Dubai Traffic Incident Analysis

In the rapidly developing landscape of Dubai, traffic safety and efficiency are paramount to supporting both daily life and the city’s long-term growth ambitions. With data science and data analytics, we can gain insights into patterns of traffic incidents, allowing us to undestand the incidents reported.

Below, is a data-driven analysis of traffic incident trends in Dubai, supported by visualizations and heat maps to underscore key findings and areas of concern. The Dataset is between 2023 to 2024 and has been pre-processed in python to organise the dataset.

Weekly distribution of incident reports

The chart below shows the distribution of the incident report of a 2 year period, as shown below, whilst most reports hover between 9,000 to 12,000 reports, on Sundays we have less than 8,000 reports, this can be attributed as it's a weekend in the U.A.E.

Hourly Distribution of Traffic Incidents in Dubai

This chart illustrates the frequency of traffic incidents recorded at each hour of the day, helping us identify peak hours and off-peak periods for traffic incidents.

Observation and General Trends:

The chart shows the distribution of traffic incidents by hour, with a noticeable pattern:

1. Morning Hours (6 AM to 9 AM): There is a gradual increase in incidents starting around 6 AM, which aligns with the start of the morning commute. Incident frequency continues to rise steadily, indicating increased traffic flow as more people are on the roads.
2. Midday Peak (12 PM to 1 PM): The highest concentration of incidents occurs between 12 PM and 1 PM, reaching over 4,000 incidents. This midday peak might be influenced by lunch hour traffic, as well as increased vehicle density on the roads during this time.
3. Afternoon Decline (After 1 PM): After peaking at midday, there is a gradual decrease in incidents through the afternoon. However, incident levels remain relatively high from 1 PM to around 7 PM, suggesting continued traffic activity during the afternoon and early evening.
4. Evening and Late Night (8 PM Onwards): Incident frequency decreases significantly after 8 PM, with the lowest levels observed between 10 PM and 5 AM. This trend reflects reduced traffic volume as fewer people are commuting or traveling during late-night hours.
5. Quiet Periods: The lowest incident rates occur between 2 AM and 5 AM, which aligns with typical overnight quiet periods when traffic is minimal.

Key Insights and Data Patterns:

• Peak incidents: Incidents reach their maximum between 12 PM and 5 AM, suggesting that the afternoon rush hour is a significant contributor to the daily traffic incident rate. During this hour, incident frequency increases by approximately 60% compared to off-peak hours
• Rush Hour Impact: The increase in incidents during morning and afternoon commute hours suggests that traffic congestion and commuting patterns are significant contributors to incident rates.
• Midday Spike: The sharp peak around midday could indicate a period of elevated risk, perhaps related to lunch-hour travel or delivery and business-related traffic.
• Off-Peak Safety: Lower incident rates during off-peak hours (late night and early morning) show that reduced traffic volumes generally correspond to fewer incidents.

Data Science Considerations:

• Time Series Analysis: Analyzing incident rates by hour highlights patterns that can inform traffic management strategies.
• Predictive Modeling: Recognizing peak times can enable predictive modeling to anticipate high-risk periods, allowing authorities to allocate resources more effectively.

Potential Actions:

• Traffic Management Strategies: To reduce incidents, implementing targeted traffic management measures around peak hours, such as increased patrols, could be beneficial.
• Public Awareness Campaigns: Educating the public about peak incident hours might encourage safer driving behavior, particularly during midday and afternoon peaks.

Comparing Incidents by Day of the Week Vs. Hour of Day

The heatmap displays the distribution of traffic incidents by both hour of the day (horizontal axis) and day of the week (vertical axis). The color intensity indicates the sum of incidents, with yellow representing higher frequencies and dark blue-purple representing lower frequencies.

Observation and General Trends:

Peak Days and Hours:

• The highest concentration of incidents (yellow areas) occurs during weekdays, particularly from Monday to Thursday between 9 AM and 5 PM. This period corresponds to typical working hours, suggesting a strong association between traffic incidents and the workday commute.

Lower Incident Periods:

• Incidents are less frequent (darker purple) during the early morning hours (12 AM to 5 AM) and later in the evening, reflecting times when there is generally less traffic on the roads.
• Weekend days (Saturday and Sunday) show consistently lower incident frequencies compared to weekdays, which may be due to reduced work-related travel and potentially lower traffic volumes.

