Zhixiang Fang

On the relationship between crowd density and movement velocity

The crowd flow pattern under emergency situations has been studied on the basis of dynamic movement principles. It has been demonstrated that the surrounding crowd density will influence the speed of an individual. The derivation of the movement equations of people with respect to the impacts at front, back and lateral directions has been given, and it shows that the impacts have substantial influence on the peoples movement speed. The logarithmic relationship between the crowd density and the speed is in good agreement with the published field data. The study also demonstrates that the influence of the crowd movement velocity by the inter-person effect at the lateral direction is much lower than that at the front-back direction.

A preliminary investigation of airflow field in designated refuge floor

Abstract Refuge floor is specially designed in high-rise buildings for the purpose of supplying a temporarily safe place for evacuees under emergency situations. The provision of such designated refuge floor is a prescriptive requirement in the fire code of Hong Kong. Such a provision appears to be desirable by the regulators as it relates to simple rules and has administrative convenience. In order to fulfill its function, the refuge floor should be a safe place for the evacuees. The safeness of refuge floors under fire situations may be impaired if the floor is affected by smoke from lower levels. The code prescribes that cross-ventilation should be provided in refuge floor so as to prevent smoke logging. However, the adequacy of such a measure and the influence of such an open floor on the rest of building have not been analytically studied. An investigation on the airflow around and inside the refuge floor is required and will provide preliminary insight on the airflow and the smoke movement patterns. In this paper, the Computational Fluid Dynamics method is employed to analyze the airflow field around and inside a refuge floor. The aim of this paper is to describe the airflow field in and around a designated refuge floor, which is the first step to explore the wind effect on the safeness of refuge floors. The study shows that airflow could be a factor affecting the smoke flow pattern.

Multiobjective evacuation route assignment model based on genetic algorithm

Emergency evacuation in public places becomes a research focus in recent decades. One major objective of evacuation is to maximize the efficiency of the whole evacuation system. The paper proposes a multiobjective evacuation route assignment model to plan an optimal egress route set for the individual evacuees. The three objectives in the proposed model are to minimize the total evacuation time, minimize the total travel distance of all the evacuees and minimize the congestion during the evacuation process. These objectives need to be satisfied simultaneously while some of them conflict with each other. The travel speed on each road segment is related with the time and the number of evacuees on it during a certain time period. The congestion of a road segment is modeled as the density of evacuees passing it in time and space dimensions. The evacuation route assignment problem can be treated as a combinatorial optimization problem. A multiobjective optimization model based genetic algorithm is adopted to solve the proposed evacuation routing problem. Wuhan Sport Center in Wuhan city of China was taken as the experiment scenario to test the performance of the proposed algorithm. The results showed that it can provide some system optimal evacuation plans. Meanwhile, the multi-objective optimization model based genetic algorithm can produce a pareto optimal set rather than single optimal point, thus the model can give alternative strategies for the evacuation policy makers.

Another tale of two cities: understanding human activity space using actively tracked cellphone location data

Activity space is an important concept in geography. Recent advancements of location-aware technologies have generated many useful spatiotemporal data sets for studying human activity space for large populations. In this article, we use two actively tracked cellphone location data sets that cover a weekday to characterize peoples use of space in Shanghai and Shenzhen, China. We introduce three mobility indicators (daily activity range, number of activity anchor points, and frequency of movements) to represent the major determinants of individual activity space. By applying association rules in data mining, we analyze how these indicators of an individuals activity space can be combined with each other to gain insights of mobility patterns in these two cities. We further examine spatiotemporal variations of aggregate mobility patterns in these two cities. Our results reveal some distinctive characteristics of human activity space in these two cities: (1) A high percentage of people in Shenzhen have a relatively short daily activity range, whereas people in Shanghai exhibit a variety of daily activity ranges; (2) people with more than one activity anchor point tend to travel further but less frequently in Shanghai than in Shenzhen; (3) Shenzhen shows a significant north–south contrast of activity space that reflects its urban structure; and (4) travel distance in both cities is shorter around noon than in regular work hours, and a large percentage of movements around noon are associated with individual home locations. This study indicates the benefits of analyzing actively tracked cellphone location data for gaining insights of human activity space in different cities.

A GIS data model for landmark-based pedestrian navigation

Landmarks provide the most predominant navigation cue for pedestrian navigation. Very few navigation data models in the geographical information science and transportation communities support modeling of landmarks and use of landmark-based route instructions for pedestrian navigation services. This article proposes a landmark-based pedestrian navigation data model to fill this gap. This data model can model landmarks in several pedestrian navigation scenarios (buildings, open spaces, multimodal transportation systems, and urban streets). This article implements the proposed model in the ArcGIS software environment and demonstrates two typical pedestrian navigation scenarios: (1) a multimodal pedestrian navigation environment involving bus lines, parks, and indoor spaces and (2) a subway system in a metropolitan environment. These two scenarios illustrate the feasibility of the proposed data model in real-world environments. Further improvements of this model could lead to more intuitive and user-friendly landmark-based pedestrian navigation services than the functions supported by current map-based navigation systems.

