Challenges in Classifying Vehicles in Mixed Traffic Environments
Mixed traffic environments—where various types of vehicles such as cars, buses, trucks, motorbikes, auto-rickshaws, and non-motorized vehicles share the same roadway—present significant complexities in traffic data analysis. Accurately classifying each vehicle type in such settings is crucial for urban planning, transport modeling, and infrastructure development.
In this blog, we’ll explore the key challenges encountered during vehicle classification in heterogeneous traffic, and how modern techniques are helping overcome them.
1. Lack of Lane Discipline
One of the most prominent features of mixed traffic, especially in developing countries, is the absence of well-maintained lane discipline. Vehicles often do not follow fixed lanes, leading to overlapping and occlusion in video footage, which complicates both manual and AI-based classification.
Impact:
Difficulty in distinguishing between adjacent vehicles
Increased chances of double-counting or misclassification
More errors during peak congestion hours
2. High Variation in Vehicle Sizes and Shapes
From two-wheelers and e-rickshaws to multi-axle trucks, the sheer diversity of vehicle types makes it challenging to develop a standard classification model that performs well across all traffic scenarios.
Impact:
AI algorithms often struggle with overlapping classes
Need for location-specific training datasets
Manual coders require enhanced expertise
3. Occlusion in Video Footage
When larger vehicles like buses or trucks obstruct the view of smaller vehicles behind or beside them, it leads to significant classification inaccuracies. Occlusion is one of the biggest hurdles in both automated and manual data extraction.
Impact:
Under-reporting of two-wheelers and pedestrians
Classification delay and accuracy drop
Incomplete datasets, affecting analytics quality
4. Lighting and Weather Conditions
Low-light, rainy, or foggy conditions severely reduce the visibility of vehicles on video recordings. Nighttime video analysis further challenges the software’s ability to identify vehicle contours, license plates, and other defining features.
Impact:
Increased false positives/negatives
Reduced data reliability
Need for enhanced IR or night-vision equipment
5. Dynamic Driving Patterns
Erratic movement, overtaking, and sudden stops—common in mixed traffic—add complexity to tracking and classifying individual vehicles, especially over longer video durations.
Impact:
Increased tracking errors
Difficulties in generating accurate turning movement counts (TMCs)
Need for frame-by-frame verification
6. Pedestrian and Non-Motorized Traffic Interference
Non-motorized vehicles (like cycles or handcarts) and pedestrians frequently interact with motor traffic in mixed environments, which can disrupt classification algorithms and skew counts.
Impact:
Class confusion between very small vehicles and pedestrians
System misfires or drops
Need for custom classification schemes
How Traffic Data Count Tackles These Challenges
At Traffic Data Count, we address these mixed traffic classification challenges through:
✅ Trained Human Analysts: Our team uses frame-by-frame verification for high-accuracy manual classification.
✅ Customized Classification Models: Tailored to specific urban or regional vehicle profiles.
✅ Redundant Quality Checks: To ensure no vehicle is misclassified or missed.
✅ Hardware Support: Recommendations on best camera angles and lighting for optimized video capture.
✅ Hybrid Approach: Combining AI-assisted tracking with manual corrections for the best balance of speed and accuracy.
Conclusion
Classifying vehicles in mixed traffic environments requires a blend of experience, technology, and process discipline. By understanding and overcoming these challenges, urban planners, transport engineers, and authorities can unlock highly reliable data for effective decision-making.
📩 Need expert help with accurate vehicle classification in your traffic study?
Contact us today at Traffic Data Count to learn how we ensure high-quality traffic data in any environment.
