|Table of Contents|

Trunk highway passenger flow forecasting method based on comprehensive transportation network(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2022年04期
Page:
259-272
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Trunk highway passenger flow forecasting method based on comprehensive transportation network
Author(s):
PEI Yu-long;  YUWEN Chong;  CHANG Zheng;  GAO Zhi-xiang;  LIU Tao
(School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, Heilongjiang, China)
Keywords:
comprehensive transportation network;  trunk highway;  passenger flow forecast;  standardization of passenger transport unit;  comprehensive traffic distribution
PACS:
U491.14
DOI:
10.19818/j.cnki.1671-1637.2022.04.020
Abstract:
A trunk highway passenger flow forecasting method integrating multiple transport modes was proposed. By introducing the standard passenger transport unit based on the man-time and the hub nodes conversion method from point to line, the sub-networks of different transport modes, such as highways, railways, airlines and waterways, were integrated, and the comprehensive transportation network model which can reflect the transfer relationship among different transport modes was built. By considering the travel economic cost, travel time, maximum travel recovery time, comfort level and other factors, the impedance functions of differenttransport modes in the comprehensive transportation network were constructed. The maximum capacities of different transport modes in the comprehensive transportation network were calibrated by using the rated passenger number and the number of departures per unit time. Based on the standard passenger transport unit and comprehensive transportation network model, the passenger flow distribution forecasting model considering the impedance of comprehensive transportation was proposed. On this basis, the passenger flow forecasting model considering the influence of different transport modes was realized. Taking Harbin, Daqing, Suihua and Qiqihar area in Heilongjiang Province as an example, the method was verified. Analysis results show that compared with the actual observation value in 2019, the average error of forecasting results of the passenger flow forecasting method based on the comprehensive transportation network is 5.47%, slightly lower than the 6.14% of the traditional four-stage method when there are no accompanying lines around the characteristic roads. However, the average error of forecasting results of the proposed method is 4.58% when the accompanying lines are around the characteristic roads, far less than the error value 11.89% of the traditional four-stage method. Compared with the traditional four-stage method, the proposed method can better reflect the influence of the transfer passenger on the traffic volume of the trunk highway after the structural change of comprehensive transportation network. Compared with adding waterways, adding high-speed or conventional railways accompanying lines have more obvious impact on the passenger flow of trunk highways, and can promote the transfer of passenger flow from highways to railways. 4 tabs, 12 figs, 32 refs.

References:

[1] 张宗博,谢君平,马金麟.江苏省徐州市域公路交通发展现状及其需求预测[J].江苏师范大学学报(自然科学版),2016,34(4):51-54.
ZHANG Zong-bo, XIE Jun-ping, MA Jin-lin. Development status and demand forecast of highway traffic in Xuzhou, Jiangsu Province[J]. Journal of Jiangsu Normal University(Natural Science Edition), 2016, 34(4): 51-54.(in Chinese)
[2] ZHONG M, HANSON B L. GIS-based travel demand modeling
for estimating traffic on low-class roads[J]. Transportation Planning and Technology, 2009, 32(5): 423-439.
[3] KRYLATOV A Y, ZAKHAROV V V, MALYGIN I G.
Competitive traffic assignment in road networks[J]. Transport and Telecommunication Journal, 2016, 17(3): 212-221.
[4] ALVES M A C, CORDEIRO R L F. Effective and unburdensome forecast of highway traffic flow with adaptive computing[J]. Knowledge-Based Systems, 2021, 212: 106603.
[5] KOLIDAKIS S, BOTZORIS G, PROFILLIDIS V, et al. Road traffic forecasting—a hybrid approach combining artificial neural network with singular spectrum analysis[J]. Economic Analysis and Policy, 2019, 64: 159-171.
[6] LI Run-mei, HUANG Yin-feng, WANG Jian. Long-term
traffic volume prediction based on k-means Gaussian interval type-2 fuzzy sets[J]. IEEE/CAA Journal of Automatica Sinica, 2019, 6(6): 1344-1351.
[7] LYU Y S, DUAN Y J, KANG W W, et al. Traffic flow prediction with big data: a deep learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2): 865-873.
[8] LIU Can-qi, HU Dong. A forecasting method of trip generation based on land classification combined with OD matrix estimation[C]∥IET. 7th Advanced Forum on Transportation of China: London: IET, 2011: 1-4.
