A speculative exploratory urban planning engine designed for activists, policymakers, and city planners looking to shake things up. Forecasting rail connectivity development in Southeast Asia, this tool is like SimCity meets real-world infrastructure—except the stakes are higher.
By transforming biodiversity and census data into hypothetical “What-if” scenarios, it reveals indiscernible pathways through challenging terrains and natural hazards like earthquakes. Whether advocating for underserved communities or optimizing railway routes, users can explore how data-driven urban futures can reshape regional connectivity.
What if you could find the best-fit route that maximizes services to the majority of the population while avoiding seasonal hurricanes?
A Project by Xuan
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Prior to commencing the construction of a rail network, feasibility studies are conducted. These studies include terrain analysis, identifying surrounding population clusters, and even weather conditions. A significant amount of investments, resources, and labor are dedicated to this rigorous pre-selection process.
Given the geographical and socio-political challenges native to Southeast Asia—ranging from unique terrains to fragmented governance— and the political risks associated with large infrastructure projects, incorporating a data-driven calculator into the pre-selection process can increase engagement to underserved regions, maximise structure longevity and have greater consideration towards sustainable infrastructure planning, while weighing the risks specific to Southeast Asia—all of which are keys in the potential realisation of such hypothetical connectivity projects.
This project aims to develop a rail routing calculator for the general public, primarily rail enthusiasts, to route train lines and determine station placements with conditions specific to Southeast Asia. Instead of focusing solely on economically dominant areas, this "what-if" scenario helps bring attention to neglected regions by leveraging data such as ground elevation and earthquake safety distances and propose a few options of varying strengths of rail routes from Point A to Point B, which are then weighted against a final, feasibility score that is computed based on its constituent criteria.
For more details, please refer to the Research Paper
Historically, Southeast Asia has struggled to develop cohesive transport networks due to natural barriers, political fragmentation, and economic disparities, which has contributed to poor urban planning and underutilized transport routes. Such pan-regional infrastructure generally requires substantial amount of funding, time and resources.
The region’s terrain, ranging from rainforests to river deltas along with its weather, characterized by monsoon seasons and high humidity—introduces new challenges to the durability and maintenance of existing infrastructure as deterioration through flooding and high humidity, has led to a necessity for frequent maintenance work and in return, their associated costs.
One of the keys to making these projects worthwhile; maximizing their utility within their limited shelf life; is to place more emphasis on the quality and inclusiveness of its planning — to get as many Southeast Asians onboard as possible.
This project, inspired by the rapid urbanization and infrastructure challenges in Southeast Asia, where uneven development has led to economic disparities and access to resources, aims to develop a data visualization-based calculator that seeks to democratize civil projects in regards to public transport lines and station placements planning across Southeast Asia.
One of the key challenges amongst infrastructure projects is public perception—many believe such projects are too ambitious, complex or costly to execute. This calculator we will develop uses combinatorial optimization—called the “slime mold” as it is a natural organism known for its efficient network-building capabilities. In the name of biomimicry, this concept is applied in this context; to propose the most sensible curvature of a rail network to be constructed, demonstrating that infrastructure planning is "not as hard as we think," even in natural disasters prone environments like Southeast Asia, where factors such as tsunami can significantly impact planning doctrine.
By visualizing these factors, the tool weighs the risks and challenges specific to Southeast Asia to assist in routing train lines and determine station placements and gauges the likelihood of the realisation of such hypothetical connectivity projects.
Historically, the focus on railway expansion has always been solely on economically dominant areas. This "what-if" scenario helps bring attention to neglected regions and propose a few options of “Best Fit Curve”, each with their own varying strengths of rail routes from Point A to Point B, which are then weighted against a final, feasibility score that is computed based on its constituent criteria. By incorporating data and deriving its results, this not only allows a more inclusive infrastructure planning but also translates to the eventual ease of movement for human capital, services, and resources, facilitating economic integration in Southeast Asia.