Notable Midday Concentration:

• There is a concentrated band of higher incidents around 12 PM to 3 PM across multiple weekdays. This midday pattern might indicate higher activity during lunch hours or increased road usage by commercial vehicles.

Key Insights and Implications:

• Weekday Commuting Influence: The distribution clearly shows a peak in incidents during weekday working hours, especially from late morning to early evening, aligning with common commuting times.
• Weekend and Nighttime Safety: The reduced incident rates during weekends and late-night hours reflect lower traffic volumes, suggesting these are generally safer periods for road travel.

Data Science Considerations:

• Heatmap Visualization: This type of visualization helps to identify and interpret patterns in incident frequency that may not be obvious in a standard line or bar chart.
• Anomaly Detection: Such patterns can be analyzed to detect any unusual spikes, such as unexpected increases on specific days or times, which may indicate temporary factors (e.g., special events or holidays).

Potential Actions:

• Traffic Management Interventions: The high incident rates during weekday hours suggest the need for traffic management solutions, such as optimized congestion mitigation measures.
• Public Awareness and Safety Campaigns: Targeted awareness campaigns could encourage safer driving during peak incident times, particularly midday on weekdays.

Comparing Incidents by Day of the Week Vs. Hour of Day

This heatmap displays the distribution of traffic incidents by hour of the day (horizontal axis) and month of the year (vertical axis). The color scale represents the sum of incidents, with yellow indicating higher frequencies and blue-purple indicating lower frequencies.

Observation and General Trends:

Peak Hours Across Months:

• Incident levels are higher between 12 PM and 7 PM across most months, shown by green and yellow colors, suggesting that midday to early evening is consistently a high-risk period for traffic incidents, possibly due to peak traffic and commuting hours.

Lower Incident Periods:

• There are fewer incidents from midnight to early morning (12 AM to 5 AM) across all months, as shown by dark purple. This trend aligns with lower road usage during overnight hours when traffic is minimal.

Seasonal Variation:

• While most months show similar hourly patterns, April stands out with a higher concentration of incidents around midday to early evening (around 12 PM to 6 PM). This pattern may suggest seasonal factors affecting traffic incidents, such as increased travel during specific months.

Missing Data:

• There is a significant gap (dark purple row) around May to June (months 5 and 6), indicating missing data. This gap affects the continuity of the data and should be taken into account when interpreting the overall trends. The lack of data in these months means we cannot assess incident patterns for this period, and any seasonal analysis should acknowledge this limitation.

Key Insights and Implications:

• Peak Hours Consistency: The consistent rise in incidents from midday to early evening across most months highlights a daily pattern likely tied to regular traffic patterns, such as work commutes and business activities.
• Data Gaps: The missing data between May and June might affect the accuracy of monthly trends. Further investigation could help understand if this gap resulted from data collection issues or another reason.
• April Anomaly: The noticeable increase in incidents during April’s midday hours.

Top 20 reported Traffic Incidents

This bar chart illustrates the top 20 types of traffic incidents recorded, with incident types listed on the vertical axis and the number of incidents on the horizontal axis. The chart is ordered by frequency, with the most common incident types at the bottom.

Most Common Incident Types:

• The "Malfunctioning vehicle in the street" category has the highest number of incidents, with over 15,000 cases. This suggests that vehicle breakdowns or malfunctions are a significant contributor to traffic issues, potentially causing obstructions and delays.
• "Parking behind vehicles (Double Park)" is the second most common, with a large number of incidents as well. Double parking is usually the result of unavailability of parking spaces, particularly in densely populated areas.

Other Frequent Incident Types:

• "Shaft impact", "Wall ramming", and "Barrier impact" also appear frequently. These incidents indicate various types of collisions with stationary objects, which could be influenced by factors such as driver inattention or challenging road conditions.
• "Collision between two vehicles" also ranks among the top types, highlighting the prevalence of multi-vehicle accidents in Dubai’s traffic incident data.

Least Common Types in the Top 20:

• The incident types with the lowest frequency in this chart include "Light signal failure," "Reviews and Burrowing," and "Door rammer". Although these incidents are included in the top 20, their relatively low numbers suggest they are less of a systemic issue compared to other types.

Map Overview of top recorded incident (Malfunctioning vehicles)

This map presents a spatial distribution of traffic incidents involving malfunctioning vehicles across Dubai during October 2024. Each marker represents an individual incident, with colors indicating the progression of time throughout the month: purple markers represent incidents occurring earlier in October, while yellow markers represent those closer to the end of the month.

