Optimal routing of pedestrian flow in a complex topological network with multiple entrances and exits

A real-world topological network consists of multiple entrances along its source nodes. Routing appropriate percentages of pedestrians from these entrances to the particular available routes with relevant arrival rates will improve the network’s performance. This paper presents a framework for finding the optimal arrival rates of pedestrians from all available entrances and routes to downstream nodes maximising the network’s throughput. The calculation of the arrival rates and movement directions is based on M/G/C/C analytical and simulation models and the network flow model and considers the real distances of the entrances along the source nodes. The framework was tested on the Tuanku Syed Putra Hall, Universiti Sains Malaysia, Malaysia. Extensive analyses of the performances of its available nodes especially on the achievable optimal throughputs were documented and discussed. Quantitative results show that the hall’s throughput is optimised when pedestrians’ arrival rates to all the available entrances and their movement directions are controlled within certain ranges

A Microscopic Simulation Laboratory for Evaluation of Off-street Parking Systems

The parking industry produces an enormous amount of data every day that, properly analyzed, will change the way the industry operates. The collected data form patterns that, in most cases, would allow parking operators and property owners to better understand how to maximize revenue and decrease operating expenses and support the decisions such as how to set specific parking policies (e.g. electrical charging only parking space) to achieve the sustainable and eco-friendly parking. However, there lacks an intelligent tool to assess the layout design and operational performance of parking lots to reduce the externalities and increase the revenue. To address this issue, this research presents a comprehensive agent-based framework for microscopic off-street parking system simulation. A rule-based parking simulation logic programming model is formulated. The proposed simulation model can effectively capture the behaviors of drivers and pedestrians as well as spatial and temporal interactions of traffic dynamics in the parking system. A methodology for data collection, processing, and extraction of user behaviors in the parking system is also developed. A Long-Short Term Memory (LSTM) neural network is used to predict the arrival and departure of the vehicles. The proposed simulator is implemented in Java and a Software as a Service (SaaS) graphic user interface is designed to analyze and visualize the simulation results. This study finds the active capacity of the parking system, which is defined as the largest number of actively moving vehicles in the parking system under the facility layout. In the system application of the real world testbed, the numerical tests show (a) the smart check-in device has marginal benefits in vehicle waiting time; (b) the flexible pricing policy may increase the average daily revenue if the elasticity of the price is not involved; (c) the number of electrical charging only spots has a negative impact on the performance of the parking facility; and (d) the rear-in only policy may increase the duration of parking maneuvers and reduce the efficiency during the arrival rush hour. Application of the developed simulation system using a real-world case demonstrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating smart parking facilities

Emergency response in complex buildings: Automated selection of safest and balanced routes

The extreme importance of emergency response in complex buildings during natural and human-induced disasters has been widely acknowledged. In particular, there is a need for efficient algorithms for finding safest evacuation routes, which would take into account the 3-D structure of buildings, their relevant semantics, and the nature and shape of hazards. In this article, we propose algorithms for safest routes and balanced routes in buildings, where an extreme event with many epicenters is occurring. In a balanced route, a trade-off between route length and hazard proximity is made. The algorithms are based on a novel approach that integrates a multiattribute decision-making technique, Dijkstra's classical algorithm and the introduced hazard proximity numbers, hazard propagation coefficient and proximity index for a route

A discrete simulation model for heterogeneous traffic including bicycles on urban road networks

Environment and health-related concerns mean that pedal-bicycles as an alternative mode of urban transport are gaining ground, with study of motorised/non-motorised traffic mix a topic of practical interest in transportation science and traffic modelling. This thesis reports on a simulation model, developed for heterogeneous traffic on city networks with AD HOC lane-sharing, characteristic of Dublin streets. While based on simple cellular automaton rules, the vehicle movement model also accounts for vehicle type heterogeneity and network-specific factors, including the resolution of conflicts and effects of driver decisions on movement dynamics. The model has been implemented as an agent-based simulation framework. Its spatial component is based on a modular design that facilitates straightforward scenario configuration and scalability. In order to perform large network simulations, the framework has been adapted for parallel processing. Issues of both static and dynamic load balancing are considered. While detailed field data are not available for heterogeneous traffic on urban networks, which precludes precise quantitative validation, sensitivity analysis of the model was performed with a wide range of parameters and values. Macroscopic whole-network measures are defined and used to study a number of scenarios, the most manifest property of which is the contrast between slow and fast, vulnerable and less vulnerable agents in the traffic mix

