Written by Georgette Virgo
For a long time, urban planning has relied on a mix of forecasts, design manuals, and professional experience. Planners estimated future traffic, applied standard cross‑sections, and checked that intersections met level‑of‑service targets. Pedestrian counts, cycling data, and near‑miss information were limited, with many decisions necessarily based on forecasts, periodic observations, and generalized assumptions about how a new street or district would operate once built.
With high‑resolution LiDAR now entering the planning toolkit, that picture is changing. Instead of designing primarily around assumed volumes and generic patterns, planners can base their decisions on detailed, anonymized, three‑dimensional (3D) data from existing real streets. They can understand with greater precision how people and vehicles move, where risk builds, and how different designs may perform before construction begins.
With a portfolio spanning ultra-long-, long-, medium-, and short-range sensing, Seyond is at the forefront of this change. The company provides the image-grade LiDAR and high-resolution 3D data needed to support planning, digital twins, and intelligent infrastructure operations. This provides the benefits of smart cities not just after the infrastructure is built, but as early as the planning and building stages, as well.
Inside the Established Planning Playbook
Traditionally, street and corridor design has been guided by regional traffic models, growth forecasts, point‑based vehicle counts, crash histories, and level‑of‑service tables, all interpreted through standard layouts and “design vehicles” in manuals. These tools remain fundamental to transportation planning, but they are most effective when complemented by richer, real-world insights into how people and vehicles actually move through the built environment.
Often, they focus mainly on vehicles, undercounting pedestrians, cyclists, and micromobility users. They capture conditions at a few points rather than giving a continuous picture along an entire route. More importantly, they reflect only incidents serious enough to appear in crash data, not the many near‑misses that signal underlying problems but never show up in official statistics.
As a result, planners could estimate how much traffic a corridor might carry, but had a limited view of how people and vehicles would interact in detail. It was hard to predict where conflicts would emerge or how a design would feel for people walking, cycling, or using public transport until the infrastructure was actually in use.
What LiDAR Adds: Analyzing How Streets Actually Work
In the past, sensors would tell planners how many vehicles passed in an hour, but not how those vehicles interacted with people walking or cycling, or where close calls were happening.
LiDAR, short for Light Detection and Ranging, changes that view. High‑resolution LiDAR produces precise, anonymized 3D information about how people and vehicles move through an intersection or along a corridor. This helps planners spot recurring problems, such as turning conflicts, blocked crosswalks, and queues backing up into other lanes, that often do not show up in basic traffic counts or crash records.
In a busy city street, LiDAR systems like Seyond’s can track the paths of vehicles, cyclists, scooters, and pedestrians, along with their speeds, directions, and spacing from one another, including near‑misses that never become collisions. Seyond’s sensors work over very long and short distances alike, giving planners a consistent 3D picture from high‑speed approaches all the way into crowded, pedestrian‑heavy areas. The result is a richer, more practical evidence base for early design decisions.
Unlike camera footage, this is done anonymously. The data describes shapes and motion, not identity, which makes it suitable for public spaces where privacy matters. “The future of urban planning is not about replacing professional judgment with technology,” says Billy Evers, VP of Sales and Marketing at Seyond. “It is about giving planners stronger, real-world evidence so they can make safer, more confident, and more effective decisions before infrastructure is built.”
With this level of detail, planners can examine existing streets that resemble those they plan to build, with similar speeds, land uses, and traffic mixes. They can see where near‑misses cluster at turns and crossings, how pedestrians actually choose routes instead of simply following marked facilities, and where buses consistently lose time along a corridor.
Design shifts from being based mainly on hypothetical patterns to being grounded in how similar streets actually work.
Planning for Smarter Day-to-Day Operations
Today, planners can pair LiDAR with what is often called a digital twin—a virtual version of a street, corridor, or district that behaves like the real place.
Think of it as a living model that can be updated with actual movement and traffic data. In this model, planners can try out different ideas before anything is built: changing lane numbers, adjusting crossing layouts, adding bike or bus lanes, or testing lower speeds. They can then see how each option would affect safety, travel times, crowding, and everyday comfort for people walking, cycling, and riding transit.
