Human behavior modeling can easily become a list of theories: KAP, TPB, HBM, PMT, SCT, SDT, COM-B, ABM, and many more. That is not enough for deep understanding.
This series is planned as a learning path. Each post should answer one practical question:
What does this model help us understand, what does it miss, and when should we use it?
The focus is transportation, urban planning, civil engineering, safety, sustainability, emergency response, and policy. The running examples can include public transit use, speeding, distracted driving, cycling, evacuation, electric vehicle adoption, car dependency, and low-carbon mobility.
The goal of the series is to understand human behavior modeling as a research strategy, not as a catalog of frameworks. A strong reader should finish the series able to:
- distinguish description, explanation, prediction, intervention, and simulation;
- choose a model based on the behavior, mechanism, data, and policy question;
- connect psychological constructs to measurable variables;
- recognize when individual-level models are not enough;
- combine theory, survey data, travel behavior data, choice models, and simulations.
The series moves from simple to complex in six layers.
| Layer | Main Question | Example Models |
|---|---|---|
| Descriptive models | What do people know, feel, and do? | KAP |
| Psychological models | Why do people intend, avoid, or act? | TPB, HBM, PMT, SCT, SDT |
| Social and environmental models | How do norms, values, routines, and systems shape behavior? | Diffusion of Innovations, NAM, VBN, Social Practice Theory |
| Choice and decision models | Which option will people choose? | Discrete Choice Models, Prospect Theory, Bounded Rationality |
| Behavior change models | What must change for behavior to change? | COM-B, BCW, TDF, Nudge Theory |
| System and computational models | How do individual behaviors produce system outcomes? | ABM, Activity-Based Models, System Dynamics, Hybrid Choice Models, Machine Learning |
Post Series Structure
Part 1: Foundations
Post 1: What Is Human Behavior Modeling?
Introduce the idea that planning and engineering systems are human systems, not only physical systems. Define behavior modeling as the study of how people make decisions, form intentions, respond to risk, develop habits, and interact with built and social environments.
Post 2: Why Knowledge Does Not Always Change Behavior
Use speeding, transit use, safety compliance, and active transportation to explain the knowledge-action gap. This post sets up why behavior modeling needs more than information campaigns.
Post 3: KAP Framework
Explain Knowledge, Attitude, and Practice as a simple descriptive model. Show how KAP can reveal awareness, misconceptions, and gaps between beliefs and behavior.
Post 4: Theory of Planned Behavior
Explain attitude, subjective norms, perceived behavioral control, intention, and behavior. Use public transit, cycling, seatbelt use, or EV adoption as examples.
Post 5: KAP vs. TPB
Compare descriptive and explanatory models. Show that KAP can tell us what people know and do, while TPB tries to explain why intention forms.
Part 2: Risk, Safety, and Protective Behavior
Post 6: Health Belief Model
Explain perceived susceptibility, severity, benefits, barriers, cues to action, and self-efficacy. Apply it to distracted driving, helmet use, seatbelt use, or disaster preparedness.
Post 7: Protection Motivation Theory
Explain threat appraisal and coping appraisal. Use evacuation, flood preparedness, or safety campaigns to show why fear alone is not enough if people lack efficacy or face high response costs.
Post 8: Transtheoretical Model
Explain behavior change stages: precontemplation, contemplation, preparation, action, and maintenance. Apply it to mode shift, safer driving, or active transportation.
Post 9: Social Cognitive Theory
Explain self-efficacy, observational learning, reinforcement, outcome expectations, and reciprocal determinism. Use cycling confidence or safe driving habits as examples.
Part 3: Motivation, Norms, and Social Influence
Post 10: Self-Determination Theory
Explain autonomy, competence, relatedness, intrinsic motivation, and extrinsic motivation. Apply it to sustainable mobility, active transportation, or public participation.
Post 11: Diffusion of Innovations
Explain how new technologies, policies, and behaviors spread through communication channels and social systems over time. Use EV adoption, e-scooters, mobility apps, or automated vehicles.
Post 12: Norm Activation Model
Explain awareness of consequences, ascription of responsibility, personal norms, and prosocial behavior. Apply it to carpooling or low-carbon mobility.
