An agent-based model of the influence of neighbourhood design on daily trip patterns
Highlights
► We develop an agent-based model of the trip influence of neighbourhood design. ► Neo-traditional and fused grid designs generally provide more pedestrian benefits. ► These benefits also depend on implementation of infrastructure and facilities. ► Designs and design features often have both positive and negative impacts. ► The study shows a meso-level approach to urban and transportation simulation.
Introduction
The relationship between neighbourhood design and daily trip patterns is an active area of research. As the basic units of cities, the design of urban neighbourhoods directly determines the characteristics of local road networks and the availability and location of local facilities. These factors in turn influence the schedule, mode and route choices of local residents. Local traffic patterns, the collective outcome of these choices, in turn have feedback influences on trip behaviour, as people choose their schedule, route and mode to minimise travel time, reduce cost, and avoid congestion. They also attempt to reduce exposure to safety hazards (Schlossberg, Agrawal, Irvin, & Bekkouche, 2007) and automobile emissions (Bhat et al., 2009, Kaur et al., 2006, King et al., 2009), and increase their level of physical activity (Brownson, Baker, Housemann, Brennan, & Bacak, 2001). A positive social environment of social cohesion and trust, a pedestrian-friendly infrastructure with connected pedestrian paths and sidewalks, and a lower volume of traffic all contribute to the choice of walking mode (Cao et al., 2006, MacDonald, 2007), while the presence of “other people walking” influences pedestrian route choice (Schlossberg et al., 2007).
Traditionally, research on the relationship between neighbourhood design and trips focused on the statistical relationship between neighbourhood characteristics and trip characteristics (Boarnet and Crane, 2001a, Boarnet and Crane, 2001b, Cao et al., 2009, Cao et al., 2006, Cervero and Duncan, 2003, Crane, 1995, McNally and Ryan, 1992, Sen and Baht, 2009, Stead, 2001, Stone et al., 1992). Personal characteristics and preferences are often neglected in such studies, the influence of the micro-environment is also rarely considered.
Urban neighbourhoods are complex systems formed as collective outcome of human behaviour. While traditional research methods like aggregate statistical analysis and equation-based methods can be used to analyse certain aspects of such a complex system, they often fail to explain many essential characteristics such as local interactions, feedback processes and emergent phenomena. Agent-based models are suitable for modelling urban neighbourhoods, as feedback processes (such as the interaction between automobile and pedestrian traffic) and bottom-up phenomena (such as the generation of dynamic traffic patterns) can be easily and intuitively simulated. Such models also make possible the evaluation of newly proposed designs such as the fused grid design, for which no real-world aggregate data are available.
Agent-based models have been widely used to simulate movement patterns and trip behaviour in the recent years, but the daily patterns of automobile and pedestrian traffic and the interactions and feedbacks between them at the neighbourhood level are rarely considered. Existing models mostly focus on either automobile traffic patterns at metro- or national-scales (Benenson, Martens, & Birfir, 2008) or pedestrian movements in constrained building environments (Batty, 2003, Castle, 2006, Helbing et al., 2000, Lee and Lam, 2008, Schelhorn et al., 1999). While popular modelling software such as TRANSIMS (Smith, Beckman, Anson, Nagel, & Williams, 1995) and MATSim (Matsim.org, 2012) support multi-modal simulations, the majority of studies based on these software still focus on a single-mode of either automobile or pedestrian (Rieser, 2010). Rieser (2010) introduced a multi-modal agent-based model that includes car, transit and other non-car modes, but the movement of non-car modes are not explicitly simulated (i.e. such agents are teleported). Interactions and feedbacks are important for complex systems, but few studies have explicitly included such processes. The MATSim package features an iterative optimisation process based on automobile traffic feedbacks (Balmer et al., 2008, Nagel and Flötterlöd, 2009), but no pedestrian-automobile interaction is considered. Agent-based studies that consider pedestrian-automobile interactions mostly took a mechanistic view of the problem and simulate how macro-scale movement characteristics such as speed and directions influence the probability of collision (For example, see Banos et al., 2005, Lotzmann et al., 2009). Recently, a few meso-scale models have appeared. Waddell, Wang, Charlton and Olsen, (2010) introduced a sub-metro scale microsimulation model of land use and transportation, but the model assigns trip demands to the network using a static approach, with no consideration of iterative dynamics and pedestrian-automobile feedbacks. A latest study (Aschwanden, Wullschleger, Muller, & Schmitt, 2012) included detailed movement characteristics of pedestrians and automobiles in the simulation of an urban neighbourhood setting, but there is still no direct interaction between pedestrian and automobile traffic; agents’ socio-economic characteristics and trip schedules are not considered either.
