Agent-based Activity-based Microsimulation Modeling of

Urban Transportation Systems

Mohammed Medhat AminDepartment of Civil Engineering

University of Toronto

Micro-simulation And Modeling Applications: Methods, Implications & Analysis (MAMAMIA)

…A Shift To the Activity-Based Approach

It recognizes that activities – from which a person’s travel derives – are themselves motivated by a complicated social environment that links them to other activities, to other people, and to the features of the built environment

An important nature of travel demand ignored by trip-based approaches is that travel is a derived demand – people travel to participate in other activities not for its own consumption value

A trip is generated to connect two spatially-separated sequential activities, and individual trips do not stand in isolations, but more motivated by the activities they enable

Simply, activity-based models express the need to travel as a function of the need to perform activities

The need for estimating the impacts of policies that affect not only the broad characteristics of urban form, but also target (directly/indirectly) the mechanisms that produce human travel behavior

Traditional approaches capture the effect of social roles and norms and the distribution of environmental resources, etc… on travel (demand) implicitly, while the activity-based approach treats these factors as endogenous to the system

In activity-based approaches, every individual is a decision-maker who deals with a huge choice set of various activity patterns in the time-space domain. Each combination of activities, their locations, starting points and durations forms a unique activity pattern

…A Shift To the Activity-Based Approach

What is an Agent?

To Describe an Agent

• Its environment

• Its sensing capabilities

• Its behavior (actions schemes)

What is an Agent (or autonomous agent)?

‘a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future’ [Franklin and Graesser, 1997 p.25]

Agents

are environment dependent. In other words, different agents belong to different environments.

sense and acts are two important properties of an agent, which determines how they behave inside their environment

• Agents independently make decisions as a function of their own attributes and the state of the system that they find themselves within.

Agent

Environment

Action Output Sensor Input

What is an Agent?

In summary,

 Input (sensor) – Output (action) entity

 Never controls its entire environment

  Just some (partial) influence on/control of a system

 Effectoric capability – ability of an agent to modify/change its environment

 Actions have pre-conditions

 May obtain complete, accurate and up-to-date information about its environment

What is an Agent?

ABM, What?

Interaction Rules: describe how agents interact with each other and the environment

Behaviors are generated through agent interaction/communication with other agents/objects and their environment(s)

Agents: are like people who have characteristics, goals and rules of behavior.

Objects: are the set of all represented passive entities that do not respond to stimuli (e.g. building)

Environment: are the topological space where agents and objects are located, move and act, and where signals (e.g. sounds) propagate

Behavioral Rules: define how agents behave and how environment changes as a result of agent activities.

Agents…

Reactive vs. Pro-active vs. Cognitive

Reactive agents are able to perceive their environment and respond according to their capabilities and the design objective

Task accomplishing behavior

Rules in the form of: if situation then action

Pro-active agents are able to take initiatives in order to reach their goals

Person Vs. Social agents

(e.g. individuals vs. employers)

Active Vs. Passive agents

Active agents interact with other agents and their environment to obtain some pay off

Passive agents, like locations, maintain a set of temporally restricted resources necessary for various activity types (i.e. time-window for an activity)

Cognitive agents have a set of perceived histories then encodes their experience in a form that can be applied to the problem of (deliberately) deciding what to do

have their activity plan

memorize what they have sensed in order to perform global planning tasks by using sources and information from their own experience

Senses and acts are two important properties of an agent, which determines how they behave inside their environment.

The range and sensitivity of their sense and the range and effectiveness of their actions determine the agent type.

Agents…

Non-binding activities, agents in activity systems tend to have relative autonomy in their activity

participation, including the ability to terminate some activities under way at will (e.g. a casual social engagement)

Recall that, in the travel demand context, the model is not intending as an optimizer problem solver, and hence the completion of activities need not to be guaranteed in order to meet some global goal.

Self-generated Vs. Driven from the environment

I want to have lunch! I need to pick up my kid from a football practice

Binding Vs. Non-binding Vs. Simultaneous

Binding activities, consider a travel activity using a train, a ‘physical’ constraint that the

agent can’t get off a moving train.

