added a new example: city walking behaviour

examples/city_walking_behaviour/Readme.md

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+# Walking Behavior Agent-Based Model
+
+An agent-based model (ABM) that simulates how people walk in cities based on socioeconomic status, environment, and social factors.
+
+## What This Model Shows
+
+This simulation demonstrates how different city layouts and social factors affect walking patterns. Key insights include:
+
+- How safety perceptions influence route choices
+- Impact of socioeconomic status on walking frequency
+- Effects of centralized vs distributed city planning
+- Relationship between social networks and walking behavior
+
+## Features
+
+- Four simulation scenarios:
+
+  - Random layout with random safety (RR)
+  - Random layout with core safety gradient (RS)
+  - Centralized layout with random safety (CR)
+  - Centralized layout with core safety gradient (CS)
+
+- Agent behaviors include:
+  - Work commutes
+  - Shopping trips
+  - Social visits
+  - Leisure walks
+
+## Outcomes in different scenarios
+
+#### RR
+![image](https://github.com/user-attachments/assets/fddda37d-d0d5-40f7-9e6d-89fa9b37898a)
+
+#### RS
+![image](https://github.com/user-attachments/assets/67112dbb-056d-4bd5-b668-a1bb389c262d)
+
+#### CR
+![image](https://github.com/user-attachments/assets/aee37477-7693-46a8-848e-5a5986db0e95)
+
+#### CS
+![image](https://github.com/user-attachments/assets/f9e58239-19a2-4fa4-bbea-2b7ccbe36c8f)
+
+## Quick Start
+
+1. Run the simulation:
+
+```python
+solara run app.py
+```
+
+## Technical Details
+
+### Model Architecture (`model.py`)
+
+#### Initialization Parameters
+
+- Grid dimensions (width and height)
+- Number of workplaces (categorized into Grocery Stores, Social Places, etc.)
+- Population composition (number of couples and singles)
+
+#### Environmental Layers
+
+1. **Safety Layer** (`safety_cell_layer`)
+
+   - Values vary based on selected scenario
+   - Impacts walking behavior and route choices
+
+2. **Aesthetic Layer** (`aesthetic_cell_layer`)
+   - Values decrease with distance from center
+   - Reflects personal preferences in route selection
+
+### Agent Implementation (`agents.py`)
+
+#### Human Class Attributes
+
+- **Demographics**: Gender, Age (18-87), Family Size (1-2), Pet Ownership (20% probability)
+- **Personal**: Walking Ability, Walking Attitude, Employment Status
+- **Social**: Network of friends and family members
+
+#### Behavioral System
+
+Walking attitude influenced by:
+
+- Social network (family and friends' attitudes)
+- Environmental factors (safety and aesthetics)
+- Local pedestrian density
+- Cumulative walking distance
+
+### Data Collection
+
+Tracks metrics across five SES levels (1-5):
+
+1. Average daily walking trips
+2. Work-related trips
+3. Basic needs trips
+4. Leisure trips
+
+## File Structure
+
+- `city_walking_behaviour/model.py`: Core simulation engine
+- `city_walking_behaviour/agents.py`: Agent behavior definitions
+
+## Based On
+
+This model extends research from ["A Spatial Agent-Based Model for the Simulation of Adults' Daily Walking Within a City"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3306662/)