考虑综合成本的常规公交客流分配方法

程国柱; 李威骏; 冯天军

doi:10.3963/j.jssn.1674-4861.2024.02.017

考虑综合成本的常规公交客流分配方法

doi: 10.3963/j.jssn.1674-4861.2024.02.017

1.
东北林业大学土木与交通学院哈尔滨 150040
2.
吉林建筑大学交通科学与工程学院长春 130118

基金项目:

国家重点研发计划项目 2018YFB1600900

吉林省科技发展计划项目 20220402030GH

详细信息

通讯作者:
程国柱（1977—），博士，教授. 研究方向：交通安全、交通规划与设计等. E-mail：guozhucheng@126.com

中图分类号: U491.1+7
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- 文章访问数: 114
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- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2023-03-10
- 网络出版日期: 2024-09-14

A Method for Allocating Bus Passenger Flow by Considering Comprehensive Cost

1.
School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China
2.
School of Transportation Science & Engineering, Jilin Jianzhu University, Changchun 130118, China

摘要

摘要: 为改善常规公交客流数据传统调查方法效率低、准确性差，以及常规公交客流分配时对出行成本考虑不全面、个体间出行成本存在较大差距的缺点，开展了考虑综合成本的常规公交客流分配方法研究。以数据即服务为基础开发的手机信令数据平台作为常规公交客流分配数据来源。通过经纬度坐标匹配，得到用户与交通小区之间的空间关系。利用数据仓库工具筛取数据字典索引，界定时间、速度、起终点类型等数据参数，通过时间匹配、路径匹配进行交通方式识别，将用户比例外推扩样至全国人口，得到常驻居民早高峰常规公交通勤起讫点（origin-destination，OD）量。分析常规公交客流个体的出行时间成本、拥挤成本、票价成本，建立以个体利益最大为原则、考虑综合成本的常规公交客流分配模型。将交通小区间常规公交客流分配问题转换为有向赋权图路径选择问题，并采用深度优先搜索与连续平均法混合算法求解，进行常规公交出行方案筛选以及客流分配。选取哈尔滨市典型交通小区为案例，开展常规公交客流分配，并与传统Logit路径选择概率模型分配结果、人工调查结果对比分析。结果表明：模型分配结果与人工调查结果的平均绝对百分比误差为4%，Logit模型为17.5%。模型分配客流后个体出行成本极差、方差、总和分别为0.03，0.000 1，1 108.35，Logit模型分别为3.28，1.58，1 127.02。验证了模型分配客流的准确性以及考虑综合成本的必要性，分配客流后个体出行成本差距更小，更符合利益最大原则。
- 交通工程 /
- 常规公交客流分配 /
- 综合成本 /
- 有向赋权图 /
- 深度优先搜索算法 /
- 连续平均法
Abstract: The inefficiencies and inaccuracies of traditional survey methods for bus passenger flow data need to be addressed. Moreover, bus passenger flow allocation methods are inadequate due to the incomplete consideration of travel cost and significant disparities in travel cost among individuals. Therefore, a study on a bus passenger flow allocation method that considers comprehensive cost is conducted. A mobile signaling data platform based on Data-as-a-Service is developed as a source of data for bus passenger flow allocation. Spatial relationships between users and traffic zones are determined by matching latitude and longitude coordinates. Using a data warehousing tool, data dictionary indexes are filtered to define parameters such as time, speed, and origin-destination types. Transportation modes are identified through time matching and path matching. User proportions are extrapolated to the national population to obtain the bus commuting origin-destination (OD) matrix during the morning peak period for permanent residents. Travel time cost, congestion cost, and fare cost for bus passengers are analyzed. A bus passenger flow allocation model is established based on the principle of maximizing individual benefits while considering comprehensive cost. The problem of bus passenger flow allocation between traffic zones is transformed into a directed weighted graph path selection problem. A hybrid algorithm combining depth-first search and successive averages method is employed to solve this problem, facilitating bus travel plan selection and passenger flow allocation. Taking typical traffic zones in Harbin as a case study, bus passenger flow allocation is conducted and compared with results from the traditional Logit path selection probability model and manual surveys. The results show that the average absolute percentage error between the proposed model and manual surveys is 4%, compared to 17.5% for the Logit model. After allocating passenger flows using the proposed model, the extreme difference, variance, and total sum of individual travel cost are 0.03, 0.000 1, and 1 108.35, respectively, compared to 3.28, 1.58, and 1 127.02 for the Logit model. These results validate the accuracy of the proposed model in allocating passenger flows and highlight the necessity of considering comprehensive cost. After passenger flow allocation, the difference in individual travel cost is smaller, aligning better with the principle of maximizing benefits.
- traffic engineering /
- bus passenger flow allocation /
- comprehensive cost /
- directed weighted graph /
- depth-first search algorithm /
- method of successive averages

HTML全文

图 1 常规公交出行OD量提取流程

Figure 1. OD extraction process of bus travel

下载: 全尺寸图片幻灯片

图 2 交通小区间常规公交可达出行方案有向图

Figure 2. Directed diagram of conventional bus reachable travel scheme between traffic zones

下载: 全尺寸图片幻灯片

图 3 DFS与MSA混合算法流程图

Figure 3. Mixed DFS and MSA algorithm flow chart

下载: 全尺寸图片幻灯片

图 4 哈尔滨市常驻居民早高峰常规公交通勤OD分布

Figure 4. The bus commuting OD during the morning peak period for permanent residents in Harbin

下载: 全尺寸图片幻灯片

图 5 常规公交内部构造

Figure 5. Conventional bus interiors

下载: 全尺寸图片幻灯片

图 6 MAPE结果图

Figure 6. MAPE results

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图 7 分配结果图

Figure 7. Assignment results

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表 1 出行方案相关数据

Table 1. Travel scheme data

方案	L_ws/m	T_g/min	L_b/m	s/个	L_sw/m	L_sws/m	T_gg/min	N_seat/人	N_stand/人	P/元
1路	200	15	8 000	12	200			32	7	2
2路	250	15	9 000	13	200			23	0	1
3路	200	10	8 500	12	300			32	20	2
4路换乘5路	100	10	10 000	14	50	50	10	15	0	2

下载: 导出CSV

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