As Batty (2003, p. 83) points out, behaviour in human systems is determined not only by personal preferences, intentions and desires, but also “by the environment which reflects the spatial or geometric structure in which the agents function as well as variability between agents, in terms of their intrinsic differences and the uncertainty that they have to deal with in making any response”. A neighbourhood-scale model that takes into account both personal preferences and environmental constraints, and that considers interactions, feedbacks and uncertainties has not been seen in the literature. Such a model will help improve our understanding of how neighbourhood design influences trip and traffic patterns and daily lives.
Section snippets
Model setup
Based on the Repast simulation platform (Repast Organization for Architecture and Development, 2003) and OpenMap GIS toolkit (BBN Technologies, 2005), an agent-based model is designed to explore the influences of neighbourhood design on trip and traffic patterns with an emphasis on pedestrian movements.
Trip and traffic patterns in the urban neighbourhoods are the collective outcome of individual residents’ mode and route choices. To simulate trip and traffic patterns, the first step is to
Estimation and calibration
Trip survey data from seven Ottawa TAZs are used for the calibration of the model. Fig. 2 shows the location of these seven TAZs in three areas: Westboro (TAZs 242 and 243), Barrhaven (TAZs 433, 434 and 435) and Bridlewood (TAZs 500 and 501). For each TAZ, road characteristics and facility locations are identified by manually interpreting Google Earth satellite imagery and Microsoft Bing Maps aerial (“bird’s eye”) imagery. Of the three areas, Westboro has a traditional grid design, and features
Experiments setup
Four types of neighbourhood designs were examined in this study. The traditional grid and post-war suburban designs are important because they are widely used throughout North America. In the recent years, the neo-traditional design has also been implemented in many neighbourhoods in the US and Canada. With the calibrated model, experiments can be carried out to find out how neighbourhood design in general, and how specific design features in detail (such as the availability and location of
Discussion
Results from the study show that the neo-traditional designs and the fused grid design are generally pedestrian-friendly, with fewer crossings, less walking distance to facilities, less traffic and pollution exposure and more social interaction opportunities for pedestrians. The implementation of a design is important, as provisions of facilities and pedestrian-only routes at different locations prove to be generating very different mode split and traffic patterns. Results also show the complex
References (58)
- et al.
Agent based evaluation of dynamic city models: A combination of human decision processes and an emission model for transportation based on acceleration and instantaneous speed
Automation in Construction
(2012) - et al.
PARKAGENT: An agent-based model of parking in the city
Computer, Environment and Urban Systems
(2008) - et al.
The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use
Transportation Research Part B
(2009) - et al.
The influence of land use on travel behavior: Specification and estimation strategies
Transportation Research Part A
(2001) - et al.
The relationship between the built environment and nonwork trave: A case study of Northern California
Transportation Research Part A
(2009) - et al.
Pedestrian route-choice and activity scheduling theory and models
Transportation Research Part B
(2004) - et al.
Exposure visualization of ultrafine particle counts in a transport microenvironment
Atmospheric Environment
(2006) - et al.
Environment measures of physical activity supports: Perception versus reality
American Journal of Preventive Medicine
(2003) - et al.
