Saudi Arabia’s urban transformation under Vision 2030 is reshaping how cities are planned, connected, and experienced. As metropolitan areas such as Riyadh, Jeddah, Dammam, and Makkah expand rapidly, mobility planning is evolving to prioritize pedestrians and cyclists. Non-motorized transport (NMT) infrastructure is now central to sustainable urban development, supported by accurate surveying, geospatial standards, and modern infrastructure analytics.
Digital platforms such as AI-powered road infrastructure management systems are increasingly helping transportation authorities analyse infrastructure conditions and plan safer, more connected urban mobility networks.
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Pedestrian and cycling infrastructure plays an important role in improving urban mobility and sustainability.
Key benefits of NMT systems include:
• Reduced urban traffic congestion
• Lower transportation-related emissions
• Healthier and more active lifestyles
• Improved accessibility for all residents
• Enhanced urban livability and social interaction
Saudi Arabia’s multimodal planning strategies increasingly integrate walking and cycling networks with public transport systems and major road corridors.
Mobility insights generated through AI-driven traffic analytics platforms help planners understand travel patterns and prioritize pedestrian-friendly infrastructure improvements.
According to Saudi Highway Code SHC 202, surveying forms the foundation for every transportation infrastructure project.
Accurate surveys ensure:
• Reliable terrain representation
• Alignment with national geodetic reference systems
• Precise mapping of sidewalks and bicycle lanes
• Safe integration of NMT infrastructure within road corridors
Digital infrastructure mapping tools such as AI roadside asset inventory systems help maintain updated digital records of road infrastructure and pedestrian facilities.
Saudi Arabia uses advanced Global Navigation Satellite System (GNSS) technologies through the KSA-CORS network to achieve centimeter-level positioning accuracy.
Mobile Mapping Systems (MMS) are increasingly used to:
• Identify gaps in pedestrian sidewalks
• Assess accessibility challenges
• Capture high-resolution street-level data
• Generate 3D models of urban infrastructure
These technologies allow planners to integrate infrastructure datasets into GIS and BIM platforms for efficient planning and lifecycle management.
SHC 202 requires strict horizontal and vertical accuracy to ensure safe pedestrian and cyclist routes.
Surveying methods commonly used include:
• Total Station measurements
• Leveling surveys
• Ground control point establishment
• GNSS positioning systems
Precise surveying prevents design issues such as unsafe slopes, uneven walkways, and hazardous gradients along pedestrian corridors.
Road quality monitoring tools such as AI pavement condition monitoring systems help agencies maintain smooth and safe pedestrian and cycling surfaces.
The SHC 202 project lifecycle ensures that pedestrian and cycling infrastructure is integrated throughout the development process.
Feasibility Stage
• Identification of walking and cycling demand corridors
• Preliminary terrain and accessibility analysis
Preliminary Design
• Planning sidewalks, crossings, and bicycle lanes
• Evaluating accessibility and safety requirements
Detailed Design
• Application of precise survey data
• Development of safe geometric design
Construction
• GNSS-based control surveys
• Verification of infrastructure alignment
Maintenance
• Infrastructure monitoring through digital mapping and analytics
Infrastructure monitoring platforms such as AI-based road damage detection tools help authorities identify defects early and maintain pedestrian corridors more effectively.
Surveying operations in pedestrian areas must follow strict safety procedures.
SHC 202 requires:
• Temporary traffic and pedestrian signage
• Protective barriers around survey work zones
• Coordination with local authorities
• Clear documentation and reporting standards
These protocols ensure public safety while allowing survey teams to collect essential spatial data.
Digital Terrain Models (DTMs) are essential for designing pedestrian-friendly and cyclist-friendly infrastructure.
DTMs help planners evaluate:
• Slope gradients and elevation changes
• Drainage patterns and flood risks
• Accessibility for pedestrians and cyclists
• Integration with surrounding infrastructure
Advanced safety analytics platforms such as AI-based traffic risk identification systems help identify hazardous locations that require improved pedestrian infrastructure.
Building Information Modeling (BIM) is increasingly used to support long-term infrastructure management.
BIM systems allow authorities to:
• Visualize infrastructure performance
• Simulate pedestrian and cyclist interactions
• Plan maintenance and repair schedules
• Improve asset lifecycle management
Engineering analysis tools such as AI-powered road safety audit platforms help identify potential safety issues during planning and design stages.
Saudi Arabia is rapidly advancing smart mobility technologies that support safer pedestrian and cyclist infrastructure.
Future developments may include:
• AI-powered pedestrian traffic analytics
• Smart crosswalk systems
• Connected GNSS-enabled infrastructure
• Digital twin simulations of urban mobility networks
These innovations will help Saudi cities maintain efficient and resilient transport networks.
Traffic planning for pedestrians and cyclists in Saudi Arabia is evolving into a data-driven, inclusive, and technologically advanced process.
By combining SHC 202 surveying standards, national geospatial frameworks, and intelligent infrastructure analytics, Saudi cities are building safer and more sustainable non-motorized transport networks.
These efforts will play a crucial role in improving urban livability and supporting the long-term goals of Vision 2030, ensuring that future Saudi cities are not only connected—but also walkable, accessible, and environmentally sustainable.
The Autonomous Road Engineer
AI that is transforming road infrastructure, from reactive to predictive.
