Comparison and Analysis of Pedestrian Service Level Evaluation Modeling for theElderly at Signalized Intersections

NAVIGATE

Table of Contents

STATISTICS

Viewed227

Downloads201

Comparison and Analysis of Pedestrian Service Level Evaluation Modeling for theElderly at Signalized Intersections

[HTML] PDF下载 (201)

[1]ZHANG Huiling,WANG Ruihao,ZHANG Rui.Comparison and Analysis of Pedestrian Service Level Evaluation Modeling for theElderly at Signalized Intersections[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):9-17.[doi:10.13705/j.issn.1671-6833.2024.06.022]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 9-17 Column: Public date: 2024-09-25

Title:: Comparison and Analysis of Pedestrian Service Level Evaluation Modeling for theElderly at Signalized Intersections

Author(s):: ZHANG Huiling; WANG Ruihao; ZHANG Rui; College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Keywords:: signalized intersection; the elderly pedestrian; multivariate nonlinear regression model; pedestrianservice level; modeling and analysis

CLC:: U491

DOI:: 10.13705/j.issn.1671-6833.2024.06.022

Abstract:: In order to analyze the needs of the elderly at the signalized intersection and evaluate the service level ofthe elderly pedestrians, a service level evaluation model based on subjective evaluation and intersection traffic characteristics was established. The traffic characteristic parameters of 33 signal-controlled crosswalks were obtainedthrough field observation and video analysis. A truncated questionnaire was used to obtain the subjective value ofpedestrian service level evaluation of the elderly crossing the street with synchronous conditions. The initial selection was based on nonlinear, linear and fuzzy linear regression models. Based on the data analysis of 30 crosswalks,Pearson correlation coefficient and Spearman correlation coefficient were used to identify the key factors affecting theservice level of the elderly crossing the street. Taking the key factors as input, comparing the nonlinear, linear andfuzzy linear regression models, the results showed that the multivariate nonlinear regression model of elderly pedestrian service level evaluation was better in terms of fit and error, and the data of 3 crosswalks in the verificationgroup further confirm the applicability of the model. The research results could provide some reference for the studyof traffic safety improvement strategies in the aging trend.