Simulating pedestrian movements at signalized crosswalks in Hong Kong
Transportation Research Part A
(2008) Vehicle-type choice and neighbourhood characteristics: An empirical study of Hamilton, Canada
Transportation Research Part D
(2008)
Stated preference in the valuation of interurban road safety
Accident Analysis and Prevention
Perceived characteristics of the neighbourhood and its association with physical activity behavior and self-rated health
Health & Place
Forecasting the effects of road user charge by stochastic agent-based modelling
Transportation Research Part A
A methodological framework for the study of residential location and travel-to-work mode choice under central and suburban employment destination patterns
Transportation Research Part A
A simultaneous two-dimensionally constraint disaggregate trip generation, distribution and mode choice mode: Theory and application for the Swiss national model
Transportation Research Part A
Associations of perceived social and physical environment supports with physical activity and walking behavior
American Journal of Public Health
Agent-based pedestrian modelling
Travel by design: The influence of urban form on travel
Environmental and policy determinants of physical activities in the United States
America Journal of Public Health
The influence of the built environment and residential self-selection on pedestrian behavior: Evidence from Austin
Transportation
Walking, bicycling and urban landscapes: Evidences from the San Francisco Bay Area
American Journal of Public Health
Induced travel demand and induced road investment: A simultaneous equation analysis
Journal of Transport Economics and Policy
Are perceptions of the physical and social environment associated with mothers’ walking for leisure and for transport? A longitudinal study
Preventive Medicine
Cited by (18)
Evolution of residents' cooperative behavior in neighborhood renewal: An agent-based computational approach
2023, Computers, Environment and Urban SystemsEvaluation of public health interventions from a complex systems perspective: A research methods review
2021, Social Science and MedicineCitation Excerpt :Models cannot truly capture the complexities and unpredictability of the real world, but they may be of use to decision-makers in anticipating likely impacts of interventions. Agent-based models (ABMs) were typically used to hypothesise and simulate how agents within a system might react and interact in response to an intervention (White and Levin, 2016; Adams and Schaefer, 2016; Atkinson et al., 2018; Beheshti et al., 2017; Combs et al., 2019; Luke et al., 2017; Jin and White, 2012; Kasman et al., 2019; Keyes et al., 2019a, 2019b; Koh et al., 2019; Lee et al., 2018; Li et al., 2018; Scott et al., 2016; Spicer et al., 2012; Yang et al., 2014; Yonas et al., 2013; Zhang et al., 2014). System dynamics (SD) modelling was used to hypothesise and simulate how an intervention may impact on and interact within a wider complex system (Allender et al., 2019; Araz et al., 2018; Biroscak et al., 2014; Caroleo et al., 2017; Chen et al., 2018; Eker et al., 2018; Guo et al., 2016; Guzman et al., 2013; Haghshenas et al., 2015; Hirsch et al., 2010; Honeycutt et al., 2019; Jalali et al., 2019; Kuo et al., 2016; Lich et al., 2017; Loyo et al., 2013; Lyon et al., 2016; Manohar et al., 2014; Nyabadza and Coetzee, 2017; Soler et al., 2016; Tengs et al., 2001; Tobias et al., 2010; Wakeland et al., 2013; York et al., 2017).
A scoping review of simulation modeling in built environment and physical activity research: Current status, gaps, and future directions for improving translation
2019, Health and PlaceCitation Excerpt :Most (11 of the 14 articles; 7 of the 10 authors) indicated that the model had not been used by practitioners or policy-makers to their knowledge. Of the remaining three, one responded that the model had been commissioned by a mortgage and housing corporation, but the author was uncertain if and how the model had been used (Jin and White, 2012). A second indicated that the city they worked with used the findings to support their investment decisions in pedestrian and bicycle infrastructure (MacDonald Gibson et al., 2015).
Size, connectivity, and tipping in spatial networks: Theory and empirics
2015, Computers, Environment and Urban SystemsSimulating choice set formation processes in a model of endogenous dynamics of activity-travel behavior: The effect of awareness parameters
2015, Computers, Environment and Urban SystemsComparing spatial metrics that quantify urban form
2014, Computers, Environment and Urban SystemsCitation Excerpt :Accessibility is a related concept, but with greater focus on the ability to access destinations (a neighborhood might have high centrality, i.e. near key activity centers, but poor accessibility because of missing street connections to the activity centers). Critics of sprawling suburban development contend that neighborhoods with winding dendritic streets, large residential blocks, and cul-de-sacs are not pedestrian friendly (Jin and White, 2012). Consequently, accessibility metrics seek to quantify street pattern and network connectivity (Ewing et al., 2002; Song & Knapp, 2004).