Activities…

Simultaneous activities, if the current activity does not ‘tie up’ other resources at the agent’s disposal, the agent should be able to apply them to another activity as

long as other criteria can be met (e.g. working on your laptop while being in the train)

Activities…

Agent-based models assume directly the behaviors of real-world decision-making units such as individual or household. Therefore, urban travel demands become the results of a multi-dimensional hierarchical choice process

The effects of inter-personal, inter-environmental, and inter-activity linkages have proven difficult to capture in existing models.

Linkages between the choices made by multiple individuals are either not represented or taken to be fixed inputs to the model.

This ultimately omits portions of the potential activity pattern space which may actually be feasible with slight modifications of the constraint space

These limitations reduce those models’ usefulness for the analysis of policy measures whose responses may be affected by such linkages

ABM, Why?

Using agent-based raises the possibility of emergent behavior; to predict outcomes that are not ‘hardwired’ into the model. For example, the generation of single-parent household

‘Turn the argument on its head, instead of artificial intelligence being inspired by social processes, we will model social processes using the techniques of AI’

Agent-based models are characterized by their ability to represent complex environment consisting of autonomous agents whose behavioral possibilities are defined by their relationship to the environment

The future state of urban systems generally can only be estimated by explicitly tracing the evolutionary path of the system over time, beginning with current know conditions.

ABM, Why?

It allows modeling space-time dynamics within urban systems, as it enables to study the relationships between micro-level individual actions and the emergent macro-level phenomenon

The MAIN IDEA

By linking an individual to a behavioral mechanism, it is possible to simulate an artificial world inhabited by interacted processes

Thus it is possible to implement simulation by transposing the populations of a real system to its artificial counterpart

Each member of the population is represented as an agent who has built-in behavior

ABM, How?

Given an initial condition, all the agents will behave based on their ‘personal’ characteristics, learning and interacting rules in the system. These actions, in turn, change the system state over time (congestion levels, housing prices…)

The system will then evolve to a pattern, perhaps an equilibrium, from which useful macro-level information can be extracted

The underlying assumption is that human behavior is adaptive, and that the engine of adaptation is the individuals’ assessment of his/her imbalance with the environment – ‘person-environment fit’

Agent-based models view the system as a population of self-directed entities that interact with each other according to specific behavioral rules that simultaneously motivates and limit behavior, thus dictating the dynamics of the system

Rules need to be constructed in such a way that these agents are able to grow, learn and interact with other agents and the environment

ABM, How?

An agent representing a person would therefore need:

 The ability to perceive information about environmental opportunities

A model to anticipate such opportunities in the future

And, a decision process that selects behavior based upon the anticipatory model of the environment to achieve some set of goals

An agent’s success in achieving its goals depends on its ability to anticipate the opportunities presented by its environment

Estimation ValidationVerification Calibration

ABM, How?

Consider, a simple work activity may involve three agents: an employee, an employer and a workplace

The P-E Fit concept describes a personal equilibration in which the individual tries to balance what s/he wants to do (i.e. the activity plan) with what the

environment allows (i.e. opportunity space)

Each agent has a time-series of activities that define his/her behavior as interactions with other agents in the environment and the environment itself

 Coupling Constraint

another employee is required to complete the task

Spatial Constraint

the employee must travel to the workplace

Authority Constraint

Obtain the permission to access work-related resources from his employer.

What should an ABM model?

Decision Process

that generates the activity plan.

decision Rules can be If-then classical statements, or may be any algorithm that maps from an interaction to an activity plan

it arises from an adaptive learning process driven by the agent’s desire to maximize some payoff through its actions over time.

Planning Process

requires that the agent can anticipate the consequences of its actions; by developing an internal model of the environment through experience

Learning Process

how the agent learns about its environment, how it learns about its options in the environment, how it learns to evaluate its own behavior in that

environment

occurs on different levels; perception, interpretation, possible actions, rules, and payoffs…

What should an ABM model?

Environment response

the agent’s activities lead to a response (a consequence) from the environment; one out of a set of all possible responses

the agent’s activities change the world state

Consider an agent’s decision to travel along a section of roadway

reduces the available capacity

the environment should reflect the impact of an additional traveler on the roadway segment

results in increasing the amount of time needed for agent’s travel activity

What should an ABM model?