Post 13: Value-Belief-Norm Theory
Explain how values and environmental beliefs shape personal norms and sustainability behavior. Use climate-related travel behavior or support for congestion pricing.
Post 14: Social Practice Theory
Move beyond individual psychology. Explain materials, competences, meanings, and routines. Use car dependency, commuting routines, parking, land use, and household travel patterns.
Part 4: Choice and Decision Models
Post 15: Discrete Choice Models
Explain alternatives, attributes, utility, probabilities, and heterogeneity. Introduce multinomial logit, nested logit, mixed logit, latent class models, and the idea of choice sets.
Post 16: Prospect Theory
Explain reference points, loss aversion, probability weighting, and framing. Apply it to tolling, congestion pricing, route choice under uncertainty, or travel-time reliability.
Post 17: Bounded Rationality
Explain satisficing, limited information, heuristics, and cognitive limits. Use route choice, parking decisions, real-time information, or emergency decisions.
Post 18: Habit Theory
Explain repetition, context cues, automaticity, and past behavior. Use car commuting, speeding, phone use while driving, or route choice.
Post 19: Dual-Process Models
Explain automatic and reflective processing. Use pedestrian crossing, distracted driving, risk-taking, and habitual travel behavior.
Part 5: Intervention Design
Post 20: COM-B Model
Explain capability, opportunity, motivation, and behavior. Show how COM-B diagnoses what must change before behavior can change.
Post 21: Behaviour Change Wheel
Connect COM-B diagnosis to intervention functions such as education, persuasion, incentivization, training, restriction, environmental restructuring, modeling, and enablement.
Post 22: Theoretical Domains Framework
Explain detailed behavioral barriers and facilitators: knowledge, skills, social influence, beliefs about consequences, environmental context, emotion, intentions, goals, memory, attention, and decision processes.
Post 23: Nudge and Choice Architecture
Explain defaults, salience, feedback, framing, simplification, social norms, and reminders. Apply it to transit pass enrollment, speed feedback signs, route guidance, parking behavior, or safety compliance.
Part 6: Computational and System-Level Modeling
Post 24: Agent-Based Modeling
Explain agents, rules, environments, interactions, heterogeneity, and emergent outcomes. Use evacuation, pedestrian movement, congestion, or land-use and transportation interaction.
Post 25: Activity-Based Travel Models
Explain how daily activities generate travel demand. Use household schedules, school drop-off, work, shopping, caregiving, time use, and accessibility.
Post 26: System Dynamics
Explain stocks, flows, feedback loops, delays, and nonlinear change. Use induced demand, transit ridership decline, car dependency, or infrastructure investment cycles.
Post 27: Hybrid Choice Models
Explain how attitudes, perceptions, trust, risk perception, and environmental concern can be represented as latent variables inside choice models.
Post 28: Machine Learning for Behavior Prediction
Explain prediction, pattern detection, segmentation, interpretability, and theory gaps. Use GPS traces, crash risk, transit demand, travel mode prediction, or social media data.
Post 29: Integrating Theory, Data, and Simulation
Show how qualitative theory, surveys, structural equation modeling, discrete choice models, machine learning, agent-based simulation, and system dynamics can complement one another.
Post 30: Final Synthesis
Return to the core lesson: the best behavior model depends on the behavior, mechanism, data, and decision context.
Model Selection Guide
| Research Goal | Best-Fit Models |
|---|---|
| Describe what people know, feel, and do | KAP |
| Explain intention | TPB |
| Explain risk or protective behavior | HBM, PMT |
| Explain confidence and learning | SCT |
| Explain motivation quality | SDT |
| Explain adoption over time | Diffusion of Innovations |
| Explain moral or environmental behavior | NAM, VBN |
| Explain routines and practices | Social Practice Theory, Habit Theory |
| Predict choices among alternatives | Discrete Choice Models |
| Explain risk and loss framing | Prospect Theory |
| Explain limited decision-making | Bounded Rationality |
| Design interventions | COM-B, BCW, TDF, Nudge Theory |
| Simulate many interacting individuals | ABM |
| Model daily travel demand | Activity-Based Models |
| Model feedback over time | System Dynamics |
| Combine attitudes with choice | Hybrid Choice Models |
| Predict behavior from large data | Machine Learning |