| Framework | Objectives | Scope | Geographic Focus | Planning Methods |
|---|---|---|---|---|
| African Continental Free Trade Area (AfCFTA) Transport Infrastructure Development | Promote intra-African trade by improving transport infrastructure and connectivity. | Road, rail, air, and maritime transport networks. | Africa | - GIS-based spatial analysis - Cost-benefit analysis - Multi-criteria decision-making (MCDM) |
| ASEAN Connectivity Master Plan (ACMP) | Enhance physical, institutional, and people-to-people connectivity within ASEAN Southeast Asia. | Transport, energy, ICT, and digital infrastructure. | Southeast Asia (ASEAN member states) | - GIS for corridor mapping - Network optimization models - Scenario planning |
| Belt and Road Initiative (BRI) | Strengthen global trade and infrastructure networks through massive infrastructure investments. | Roads, railways, ports, energy, and telecommunications. | Global (primarily Asia, Europe, Africa) | - Combinatorial optimization - GIS for spatial analysis |
| NAFTA / USMCA Transport Corridors | Facilitate trade and transport efficiency among the U.S., Mexico, and Canada. | Road, rail, and maritime transport corridors. | North America | - Traffic flow modeling - GIS for corridor analysis - Simulation-based optimization |
| Pacific Alliance Infrastructure Integration Initiative | Strengthen economic integration and infrastructure connectivity among member countries. | Transport, energy, and telecommunications. | Latin America (Chile, Colombia, Mexico, Peru) | - GIS for spatial planning - Economic impact analysis - Network optimization |
| SAARC Regional Multimodal Transport Study | Develop a regional multimodal transport system to enhance connectivity and trade. | Road, rail, air, and maritime transport. | South Asia (SAARC member states) | - GIS for corridor identification - Multi-modal network optimization - Cost-effectiveness analysis |
| Trans-European Transport Network (TEN-T) | Create a seamless and efficient transport network across the EU. | Road, rail, air, and maritime transport, as well as logistics hubs. | Europe (EU member states) | - Combinatorial optimization - GIS for spatial planning - Traffic simulation models - Lifecycle cost analysis |
| Concept | Description |
|---|---|
| Combinatorial Optimization | A subset of Network Optimization Models and Urban Network Analysis (UNA) that uses mathematical approaches to find the most efficient or optimal solution from a finite set of possible options. |
| Geographic Information Systems (GIS) | A tool for spatial analysis and visualization to make informed decisions about infrastructure development. |
| Multi-Criteria Decision-Making (MCDM) | Maps levels of priority for projects based on multiple factors like cost, environmental impact, social benefits, and technical feasibility. |
| Traffic Flow Modeling and Simulation | Used to predict and optimize traffic and logistics in transport corridors by simulating different scenarios. |
| Climate-Resilient Infrastructure Frameworks | Extra considerations placed on designing and building infrastructure that can withstand the impacts of extreme weather. |
Ground Elevation: Digital elevation models (DEMs) and LiDAR for terrain and topography. https://opentopography.org/
Historical Earthquakes: Seismic data to spot earthquake hot zones. https://earthquake.usgs.gov/
Historical Tsunami: Records of historic tsunamis to determine safety distance away from coastlines. https://www.ngdc.noaa.gov/hazard/tsu_db.shtml
Forested Areas: Green forested cover to annotate conversation zones and mapped to evaluate environmental impact. https://globalforestwatch.org/
Coastlines: Shapefiles for coastal mapping. https://www .openstreetmap.org/
Humidity: Climate datasets for humidity levels. https://climateknowledgeportal.worldbank.org/
Biodiversity Indicators: Datasets on protected areas and species distribution. https://data.unep-wcmc.org/
Population Counts: Demographic data to spot high-density areas. https://human-settlement.emergency.copernicus.eu/
Land Area: Geospatial data as a base for overlaying other data. https://www.naturalearthdata.com/
Economic Activity: Economic data to identify high-demand areas. https://data.worldbank.org/ / https://www.oecd.org/en/data.html / https://data.adb.org/
Existing Network: Point/Line shapefiles on roads, railways to assess connectivity and avoid redundancy and repetition of new lines on existing networks. https://data.opendatasoft.com/pages/home/
Measures the risk of tsunamis based on ground elevation, proximity to the coastline, and historical tsunami occurrences.
