7高德地图 中 国 主 要 城 市 交 通 分 析 报 告 l 高德拉图 4\研究报告由高德地图大云图业务 中心数据分析团队撰写,所载全部内容仅 供参考。 报告是基于高德地图及行业浮动车数 据,通过大数据挖掘技术结合交通算法及 交通理论编制,保证报告合理性与科学性。 报告中地面道路交通通行时间计算方法。 是考虑融合道路交叉口延误时间（即信号 灯等待时间）,从时间、空间、效率三个 维度客观、综合地反映了城市道路交通健 康状况并提出诊断方案的研究。报告力争 做到精准、精细、精确,为公众出行、机 构研究及政府决策提供有价值的参考依据。 报告中所涉及的文字、数据、图片 及标识等所有内容均受到中国著作权法、 专利法、商标法等知识产权法律法规以 及相关国际条约的保护。未经高德事先 书面许可,任何组织和个人不得将本报 告中的任何内容用于任何商业目的。经 高德事先书面许可的引用发布,需明确 出处为"高德地图《2024年中国主要城 市交通分析报告》"，不得对报告进行 有悖原意的引用、删节和修改，且引用 本报告的文件发布前应当经高德审核。 " 交通评价是一个极其复杂的工程,虽然大数据可以反映城市运行规律和特征,但源于数据来源和样 ...

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report focuses on urban traffic analysis using big data from Gaode Map, aiming to describe current traffic conditions, evolution patterns, and future trends, with a particular emphasis on congestion causes and solutions [8] - The report is a collaborative effort involving multiple institutions, including the National Information Center and Tsinghua University, indicating a comprehensive approach to urban traffic research [8] - The report introduces the "Public Transport Happiness Index" and "Traffic Health Index" as key metrics for evaluating urban transportation systems [42][76] Summary by Sections Urban Public Transport Analysis - The report utilizes a "Public Transport Happiness Index" based on six indicators to evaluate the overall performance of urban public transport systems [42] - In 2024, Lanzhou achieved the highest Public Transport Happiness Index at 81.48%, indicating superior public transport efficiency and reliability compared to other cities [44] - The average transfer coefficient across cities shows a slight decline, indicating improved convenience in public transport [51] Urban Ground Traffic Analysis - The "Traffic Health Index" is introduced to provide a comprehensive evaluation of urban traffic conditions, incorporating six key performance indicators [76] - The report ranks cities based on their Traffic Health Index, with Nantong leading at 73.3%, indicating a healthy urban traffic environment [80] - The report highlights that 42% of the 50 major cities experienced a decrease in congestion compared to the previous year, with Changchun showing the most significant improvement [85]

