Traffic_Analysis_Report_2018Q1_48页_4mb

2018 Q1 Traffic Analysis Report of Major Cities in China

Core Content

This report, published by AutoNavi Traffic Big-data Team, presents an analysis of traffic congestion in major cities in China during the first quarter of 2018. It uses the "congestion delay index" as a metric, which measures the ratio of actual travel time to free-flow travel time. The report provides insights into congestion patterns, travel characteristics, and the impact of weather and seasonal factors on traffic flow.

Main Points

• Congestion Delay Index: A key metric to evaluate urban congestion. A higher index indicates greater congestion.
• Time Periods:
  • Whole Day: 06:00~22:00
  • Morning Peak: 07:00~09:00
  • Evening Peak: 17:00~19:00
• Data Sources: AutoNavi's massive traffic and travel data, including 700 million users, and supported by Ali Cloud and data mining technologies.

Key Information

Congestion Overview

• Out of 361 cities, 65 had a congestion delay index >1.8, indicating severe congestion.
• 231 cities had a congestion delay index between 1.5 and 1.8, showing moderate congestion.
• 65 cities had a congestion delay index <1.5, indicating minimal congestion.

Top 10 Congested Cities in Peak Hours

1. Yinchuan – Congestion delay index 1.966, average speed 21.41 km/h
2. Jinan – Congestion delay index 1.954, average speed 23.59 km/h
3. Luoyang – Congestion delay index 1.941, average speed 21.35 km/h
4. Beijing – Congestion delay index 1.935, average speed 24.52 km/h
5. Hohhot – Congestion delay index 1.920, average speed 24.30 km/h
6. Ganzhou – Congestion delay index 1.917, average speed 21.27 km/h
7. Hefei – Congestion delay index 1.896, average speed 23.37 km/h
8. Harbin – Congestion delay index 1.883, average speed 23.35 km/h
9. Maoming – Congestion delay index 1.862, average speed 20.21 km/h
10. Shantou – Congestion delay index 1.849, average speed 20.27 km/h

Congestion Changes Compared to 2017 Q1

• 43 cities showed an increase in congestion delay index.
• 23 cities showed no significant change.
• 34 cities showed a reduction in congestion delay index.
• Shijiazhuang had the largest reduction (11.9%).
• Jinan reduced its congestion degree by 5.6%.

Congestion Reasons

• Snow and Haze Weather: Contributed to increased congestion in cities like Yinchuan and Luoyang.
• Spring Festival Travel: Increased passenger flow and shopping activities led to congestion in many cities.
• Cross-city Travel Behaviors: Influenced congestion patterns in some cities.

Travel Characteristics

• 66% of travel concentrated in the Gu Lou area and its surroundings.
• Yinchuan had the highest congestion delay index during the evening peak (2.145).
• Jinan had the highest congestion delay index during the morning peak (1.954).
• Luoyang had the highest congestion delay index during holidays.

Congestion Reduction

• Shijiazhuang had the largest reduction in congestion delay index (11.9%).
• Jinan had a significant reduction in congestion degree, moving from the most congested city to 6th in the reduction list.

Congestion Increase

• Nanyang had the largest increase in congestion delay index (12.8%).
• Suqian and Shantou also showed notable increases.

Congestion by City Scale

• Large and Middle Cities experienced higher congestion than first-tier cities and provincial capitals.
• Yinchuan and Luoyang showed high congestion despite relatively low city scale.

Key Insights

• Yinchuan was the most congested city in the peak hours and evening peak.
• Jinan was the most congested city in the morning peak but showed a reduction in congestion in 2018 Q1.
• Luoyang was the most congested city during holidays due to peony flower season and high travel demand.
• Shanghai had a notable increase in congestion during the morning peak in March, but its overall congestion level was lower than some other cities.
• Weather played a significant role in congestion patterns, especially in January and February.
• Spring Festival had a mixed impact on congestion, with some cities experiencing reduced congestion due to decreased travel activity.

Summary Table

Conclusion

The report highlights that congestion levels in Chinese cities are influenced by a combination of seasonal factors, weather conditions, and travel behavior. While some cities experienced a reduction in congestion, others saw a significant increase, particularly those with high travel demand and adverse weather. The congestion delay index and average speed are essential metrics for understanding and managing urban traffic congestion.