| Risk Category | Score (Min 0 - Max 1) | Interpretation |
|---|---|---|
| High Risk | 0.00 - 0.33 | - Frequent past tsunamis - Low elevation - Near the coast |
| Moderate Risk | 0.34 - 0.66 | - Occasional tsunami activity - Medium elevation |
| Low Risk | 0.67 - 1.00 | - No tsunami history - High elevation - Far from coast |
Where:
Tsunami Risk Index - Tsunami Prevalence Score
Few Tsunami Count – Score ~ 0.67 - 1.0
Some Tsunami Count – Score ~ 0.34 - 0.66
Frequent Tsunamis – Score < 0.33
Where:
Tsunami Risk Index - Coastline Proximity Score
> 10 km from Coast – Score ~ 0.67 - 1.0
5 - 10 km from Coast – Score ~ 0.34 - 0.66
0 - 5 km from Coast – Score < 0.33
Where:
Tsunami Risk Index - Ground Elevation Score
Low elevation (Below sea level or < 10 m above sea level) – Score < 0.33
Moderate elevation (10 m – 50 m) – Score ~ 0.34 - 0.66
High elevation (> 50 m) – Score 0.67 - 1.0
Evaluates the durability of structures considering seismic activity, ground elevation, proximity to the coastline, and humidity levels.
| Durability | Score (Min 0 - Max 1) | Interpretation |
|---|---|---|
| Poor Durability | 0.00 - 0.33 | - High seismic activity - Low elevation - High humidity |
| Moderate Durability | 0.34 - 0.66 | - Some seismic activity - Moderate elevation - Moderate humidity |
| High Durability | 0.67 - 1.00 | - Low seismic activity - High elevation - Low humidity |
Where:
> 150 km Away – Score ~ 0.67 - 1.0
50 - 150 km Away – Score ~ 0.34 - 0.66
0 - 50 km Away – Score < 0.33
Where:
> 10 km from Coast – Score ~ 0.67 - 1.0
5 - 10 km from Coast – Score ~ 0.34 - 0.66
0 - 5 km from Coast – Score < 0.33
Where:
Low elevation (Below sea level or < 10 m above sea level) – Score < 0.33
Moderate elevation (10 m – 50 m) – Score ~ 0.34 - 0.66
High elevation (> 50 m) – Score 0.67 - 1.0
Where:
High Humidity (> 80%) – Score < 0.33
Moderate Humidity (50% - 80%) – Score ~ 0.34 - 0.66
Low Humidity (< 50%) – Score 0.67 - 1.0
Assesses the environmental impact based on land use changes and biodiversity loss.
| Impact Level | Score (Min 0 - Max 1) | Interpretation |
|---|---|---|
| High Impact | 0.00 - 0.33 | - Significant land conversion - High biodiversity presence |
| Moderate Impact | 0.34 - 0.66 | - Some land conversion - Moderate biodiversity presence |
| Low Impact | 0.67 - 1.00 | - Minimal land conversion - Low biodiversity presence |
Where:
Land Use Change < 10% – Score ~ 0.67 - 1.0
Land Use Change 10 - 25% – Score ~ 0.34 - 0.66
Land Use Change > 25% – Score < 0.33
Where:
Species Density < 10% – Score ~ 0.67 - 1.0
Species Density 10 - 35% – Score ~ 0.34 - 0.66
Species Density > 30% – Score < 0.33
Determines the operational feasibility considering ground elevation, network density, urban proximity, and population density.
| Operability Level | Score (Min 0 - Max 1) | Interpretation |
|---|---|---|
| Low Operability | 0.00 - 0.33 | - Isolated - High elevation - Low accessibility, not emergency-ready - Low population |
| Moderate Operability | 0.34 - 0.66 | - Moderately connected - Moderate elevation - Medium accessibility - Average population |
| High Operability | 0.67 - 1.00 | - Well-connected - Low elevation - High accessibility, emergency-ready |
Where:
Elevation <5m or >50m - Score 0
Elevation between 5-10m - Linear increase from 0 to 1
Elevation between 10-50m - Score 1
Elevation between 50-60m - Linear decrease from 1 to 0; transition to undesirable
Elevation >60m - Score 0
Where:
High Accessibility (> 0.5 km/km²) – Score < 0.33
Moderate Accessibility (0.1-0.5 km/km²) – Score ~ 0.34 - 0.66
Low Accessibility (<0.1 km/km²) – Score 0.67 - 1.0
Where:
Low Accessibility (> 50 km from urban center) – Score < 0.33
Moderate Accessibility (15 km - 50 km from urban center) – Score ~ 0.34 - 0.66
High Accessibility (< 15 km from urban center) – Score 0.67 - 1.0
Where:
Low Demand (<500 people/km²) – Score < 0.33
Moderate Demand (500-5,000 people/km²) – Score ~ 0.34 - 0.66
High Demand (>5,000 people/km²) – Score 0.67 - 1.0
Highlights the economic and population significance based on population density, land area, and GDP per capita.