References:: [1] 国家统计局. 中华人民共和国 2023 年国民经济和社会发展统计公报[ EB / OL] . ( 2024- 02- 29) [ 2024- 03- 20 ] . https: ∥ www. stats. gov. cn / sj / zxfb / 202402 /t20240228_1947915. html.
National Bureau of Statistics. Statistical bulletin of thePeople′s Republic of China on the 2023 national economic and social development [ EB / OL] . ( 2024 - 02 - 29)[2024 - 03 - 20 ] . https:∥www. stats. gov. cn / sj / zxfb /202402 / t20240228_1947915. html.
[2] 黄建中, 张芮琪, 胡刚钰. 基于时空间行为的老年人日常生活圈研究: 空间识别与特征分析[ J] . 城市规划学刊, 2019(3) : 87-95.
HUANG J Z, ZHANG R Q, HU G Y. A research of theelderly′s daily life circle based on spatial-temporal behaviors—analysis of place recognition and spatial features[ J] . Urban Planning Forum, 2019(3) : 87-95.
[3] 潘义勇, 吴静婷, 施颖. 交叉口处老年人碰撞事故伤害严重程度分析[ J] . 重庆交通大学学报( 自然科学版) , 2022, 41(9) : 39-44.
PAN Y Y, WU J T, SHI Y. Injury severity analysis ofthe elderly in collision accidents at intersections [ J ] .Journal of Chongqing Jiaotong University ( Natural Science) , 2022, 41(9) : 39-44.
[4] ZHANG H L, ZHANG D K, WANG R H. Evaluation ofpedestrian level of service at signalised intersections fromthe elderly perspective[ J] . Promet-Traffic & Transportation, 2023, 35(3) : 434-445.
[5] TRANSPORTATION R B. Highway capacity manual2000[M] . Washington: Transportation Research Board,2000.
[6] 刘孟歆, 秦华, 岳晨, 等. 右转车辆与过街行人交互过程的影响因素研究[ J] . 包装工程, 2023, 44( 12) :118-125.
LIU M X, QIN H, YUE C, et al. Influencing factors ofinteraction process between right-turning vehicles and pedestrians crossing the street[ J] . Packaging Engineering,2023, 44(12) : 118-125.
[7] LI W F, HE J W, YU Q, et al. Using POI data to identify the demand for pedestrian crossing facilities at midblock[ J] . Sustainability, 2021, 13(23) : 13256.
[8] JIA X L, FELICIANI C, MURAKAMI H, et al. Revisiting the level-of-service framework for pedestrian comfortability: velocity depicts more accurate perceived congestion than local density[ J] . Transportation Research PartF: Traffic Psychology and Behaviour, 2022, 87: 403-425.
[9] TRANSPORTATION R B. Highway capacity manual2016 [ M ] . Washington: Transportation ResearchBoard, 2016.
[10] AHMED T, MOEINADDINI M, ALMOSHAOGEH M, etal. A new pedestrian crossing level of service ( PCLOS)method for promoting safe pedestrian crossing in urban areas[ J] . International Journal of Environmental Researchand Public Health, 2021, 18(16) : 8813.
[11] SAHANI R, BHUYAN P K. Modelling pedestrian perspectives in evaluating satisfaction levels of urban roadwaywalking facilities[ J] . Transportation Research Procedia,2020, 48: 2262-2279.
[12] DIVISION T A, BOARD T R. Innovative Applications ofthe Highway Capacity Manual 2010 [ M ] . Washington:Transportation Research Board, 2014.
[13] 边扬, 王炜, 陆建. 人行道行人服务水平评价方法[ J] . 东南大学学报(自然科学版) , 2007, 37(4) : 695-699.
BIAN Y, WANG W, LU J. Pedestrian level of service forsidewalks[ J] . Journal of Southeast University ( NaturalScience Edition) , 2007, 37(4) : 695-699.
[14] YE X F, CHEN J, JIANG G Y, et al. Modeling pedestrian level of service at signalized intersection crosswalksunder mixed traffic conditions[ J] . Journal of the Transportation Research Board, 2015, 2512(1) : 46-55.
[15] MARISAMYNATHAN S, VEDAGIRI P. Modeling pedestrian level of service at signalized intersection undermixed traffic conditions[ J] . Journal of the TransportationResearch Board, 2017, 2634(1) : 86-94.
[16] RAAD N, BURKE M I. What are the most important factors for pedestrian level-of-service estimation? A systematic review of the literature[ J] . Journal of the Transportation Research Board, 2018, 2672(35) : 101-117.
[17] 景鹏, 王伟, 吴麟麟. 老年人对自动驾驶汽车接受度研究[ J] . 中国公路学报, 2021, 34(6) : 158-171.
JING P, WANG W, WU L L. Acceptance of autonomousvehicles for the elderly [ J ] . China Journal of Highwayand Transport, 2021, 34(6) : 158-171.
[18] SACCENTI E, HENDRIKS M H W B, SMILDE A K.Corruption of the Pearson correlation coefficient by measurement error and its estimation, bias, and correction under different error models[ J] . Scientific Reports, 2020,10: 438.
[19] XIAO C W, YE J Q, ESTEVES R M, et al. UsingSpearman′s correlation coefficients for exploratory data analysis on big dataset[ J] . Concurrency and Computation:Practice and Experience, 2016, 28(14) : 3866-3878.
[20] MIROSHNICHENKO V, MAIBORODA R. Asymptoticnormality of modified LS estimator for mixture of nonlinearregressions[ J] . Modern Stochastics: Theory and Applications, 2020: 435-448.
[21] 郝翠萍. 基于多元线性回归模型的考试评价与预测研究[ J] . 电子设计工程, 2021, 29(15) : 142-145.
HAO C P. Research on examination evaluation and prediction based on multiple linear regression model[ J] . Electronic Design Engineering, 2021, 29(15) : 142-145.
[22] LI Y F, HE X X, LIU X Q. Fuzzy multiple linear leastsquares regression analysis[ J] . Fuzzy Sets and Systems,2023, 459: 118-143.
[23] 武继刚, 李妙君, 赵淑平. 基于低秩稀疏表达的弹性最小二乘回归学习 [ J] . 郑州大学学报 ( 工学版) ,2023, 44(6) : 25-32.
WU J G, LI M J, ZHAO S P. Low-rank sparse representation based on elastic least squares regression learning[ J] . Journal of Zhengzhou University ( Engineering Science) , 2023, 44(6) : 25-32.

Similar References:

Memo

Last Update: 2024-09-29