不同城市的社会经济状况和形态不尽相同，因此其公交分担率指标也不应是统一不变的，而应结合该城市自身特点，制定与之相适应的公交分担率指标。鉴于此，基于城市整体的交通供需条件，考虑地面公共交通与私家车的竞争关系，以系统出行效率最优为目标，建立公交分担率指标优化模型。通过对模型分析可得出，城市公交分担率发展目标与该城市的交通供需系数密切相关，当供需系数较低时（算例取G≤1），建议公交分担率为0；当供需系数较高时（算例取G≥3.5），建议公交分担率为100%；当1＜G＜3.5时，适宜的公交分担率在0~100%之间取得；适宜的公交分担率随着交通供需系数的增大呈上升趋势，且上升幅度逐渐减小；随着私家车-公交速度比增大，适宜的公交分担率呈下降趋势，两者关系曲线呈倒S形；公交平均载客率对适宜的公交分担率影响不明显。
Abstract
Different cities have different social, economic and pattern status. Therefore, the public transportation share ratio index of different cities should not be invariant. The appropriate public transportation share ratio should be formulated according to the characteristics of a particular city. Based on the traffic supply and demand conditions of cities, an optimization model of public transportation share ratio index aiming at optimizing the travel efficiency of whole system was established, considering the competitive relationship between ground public transportation and private cars. Through model analysis, it can be seen that the public transportation share ratio index is closely related to traffic supply-demand index. When the supply-demand index is low (G≤1 for example), it is suggested that the public transportation share ratio should be 0. When the supply-demand index is high (G≥3.5 for example), it is suggested that the public transportation share ratio should be 100%. When 1<3.5, the appropriate share ratio should be between 0 and 100%. The suitable public transportation share ratio increases with the increase of supply-demand index and the increase range gradually decreases. With the increase of the speed ratio of private car to bus, the suitable public transportation share ratio decreases and the relation curve shows inverted S shape. Average bus carrying rate has no obvious influence on proper public transportation share ratio.
关键词
城市交通 /
公共交通 /
公交分担率 /
优化模型 /
供需指数
Key words
urban traffic /
public transportation /
public transportation share ratio /
optimization model /
supply-demand index
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1]Vanier D J, Sciglimpaglia D. Development and Application of the Concept of User Image: The Case of Mass Transit Ridership[J]. Journal of the Academy of Marketing Science, 1981, 9(4): 479-489.
[2]鲜于建川，隽志才，朱泰英. 基于贝叶斯网络的出行选择行为分析[J]. 交通运输系统工程与信息，2011，11（5）：167-172.
[3]殷焕焕，关宏志. 基于BP神经网络的居民出行方式选择模型[J]. 交通信息与安全，2011，29（3）：47-50.
[4]宗芳，隽志才. 基于活动的出行方式选择模型与交通需求管理策略[J]. 吉林大学学报（工学版），2007，37（1）：48-53.
[5]叶倩文，陈旭梅，柏喜红. 基于NL模型的北京公共交通票价对出行方式影响研究[J]. 山东科学，2019，32（2）：79-88，114.
[6]Gomez-Ibanez J A. Big-City Transit Rider snip, Deficits, and Politics: Avoiding Reality in Boston[J]. Journal of the American Planning Association, 1996, 62(1): 30-50.
[7]Hendrickson C. A Note on Trends in Transit Commuting in the United States Relating to Employment in the Central Business District[J]. Transportation Research Part A: General, 1986, 20(1): 33-37.
[8]Morral J, Bolger D. The Relationship Between Downtown Parking Supply and Transit Use[J]. ITE Journal, 1996, 66(2): 32-34.
[9]Xiao H, Peng R. Load Dependent Series-Parallel Systems with Common Bus Performance Sharing Mechanism[J]. Journal of Donghua University, 2014, 31(6): 770-773.
[10]李瑶. 关于提高公交分担率的相关因素研究[J]. 西部交通科技，2017（3）：94-97.
[11]温旭丽，杨涛，陶雨濛. 基于公交分担率的公交系统评价指标体系研究[J]. 现代城市研究， 2017（12）：18-23.
[12]杨新新. 中等规模城市居民出行特性与公交最佳分担率研究[D]. 西安：长安大学，2017.
[13]马小毅，李彩霞，郑炜. 对城市公交优先发展评价指标的思考[J]. 交通工程，2017，17（1）：18-21.
[14]姜毅，王路，尹锦明. 中等城市公共交通优先发展的策略研究——以泰州市为例[J]. 交通运输工程与信息学报，2019，17（1）：117-121，146.
[15]章玉，黄承锋，许茂增. 中国城市公交的运营效率与服务效果研究[J]. 公路交通科技，2017，34（1）：154-158.
[16]建设部. 建设部关于优先发展城市公共交通的意见（建城〔2004〕38号）[Z]. 北京：建设部，2004.
[17]Seely B E. Engineers and Government-Business Cooperation: Highway Standards and the Bureau of Public Roads, 1900-1940[J]. Business History Review, 1984, 58(1): 51-77.
PDF(1504 KB)
京公网安备 11010102003417号
京ICP备17006742号-3
