Macro Prediction Model of Road Traffic Accident Based on Neural Network and Genetic Algorithm

Evaluation indices and model of highway transportation sustainable development

Reliability of Signalized Intersection Capacity Under Mixed Traffic