AgentCity. An Agent-Based Modeling Approach to City Planning and Population Dynamics

Transcription

1 AgentCity An Agent-Based Modeling Approach to City Planning and Population Dynamics George Mudrak Department of Computer Science University of Colorado at Colorado Springs Colorado Springs, CO Sudhanshu Kumar Semwal Department of Computer Science University of Colorado at Colorado Springs Colorado Springs, CO, Abstract Cities spend time, revenue and resources in an effort to optimize the geographic and physical layout of property zones and services for the population. As physical construction of these various city models is prohibitive, we can create computational models for testing and experimentation. These computational models include human elements and simulate natural complex social system. These computational-models also provide opportunities to study emergent behaviors similar to the real world. These computational emergent behaviors can give us insight into the effectiveness of our city model. This paper will focus on creating a computational model of a virtual city with zoning and a heterogeneous population using NetLogo modelling environment. Results of our implementation are described and future opportunities are identified. Keywords-NetLogo simulation of city planning, emergence, collobaration I. INTRODUCTION Real cities contain organic structures evolving through a lifecycle that might be unique to the city even though not unique in concept. These organic structures start out small and with factors, such as resource availability, time, planning and people, and can represent magnificence of its inhabitants. While growth and death occur naturally, it is desirable to ensure the longevity of a city for its inhabitants. This planning on the surface represent a straightforward approach to meeting needs. For example, build a road here, a school there and place traffic lights in these locations, lower taxes or raise taxes and suddenly a magical concoction yields a city of beauty or city where even the traffic cannot move properly. Even if city planning can be a straightforward construction project, no city is complete without its people. Once we inject people into this magical concoction, it no longer represents a linear system expressed through many simple formulae. The inclusion of a population brings about a dynamic, evolving complex system that gains its complexity by operating within and even beyond the constraints brought about by simple city planning, creating structures and emergent behaviors [1]. This complexity means that original plans as laid out may no longer apply as a dynamic population goes about its days, nights and lives within the city. In this paper, we use Agent-Based Modelling (ABMs) to begin exploring the dynamics of a city's population with support for residential, commercial and industrial zoning. Agent-based modeling (ABM) is a form of computational social simulation particularly suited to tasks where understanding processes and their consequences is important and concrete experimentation may not be a viable option [2]. ABMs do not take a purely reductionist approach to decomposition of a given model/problem into its primitive constituent parts but rather work towards definitions of simple behavioral rules executed by autonomous entities, allowing behaviors to emerge through interactions between autonomous entities [3]. As the agents go about their "lives" within the simulation, the execution of these base-behaviors can yield a dynamic and emergent pattern of interactions; with these emergent patterns providing great insight into the real-world systems in question and allowing this model to operate as a principle investigative tool [4]. The computation model presented in this paper draws its influences from the wonderful 1989 game, SimCity, released by Maxis software. The SimCity game, or more correctly, simulated city, featured the player as the city's mayor, to decide the destiny of the businesses and the people [5]. Over the years, SimCity has seen four versions and numerous simulation game follow-ups. From a gaming perspective, SimCity launched the "Sim" game era. Our implementation is based on NetLogo which is much simpler visually in comparison to SimCity yet provides toolset to implement an Agent Based modelling environment. II. AGENCY DETAILS A. What is AgentCity AgentCity [6] was developed using NetLogo 3D v4.1.3 (April 3, 2011) on a " imac 2.8 Ghz core i7 with 16 Gb RAM and an ATI Radeon HD 4850 with 512 Mb RAM. NetLogo, itself was developed by Uri Wilensky and provides a complete 2D and 3D development environment with the necessary graphical elements to support end user interaction and visual presentation of the agent based model. We choose a multi-layer approach to model the 3D environment for AgentCity to support 3D-data visualization in NetLogo [7]. AgentCity refers to the 3D visualization in which the business and people agents interact. Two additional layers within AgentCity assist with data visualization. The agent interactions happen according to a time scale we established as NetLogo's "tick" concept has no direct correlation to a fixed unit of time.

