At a busy city intersection, problems rarely arrive one at a time. A bus falls behind schedule after getting caught in a queue. A driver enters the junction on green but cannot clear it before the signal changes. Parents and children step into the crosswalk while a vehicle turns across their path.
To someone on the street, this may feel like an ordinary morning rush. To a city, it is a set of connected safety, operational, and planning challenges. The problem is not simply that too many people and vehicles are in one place. It is that a small disruption at one corner—a blocked bus stop, a queue that reaches into the intersection, or a vehicle turning through a crossing—can quickly affect an entire corridor.
Cities already use a range of tools to understand what is happening on their streets, including cameras, traffic loops, radar, vehicle counts, and connected infrastructure. Each serves a useful purpose.
However, in an era of increasingly complex transportation systems, how can agencies and planners move beyond recognizing that congestion or risk exists to understanding where it starts, who it affects, and what practical changes could improve the situation?
The answer is Light Detection and Ranging or LiDAR.
LiDAR is not meant to replace the already built ecosystem in streets. In fact, it adds a complementary layer of spatial intelligence: an anonymous, three-dimensional (3D) view that helps agencies better understand how vehicles, buses, pedestrians, cyclists, scooters, and curbside activity move together in the same space.
This article explores five everyday street challenges where using LiDAR can strengthen the information cities already collect to make better decisions. It also considers how a California-based LiDAR company, Seyond, brings high-fidelity LiDAR into intelligent transportation systems to help cities make streets safer, more reliable, and easier to navigate.
1. Near-misses that can become serious crashes
Crash records are essential, but they tell cities where harm has already occurred. A serious incident recorded months or years after people have started avoiding an intersection because it feels unsafe. Smaller, recurring conflicts may never appear in official crash data at all.
LiDAR can help cities interpret those earlier warning signs. By using laser pulses to create an anonymous 3D model of a street, LiDAR can simultaneously track the movement, direction, speed, and spacing of multiple road users. It helps cities understand vehicles, pedestrians, and curbside activity as parts of the same shared environment rather than as separate categories of traffic.
At a busy turn, for example, it can identify repeated close interactions between vehicles and pedestrians in a crosswalk, or between turning vehicles and cyclists moving straight through an intersection. It can show whether these conflicts happen at a particular signal phase, at a certain time of day, or under specific traffic conditions.
Seyond’s LiDAR technology is designed for this kind of high-detail perception in complex, high-traffic environments. By combining long-range coverage of approaching traffic with closer monitoring around crossings, bike lanes, and curbside activity, its sensors can help agencies identify where risk is building and what is contributing to it.
That gives planners and traffic engineers a stronger basis for action, whether the appropriate response is a protected turning phase, a revised signal sequence, a shorter crossing distance, or a safer bike-lane design.
The goal is to act on recurring risks before they become serious crashes—not after.
2. Crowded intersections
A blocked intersection is a familiar urban frustration. A driver enters on green, expecting traffic ahead to move, but stops in the middle of the junction when there is no room to clear it. When the light changes, cross traffic cannot proceed. Soon, one blocked box affects the next signal, then the next block, until a localized delay becomes corridor-wide congestion.
Traditional traffic counts can show that a street is congested. However, LiDAR can help reveal how the congestion formed.
It can show when queues extend backward into an intersection, how long they remain there, which turning movement is causing the blockage, and whether buses, cyclists, or pedestrians are affected as vehicles try to maneuver around the obstruction. It can also help distinguish between a short, occasional queue and a recurring spillback problem that appears at the same time every day.
These detailed insights give traffic operators options beyond treating congestion as an unavoidable fact of city life. They can adjust signal coordination, revise green times, change turn restrictions during peak periods, and improve lane assignments where it is most likely to improve flow.
3. Risks of navigating multimodal streets
Today’s streets must accommodate more than private vehicles. At the same intersection, people may be walking to a bus stop, cycling to work, using a scooter, driving children to school, crossing with a stroller, or waiting to turn in a vehicle. Managing that mix safely requires a fuller understanding of how different movements overlap.
LiDAR contributes additional spatial intelligence to this picture. It can help agencies understand where pedestrians wait, how long people remain at a crossing, how cyclists move through an intersection, and where these paths intersect with vehicle movements. When combined with other sensing and traffic-management tools, this information can provide a more complete view of the street.