Investment Rating - The report does not provide a specific investment rating for the industry Core Insights - The report predicts significant traffic congestion during the 2025 Spring Festival travel season, with peak congestion expected on January 18, 19, and January 25, 2025, indicating a more dispersed travel pattern compared to previous years [13] - The report highlights the expected return peak on February 3 and 4, 2025, with a notable increase in public awareness of staggered travel [13] - The analysis of the growth of electric vehicle charging stations indicates a strong increase in infrastructure, particularly in regions like Guangdong and Jiangsu, with Inner Mongolia showing the highest growth rate at 157% [41] Summary by Sections 1. National Highway Congestion Prediction - The report forecasts peak congestion periods and identifies specific highway segments likely to experience delays, particularly in the Guangdong-Hong Kong-Macao Greater Bay Area and Yangtze River Delta regions [14][15] - Specific highway segments such as G4 Jinggang'ao Expressway and S49 Xinyang Expressway are highlighted for their expected congestion levels [15] 2. Popular Travel Destinations During Spring Festival - The report lists the top 10 popular driving destinations for the Spring Festival, including ancient towns and scenic spots, with notable mentions like Fenghuang Ancient Town and Dali Ancient City [21][23] - It also predicts high traffic to popular islands and snow-viewing destinations, emphasizing the need for travelers to adhere to safety regulations [26][29] 3. Electric Vehicle Charging Station Growth Analysis - The report details the growth of electric vehicle charging stations, with a focus on regions with the highest numbers and growth rates, indicating a shift towards sustainable transportation [41][40] - The analysis shows that the highest growth in charging stations is occurring in less urbanized areas, suggesting a strategic expansion of infrastructure to support electric vehicles [41]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report focuses on urban traffic analysis in major Chinese cities, utilizing big data from Gaode Map to assess traffic conditions, evolution patterns, and future trends, emphasizing congestion causes and solutions [6][8] - The report highlights the collaboration with various institutions, including the National Information Center and Tsinghua University, to enhance the credibility and scientific basis of the findings [6] - The report introduces the "Public Transport Happiness Index" and "Traffic Health Index" as key metrics for evaluating urban transportation efficiency and health [40][73] Summary by Sections Public Transport Analysis - The "Public Transport Happiness Index" is calculated using six indicators, including average transfer coefficient and peak travel time ratio, to evaluate the overall performance of public transport in cities [40][41] - In Q3 2024, Lanzhou, Shenzhen, and Nanjing ranked highest in the Public Transport Happiness Index, indicating better operational efficiency and reliability compared to other cities [44] - The average transfer coefficient for public transport in major cities showed a slight increase, indicating a trend towards more transfers required for public transport users [46][47] - The report notes that the average waiting time for public transport during peak hours is lowest in Lanzhou, reflecting efficient service [61] Ground Traffic Analysis - The "Traffic Health Index" is developed to provide a comprehensive evaluation of urban traffic conditions, incorporating six key indicators [73] - Urumqi ranked lowest in the Traffic Health Index at 49.3%, indicating significant traffic issues, while Nantong topped the list at 74.6% [76][77] - The report indicates that 32% of the 50 major cities experienced a decrease in congestion compared to the previous year, with Changchun showing the most significant improvement [82][84] - The average peak speed in major cities is analyzed, with Nantong and Shaoxing leading in peak average speed [88][89] Data and Methodology - The report utilizes data from Gaode Map and various governmental sources to ensure accuracy and reliability in traffic assessments [6][79] - The methodology for calculating the indices follows international standards, ensuring comparability and relevance [73][74]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report focuses on urban traffic analysis in major Chinese cities, utilizing big data from Gaode Map to assess traffic conditions, evolution patterns, and future trends, emphasizing congestion causes and solutions [6][8] - The report highlights the collaboration with various institutions, including the National Information Center and Tsinghua University, to enhance the credibility and scientific basis of the findings [6] - The report introduces the "Public Transport Happiness Index" and "Traffic Health Index" as key metrics for evaluating urban transportation efficiency and health [40][73] Summary by Sections Public Transport Analysis - The "Public Transport Happiness Index" is calculated using six indicators, including average waiting time and transfer coefficient, to evaluate the overall performance of public transport in cities [40][41] - In Q3 2024, Lanzhou had the highest Public Transport Happiness Index at 79.82%, indicating superior public transport efficiency compared to other cities [44] - The average transfer coefficient across major cities showed a slight increase, with super-large cities averaging 1.574 [46] Ground Traffic Analysis - The "Traffic Health Index" is developed to provide a comprehensive evaluation of urban traffic conditions, incorporating six key indicators [73] - Urumqi ranked first in the traffic sub-health index with a score of 49.3%, indicating significant congestion issues [76] - The report notes that 32% of the 50 major cities experienced a decrease in peak travel delay index compared to the previous year, with Changchun showing the most significant improvement [84] Traffic Efficiency Metrics - The report identifies the top cities for peak average speed, with Nantong leading at 35.56 km/h [88] - The report also highlights that Haikou had the highest road speed deviation rate at 9.18%, indicating significant fluctuations in travel times [93]

Investment Rating - The report does not provide a specific investment rating for the industry Core Insights - The report predicts significant traffic congestion during the 2024 National Day holiday, particularly on October 1st for outbound travel and October 5th and 6th for return travel, due to increased travel demand and free highway access [4][5] - Major congested routes have been identified, with specific highways and segments expected to experience severe delays, particularly in cities like Suzhou and Shanghai [6][8] - Popular self-driving travel destinations during the holiday include major cities and scenic spots, indicating a strong trend in domestic tourism [11][13][18] Summary by Sections 1. National Highway Congestion Forecast - Peak congestion times are expected on October 1st from 9 AM to 12 PM for outbound travel and on October 5th and 6th from 3 PM to 7 PM for return travel [4] - Specific congested highway segments have been identified, such as G1522 in Suzhou with an average speed of 6 km/h on October 1st [6][7] 2. Popular Self-Driving Destinations - The top 10 popular self-driving cities during the holiday include Beijing, Chengdu, and Shanghai, reflecting a trend towards domestic travel [11][12] - The top scenic spots include West Lake in Hangzhou and the ancient town of Fenghuang, indicating high tourist interest in cultural and natural attractions [13][14] - Popular ancient towns include Fenghuang and Pingyao, showcasing a preference for historical sites among travelers [18][19]

National Highway Traffic Prediction - The peak congestion period for outbound traffic is predicted to be from 9:00 to 12:00 on October 1st, with the peak occurring between 10:00 and 11:00 [5] - The peak congestion period for return traffic is predicted to be from 15:00 to 19:00 on October 5th and 6th, with the peak occurring between 16:00 and 17:00 [5] - The outbound traffic pressure is higher than the return traffic pressure [5] - The most congested outbound highway sections are predicted to be G1522 Changtai Expressway in Suzhou, G1503 Shanghai Ring Expressway, and G15 Shenhai Expressway [7][8] - The most congested return highway sections are predicted to be G0421 Xuguang Expressway in Qingyuan, G2 Jinghu Expressway in Taizhou, and G78 Shanzhan Expressway in Hezhou [9][10] Popular Self-Driving Destinations - The top 10 popular self-driving cities are predicted to be Beijing, Chengdu, Shanghai, Hangzhou, Suzhou, Guangzhou, Chongqing, Shenzhen, Xi'an, and Ningbo [12][13] - The top 10 popular self-driving scenic spots are predicted to be West Lake in Hangzhou, Datang Never Sleeps City in Xi'an, and Fenghuang Ancient Town in Xiangxi [14][15] - The top 10 popular self-driving grottoes are predicted to be Longmen Grottoes in Luoyang, Yungang Grottoes in Datong, and Mogao Grottoes in Jiuquan [16][17] - The top 10 popular self-driving ancient towns are predicted to be Fenghuang Ancient Town in Xiangxi, Pingyao Ancient Town in Jinzhong, and Langzhong Ancient Town in Nanchong [19][20] Data Source and Methodology - The report is based on massive traffic data from Gaode Map and industry floating car data, combined with historical and recent data using big data technology, machine learning, and comprehensive analysis [2] - The data is protected by Chinese intellectual property laws and international treaties, and any commercial use requires prior written permission from Gaode [2]

