When I use AI in a transit project, I treat it as a fast option generator with incomplete knowledge. It can help compare a rail station, bus interchange, airport connector, or mixed-use hub, but it cannot replace survey data, traffic modeling, accessibility review, or operating requirements. My workflow keeps those boundaries visible.
1. Define the Movement Program
I list every mode before opening an image tool: rail, local bus, bus rapid transit, airport shuttle, taxi, rideshare, bicycle, micromobility, walking, service vehicles, and emergency access. For each mode I record peak demand, dwell time, transfer direction, and weather exposure. A station that solves rail boarding but blocks buses is not a complete option.
I also name the public uses that may share the site: housing, retail, offices, a clinic, a library, classrooms, or a market. These uses need independent entrances, deliveries, security, and operating hours.
2. Capture the Site Context
I select the relevant area on a map in Shapezo and use its AI to generate an initial 3D model. The output helps me inspect street connections, terrain, nearby building heights, parcels, open space, and existing transit edges. I save the selection boundary, date, prompt, and source notes with the image.
The model is a visual context layer only. It is not survey control, a right-of-way record, a traffic model, a utility plan, or a permit base. Survey, civil, geotechnical, and operations teams verify the facts that control the design.
3. Generate Two Comparable Schemes
I keep the camera, site boundary, and major station footprint stable. Scheme A is a single-purpose facility with platforms, bus bays, ticketing, elevators, stairs, bike parking, and weather cover. Scheme B is a transport hub plus city living room with a public plaza, shops, community rooms, housing or offices, and a stronger pedestrian connection to the surrounding block.
I label each output as concept, effect, or scheme. The label tells the team how far the image can travel. I store the model name, prompt, reference data, date, editor, and intended use.
4. Check Climate and Comfort
Hot and Dry Regions
Test shade depth, tree canopy, low-glare materials, drinking water, air movement, and a route between the platform and cooled rooms.
Wet and Temperate Regions
Test canopy overlap, drainage, slip resistance, covered bike storage, and places for passengers to wait without narrowing the main path.
Cold Regions
Test heated indoor waiting, wind protection, snow storage, door vestibules, visibility, and a direct accessible route that remains clear after storms.
These are early design checks, not a substitute for energy modeling or operations planning.
5. Run Access and Safety Checks
Trace a continuous accessible route from every arrival mode to every platform, restroom, ticket area, public room, and exit. Check slopes, turning spaces, tactile guidance, audible information, lighting, seating, elevator redundancy, and crossing conflicts. Then test emergency access, fire lanes, evacuation, platform edges, guardrails, smoke control, and service routes.
AI commonly produces stairs that connect to nowhere, ramps that are too steep, elevators hidden behind retail, bike storage in an exit path, and canopies that do not cover the actual waiting area. I mark each issue as confirmed, open, or rejected. “Looks plausible” is not a review status.
6. Assign Owners for the Handoff
The architect coordinates program, form, and public-realm design. Transportation engineers confirm capacity, dwell, traffic, and operations. Structural, civil, mechanical, electrical, and fire specialists verify their systems. Accessibility reviewers test the route in detail. A public agency decides what becomes a controlled document and what remains illustrative.
7. Test the Neighborhood Outcome
I compare ridership access, housing, retail, public services, street safety, and maintenance for both schemes. A plaza that looks generous may be expensive to clean. A housing block above the station may improve land use but require a separate loading plan. A new bike route may work for commuters but fail at a dangerous crossing.
The workflow is simple: define modes, capture context, generate comparable options, test climate and access, assign reviewers, and record the decision. AI makes the first round faster. The professional and public review determine whether the hub is ready for the next stage.
I also run a plain baseline against both schemes: keep the existing facility, rebuild only the platform edge, or move the transfer to another parcel. That comparison catches hidden assumptions about land value, construction phasing, temporary service, and maintenance. A visually strong hub is not automatically the best investment if it cannot operate during construction or serve the people who use the route every day.
Top comments (0)