2 B. Time Scale The default time measurement in NetLogo is the "tick" unit and a corresponding real-world time value cannot be automatically inferred. Therefore, a deliberate mapping of "ticks" per time unit must be calculated. We started by choosing a movement pace of 0.2 steps per tick. This meant that a person would cover 1 patch in 5 ticks, assuming straight entry / exist on the four compass sides (N, S, E, W) and nondiagonal travel. This 0.2 value also had the added benefit of assisting with a smooth visual representation of movement. Next, we choose an average employee drive time of 30 minutes and arbitrarily assigning 10 patches to correspond to that. For example, using 5 ticks per patch, ten patches per thirty minutes, and sixty minutes per hour, NetLogo ticks per hour (tph) will equal 100, and NetLogo ticks per day (tpd) assuming 24 hours per day will be This allows time scale and work-shifts to be described in terms of ticks in NetLogo. C. The City Layer Four distinct visual elements compose AgentCity. Blue and Pink spheres represent gender aware people with blue males and pink females. Each of the businesses or "zones" is represented as colored cubes. Green for residential businesses, Blue for Commercial and Yellow for Industrial businesses. All of the businesses and residents occupy a "0" base plane within the 3D cube. Zones are randomly generated and based on a percentage of the useable land as indicated during initialization. L a = C w * C h L u = La * P l Z r = L u * P r Z c = L u * P c Z i = L u * P i P l, P r, P c, P i are constants representing the percentages used for usable land, residential land, commercial land and industrial land. La is the land available for use based on the City's width times the City's height. Lu is the land allocated for use by the zones. P l is the percentage of the land available for use or land to use. Z r is the land allocated for residential zones. P r is the percentage of the zone available land to use for residential purposes. Z c is the land allocated for commercial zones. P c is the percentage of the zone available land to use for commercial purposes. Z i is the land allocated for industrial zones. P i is the percentage of the zone available land to use for industrial purposes. D. The Growth Layer The next layer down (-z axis) from the city layer is the Growth Layer. This layer shows in real-time the dynamics of population movement as residents accept and leave jobs, and move into and out of residences. This layer visually presents population gains and population losses for a given zone. P z is the sum of all people affiliated with this zone. Growth or loss itself is calculated as the difference of the current population minus the prior population. A negative value shows a decrease in population and positive value indicates an increase. The delta change can be mapped onto a color and displayed. E. The Pollution Layer The lowest layer presents current pollution of the associated zones as a heat map of pollution intensity. Pollution amounts are based on the Energy Information Administration's (EIA) annual sum amounts for each listed industry found in the U.S. Carbon Dioxide Emissions from Energy Sources 2008 Flash Estimate [8]. These annual values were broken down to daily values across all like zones. As pollution values needed to correspond to ticks, we calculated pollution per NetLogo tick as P r by using the value of the total population in the industry (P z per year) divided by 365 days per year to obtain total population per day. This can be further divided by 2400 to convert to population per NetLogo tick. These values alone per industry do not give enough information yet as they represent the aggregate for that industry. However, the prior formula combined with the national aggregated EIA pollution data yields (P t ) values of: residential P t = ; commercial P t = ; industrial P t = Therefore, to know at a per business (zone) level, we need to take the pollution per tick value of P t and divide that by the number of the type of zone in question for a per zone pollution value. Now with individual zone pollution values present, their visual representation was mapped to a scale of 9 max-number system in which 0 represents the absence of pollution and 9 represented the maximum pollution possible. III. SETUP AND VISUALIZATION Our model utilizes two sets of user-controlled values to determine how the initial city is created and then how the user can interact with the city once the simulation has begun. A. The setup Initialization parameters provide the initial world settings for the city and the agents within our environment. These parameters determine the number of people, amount of usable, zoned, city land, our gender ratios, energy, search vision, etc. Initialization of our city occurs over two synchronous passes.