Pedestrian Mobility Mining with Movement Patterns

In street-based mobility mining, pedestrian volume estimation receives increasing attention, as it provides important applications such as billboard evaluation, attraction ranking and emergency support systems. In practice, empirical measurements are sparse due to budget limitations and constrained mounting options. Therefore, estimation of pedestrian quantity is required to perform pedestrian mobility analysis at unobserved locations. Accurate pedestrian mobility analysis is difficult to achieve due to the non-random path selection of individual pedestrians (resulting from motivated movement behaviour), causing the pedestrian volumes to distribute non-uniformly among the traffic network. Existing approaches (pedestrian simulations and data mining methods) are hard to adjust to sensor measurements or require more expensive input data (e.g. high fidelity floor plans or total number of pedestrians in the site) and are thus unfeasible. In order to achieve a mobility model that encodes pedestrian volumes accurately, we propose two methods under the regression framework which overcome the limitations of existing methods. Namely, these two methods incorporate not just topological information and episodic sensor readings, but also prior knowledge on movement preferences and movement patterns. The first one is based on Least Squares Regression (LSR). The advantage of this method is the easy inclusion of route choice heuristics and robustness towards contradicting measurements. The second method is Gaussian Process Regression (GPR). The advantages of this method are the possibilities to include expert knowledge on pedestrian movement and to estimate the uncertainty in predicting the unknown frequencies. Furthermore the kernel matrix of the pedestrian frequencies returned by the method supports sensor placement decisions. Major benefits of the regression approach are (1) seamless integration of expert data and (2) simple reproduction of sensor measurements. Further advantages are (3) invariance of the results against traffic network homeomorphism and (4) the computational complexity depends not on the number of modeled pedestrians but on the traffic network complexity. We compare our novel approaches to state-of-the-art pedestrian simulation (Generalized Centrifugal Force Model) as well as existing Data Mining methods for traffic volume estimation (Spatial k-Nearest Neighbour) and commonly used graph kernels for the Gaussian Process Regression (Squared Exponential, Regularized Laplacian and Diffusion Kernel) in terms of prediction performance (measured with mean absolute error). Our methods showed significantly lower error rates. Since pattern knowledge is not easy to obtain, we present algorithms for pattern acquisition and analysis from Episodic Movement Data. The proposed analysis of Episodic Movement Data involve spatio-temporal aggregation of visits and flows, cluster analyses and dependency models. For pedestrian mobility data collection we further developed and successfully applied the recently evolved Bluetooth tracking technology. The introduced methods are combined to a system for pedestrian mobility analysis which comprises three layers. The Sensor Layer (1) monitors geo-coded sensor recordings on people’s presence and hands this episodic movement data in as input to the next layer. By use of standardized Open Geographic Consortium (OGC) compliant interfaces for data collection, we support seamless integration of various sensor technologies depending on the application requirements. The Query Layer (2) interacts with the user, who could ask for analyses within a given region and a certain time interval. Results are returned to the user in OGC conform Geography Markup Language (GML) format. The user query triggers the (3) Analysis Layer which utilizes the mobility model for pedestrian volume estimation. The proposed approach is promising for location performance evaluation and attractor identification. Thus, it was successfully applied to numerous industrial applications: Zurich central train station, the zoo of Duisburg (Germany) and a football stadium (Stade des Costières Nîmes, France)

Wayfinding and Perception Abilities for Pedestrian Simulations

Computer simulations of pedestrian dynamics are common and reliable tools in order to evaluate safety risks of facilities. However, still many soft- ware frameworks for evacuation simulations imply the assumption that all simulated pedestrians are familiar with their environment and therefore take the shortest path to the outside. In fact, the spatial knowledge of people generally varies. Thus, the assumption that all persons of a build- ing possess comprehensive spatial knowledge is a rough approximation of the reality. Especially for simulations in complex buildings the reliability of this approximation is questionable. In order to make simulations of pedestrian dynamics more reliable in this regard, this thesis introduces a new software framework. This framework provides the possibility to predict route choices of a group of people with varying spatial knowledge degrees. Therefor, the framework also considers selected wayfinding strategies that are applied beside the use of spatial memories. These are using signage, using generalized knowledge about the structure of buildings, and search strategies. In addition, three studies have been conducted in order to investigate wayfinding abilities and strategies of people in office buildings and subway stations. The results of the studies are discussed and are used to calibrate and test the models of the new software framework. Finally, the framework is applied to conduct a case study of an evacuation scenario in a subway station. The case study turns out that the egress time in the station is strongly dependent on the wayfinding strategies and abilities of the occupants. This outcome suggests that the proper consideration and prediction of route choices is relevant and necessary for reliable evacuation simulations.Computersimulationen von Fußgängerströmen sind heutzutage ein gängiges Hilfsmittel, wenn es darum geht, Sicherheitsrisiken eines geplanten Neubaus oder Bestandsobjektes im Vorfeld zu erkennen und zu analysieren. Die Mehrheit der Modelle für die Routenwahl von Fußgängern legt die Annahme zugrunde, dass Menschen sich für einen Weg entscheiden, deren zurückzulegende Strecke möglichst kurz ist oder deren Reisezeit möglichst klein ist. Dies impliziert, dass sämtliche Räume, Ausgänge, Korridore, etc. jedem Fußgänger bekannt sind. Diese Annahme kann im Besonderem bei der Betrachtung von komplexen Gebäuden nur als starke Vereinfachung der menschlichen Orientierung bzw. Wegfindung angesehen werden. Um Evakuierungssimulation diesbezüglich zu verbessern bzw. belastbarer zu machen, stellt die vorliegende Thesis ein neues Software-Framework vor. Dieses bietet die Möglichkeit, auch Fußgänger bzw. deren Routenwahl abzubilden, die nur Teile des Gebäudes kennen oder denen das Gebäude unbekannt ist. Die Modelle des Frameworks berücksichtigen hierbei die Anwendung von räumlichem Wissen (kognitive Karte), die Nutzung der Fluchtwegsbeschilderung und die Verwendung von generalisiertem Wissen über Gebäudestrukturen. Des Weiteren wurden drei Studien zur Untersuchung der Wegewahl von Personen in Bürogebäuden und U-Bahnhöfen durchgeführt. Die Ergebnisse der Studien werden in dieser Thesis diskutiert und zur Kalibrierung und Prüfung der Modelle herangezogen. Schließlich wird das Framework im Rahmen einer Simulationsstudie in einer U-Bahnstation angewendet. Diese Studie zeigt, dass die Räumungszeit der Station in Abhängigkeit der Wegfindungsstrategien und -fähigkeiten der Personen stark variieren kann und daher die Berücksichtigung menschlicher Wegfindung in Evakuierungssimulationen relevant ist

