Public mobility systems are typically optimized for:

efficiency

travel time

cost

predictable, high-density demand

Human-Centered Mobility Systems

Halina™
Case Study

While effective at scale, these optimization models often fail to represent human capability, safety, and real accessibility, particularly outside major urban hubs.

HALINA™ was developed as a conceptual system framework addressing this structural gap.

Common characteristics include:

lack of transport at the origin point

long last-mile distances

late-night or low-frequency connections

extended waiting times in isolated locations

journeys requiring sustained cognitive and physical alertness

When mobility works in theory, but not in practice.

In many regions, especially rural and semi-rural areas, public transport routes are technically available but functionally inaccessible.

Problem

As a result, safety becomes probabilistic, not guaranteed.

If a person cannot safely and predictably participate in the system, the limitation lies in the system design — not the individual.

Observed System Pattern

Current mobility logic
systematically prioritizes:


large transport hubs

dense populations

profitable routes

predictable commuter flows

This creates structural
disadvantages for:


villages and small towns

people without access to private vehicles

users traveling outside standard hours

routes dependent on last-mile connectivity

Structural bias in mobility planning

In practice, geography determines opportunity more strongly than individual capability.

No public transport at the starting location

Several kilometers required to reach the first stop

Evening or night departure

Multiple transfers across long time spans

Extended waiting periods in low-activity environments

Inter-regional route between a rural area and a major city.

Example Scenario

System status: technically available
Human status: operationally unsafe

This discrepancy highlights a missing layer in current mobility systems.

Traditional planning tools model:

vehicles

timetables

distances

Transport failure vs. intelligence gap.

The issue is not the absence of infrastructure. It is the absence of human-aware intelligence within existing infrastructure.

Design Insight

They do not model:

human risk

safety degradation over time

cognitive load & continuity of access

HALINA™ allows differentiated readiness without ranking participants, enabling escalation roles while preserving autonomy and preventing silent dependency.

It does not replace current infrastructure.
It augments it with human-factor awareness.

HALINA™ is not:

a navigation app

a timetable engine

a route visualizer

HALINA™ is a predictive intelligence layer designed to operate on top of existing public transport systems.

Solution Concept
HALINA™ — Human-Centered Mobility Intelligence Layer

It is a decision-support system for mobility planning and policy.

Core Functions

HALINA™ identifies where journeys become unsafe before failure occurs, including:

long night gaps between connections

extended waiting times in isolated locations

transfers with elevated human risk

routes requiring prolonged alertness

Safety Intelligence

Result: route evaluation based on human safety thresholds, not only travel time.

HALINA™ addresses segments invisible to standard planning models by integrating:

micro-mobility solutions

vetted shared transport

community-verified local routes

local transport operators

Last-Mile Logic

Result: continuity of access when standard public transport disappears.

HALINA™ introduces a metric that measures real opportunity, not distance.
Each route receives an accessibility score reflecting:

safety

continuity

feasibility for real users

Opportunity Access Index

This index exposes where mobility systems exclude participation, despite formal availability.

HALINA™ is designed for integration with:

national mobility planners

public transport authorities

large infrastructure programs

GovTech decision frameworks

System Integration Perspective

HALINA™ functions as a human-reality recorder within the mobility ecosystem.

It:

reveals blind spots in existing data

adds real-time human-factor intelligence

supports evidence-based policy refinement

provides feedback loops grounded
in lived conditions

improved access for rural and underserved areas

reduced safety risks in off-peak travel

more accurate assessment of national mobility coverage

policy decisions informed by real accessibility, not assumptions

transport systems aligned with human capability

Impact Potential

Mobility becomes predictable, safe, and inclusive, rather than conditionally available.

HALINA™ reframes public transport as a human-centered system, where access is defined by safety and feasibility — not theoretical reach.

By introducing an intelligence layer focused on human factors, mobility systems can evolve from logistical networks into inclusive public infrastructure.