3 B. Zones The first initialization pass creates the city's residential, commercial and industrial agents per the equations described prior. These agents are distributed randomly throughout the full city plain. The input values, specified by the user, include the percentage of usable land and the percentages indicating the residential, commercial and industrial zones. While not mobile, we wanted our zones to demonstrate preferential affinity towards each other, similar that found in the Boids work [9]. We then extend this concept to use an affinity algorithm based on a cellular automata by generating a neighbor-likeness value N i. The use selects the value of likeneighbors ratio N w. When N i is greater than N w the zone settles down and no longer moves. This provides an 'anchor' so that other zones can attach as neighbors assuming their affinity needs are met. However, when N i is less than or equal to N w the zone will continue looking for a place to settle as the current neighbors do not meet this zones affinity needs. Additional zone attributes are set to include the genderpreference indicating the ratio of males to females desired by the zone. A self-pollution value indicating the pollution this zone inherently creates and a maximum population supported by this zone. Once all zones have settled, city initialization moves to the next step of placing people. C. Resdidents The second initialization pass creates the city's residents. We create the specified number of residents and assign a gender in alignment with the specified gender ratio, a health preference percentage to indicate the person's tolerance to a poor environment, a happiness value and a vision value ranging from 1 to the user specified search vision value. Our agents scan within their vision to locate an employer and a residence. After the attributes have been set and the residents locate a starter resident and job, the simulation setup is complete and ready for execution. D. Runtime options The current runtime options assist with the visualization of the environment and ongoing activity. As described earlier, AgentCity was developed with the 3D version of NetLogo to facilitate the visualization of data in additional cube layers. During execution, the user can choose toggle the visualization rockers to show or hide the display of people, zones, growth and pollution layers. Further, built-in features of NetLogo allow the user to rotate and zoom in and out of the cube space visualization. IV. RESULTS: AGENTS AND THEIR BEHAVIOR An agent-based model seeks to understand agent behavior given a variety of parameters. For this model, the people and various zones; residential, commercial, industrial, represent the agents within our city. Agents have attributes typically maintaining state and agents have behaviors driven by their attributes and environmental parameters. A. Zones Recall that zones are effectively the businesses within the city meeting residential and employment needs. Zones follow their own behaviors when interacting with other agents. A zones initial state and type are established when created leaving behavior to drive runtime interactivity. All zones will pollute to varying degrees as a combination of their inherent pollution plus an adjusting factor based on contained population. Zones actively manage their gender preferences and populations and when they deviate from the desired values, will act to bring them back into alignment. B. Residents Our residents by nature represent the mobile aspect of our modeled-city and like zones, have a range of initial attributes established when they are created. During execution, residents are influenced by other agents and seek to be happy productive citizens of our town. In doing this, residents search for employment (shift based work), search for housing, and will manage their happiness and health. Loss of employment or housing affects a person's happiness. An excessively polluted work or housing environment that exceeds tolerances of an individual will prompt that person into seeking a new employer or a new residence, as appropriate. People also adjust their employment and residence based on their personal gender affinity. Throughout the course of a given day, people can be found at home, work, transitioning between or simply wandering around.

4 V. COMPUTATIONAL MODEL The model essentially operates in a continuous loop asking the agents to perform their behaviors. A. Main loop The main loop establishes the agent activity methods that will be called ongoing for the respective agents. With each discrete agent type providing its own looping cycle, the main loop calls each in turn. main loop update day of week people zones update visuals end The day of the week is updated based on the number of ticks that have passed since the start of the simulation. Remember from the time frame calculations above, that 1 day = 2,400 ticks. This activity takes this information and displays a named day of the week for the user. The main loop further calls an agent method for agent specific processing and then completes the current cycle with updated visuals. B. People This method iterates all the residents giving each an opportunity to calculate and act. people end find a home find a job update happiness update health If a person does not have a home or job, that individual will look within their search vision radius for a home or job. A person s health, employment and home play a role in core happiness. As with real life circumstances, poor health, unemployment or homelessness brings happiness down. Further, a standard 2-sigma distribution to generate an element of randomness is used to affect a persons' happiness. Updating health allows the person to measure their current employment and home pollution to determine if they move. This check does not involve a random element. Once the pollution exceeds the individual s preferences, they act to relocate. C. Zones This method iterates all zones giving them a chance to update attributes and act on behaviors. zones loop balance genders balance population position zone apply pollution end Balancing genders and population follow a simple rule such that if the current value exceeds the desired value, then people of the appropriate gender are let go. Zone-positioning refers to "unsettled" zones and gives them a chance to move, locating themselves based on their affinity setting. Apply the pollution for not only this zone but its impact on neighboring zones is also performed. The impact can be measured by an estimate of falloff pollution. We made use of a following equation adapted from the Astrophysics, Gravity formula but using higher power to tighten the falloff. Let P i is the pollution for the specific non-originating zone. Let falloff = R -5 where R is the radial distance. Then P i = falloff * P s where P s is the pollution of the specific non-originating zone. VI. RESULTS AND COMPUTATIONAL MODEL IN ACTION Once the initialization completes we have the starting state for our simulation. As the model executes in NetLogo, the city itself will change appearance based on modeled resident behaviors. These adjustments will typically be viewed as changes in the density of a zone as indicated by its vertical height. The zone visuals themselves do not provide depth as they function more in aggregate, as such additional visuals are supported by AgentCity to provide greater operational insight into the city's residents' activities. A. Agent city views There are various views of AgentCity that can be captured. Here we demonstrate a sequence of views starting with our initial city having completed setup, then a state after the population has located jobs and homes, then finally a view of the city after the simulation has run for a while. B. Visualized insight into the model The 3D city model provides visualization in aggregate and allows us to see broader trends such as heavily populated zones. This aggregate view does not give us numerical feedback into the model and what activities are taking place. Therefore we added several additional numerical data visualization elements to watch data of interest such as the unemployment and homeless rates, work-shift transitions, resident s happiness, pollution levels and detailed numeric's.