Investment Rating - The report does not provide a specific investment rating for the industry Core Insights - The report predicts significant traffic congestion during the 2024 Dragon Boat Festival holiday, with peak travel times identified for both outbound and return trips [5][6] - Major congested routes have been forecasted, highlighting specific highways and their expected congestion indices and average speeds [7][10][13] - Popular self-driving destinations during the holiday have been identified, including scenic grasslands, beaches, amusement parks, and cultural streets [17][20][23][27] Summary by Sections National Highway Congestion Prediction - The report outlines the expected congestion trends for national highways during the Dragon Boat Festival, with historical data from 2019 to 2023 indicating consistent patterns of congestion [3] - Peak outbound congestion is expected on June 8, 2024, between 10 AM and 11 AM, with significant congestion also anticipated on June 7 and June 10 [5][6] Congested Routes - Specific routes predicted to experience heavy congestion include G1522 Changtai Expressway in Suzhou and G93 Chengyu Ring Expressway in Chongqing, with detailed congestion indices provided [7][10][13] Popular Self-Driving Destinations - The report lists the top self-driving destinations, including grasslands, beaches, amusement parks, and cultural streets, with recommendations for travelers to plan their routes and avoid fatigue [17][20][23][27]

1 本 研究报告由高德地图大云图业务 报告中所涉及的文字、数据、图片及 中心数据分析团队撰写，所载全部内容仅 标识等所有内容均受到中国著作权法、专 供参考。 利法、商标法等知识产权法律法规以及相 关国际条约的保护。未经高德事先书面许 报告是基于高德地图及行业浮动车数 可，任何组织和个人不得将本报告中的任 据，通过大数据挖掘技术结合交通算法及 何内容用于任何商业目的。经高德事先书 交通理论编制，保证报告合理性与科学性。 面许可的引用发布，需明确出处为“高德 报告中地面道路交通通行时间计算方法， 地图《2024Q1中国主要城市交通分析报 声 明 是考虑融合道路交叉口延误时间（即信号 告》”，不得对报告进行有悖原意的引用、 灯等待时间），从时间、空间、效率三个 The Statement 删节和修改，且引用本报告的文件发布前 维度客观、综合地反映了城市道路交通健 应当经高德审核。 康状况并提出诊断方案的研究。报告力争 做到精准、精细、精确，为公众出行、机 构研究及政府决策提供有价值 ...

Core Insights - The report predicts significant traffic congestion on highways during the 2024 May Day holiday, particularly on May 1st for outbound travel and May 4th for return travel [2][11][14] - Major congested routes include S20 Outer Ring Expressway, G1522 Changtai Expressway, and S39 Jiangyi Expressway, with expected delay indices of 18.2, 17.7, and 14.4 respectively [2][3][14] - The report emphasizes the importance of real-time traffic updates and suggests that travelers plan their journeys to avoid peak congestion times [11][14] Outbound Travel Predictions - On May 1st, the peak congestion time is expected between 10 AM and 11 AM, with significant delays anticipated on major highways [11][14] - Specific routes predicted to experience heavy congestion include: - S20 Outer Ring Expressway (11 km, delay index 18.2, average speed 5 km/h) [3] - G1522 Changtai Expressway (8 km, delay index 17.7, average speed 6 km/h) [3] - S39 Jiangyi Expressway (9 km, delay index 14.4, average speed 8 km/h) [3] Return Travel Predictions - For return travel on May 4th, the peak congestion is expected between 4 PM and 5 PM, with additional congestion on May 5th from 3 PM to 4 PM [11][14] - Congested routes for return travel include: - G0425 Guangao Expressway (17 km, delay index 7.5, average speed 13 km/h) [15] - G40 Hushan Expressway (23 km, delay index 6.9, average speed 14 km/h) [15] - S19 Tongxi Expressway (24 km, delay index 4.7, average speed 21 km/h) [15] Popular Destinations - The report identifies the top 10 popular self-driving destinations during the holiday, including: - Hangzhou West Lake Scenic Area - Xi'an Datang Night City - Shantou Nanao Island Ecological Tourism Area [20][22][37] - It also highlights the top 10 ancient towns and 5A-level mountain tourism scenic spots expected to attract significant visitor numbers [22][34][37]