To close the gap with the world in intelligent transportation systems (ITS), Vietnam’s challenge lies not only in installing more cameras, but also in the ability to transform observed data into solutions: how do vehicles move between areas and how should traffic be organized?
Successful models around the world
Globally, advanced cities have long used traffic demand data to understand traffic flow, inform planning, and select traffic management solutions. In Stockholm, Sweden, demand modeling combined with route selection has helped predict and successfully implement congestion pricing, reducing traffic volume in and out of the city center by 20% during the trial period.
Meanwhile, in London (UK), transport authorities use demand analysis models and traffic flow allocation across the road network to calculate traffic volume, delays, and support long-term planning. Despite the different implementations, these examples demonstrate that traffic demand data is only truly valuable when it is translated into a basis for traffic management decisions.
From studying successful models, the Viettel ITS Traffic Demand Management subsystem development team realized a key lesson: to organize traffic effectively, one must first understand the travel needs between areas and how vehicle flows are distributed across the network, rather than just focusing on solving immediate congestion points.
According to experts, Vietnam can certainly learn from international approaches, but cannot mechanically apply a model designed for different traffic conditions. Vietnam’s urban traffic has a very high volume of motorbikes, flexible movement patterns, and a dense network of alleys and small roads. In locations with surveillance cameras, authorities can see how busy an intersection is and how many vehicles pass through. However, data from a single location is insufficient to determine where the traffic is coming from, where it’s heading, or whether it’s simply passing through congested areas.
Furthermore, a vehicle might appear on a camera on a main road, then turn into an unmonitored alley, and then reappear at a completely different location. The system cannot consider these two discontinuous appearances as a complete journey. Simply connecting camera points to create a movement map will encounter many problems when the data does not cover all routes.
Given this reality, the challenge for Viettel’s engineering team was how to piece together incomplete movement traces, identify likely trips, and then synthesize them into travel demand between urban areas. To achieve this, the system needed to combine camera data with analytical and estimation methods suitable for Vietnamese traffic conditions.
Overcoming data blind spots
To build the matrix, Viettel ITS had to solve a complex problem: how to know where vehicles are traveling when the camera system cannot observe the entire journey? The solution lies in combining three technologies, transforming fragmented traffic data into a comprehensive picture of urban transportation needs
First, the system divides urban areas into transportation zones based on geographical characteristics, population density, and road networks. Areas with similar characteristics are grouped together, while ensuring appropriate size and population density. This division provides a basis for determining transportation needs between zones, rather than simply looking at individual routes or intersections.
The next step, and one of the most significant challenges, is reconstructing the journey from interrupted camera data. For example, a vehicle is recorded by a camera at point A, then travels through several unrecorded routes before appearing at point B. The system will use recording times, distances, and road network characteristics to assess whether the two appearances belong to the same journey. Recordings that are too far apart or have inconsistent travel times will be separated into distinct journeys.
For journey segments not covered by cameras, the system continues to analyze the routes the vehicle likely took. By comparing travel times and real-time traffic data, the technology selects the most probable route to reconstruct the missing part of the journey. This allows seemingly isolated occurrences to be linked into coherent trips, rather than simply assuming the vehicle always chooses the shortest path.
Finally, when sufficient observational data is gathered for estimation, trips are aggregated by origin, destination, and time frame to form the initial OD matrix. The system then compares this data with actual traffic flow at routes and intersections, adjusting the distribution of traffic onto routes and the number of trips between regions. This process is repeated to narrow the gap between the calculated results and the observed data.
Instead of simply knowing how many vehicles pass through a location, the OD matrix helps authorities estimate the flow of vehicles between different areas, its scale, and how it changes over time. This information provides a basis for assessing travel demand and traffic pressure on the urban road network.
For example, if the volume of vehicles from residential areas heading towards the city center increases sharply during peak hours, the OD matrix helps identify traffic flows that put pressure on connecting routes. Based on this, authorities can identify priority areas, consider traffic diversion options, and organize traffic more appropriately to meet actual needs.
From traffic zoning and trip reconstruction to aggregating and verifying estimation results, Viettel ITS is progressively transforming distributed camera data into usable information for analyzing urban mobility needs. In the realm of comprehensive intelligent transportation solutions, in 2026, Viettel ITS won a gold medal at the International Business Awards (IBA) in the Transportation Solutions category; a silver medal at the Globee Awards for Technology; and the Best of Category award for technology products and services serving the Government and public sector.
Original article
https://thanhnien.vn/kham-pha-cong-nghe-phan-tich-du-lieu-giao-thong-thong-minh-185261013113900393.htm
Tag:trafficVietnamese trafficintelligent transportation#Viettel ITS
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