The “Interaction” View

it permits greater flexibility for the representation of the universe of possible activities and their associated constraints.

For example, it recognizes that activities will have ‘agent-specific meanings’; a particular activity episode might be a shopping activity to a shopper,

while simultaneously being a work activity to a cashier at the store, an economic transaction for the shop keeper, and so on.

Role-based activity generation

the need to engage in activities should be an endogenous feature of the model. This will open the door to exploring the relationship between urban

design, socio-economic structure, and human behavior.

  Representation of resource distribution

activities are simply interactions involving the exchange or consumption of resources between entities in the environment. As a result, explicit

representation of resources distribution will enhance model expressiveness

Issues in ABMs…

Longitudinal

the dynamics of urban systems are likely to exhibit sensitivity to the initial conditions

Longitudinal models will ultimately be necessary to explore how path-dependent trajectories impact the effectiveness of policy measures.

Experiential learning

agents collect information about their environment as they interact with it, and use it to develop anticipatory models of the environment

Generality of design

the model should be expressive enough to enable representation of a broad range of local and global interactions, on a variety of timescales, with a view toward exploratory analysis of possible trajectories rather than the prediction of specific ones.

Issues in ABMs…

Learning about the opportunity space

Activities depend on resources (since they involve the allocation or transfer of resources between agents)

Therefore, an agent’s perception of the opportunity space will depend on its knowledge of the resources it has available to it, which it continuously updates over time through experience

For instance, an individual recognizes that a particular activity meets particular

requirements

Mainly important where there is no a one-to-one mapping between activities and requirements – like particular activities that satisfy multiple needs

e.g. having dinner with friends may satisfy both meal and social leisure needs

Issues in ABMs…

Learning about the Decision Rules The agent’s rule set arises from learning. In complex environment, the space of

perceived states and possible actions can get very large

Furthermore, the agent’s ability to perceive constraints may also limit the development of list of rules, and make such lists probabilistic (or Fuzzy!), i.e. I think I can do this!

Learning about decision rules is a complex process, possible involving insights and innovation, but is generally a function of the existing rule set and the agent’s experience

Issues in ABMs…

Learning about interpretations of Historical Trajectories

As agents gain experience, they develop better knowledge about casual linkages in the environment according to some process

Thus, a prior activity trajectory is used to update the agent’s anticipatory model about what to expect from the environment

A simple example of this is a model of expected travel time. As a individual makes trips in the transportation system, s/he will learn about areas of congestion in the system, perhaps linking time-of-day to anticipated travel times.

Issues in ABMs…

    “ Agent-based modeling is a third way of doing science. Like deduction, it starts with a set of explicit assumptions. But unlike deduction, it does not (try to) prove theorems. Instead an ABM generates simulated data that can be analyzed inductively. Unlike typical induction, however, the simulated data come from a rigorously specified set of rules rather than direct measurement of the real world. Whereas the purpose of induction is to find patterns in data and that of deduction is to find consequences of assumptions, the purpose of ABM is to aid intuition.

…Agent-based modeling is a way of doing through experiment. Although the assumptions may be simple, the consequences may not be at all obvious. Numerous examples…of locally interacting agents are called “emergent properties” of the system. Emergent properties are often surprising because it can be hard to anticipate the full consequences of even simple forms of interaction”

– Robert Axelrod (1997), emphasis added

Interesting!

Questions?

References…

• Agent-Based Approach to Modeling Environmental and Urban systems within GIS, (B. Jiang, 2000), Institutionen for Teknik, University of Gavel, Sweden

• An Agent-based Activity Micro-simulation Kernel Using a Negotiation Metaphor, (C. R. Rindt, J. E. Marca and M. G. McNally, August 2002): Institute of Transportation Studies, University of California, Irvine

• Is it an Agent? Or Just a Program?: A Taxonomy for Autonomous (Franklin S. and Graesser A., 1977)

•Microsimulation, (E. Miller, 2003)

• The Complexity of Corporations, (Robert Axelrod, 1997): Princetion University Press

• Toward Dynamic, Longitudinal, Agent-Based Microsimulation Models of Human Activity in Urban Settings, (C. R. Rindt, J. E. Marca and M. G. McNally, 2002): Institute of Transportation Studies, University of California, Irvine