Patterns and Observations:

Concentration on Highways and Main Arterial Roads:

• The majority of incidents are clustered along major highways and main roads, indicating a higher occurrence of vehicle malfunctions in high-traffic corridors. These roads are essential for movement across the city, suggesting that vehicle malfunctions in these areas could significantly impact traffic flow and cause delays.
• Highways such as Sheikh Zayed Road, Al Khail Road, and Emirates Road appear prominently, with frequent markers indicating recurring incidents in these areas. This concentration aligns with heavy usage and high speeds on these routes, which may lead to increased vehicle wear and tear, contributing to breakdowns

Sparse Incidents in Residential, Commercial, and Industrial Areas:

• While there are some markers within residential, commercial, and industrial zones, they are relatively sparse compared to the highways. This lower frequency could be due to reduced traffic volumes, lower speed limits, or a shorter average travel distance within these areas, reducing the likelihood of malfunctions.
• Some incidents in residential areas may be due to vehicles breaking down as they leave or return, but these are less common than on major roads.

Temporal Pattern of Incidents:

• The color gradient from purple to yellow provides insights into the temporal progression of incidents. It appears that incidents are somewhat evenly distributed across the month, as we see both early-month (purple) and late-month (yellow) incidents throughout the city.
• However, certain areas have clusters of newer markers (yellow), which could indicate an increase in malfunctions towards the end of the month in those locations.
• Tracking the progression of incidents across the month provides insights into whether malfunctions are increasing or if there are specific periods within the month when incidents spike. Further investigation could reveal if factors such as weather, traffic volume changes, or road conditions influence these patterns.

Insights on Collision between two vehicles incidents

This heatmap displays the frequency of vehicle collisions across a section of Dubai, with the highest density areas shown in red, indicating more frequent incidents. The pattern reveals that collisions occur most frequently near highway ramps, exits, and merging lanes, which are high-risk zones due to complex traffic maneuvers, speed changes, and increased interactions between vehicles entering or exiting the highway.

This heatmap below visualizes vehicle collision hotspots around Dubai International Airport (DXB). The color intensity, with red and orange indicating higher frequency, reveals that collision incidents are concentrated on major roads surrounding the airport.

Key Observations:

Highway and Major Road Intersections:

• The red clusters surrounding DXB indicate high collision frequencies near intersections of major roads, especially where vehicles are merging or turning. High traffic volume, combined with complex intersections, likely contributes to these frequent incidents.

Airport Access Routes:

• The areas close to airport access points show dense red and orange spots, reflecting the high traffic flow as vehicles approach or leave the airport. These zones experience frequent speed adjustments and lane changes, leading to increased accident risks.

Highway Ramps and Exits:

• High incident areas are also visible at highway ramps and exits around the airport. This suggests that merging and diverging traffic near these ramps, where speed differences are prominent, creates potential collision points.

Surrounding Districts:

• Beyond the airport, residential and commercial areas intersecting main roads also display moderate collision activity. This may be due to local traffic mixing with faster-moving vehicles from major routes.

Addressing Data Gaps and Outliers in Traffic Incident Analysis

The dataset reveals notable gaps in data and potential outliers that could affect the validity of the analysis. In the yearly incidents bar chart, there is a stark contrast between 2023, with just over 10,000 recorded incidents, and 2024, which shows nearly 60,000 incidents. This sudden increase may suggest inconsistencies in reporting or the presence of outliers, raising questions about the reliability of incident counts between years.

In the month-by-month records, there are substantial data gaps, with only March and April available for 2023, followed by a complete absence of data from May 2023 to February 2024, and no records for May 2024. These missing months limit trend analysis, as the dataset may lack key incident periods, potentially skewing insights. Addressing these data gaps and identifying possible outliers will be essential for achieving a more accurate interpretation.

Conclusion: Ensuring Data Integrity for Reliable Traffic Analysis

In conclusion, while the dataset provides valuable insights into traffic incidents, the presence of missing data and outliers highlights the importance of careful data handling in analysis. Missing records, particularly the gaps between May 2023 and February 2024, and the noticeable spike in reported incidents in 2024, suggest potential issues in data collection or reporting processes. Addressing these inconsistencies is essential for ensuring accurate and reliable analysis, as well as for drawing meaningful conclusions from the data. Future studies could benefit from a more comprehensive dataset to support robust findings and inform evidence-based strategies for traffic management and safety improvements.