基于BPR函数的城市道路间断流动态路阻模型

doi:10.13229/j.cnki.jdxbgxb.20211061

Abstract

Abstract:

In view of the limitation of BPR （Bereau of Public Roads） function that it is not suitable for oversaturated traffic flow and does not meet the characteristics of urban intermittent flow， some improvements have been made. The paper redefines the traffic demand， and proposes a dynamic resistance function form of urban road intermittent flow that takes into account the influence of upstream and downstream signal timing. Based on the data obtained by VISSIM road section simulation， the SMOTE oversampling algorithm is used to balance the simulation data set， and the improved road resistance function is calibrated. Our research shows that adding three variables which include percentage of green wave $ω$ ， green split for downstream research phase $s g$ ， and steering factor $s l$ into the BPR function can simultaneously reflect the influence of signal timing and traffic demand on travel time. In the case of zero traffic flow， the zero-flow travel time is related with the percentage of green wave $ω$ . With the increase of $ω$ ， the degree of coordination increases， and the zero-flow travel time decreases.

Key words: transportation planning and management, traffic demand, dynamic road resistance, signal timing, coordination, travel time

CLC Number:

U491

Dian-hai WANG,You-wei HU,Zheng-yi CAI,Jia-qi ZENG,Wen-bin YAO. Dynamic road resistance model of intermittent flow on urban roads based on BPR function[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 1951-1961.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Table 2

Fig.6

Fig.7

Table 3

Fig.8

References 16

1	Bureau of Public Roads. Traffic Assignment Manual[M]. Washington D C: Urban Planning Division, US Department of Commerce, 1964.
2	Zhao F, Fu L, Zhong M, et al. Development and validation of improved impedance functions for roads with mixed traffic using taxi GPS trajectory data and simulation[J]. Journal of Advanced Transportation, 2020(1):1-12.
3	Neuhold R, Fellendorf M. Volume delay functions based on stochastic capacity[J]. Transportation Research Record: Journal of the Transportation Research Board, 2014, 2421(1): 93-102.
4	Rafal K, Arkadiusz D. Estimating macroscopic volume delay functions with the traffic density derived from measured speeds and flows[J]. Journal of Advanced Transportation, 2017, 2017(3): 1-10.
5	So J J, Stevanovic A, Koonce P. Estimating performance of traffic signals based on link travel times: travel time-based signal performance measure[J]. Journal of Advanced Transportation, 2016, 50(5): 786-801.
6	Yuan G, Chen Y Y, Lu Y, et al. Research on the road resistant function model of urban express way[C]∥The 19th COTA International Conference of Transportation Professionals, Nanjing,China, 2019: 5257- 5268.
7	潘义勇, 余婷, 马健霄. 基于路段与节点的城市道路阻抗函数改进[J]. 重庆交通大学学报: 自然科学版, 2017, 36(8):76-81.
	Pan Yi-yong, Yu Ting, Ma Jian-xiao. Improvement of urban road impedance function based on section impedance and node impedance[J]. Journal of Chongqing Jiaotong University: Natural Science, 2017, 36(8): 76-81.
8	温惠英, 卢德佑, 汤左淦. 考虑行程时间波动性的城市道路阻抗函数模型[J]. 公路工程, 2019, 44(3): 27-32.
	Wen Hui-ying, Lu De-you, Tang Zuo-gan. Urban road impedance function model considering travel time volatility[J]. Highway Engineering, 2019, 44(3): 27-32.
9	刘宁, 赵胜川, 何南. 基于BPR函数的路阻函数研究[J]. 武汉理工大学学报:交通科学与工程版, 2013, 37(3): 545-548.
	Liu Ning, Zhao Sheng-chuan, He Nan. Research on resistance function based on BPR function[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2013,37(3):545-548.
10	王飞, 徐维祥. 基于LSTM神经网络改进的路阻函数模型[J]. 浙江大学学报:工学版, 2021, 55(6): 1065-1071.
	Wang Fei, Xu Wei-xiang. Improved model of road resistance function based on LSTM neural network[J]. Journal of Zhejiang University(Engineering Science Edition), 2021, 55(6): 1065-1071.
11	李彦瑾, 罗霞. 基于模糊神经网络的混合交通流路阻测算模型[J]. 吉林大学学报: 工学版, 2019, 49(1): 53-59.
	Li Yan-jin, Luo Xia. Road resistance measurement model of mixed traffic flow based on fuzzy neural network[J]. Journal of Jilin University( Engineering and Technology Edition), 2019, 49(1): 53-59.
12	Huntsinger L F, Rouphail N M. Bottleneck and queuing analysis: calibrating volume-delay functions of travel demand models[J]. Transportation Research Record, 2011, 2255(1): 117-124.
13	So J J, Stevanovic A, Ostojic M. Methodology to estimate volume-capacity ratios at traffic signals based on upstream-link travel times[J]. Journal of Transportation Engineering, 2017, 143(4):1-13.
14	姜桂艳, 李继伟, 张春勤. 城市主干路路段行程时间估计的BPR修正模型[J]. 西南交通大学学报, 2010, 45(1): 124-129.
	Jiang Gui-yan, Li Ji-wei, Zhang Chun-qin. Modified BPR functions for travel time estimation of urban arterial road segment[J]. Journal of Southwest Jiaotong University, 2010, 45(1): 124-129.
15	Publications TRB. Highway capacity manual 2010[M]. Washington D C: National Research Council, 2011.
16	王进, 白玉, 杨晓光. 关联信号交叉口排队长度计算模型[J].同济大学学报:自然科学版, 2012, 40(11): 1634-1640.
	Wang Jin, Bai Yu, Yang Xiao-guang. Calculating model of queue length at correlated signalized intersections[J]. Journal of Tongji University (Natural Science Edition), 2012, 40(11): 1634-1640.