[9] WU Y K, TAN H C, QIN L Q, et al. A hybrid deep learning based traffic flow prediction method and its under standing[J]. Transportation Research Part C: Emerging Technologies, 2018,90: 166-180.
[10] 聂佩林,龚峻峰.一种路网交通流参数的融合预测方法[J].交通运输系统工程与信息,2015,15(6):39-45.
NIE Pei-lin, GONG Jun-feng. A combined traffic network flow prediction method[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6): 39-45.(in Chinese)
[11] 葛梦雪,宋国华,王芃森,等.基于手机应用软件测算OD数据的区域交通需求预测[J].公路交通科技,2019,36(5):152-158.
GE Meng-xue, SONG Guo-hua, WANG Peng-sen, et al. Regional traffic demand forecasting based on OD data calculated by mobile phone application software[J]. Journal of Highway and Transportation Research and Development, 2019, 36(5): 152-158.(in Chinese)
[12] 李双宝,邱万军.公路沿线机场集疏交通量预测方法研究[J].公路,2020,65(2):177-182.
LI Shuang-bao, QIU Wan-jun. Research on the prediction method of airport traffic volume along highway[J]. Highway, 2020, 65(2): 177-182.(in Chinese)
[13] 裴玉龙,徐慧智.基于城市区位势能的路网密度规划方法[J].中国公路学报,2007,20(3):81-85.
PEI Yu-long, XU Hui-zhi. Planning method of road network density based on urban area position potential energy[J]. China Journal of Highway and Transport, 2007, 20(3): 81-85.(in Chinese)
[14] 傅成红,张 阳.基于参数优化的SVR城市群交通需求预测方法[J].系统工程,2016,34(2):114-120.
FU Cheng-hong, ZHANG Yang. An improved SVR algorithm based on the nuclear parameter optimization for traffic demand forecasting of urban agglomeration[J]. Systems Engineering, 2016, 34(2): 114-120.(in Chinese)
[15] 袁 华,陈泽濠.基于时间卷积神经网络的短时交通流预测算法[J].华南理工大学学报(自然科学版),2020,48(11):107-113,122.
YUAN Hua, CHEN Ze-hao. Short-term traffic flow prediction based on temporal convolutional networks[J]. Journal of South China University of Technology(Natural Science Edition), 2020, 48(11): 107-113, 122.(in Chinese)
[16] 高华睿,郝 龙,王明明,等.基于Att-Bi-LSTM的高速公路短时交通流预测研究[J].武汉理工大学学报,2020,42(9):59-64.
GAO Hua-rui, HAO Long, WANG Ming-ming, et al. Research on short-term traffic flow forecast of expressway based on Att-Bi-LSTM[J]. Journal of Wuhan University of Technology, 2020, 42(9): 59-64.(in Chinese)
[17] 温惠英,张东冉,陆思园.GA-LSTM模型在高速公路交通流预测中的应用[J].哈尔滨工业大学学报,2019,51(9):81-87,95.
WEN Hui-ying, ZHANG Dong-ran, LU Si-yuan. Application of GA-LSTM model in highway traffic flow prediction[J]. Journal of Harbin Institute of Technology, 2019, 51(9): 81-87, 95.(in Chinese)
[18] 李桃迎,王 婷,张羽琪.考虑多特征的高速公路交通流预测模型[J].交通运输系统工程与信息,2021,21(3):101-111.
LI Tao-ying, WANG Ting, ZHANG Yu-qi. Highway traffic flow prediction model with multi-features[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 101-111.(in Chinese)
[19] 徐 凤,朱金福,杨文东.复杂网络在交通运输网络中的应用研究综述[J].复杂系统与复杂性科学,2013,10(1):18-25.
XU Feng, ZHU Jin-fu, YANG Wen-dong. The complex networks' applications in transportation networks: a survey[J]. Complex Systems and Complexity Science, 2013, 10(1): 18-25.(in Chinese)
[20] 李成兵.城市群交通运输系统供需非均衡模型研究[J].交通运输系统工程与信息,2017,17(1):47-53.
LI Cheng-bing. Supply and demand non-equilibrium model of city agglomeration transportation system[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(1): 47-53.(in Chinese)
[21] 陈晓明,李引珍,沈 强,等.基于双层复杂网络的城市交通网络协同优化方法[J].计算机应用,2019,39(10):3079-3087.
