Modelling public transport accessibility with Monte Carlo stochastic simulations: A case study of Ostrava

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin-destination (O-D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance-decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.Web of Science1124art. no. 709

Local movement: agent-based models of pedestrian flows

Modelling movement within the built environment has hitherto been focused on rather coarse spatial scales where the emphasis has been upon simulating flows of traffic between origins and destinations. Models of pedestrian movement have been sporadic, based largely on finding statistical relationships between volumes and the accessibility of streets, with no sustained efforts at improving such theories. The development of object-orientated computing and agent-based models which have followed in this wake, promise to change this picture radically. It is now possible to develop models simulating the geometric motion of individual agents in small-scale environments using theories of traffic flow to underpin their logic. In this paper, we outline such a model which we adapt to simulate flows of pedestrians between fixed points of entry - gateways - into complex environments such as city centres, and points of attraction based on the location of retail and leisure facilities which represent the focus of such movements. The model simulates the movement of each individual in terms of five components; these are based on motion in the direction of the most attractive locations, forward movement, the avoidance of local geometric obstacles, thresholds which constrain congestion, and movement which is influenced by those already moving towards various locations. The model has elements which enable walkers to self-organise as well as learn from their geometric experiences so far. We first outline the structure of the model, present a computable form, and illustrate how it can be programmed as a variant of cellular automata. We illustrate it using three examples: its application to an idealised mall where we show how two key components - local navigation of obstacles and movement towards points of global locational attraction - can be parameterised, an application to the more complex town centre of Wolverhampton (in the UK West Midlands) where the paths of individual walkers are used to explore the veracity of the model, and finally it application to the Tate Gallery complex in central London where the focus is on calibrating the model by letting individual agents learn from their experience of walking within the environment

Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment

Gibson's ecological theory of perception has received considerable attention within psychology literature, as well as in computer vision and robotics. However, few have applied Gibson's approach to agent-based models of human movement, because the ecological theory requires that individuals have a vision-based mental model of the world, and for large numbers of agents this becomes extremely expensive computationally. Thus, within current pedestrian models, path evaluation is based on calibration from observed data or on sophisticated but deterministic route-choice mechanisms; there is little open-ended behavioural modelling of human-movement patterns. One solution which allows individuals rapid concurrent access to the visual information within an environment is an 'exosomatic visual architecture" where the connections between mutually visible locations within a configuration are prestored in a lookup table. Here we demonstrate that, with the aid of an exosomatic visual architecture, it is possible to develop behavioural models in which movement rules originating from Gibson's principle of affordance are utilised. We apply large numbers of agents programmed with these rules to a built-environment example and show that, by varying parameters such as destination selection, field of view, and steps taken between decision points, it is possible to generate aggregate movement levels very similar to those found in an actual building context

Agent based approach to land use mix

Modelling and simulating the dynamics of crowd movement within the complex built environment such as a city centre is an evolutionary, processing research task. Recent methodological and theoretical advances have provided the opportunity to explore and provide answers to various crucial problems on land use mix. Daily in our urban settlements we seek for resources and attractions. Our search behaviour is complex and emergent, related to urban morphology and land use patterns as this is generated by our daily movement and activities. This report discusses a pedestrian movement study which examines the ways pedestrian behaviour and flows affect and are affected by the formation of the built environment and the land uses. The focus is in retailing uses and especially shopping. For the formulation of the model, an agent based simulation approach is adapted based on object oriented analysis and programming. Agents are given long distance vision and direct their movement and behaviour in response to the information retreat from their vision field, morphology of the local environment, and their individual desire for retail or exploration of the area. The simulations are used to extract meaningful conclusions on the pedestrian behaviour and factors that have an impact on it. Various formations of retail location patterns in a 7 x 7 grid are explored and three different approaches of agents’ behaviour are used in order to get meaningful conclusions

Crowd simulation for dynamic environments based on information spreading and agents’ personal interests

In this work a novel crowd simulation framework that incorporates information of the dynamic environment is introduced. It supports knowledge spreading and allows the simulated agents to behave according to their personal needs that are affected by the surroundings. Each agent has their own personal interests and needs, which affects its goals and interactions with the environment. Genetic algorithms are used to simulate the dynamic behaviour of the environment and the knowledge spreading. As a result more accurate and realistic simulations are obtained improving a wide range of industrial and research applications that require accurate crowd simulation and modelling

Agent-based Models in Supporting Pedestrian Transportation Planning and Design

Agent-based models offer a new approach to understanding human-urban interactions in transportation systems, allowing individual entities within a system to be characterized with cognitive and behavioral properties. This paper discussed the role of agent-based representations of pedestrian transportation systems, detailing the underlying assumptions and techniques behind different types of pedestrian models and illustrating the differences between aggregate and individual agent representations. It then turns attention to the case study and the development of a cognitive pedestrian model as a way to illustrate the spectrum of potential spatial behaviors that are enabled by material changes to the transportation network. The paper concludes with a discussion and specific frameworks for employing agent-based models to support transportation planning decisions

Role Playing Learning for Socially Concomitant Mobile Robot Navigation

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method