Related Articles 15

[1]	Yan-bo LI,Bai-song LIU,Bo-bin YAO,Jun-shuo CHEN,Kai-fa QU,Qi-sheng WU,Jie-ning CAO. Location of electrical changing station of expressway considering stochastic characteristics of road network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1364-1371.
[2]	Ying HU,Chun-fu SHAO,Shu-ling WANG,Xi JIANG,Hai-rui SUN. Identification of road riding quality based on shared bike trajectory data [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1040-1046.
[3]	Kai LU,Guang-hui XU,Zhi-hong YE,Yong-jie LIN. Algebraic method of bidirectional green wave coordination control for the head of the platoon considering the clearance time [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 421-429.
[4]	Zhan-zhong WANG,Ting JIANG,Jing-hai ZHANG. Evaluation of road transportation efficiency based on fuzzy double frontiers network model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 385-395.
[5]	Xian-min SONG,Shu-tian YANG,Ming-xin LIU,Zhi-hui LI. Fluctuation characteristics and prediction method of bus travel time between stations [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1792-1799.
[6]	Hong-feng XU,Hong-jin CHEN,Dong ZHANG,Qian-hui LU,Na AN,Xian-cai Geng. Fully⁃actuated signal timing technique for isolated signalized intersections in connected vehicle environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1324-1336.
[7]	Yun-juan YAN,Wei-xiong ZHA,Jun-gang SHI,Jian LI. Mixed network equilibrium model with stochastic charging demand [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 136-143.
[8]	Feng XUE,Chuan-lei HE,Qian HUANG,Jian LUO. Coordination degree of multimodal rail transit network [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2040-2050.
[9]	Yan-feng JIA,Da-yi QU,Lu LIN,Rong-han YAO,Xiao-long MA. Coordinated speed control of connected mixed traffic flow based on trajectory [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2051-2060.
[10]	Hao LI,Hao CHEN. Mixed traffic network equilibrium with battery electric vehicles considering charging queuing time [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1684-1691.
[11]	Zuo-an HU,Yi-ming XIA,Jia CAI,Feng XUE. Optimization of urban rail transit operation adjustment based on multiple strategies under delay [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1664-1672.
[12]	Cai-hua ZHU,Xiao-li SUN,Yan LI. Forecast of urban public bicycle traffic demand by station classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 531-540.
[13]	Wei-xiong ZHA,Qi-yan CAI,Jian LI,Li-xin YAN. Optimization of offset of urban arterial signal coordination under condition of vehicle entry and exit on side road [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 565-574.
[14]	Qing-yu LUO,Wan-li TIAN,Hong-fei JIA. Location and capacity model of electric vehicle charging station considering commuting demand [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1471-1477.
[15]	Qiang TU,Lin CHENG,Fen LIN,Chao SUN. Finding shortest path considering traveler′s risk attitude [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 720-726.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

流量方案	方向1	方向2	方向3
1	46	427	54
2	93	854	107
3	139	1280	161
4（真实情况）	185	1707	214
5	231	2134	268
6	278	2561	321
7	324	2987	375
8	370	3414	428
9	416	3841	482
10	463	4268	535
11	92	427	54
12	186	854	107
13	278	1280	161
14	370	1707	214
15	462	2134	268
16	556	2561	321
17	648	2987	375
18	740	3414	428
19	832	3841	482
20	926	4268	535
21	1018	4694	589

上游直行相位绿灯时长/s	上游周期时长 /s	下游直行相位绿灯时长/s	下游周期时长 /s	直行相位的相位差 /s
80	180	50	180	［0，10，20，…，180］
80	180	60	180	［0，10，20，…，180］
80	180	70	180	［0，10，20，…，180］
80	180	80	180	［0，10，20，…，180］
70	180	80	180	［0，10，20，…，180］

项目	自变量	R2	MAE	MAPE	RMSE
BPR函数	流量	0.219	26.896	0.399	37.816
BPR函数	交通需求	0.732	19.634	0.258	26.015
本文模型	流量	0.612	15.327	0.230	23.723
本文模型	交通需求	0.858	9.835	0.161	14.327

Dynamic road resistance model of intermittent flow on urban roads based on BPR function

RICH HTML

PDF (PC)