Evaluation of Cognitive Load Differences between Elderly and Young Pedestrians at the Signalised Intersection

Huiling ZHANG; Dan PENG; Xinyi SHI

doi:10.7307/ptt.v36i4.519

Authors

Huiling ZHANG Chongqing Jiaotong University, Transportation School
Dan PENG Chongqing Vocational and Technical University of Mechatronics, College of Business Administration
Xinyi SHI Chongqing Jiaotong University, Transportation School

DOI:

https://doi.org/10.7307/ptt.v36i4.519

Keywords:

traffic safety, pedestrian crossing, cognitive load, data envelopment analysis, eye tracking experiment, physiological experiment

Abstract

The pedestrian cognitive load has an important effect on the pedestrian crossing decision making. Compared with young adults, old people are characterised by declining physical function and slower reaction ability, which makes them prone to traffic accidents when crossing the street. This study aims to compare the visual information-mental load correlation between elderly and young adults waiting at the signalised intersections and evaluate their cognitive load conditions. Therefore, two signalised intersections with different traffic scenes in the Nan’an District, Chongqing, China were selected. The eye-tracking, electrocardiographic and electrodermal activity data of young and old pedestrians were collected using eye-tracking and physiological instruments. The visual indexes (the total duration of fixations, the number of fixations, the average pupil diameter changing rate, the number of saccades, the average peak speed of saccades, the average amplitude of saccades and the total amplitude of saccades) and physiological indicators (the average growth rate of heart rate, the time-domain analysis indicator of HRV and HRV frequency domain analysis indicators, electrodermal response amplitude and rise time of the EDR amp.) were taken as inputs and outputs parameters, respectively. Then, the comprehensive cognitive load evaluation model for pedestrians was constructed when waiting to cross the street based on the data envelopment analysis method. And the cognitive load characteristic differences between the young adults and the elderly were compared. The results show that in the same crossing scene, compared with the young pedestrian, the elder pedestrian exhibited lower overall perceptual efficiency, lower fixation durations and higher cognitive loads. These results can provide certain references on improving the street crossing safety for the elderly pedestrian.

References

Traffic management research institute of the ministry of public security. Annual report of traffic accident statistics of the people’s republic of China (2020). Traffic Management Research Institute of the Ministry of Public Security. Report number: 2020.

Trpković A, et al. The crossing speed of elderly pedestrians. Promet-Traffic & Transportation. 2017;29(2):175-183. DOI: 10.7307/ptt.v29i2.2101.

Webb EA, et al. Crossing the road in time: Inequalities in older people’s walking speeds. Journal of Transport & Health. 2017;5(2):77-83. DOI: 10.1016/j.jth.2017.02.009.

Lan LT, et al. Evaluation of conflict severity between pedestrians and right-turning vehicles. Journal of Chongqing University of Technology (Natural Science). 2022;36(7). http://clgzk.qks.cqut.edu.cn/CN/Y2022/V36/I7/70 [Accessed 17th Aug. 2022].

Jain A, Gupta A, Rastogi R. Pedestrian crossing behaviour analysis at intersections. International Journal for Traffic and Transport Engineering. 2014;4(1):103-116. DOI: 10.7708/ijtte.2014.4(1).08.

Biassoni F, et al. Visual exploration of pedestrian crossings by adults and children: Comparison of strategies. Transportation Research Part F: Traffic Psychology and Behaviour. 2018;56(7):227-235. DOI: 10.1016/j.trf.2018.04.009.

Tapiro H, Oron-Gilad T, Parmet Y. Pedestrian distraction: The effects of road environment complexity and age on pedestrian’s visual attention and crossing behavior. Journal of Safety Research. 2020;72(12):101-109. DOI: 10.1016/j.jsr.2019.12.003.

Tapiro H, et al. Where do older pedestrians glance before deciding to cross a simulated two-lane road? A pedestrian simulator paradigm. The Human Factors and Ergonomics Society Annual Meeting, 19-23 Sept. 2016, Los Angeles, USA. 2016. p. 11-15. DOI: 10.1177/1541931213601003.

