Perception of urban subdivisions in pedestrian movement simulation

Abstract

The perception of urban subdivisions, deriving from regionalisation processes and the identification of separating elements (barriers), has proven to dynamically shape peoples’ cognitive representations of space and route choice behaviour in cities. However, existing Agent-Based Models (ABMs) for pedestrian simulation have not accounted for these particular cognitive mapping processes. The aim of this paper is to explore the behaviour of pedestrian agents endowed with knowledge about urban subdivisions. Drawing from literature in spatial cognition, we adapted a region-based route choice model, which contemplates a high- and a local planning level, and advanced a barrier-based route choice model, wherein the influence of separating elements is manipulated. Finally, we combined these two approaches in a region-barrier based model. The patterns emerging from the movement of agents employing such approaches were examined in the city centres of London and Paris. The introduction of regions in the routing mechanisms reduced the unbalanced concentration of agents across the street network brought up by the widely employed least cumulative angular change model (-.08 Gini coefficient). The inclusion of barriers further raised the dispersal of the agents through secondary roads, while leading agents to walk along waterfronts and across parks; it also yielded a more regular usage of pedestrian roads. Moreover, the region- and the region-barrier based routes showed deviation ratio values from the road distance shortest path (region-based: 1.18 London, 1.16 Paris, region-barrier based: 1.43 London, 1.33 Paris) consistent with empirical observations from pedestrian behaviour research. A further evaluation of the model with macro-level observational data may enhance the understanding of pedestrian dynamics and help tuning the interplay amongst urban salient elements at the agent level. Yet, we consider the movement flows arising from our current implementation insightful for assessing the distribution of pedestrians and testing possible interventions for the design of legible and walkable spaces.

Evaluative image 2.0: A web mapping approach to capture people’s perceptions of a city

“…The most liked and disliked areas were examined with regard to the presence of some urban elements included within their boundaries. We used a methodology presented by Filomena and colleagues (Filomena et al, 2019; Filomena, Manley, & Verstegen, 2020) to extract, on the one hand, natural elements such as parks, waterbodies or sea coast, and, on the other hand, human‐made elements, or so‐called severing barriers . A large body of research in environmental psychology and urban geography, including by Nasar himself, has reported a preference for urban spaces such as parks, green areas and waterfronts or areas featured by the presence of trees, grass, and water (e.g., Kaplan, 1985; Lynch, 1960; Nasar, 1990; Ulrich, 1986).…”

From dawn to dusk: daily fluctuations in pedestrian traffic in the city center

“…Among the most recent studies, Filomena et al 33 developed a region-based route choice model. The authors claim that some features such as parks or water bodies together with urban subdivisions impact route choice decisions of pedestrians.…”

“…Real 500 m × 500 m region including buildings in downtown Osaka city/pedestrians are modeled as ''aggregate flows'' Johansson et al 27 Social force model calibrated with evolutionary optimization algorithm to determine optimal parameter specifications Jamaraat Bridge-pedestrian bridge in Mina, Saudi Arabia/very specific outdoor scenario Torrens 28 Behavioral fidelity, dimensionality, analytical finesse, dynamic user experience, multi-spatial representation, dynamic information scaling, spatial extensibility, and efficient computing Realistic urban environment: synthetic downtown setting with sidewalks, roads, and road crossings/presentation of general methodology rather than specific real-life case Crooks et al 29 Use real scene mobility information to improve performance and accuracy of pedestrian ABM Open space in center of Edinburgh/sensitive and hard-to-access data used for calibration Tordeux et al 19 Mesoscale pedestrian model/pedestrian movements described with aggregate density-flow relationships One of real-world scenarios involving Indonesian city of Padang: area of 7 km × 4 km/no interaction between agents; closer to macroapproach with aggregate flow rates of agents Hussein and Sayed 30 Microscopic model calibrated with data extracted from real pedestrian trajectories Signalized intersection in Oakland, California (four conventional crosswalks in addition to two diagonal crosswalks)/very small area; lacks interaction with other road users Knura 31 ABM model based on social forces concept with urban environments represented by GIS Inner city of Hamburg, Germany/insufficient details on model mechanics and calibration Filomena et al 33 ABM of pedestrian movement deriving from perception of regions and barriers in pedestrian navigation…”

“…If they reach the point, they permanently leave the sandbox. The concept is similar to the gateways described by Knura 31 or Filomena et al 33 However, we did not associate probability values with entry/exit points as in the study of Crooks et al 29 In the model, the gateways are situated in the locations linked to transport (tram, bus stops) as well as parking facilities. In general, pedestrian flows are usually observed from and to these points.…”

Neighbourhood-level pedestrian navigation using the construal level theory

“…This approach is also limited to graph-based navigation and cannot incorporate the navigation of continuous spaces. Filomena et al (2020) model pedestrian navigation using a theoretically robust multi-scale representation of urban environments, but this work lacks the granularity required to represent street-level movement. Torrens (2012) presents a highly granular agent-based model of pedestrian movement in continuous spaces that is well justified in terms of pedestrian spatial cognition.…”