That insight can inform practical changes: signal timing that better reflects pedestrian demand, crossings that account for real walking speeds and crowding, safer transitions into and out of bicycle facilities, and better evidence for where a new crossing, refuge island, or protected signal phase may be needed.
In this scenario, Seyond’s ability to provide high-precision 3D perception becomes especially useful. Its LiDAR systems are designed to detect multiple types of road users within the same scene, including in visually complex conditions, such as a street corner with moving vehicles, a bus stop, people walking, cyclists, and curbside activity.
The result is a more inclusive understanding of traffic flow—one that does not assume the only person worth detecting is a driver. For someone walking a child to school, using a wheelchair, or cycling to work, the benefit is straightforward: the street is more likely to recognize their presence and provide a safer, more predictable route.
4. Unpredictable everyday transportation scenarios
For commuters, the reliability of public transportation matters. A bus that is consistently five or 10 minutes late can make them late for work, school, or important appointments.
Yet that delay may not stem from a single major incident. It can build gradually: a bus misses a signal by a few seconds, waits behind a queue near a curbside stop, gets delayed by a vehicle blocking a bus lane, then encounters the same conditions at the next intersection.
LiDAR helps agencies identify where that lost time begins and why it continues. It can show how buses approach and move through intersections, where they are delayed by general traffic, how long they stay at stops, and whether parking, loading activity, or ride-hail pickups are obstructing access to the curb.
This helps cities make targeted changes rather than assuming that an entire route requires reconstruction. In some cases, a short transit-priority signal phase, a clearer curb rule, a relocated loading zone, or a small lane adjustment at a recurring bottleneck may significantly improve transit performance.
Seyond’s LiDAR-based Intelligent Transportation Systems (ITS) approach can support this kind of operational decision-making by providing real-time information about transportation, traffic queues, and intersection conditions. When that information is integrated with signal operations, agencies can give delayed buses a brief green extension or an earlier phase, helping them recover time while keeping the intersection operating for other users.
For commuters, the outcome is a more dependable trip: less time spent wondering when the bus will arrive, more reliable connections, and a stronger reason to choose transit for everyday travel.
5. Blueprint vs Real-World Movement
Some of the most expensive street problems begin even before the actual construction starts.
A new intersection can look effective in a design drawing, but perform poorly once people begin using it. When cities discover these problems after construction, the response is often expensive and disruptive. They may need to repaint lanes, relocate signs, retime signals, or launch a second construction project to address issues that were not visible in the original planning process.
These repeated adjustments cost money, inconvenience residents and businesses, and can weaken public confidence in a project. LiDAR helps planners begin with observed behavior rather than assumptions. Agencies can study comparable streets and corridors to analyze where people actually cross, where queues form, how buses lose time, how delivery activity affects traffic, and where cyclists experience close passes or sudden evasive movements.
They can compare lane configurations, crossing locations, bike and bus facilities, curb uses, signal plans, or speed-management strategies. Instead of asking only whether a design appears workable on paper, they can ask how it is likely to perform under the movement patterns that people experience every day.
Seyond’s role fits naturally at the beginning of this process: its LiDAR provides the detailed 3D movement information that makes planning models more realistic. Rather than testing a street design against generic assumptions, agencies can test it against the conditions people are likely to experience.
Seyond believes that, through these insights available before construction, it can reduce the likelihood of costly retrofits, repeated design adjustments, avoidable delays, and capital spending on solutions that fail to address the root cause of a problem.
A Better View of the Street
Smart transportation systems rely on a mix of data points from various infrastructure data sources: cameras, radar, loops, and others. LiDAR helps connect those individual data points into a more complete picture of how a street behaves. It adds precise, anonymized 3D movement intelligence to this ecosystem, helping agencies better understand how road users interact and make more informed operational and planning decisions.
For Seyond, using LiDAR means providing cities with reliable, anonymized 3D movement data that supports real and practical decisions: where to improve a crossing, how to keep an intersection from locking up, when to give a late bus priority, or which curb changes would make a busy block work better.
The future of urban mobility is not about adding new technology. It is about using these new technologies to make streets safer, easier to navigate, and more dependable for the people who rely on them. When agencies can understand how vehicles, pedestrians, cyclists, and transit services truly interact, they can move from reacting to problems after the fact to preventing them in the first place.