Reliable Space–Time Prisms Under Travel Time Uncertainty

Time geography is a powerful framework for analyzing human activities under various space–time constraints. At the core of time geography is the concept of the space–time prism, which delimits an individuals potential activity locations in space and time. The classical space–time prism, however, admits only deterministic travel speeds and ignores the stochastic nature of travel environments. In this article, the classical space–time prism model is extended to congested road networks with travel time uncertainty. A reliable space–time prism is proposed to consider explicitly an individuals on-time arrival probability concerns in the face of travel time uncertainty. The reliable space–time prism is defined as the set of space–time locations where an individual can participate in an activity and return to his or her destination with a given on-time arrival probability. To construct such a reliable space–time prism in a road network, a solution algorithm is developed. A case study using real-world traffic information is carried out to demonstrate the applicability of the proposed prism model. The results of the case study indicate that the proposed prism model can represent well individuals’ space–time taking into account various on-time arrival probability concerns.

Functionally critical locations in an urban transportation network: identification and space-time analysis using taxi trajectories

This paper studies the space-time properties of locations that are critical to travel activities in an urban environment. Specifically, we analyze locations on the urban street network from the perspective of the distribution of peoples travel trajectories. We identify the intersections of an urban transportation network which are characterized by good connectivity, serving a high density of trip trajectories, and exhibiting multiple traversing patterns of trip trajectories as potential functionally critical network locations (FCNLs). A geospatial method is proposed to extract FCNLs from peoples moving trajectories based on the street network. Two groups of quantitative indices are introduced to measure the evolution of the spatial extent and temporal variation patterns of different criticality levels of FCNLs. A case study using taxi trajectory data from Wuhan, China has been implemented. The results show that the FCNLs are very powerful in uncovering the space-time traveling patterns of a particular population and studying the relationship between urban functional structures and peoples activities. Language: en

FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices

Defining a relation between granules and computing ever-changing granules are two important issues in granular computing. In view of this, this work proposes a partial order relation and lattice computing, respectively, for dealing with the aforementioned issues. A fuzzy lattice granular computing classification algorithm, or FL-GrCCA for short, is proposed here in the framework of fuzzy lattices. Algorithm FL-GrCCA computes a fuzzy inclusion relation between granules by using an inclusion measure function based on both a nonlinear positive valuation function, namely arctan, and an isomorphic mapping between lattices. Changeable classification granules are computed with a dilation operator using, conditionally, both the fuzzy inclusion relation between two granules and the size of a dilated granule. We compare the performance of FL-GrCCA with the performance of popular classification algorithms, including support vector machines (SVMs) and the fuzzy lattice reasoning (FLR) classifier, for a number of two-class problems and multi-class problems. Our computational experiments showed that FL-GrCCA can both speed up training and achieve comparable generalization performance.

Finite Markov chain analysis of classical differential evolution algorithm

Theoretical analyses of algorithms are important to understand their search behaviors and develop more efficient algorithms. Compared with the plethora of works concerning the empirical study of the differential evolution (DE), little theoretical research has been done to investigate the convergence properties of DE so far. This paper focuses on theoretical researches on the convergence of DE and presents a convergent DE algorithm. First of all, it is proved that the classical DE cannot converge to the global optimal set with probability 1 by using the property that it cannot escape from a local optimal set. Inspired by the characteristics of the elitist genetic algorithm, this paper proposed a modified DE to overcome the disadvantage. The proposed algorithm employs two operators that assist it in escaping from a local optimal set and enhance the diversity of the population. And it is then verified that the proposed algorithm is capable of converging to global optima with probability 1. The theoretical research of this paper is undertaken in a finite discrete set, and the analysis tool used is the Markov chain. The numerical experiments are conducted on a deceptive function and a set of benchmark functions. The experimental results support the theoretical analyses on the convergence performances of the classical and modified DE algorithm.

Multi-ant colony system for evacuation routing problem with mixed traffic flow

Evacuation routing problem with mixed traffic flow is complex due to the interaction among different types of evacuees. The positive feedback mechanism of single ant colony system may lead to congestion on some optimum routes. Like different ant colony systems in nature, different components of traffic flow compete and interact with each other during evacuation process. In this paper, an approach based on multi-ant colony system was proposed to tackle evacuation routing problem with mixed traffic flow. Total evacuation time is minimized and traffic load of the whole road network is balanced by this approach. The experimental results show that this approach based on multi-ant colony system can obtain better solutions than single ant colony system and solve mixed traffic flow evacuation problem with reasonable routing plans.