Instead of debating drawings in the abstract, they compare scenarios in a twin that responds like a real corridor, with LiDAR‑based movement patterns giving each scenario a solid, real‑world foundation.
“Instead of studying cross‑sections in the abstract, planners can run scenarios in a twin that behaves like a real corridor,” Evers notes. “A digital twin allows planners to compare design options before construction begins, while LiDAR-derived movement data gives those scenarios a stronger real-world foundation.”
The same information also helps the city run day‑to‑day operations more intelligently, an idea often described as physical AI. Here, systems use anonymized 3D movement data to react to what is happening on the street in real time. Signals can give a little extra green when queues build up or when people are still crossing. Buses and trams that are running late can receive brief priority to catch up without redesigning the entire route. Speed limits near schools or busy crossings can be adjusted based on how many people are actually present, not just on fixed rules.
Because planners can simulate these operational strategies inside the digital twin, they can design streets and control logic as a single package. They can examine how a particular layout performs if signals adapt in real time, where bus priority offers the best reliability gains, and which physical features support safe dynamic speed control.
Compared with older methods, this changes practice in important ways. Agencies have a stronger basis for evaluating safety, operational performance, and long-term infrastructure investment. Designs are evaluated against measured behavior before construction, not only after. Safety and efficiency implications become apparent earlier, making it easier to spot likely points of conflict or bottlenecks and adjust plans proactively. That reduces the need for major retrofits and avoids spending on layouts that do not perform as intended.
Designing With People in Mind
LiDAR’s impact on planning becomes most visible when the focus shifts from vehicles to people. Instead of relying only on periodic counts or observations, planners can finally understand the everyday patterns that shape how people actually use streets and public spaces using LiDAR-based movement data.
They can better understand where people naturally cross, how long different users need to move safely through an intersection, where cyclists experience recurring close passes, and where buses consistently lose time. These insights can inform the placement of crosswalks and refuge islands, the design of protected cycling facilities, and changes to bus stops, lanes, or signal timing.
Seyond’s image‑grade LiDAR, with its ability to distinguish detailed movement types in complex environments, is central to building these user‑focused measures that inform design choices. Digital twins informed by Seyond’s data can reveal where near‑misses, delays, and discomfort consistently cluster. Projects can then be targeted at those locations, and their expected impact quantified more accurately.
These details were once difficult or impossible to observe consistently; now they are available as continuous, anonymized data that can be explored, tested, and built directly into the design process. As a result, decisions about geometry, signal timing, and space allocation can be grounded in how real journeys unfold, not just in how planners hope they will work.
Because of this richer understanding, both physical structures and day‑to‑day operations can be genuinely people‑centered—planned by people for the people who use them. Over time, these choices add up to streets and public spaces that feel reliable and intelligent, places that respond to how life actually happens on the ground. When people see that the city “gets” their daily experience, it builds trust: the infrastructure feels like it was made with them in mind, not just drawn on a plan.
However, the result is more than increased public trust. From a business and agency perspective, it also helps reduce costly redesigns and retrofits by catching design issues earlier in the process. It improves return on investment by directing limited capital to the interventions that address clearly observed problems. And it provides clearer evidence to support decisions that may be politically sensitive, such as reallocating lanes or changing speed limits.
What Has Truly Changed
LiDAR does not replace the established tools of urban planning, but it has changed the way planners work. It strengthens them by adding continuous, high-resolution evidence about how different road users actually interact. With this, they no longer have to design complex streets and smart‑city projects primarily based on assumptions and averages.
Seyond, as the industry leader in directional LiDAR, delivers the anonymized, detailed 3D information that planners can depend on. Digital twins turn that data into living virtual environments where ideas can be tested. Physical AI uses the same data to keep streets safer and more efficient once they are built.
The result is a more evidence-based approach to urban planning. LiDAR is becoming a foundational data layer for cities seeking to make safer, more resilient, and more confident planning decisions. The results are streets that may not be perfect, but are designed, tested, and operated with a deeper understanding of how people and vehicles actually move.