5 We present some of these additional UI elements (Figure 4) below. Figure 4. Chart indicating homeless and jobless Figure 1. Initial City, setup complete Figure 5. Population activities Figure 2. Population layout after homes and jobs are added Figure 6. Gender and capacity statistics during simulation of AgentCity. VII. DISCUSSION OF EMERGENT BEHAVIOR The computational model itself proved reasonable in terms of implementation and execution. Over the course of several executions we observed interesting behaviors not originally expected. Figure 3. Zone density and pollution heat map three layers. We noticed on several occasions, that even with enough housing and employment capacity, there was always some portion of the population homeless and unemployed. This contradicts the base behavior for individuals to find and accept a job and for employers to hire any applicant. With no economic means rules in place, the cost of housing or having an employee played no role. In other words, it makes no

6 obvious sense why people would remain homeless or jobless in a society with enough capacity. This requires further study. We also noticed that some situations existed in which the current population exceeded the zone capacity and surprisingly, it was very possible to find underutilized zones. For example, a population of 4,000 people with a residential capacity of 3,000 would still show residential zones operating at 25% of capacity. Why would this occur when the need far outstrips the capacity? One of the issues we discovered during various sample runs was that our chosen "tick-to-time" relationship was too granular. Requiring 2,400 ticks per day meant that progress and observable results took more real-time than necessary. In hindsight, making some assumptions such as "instant" travel from home to work would have allowed us to cut down on the number of downtime ticks. Future model versions will need to revisit this issue. VIII. FUTURE DIRECTIONS While we implemented a pollution model, we did not implement a corresponding pollution dispersion model. The end result of the current model is that pollution simply accrues and given enough time, no zone remains hospitable. An enhancement would be to add pollution dispersion to the model. Additional efforts could be made to increase the relationships between the agents in terms of greater producer / consumer behaviors. A further enhancement would be to model from existing city zoning data. This enhancement would allow for modeling specific cities, such as Denver, by extending the model to load city zoning information from a saved data file. IX. COCLUSIONS Agent Based Models allowed us to explore and experiment without any restrictive bounds, we could change parameters and visualization paradigms easily using NetLogo environment. ABMs create a way for us to quickly and dynamically evaluate impacts of changes in environmental settings on given models. These provided an insight into dynamic economic, social and psychological systems in a living environment which we modeled which was not obvious before we started our modeling. We have presented AgentCity as an agent based model for the complex systems task of city planning and testing. The very nature of a complex systems makes direct testing and evaluation at times prohibitive and in some cases, impossible as many of the parameters used in our experiment are based on some values which can dynamically change depending upon real-life situations. Modeled as an ABM, we were able to experiment with various starting conditions and observe the results of the heterogeneous population activities. Our model provided enough emergent behaviors, which sometime are relevant in the real world, prompting the need for further exploration and evaluation. We also feel that AgentCity can provide a starting platform for complimentary work such as residential real-estate, traffic patterns, and potential testing of zoning for specific use such as biking, pedestrians inclusive of sighted and non-sighted population, possibly helping with future city planning. REFERENCES [1] Mitchell, Melanie, "Complexity, A Guided Tour, Oxford University Press, pp (2009). [2] Gilbert, Nigel, "Agent-Based Models", SAGE Publications, pp. 2-3 (2008). [3] John H. Holland and John H. Miller, Artificial Adaptive Agents in Economic Theory, AEA Papers and Proceedings, Vol. 81 No.,2, pp (May 1991). [4] Epstein, Joshua, Axtell, Robert, "Growing Artificial Societies", Brookings Institution Press, p. 177 (1996). [5] Wilson, Johnny, "The Official SimCity Planning Commission Handbook", Osborne McGraw-Hill, pp. xxii-xxiii, (1994). [6] Mudrak, G, AgentCity modeling, term report, Animation and Visualization (CS5770) Dr. S.K.Semwal, UCCS, pp (2011). [7] Wilensky, Uri, "NetLogo", Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, [8] U.S. Carbon Dioxide Emissions from Energy Sources 2008 Flash Estimate, [9] Lalena, M. [Online] Java Flocking Boids.