| Importance Score (Min 0 - Max 1) | Interpretation |
|---|---|
| Low Importance (0.00 - 0.33) | Sparse population, High level of economic activity |
| Moderate Importance (0.34 - 0.66) | Balanced population and economic activity |
| High Importance (0.67 - 1.0) | High population density, Low level of economic activity |
Where:
Low Economic Importance (< 500 people/km²) – Score < 0.33
Moderate Economic Importance (500 - 5,000 people/km²) – Score ~ 0.34 - 0.66
High Economic Importance (> 5,000 people/km²) – Score 0.67 - 1.0
Where:
Where:
Low Economic Importance (< $5,000 USD) – Score < 0.33
Moderate Economic Importance ($5,000 - $40,000 USD) – Score ~ 0.34 - 0.66
High Economic Importance (> $40,000 USD) – Score 0.67 - 1.0
In this context, the 5 constituent indexes are combined, forming a new index which determines the score of the route from Point A to Point B. The goal is to minimise construction effort (ie. distance, terrain difficulty) while maximising regional connectivity, economic impact, and population engagement. The value here states how well the route adheres to identified constraints such as geographical hazards, population coverage, and structural resilience as the algorithm avoids areas prone to flooding or tsunamis
The Final Feasibility Index is an index that aggregates and integrates all 5 indexes into a single score:
1. Tsunami Risk Index (Hazard Vulnerability)
2. Structure Durability Index (Structural Resilience)
3. Environmental Impact Index (Environmental Sustainability)
4. Operability Index (Overall Usefulness)
5. Population-Economic Importance Index (Economic and Demographic Value)
The weightage assigned to each of the 5 indexes in the Final Feasibility Index is based on its significance when it comes to the feasibility of the route.
The Tsunami Risk Index (TSI), Population-Economic Importance Index (PEI), and Structure Durability Index (SDI) are all weighted the same due to an equal emphasis on safety and prioritization serving the majority of the population, which is essential for long-term viability (Jonkman et al. 2005).
The Operability Index (OPI) is weighted the highest as it directly affects the functionality and accessibility of the area, with access to emergency services being critical to its success as a conduit (OECD 2020). The Environmental Impact Index (E2I) is weighted the lowest as environmental sustainability is crucial but less immediately impactful in the operational aspects of such connection (World Bank 2020).
In the FFI, the higher the value, the more feasible the project.
| Feasibility Final Score (Min 0 - Max 1) | Interpretation (aka Reality Check) |
|---|---|
| "Why Bother?" Tier (0.00 - 0.33) | Costs more than your annual coffee budget. Requires solving cold fusion. Permitting process involves blood sacrifices. |
| "Maybe If We Stretch" Tier (0.34 - 0.66) | Budget will hurt but won't kill you. Engineers will complain but do it. Only needs 3 miracle approvals. |
| "Shut Up and Take My Money" Tier (0.67 - 1.0) | Basically IKEA assembly instructions. Approval process involves one bored intern. ROI before lunch. |
The results are visualized using interactive network graphs, allowing users to explore the proposed network. Key features incorporated include:
-Optimal rail lines and station placements that maximize accessibility and economic impact while minimizing exposure to potential risks.
-Index value settings to amend the final result.
-Interval distances / population density threshold for station placement.
-The projected benefits of the project, one route for each strength:
- Reduced travel time
- Improved personal safety
- Greater accessibility to specific regions.
-Terrain Cross Section
-Annotation on additional infrastructure required for project realisation ie. bridge, tunnel
3. Mockup Preview
4. Prototype Preview
5. Final Product Preview
demo.mp4
The project demonstrates that combinatorial optimization can be a powerful tool in planning public transport networks in Southeast Asia, particularly in the face of the region's challenging terrain and climate. By integrating indexes and key indicators, the tool provides a data-driven approach to rail line and station placement. The proposed rail network gives space for the general public to conceptualise hypothetic connections that has yet to exist but should have existed.
Given the multidisciplinary nature and numerous expertise involved—engineers, urban planners, environmental scientists and policymakers, in projects of such scale, it is prudent to note that this visualization will serve as a hypothetical planning tool that democratize access to information necessary to its realisation. This would allow the public to explore different possibilities and simulate outcomes based on varying parameters (e.g. environmental factors, or population needs), while considering factors and challenges specific to Southeast Asia.
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