The design and simulation of traffic networks in virtual environments

For over half a century, researchers from a diverse set of disciplines have been studying the behaviour of traffic flow to better understand the causes of traffic congestion, accidents, and related phenomena. As the global population continues to rise, there is an increasing demand for more efficient and effective transportation infrastructures that are able to accommodate a greater number of civilians without compromising travel times, journey quality, cost, or accessibility. With recent advances in computing technology, transportation infrastructures are now typically developed using design and simulation packages that enable engineers to accurately model large-scale road networks and evaluate their designs through visual simulation. However, as these projects increase in scale and complexity, methodologies to intuitively design more complex and realistic simulations are highly desirable. The need of such technology translates across to the entertainment industry, where traffic simulations are integrated into computer games, television, film, and virtual tourism applications to enhance the realism and believability of the simulated scenario. In this thesis two significant challenges related to the design and simulation of traffic networks for use in virtual environments are presented. The first challenge is the development of intuitive techniques to assist the design and construction of high-fidelity three-dimensional road networks for use in both urban and rural virtual environments. The second challenge considers the implementation of computational models to accurately simulate the behaviour of drivers and pedestrians in transportation networks, in real time. An overview of the literature in the field is presented in this work with novel contributions relating to the challenges defined above

Integrated Special Event Traffic Management Strategies in Urban Transportation Network

How to effectively optimize and control spreading traffic in urban network during the special event has emerged as one of the critical issues faced by many transportation professionals in the past several decades due to the surging demand and the often limited network capacity. The contribution of this dissertation is to develop a set of integrated mathematical programming models for unconventional traffic management of special events in urban transportation network. Traffic management strategies such as lane reorganization and reversal, turning restriction, lane-based signal timing, ramp closure, and uninterrupted flow intersection will be coordinated and concurrently optimized for best overall system performance. Considering the complexity of the proposed formulations and the concerns of computing efficiency, this study has also developed efficient solution heuristics that can yield sufficiently reliable solutions for real-world application. Case studies and extensive numerical analyses results validate the effectiveness and applicability of the proposed models

Integration of micro- and macroscopic models for pedestrian evacuation simulation

Simulation of pedestrian evacuations of smart buildings in emergency is a powerful tool for building analysis, dynamic evacuation planning and real-time response to the evolving state of evacuations. Macroscopic pedestrian models are low-complexity models that are and well suited to algorithmic analysis and planning, but are quite abstract. Microscopic simulation models allow for a high level of simulation detail but can be computationally intensive. By combining micro- and macro- models we can use each to overcome the shortcomings of the other and enable new capability and applications for pedestrian evacuation simulation that would not be possible with either alone. We develop the EvacSim multi-agent pedestrian simulator and procedurally generate macroscopic flow graph models of building space, integrating micro- and macroscopic approaches to simulation of the same emergency space. By “coupling” flow graph parameters to microscopic simulation results, the graph model captures some of the higher detail and fidelity of the complex microscopic simulation model. The coupled flow graph is used for analysis and prediction of the movement of pedestrians in the microscopic simulation, and investigate the performance of dynamic evacuation planning in simulated emergencies using a variety of strategies for allocation of macroscopic evacuation routes to microscopic pedestrian agents. The predictive capability of the coupled flow graph is exploited for the decomposition of microscopic simulation space into multiple future states in a scalable manner. By simulating multiple future states of the emergency in short time frames, this enables sensing strategy based on simulation scenario pattern matching which we show to achieve fast scenario matching, enabling rich, real-time feedback in emergencies in buildings with meagre sensing capabilities