高德地图-2018Q3中国主要城市交通分析报告-英文版-2018.12-57页-5mb

2018 Q3 Traffic Analysis Report Summary

Core Content

This report is a traffic analysis conducted by AutoNavi Traffic Big-data Team, utilizing massive traffic and user data from Amap. It provides a comprehensive and multidimensional evaluation of urban congestion across 360+ cities in China. The report introduces a new congestion delay index as a key metric, which measures the ratio of peak travel time to free-flow travel time, and evaluates congestion through multiple indexes including time, space, and efficiency dimensions.

Main Views

1. Congestion Measurement Metrics

• Congestion Delay Index is defined as the ratio of peak travel time to free-flow travel time, indicating the time costs imposed by traffic congestion.
• The report has updated the congestion measurement from a single index to 9 indexes, which includes:
  • Time Dimension: Ratio of peak time, commuter pressure index, and economic loss due to congestion.
  • Space Dimension: Mileage ratio of congestion, mileage ratio of frequent congested roads, and mileage ratio of slow driving.
  • Efficiency Dimension: Average speed, standard deviation coefficient of peak speed, and congestion delay index.

2. Congestion Trends

• Overall National Congestion Trend: The national congestion level decreased by 3.9% year-on-year and 3.6% month-on-month in 2018 Q3.
• Congestion Reduction: The congestion delay index significantly dropped from July to September, with the largest reduction in August at 5.1%. This is attributed to city government efforts, road network optimization, and improved traffic management using big data.
• Regional Comparison: The congestion level in the Yangtze River Delta region was lower than the national average, while the Chengdu-Chongqing region had the highest congestion level.

3. City Congestion Rankings

• Top 10 Congested Cities: Beijing, Chongqing, Guiyang, Foshan, Harbin, Chengdu, Shenzhen, etc., were ranked in the top 10 based on congestion delay index.
• Congestion Level Classification: Cities are classified into five groups based on the number of indexes in the top 10:
  • Extremely High: Beijing, Guangzhou, Shanghai (more than 7 indexes in top 10)
  • High: Shenzhen, Chongqing, Guiyang, Foshan, Harbin, Chengdu
  • Middle: Changchun, Nanning, Dalian, Shenyang, Hohhot, Hangzhou
  • Low: Hefei, Nanjing, Lanzhou, Kunming, Xi’an, Jinan, etc.
  • Extremely Low: Changsha, Wuhan, Taiyuan, Yantai, Nanchang, Zhengzhou, etc.

4. City-Specific Congestion Analysis

• Beijing:
  • Peak congestion remains concentrated during morning and evening peak hours.
  • Xicheng, Dongcheng, Chaoyang, and Haidian districts had the highest congestion levels.
  • Congestion delay index for Beijing was 1.982, the highest among super mega-cities and mega-cities.
• Jinan:
  • Jinan exited the top 10 congestion list due to improved traffic infrastructure and better management.
  • The average speed increased by 33% compared to 2017 Q3.
• Shanghai:
  • The opening of the S26 entering-city road section reduced congestion delay index by 10% and increased average speed by 11%.
• Foshan:
  • Chancheng District had the highest congestion delay index, while Nanhai District showed a significant increase in congestion compared to the previous quarter.

Key Information

• Data Sources: Amap traffic data and 400 million+ users’ data.
• Time Period: The statistical time periods were from July 1st to September 30th, 2018, unless otherwise specified.
• Congestion Delay Index: A higher index indicates a higher congestion level.
• Traffic Flow Reduction: In some cities, traffic flow decreased due to new vehicle restriction policies, such as in Shijiazhuang, Tangshan, and Qingdao.
• Road Usage and Congestion: Urban expressways are highly saturated and have higher congestion rates, while secondary and branch roads show less congestion but higher usage rates.
• Congestion Causes: Excessive traffic flow on urban expressways, insufficient traffic guidance on secondary and branch roads, and congestion concentrated on non-urban roads in Changping District.

Conclusion

The report highlights the use of big data for analyzing urban congestion and provides insights into the effectiveness of traffic management and infrastructure improvements. It emphasizes the importance of a multidimensional approach to congestion evaluation and offers valuable reference for government and city planners in decision-making.