CHEN Xiao-ming, LI Yin-zhen, SHEN Qiang, et al. Urban traffic networks collaborative optimization method based on two-layered complex networks[J]. Journal of Computer Applications, 2019, 39(10): 3079-3087.(in Chinese)
[22] 陈峰林.多模式客运交通综合阻抗模型研究[J].江苏科技信息,2017,4(13):34-35.
CHEN Feng-lin. Study on integrated impedance model of multi-mode passenger traffic[J]. Jiangsu Science and Technology Information, 2017, 4(13): 34-35.(in Chinese)
[23] 王秋平,王思颖,任歆雨,等.基于城市交通网络的历史街区单向交通组织优化[J].西安建筑科技大学学报(自然科学版),2014,46(3):342-347.
WANG Qiu-ping, WANG Si-ying, REN Xin-yu, et al. One-way traffic organization optimization of the historic district based on urban traffic network[J]. Journal of Xi'an University of Architecture and Technology(Natural Science Edition), 2014, 46(3): 342-347.(in Chinese)
[24] 刘 攀,刘志远,田 琼,等.城市多模式交通供需平衡机理与仿真系统研究[J].中国基础科学,2021,23(1):57-66.
LIU Pan, LIU Zhi-yuan, TIAN Qiong, et al. Study on supply-demand balance mechanism and simulation system of urban multi-modal transportation[J]. China Basic Science, 2021, 23(1): 57-66.(in Chinese)
[25] 公维勇,高建杰,焦海贤.城市交通空间需求与交通网络双均衡研究[J].重庆交通大学学报(自然科学版),2011,30(3):429-431,444.
GONG Wei-yong, GAO Jian-jie, JIAO Hai-xian. Study on the Bi-directional equalization between urban traffic spatial demands and networks[J]. Journal of Chongqing Jiaotong University(Natural Science Edition), 2011, 30(3): 429-431, 444.(in Chinese)
[26] 王 炜,华雪东,郑永涛.综合交通系统“多网合一”交通分析模型与算法[J].交通运输工程学报,2021,21(2):159-172.
WANG Wei, HUA Xue-dong, ZHENG Yong-tao. Multi-network integrated traffic analysis model and algorithm of comprehensive transportation system[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 159-172.(in Chinese)
[27] 张 锐,姚恩建,杨 扬.多方式条件下城市交通分配研究[J].交通运输系统工程与信息,2014,14(6):107-112,170.
ZHANG Rui, YAO En-jian, YANG Yang. Multimodal urban traffic assignment analysis[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6): 107-112, 170.(in Chinese)
[28] 徐君翔,郭静妮,张 锦.区域多枢纽单分配混合轴辐式综合交通网络优化及实证[J].中国管理科学,2019,DOI:10.16381/j.cnki.issn1003-207x.2019.1911.
XU Jun-xiang, GUO Jing-ni, ZHANG Jin. Optimization and demonstration of regional mixed hub and spoke comprehensive transportation network[J]. Chinese Journal of Management Science, 2019, DOI:10.16381/j.cnki.issn1003-207x.2019.1911.(in Chinese)
[29] 王世广,于德新,王树兴,等.基于改进度的城市路网元素连接特性[J].交通运输工程学报,2018,18(2):101-110.
WANG Shi-guang, YU De-xin, WANG Shu-xing, et al. Connectivity characteristics of urban road network elements based on improved degree[J]. Journal of Traffic and Transportation Engineering, 2018, 18(2): 101-110.(in Chinese)
[30] 何 南,李季涛.考虑运输方式间影响关系的公路客运交通需求预测[J].公路交通科技,2017,34(7):153-158.
HE Nan, LI Ji-tao. Highway passenger transport demand forecasting considering relationship among transport modes[J]. Journal of Highway and Transportation Research and Development, 2017, 34(7): 153-158.(in Chinese)
[31] 张 翔.基于SUE客流分配的城市轨道交通可达性研究[D].成都:西南交通大学,2019.
ZHANG Xiang. Research on accessibility of urban rail transit based on SUE passenger flow assignment[D]. Chengdu: Southwest Jiaotong University, 2019.(in Chinese)
[32] 何宇强,毛保华,陈团生,等.高速客运专线客流分担率模型及其应用研究[J].铁道学报,2006,28(3):18-21.
HE Yu-qiang, MAO Bao-hua, CHEN Tuan-sheng, et al. The mode share model of the high-speed passenger railway line and its application[J]. Journal of The China Railway Society, 2006, 28(3): 18-21.(in Chinese)

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Last Update: 2022-09-01