文章编号： 1672-5328（2022）06-0021-08

朱洪，刘明姝，王磊
(上海市城乡建设和交通发展研究院，上海200040)

摘要： 公共汽电车客运量大幅下降、行业运营及补贴压力日益加大成为中国大城市普遍面临的问 题。发现隐藏在客运量下降背后的实质性特征是公共汽电车交通行业改革的关键。以上海市为例， 通过大数据挖掘理清纷繁杂乱的数据指标，更好地认识公共汽电车交通转型发展的特征。研究显 示：虽然公共汽电车客运量较低，但其仍承担较大比例的公共交通出行量；公共汽电车出行需求以 接驳城市轨道交通或不适合城市轨道交通承担的中短距离为主；在客流低密度区域，公共汽电车运 能利用率及能源利用效率问题突出。最后，依据运行特征分析结果提出相关建议：在行业总体投入 上坚持公交优先政策保障；在出行服务中凸显公共汽电车灵活便捷的优势；在运营组织模式上需兼 顾服务水平和运营效率，特别是在客流低密度区域。

关键词： 公共汽电车；大数据；运行特征；客运量；城市轨道交通网络化；上海市

中图分类号： U491.1+7

文献标识码：A

Operational Characteristics of Bus Transit in Shanghai

ZHU Hong, LIU Mingshu,WANG Lei
(Shanghai Urban- Rural Construction and Transportation Development Research Institute, Shanghai 200040, China)

Abstract： A substantial decline in the number of passengers using bus transit and the growing pressure of industrial operations and subsidies become common issues in large cities in China. Identifying substantive characteristics of the reduced passenger volumes is critical for the reform of bus transit industry. Taking Shanghai as an example, this paper clarifies complex indicators through big data mining and discusses the characteristics of transformation and development of bus transit. Despite reduced passenger volumes of bus transit, the results suggest similar number of trips by bus transit and urban rail transit. The travel demand of bus transit is mainly from the trips connecting urban rail transit and medium- or short-distance trips for which urban rail transit is not suitable. Areas with low density of passengers flow are also subject to prominent issues related to the utilization ratio of capacity and energy efficiency of bus transit. Finally, based on the analysis of operational characteristics, the paper suggests the following: prioritizing public transit as the overall industrial investment strategy; highlighting the flexibility and convenience of bus transit among different types of travel services; and balancing the level of service and operational efficiency in terms of operational patterns, especially in areas with low density of passengers flow..

Keywords： bus transit; big data; operational characteristics; passenger volumes; urban rail transit network; Shanghai