Zeeger CV, et al. Analysis of elderly pedestrian accidents and recommended countermeasures. Transportation Research Record. 1996;27(2):56-63. DOI:10.1016/0022-4375(96)86964-7.

Dommes A, Cavallo V, Oxley J. Functional declines as predictors of risky street-crossing decisions in older pedestrians. Accident Analysis & Prevention. 2013;59(10):135-143. DOI:10.1016/j.aap.2013.05.017.

Oxley J, Fildes BN, Ihsen E. Older pedestrian safety: The role of perceptual and cognitive factors and the ability to compensate for age-related changes. Proceedings of the Australasian road safety research, policing and education conference, Monash University. 2000. p. 209-215.

Dommes A, et al. Age-related differences in street-crossing safety before and after training of older pedestrians. Accident Analysis & Prevention. 2012;44(1):42-47. DOI:10.1016/j.aap.2010.12.012.

Sun CY. Theoretical and empirical research on cognitive load. Liaoning, CN: Liaoning People’s Publishing House; 2014.

Lee Y, Lee JD, Boyle LN. The interaction of cognitive load and attention-directing cues in driving. Human Factors. 2009;51(3):271-280. DOI: 10.1177/0018720809337814.

Mikula L, et al. Eye-head coordination and dynamic visual scanning as indicators of visuo-cognitive demands in driving simulator. PloS One. 2020;15(12):1-21. DOI: 10.1371/journal.pone.0240201.

Ahn JW, Ku Y, Kim HC. A novel wearable EEG and ECG recording system for stress assessment. Sensors. 2019;19(9):1991-1991. DOI: 10.3390/s19091991.

Barua S, Ahmed MU, Begum S. Towards intelligent data analytics: A case study in driver cognitive load classification. Brain Sciences. 2020;10(8):1-19. DOI: 10.3390/brainsci10080526.

Bailey BP, Iqbal ST. Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. ACM Transactions on Computer-Human Interaction (TOCHI). 2008;14(4):1-28. DOI: 10.1145/1314683.1314689.

Liu X. Measuring cognitive load level based on eye movement data. PhD thesis. Southwest University; 2017.

Liu X, Wang H-Z. Research on security check visual search based on image saliency. Journal of Chongqing University of Technology (Natural Science). 2019;33(5). http://clgzk.qks.cqut.edu.cn/CN/Y2019/V33/I5/84 [Accessed 17th Jul. 2019].

Liang GH, et al. Evaluating the influence of approaching vehicles on pedestrian’s visual patterns and crossing behaviors at an uncontrolled crosswalk. Transportation Research Part F: Traffic Psychology and Behaviour. 2022;88(5):236-247. DOI: 10.1016/j.trf.2022.05.026.

Lanzer M, et al. Assessing crossing and communication behavior of pedestrians at urban streets. Transportation Research Part F: Traffic Psychology and Behaviour. 2021;80(2):341-358. DOI: 10.1016/j.trf.2021.05.001.

Bianchi LJ, Kingstone A, Risko EF. The role of cognitive load in modulating social looking: a mobile eye tracking study. Cognitive Research Principles and Implications. 2020;5(1):44. DOI: 10.1186/s41235-020-00242-5.

Zhu P, Chang RS. Effects of gender and situational hazard level on pedestrian hazard perception: Evidence from ERP. Studies of Psychology and Behavior. 2020;18(1). https://psybeh.tjnu.edu.cn/CN/Y2020/V18/I1/113 [Accessed 20th Jan. 2020].

He XM, Li JY. Cause analysis and design strategies of cognitive load in human-computer interaction. Packaging Engineering. 2020;41(10):24-30. DOI: 10.19554/j.cnki.1001-3563.2020.10.005.

Xu ZM, et al. A review of cognitive workload assessment based on EEG. Space Medicine & Medical Engineering. 2021;34(4):339-348. DOI: 10.16289/j.cnki.1002-0837.2021.04.008.

Marquart G, Cabrall C, Winter J. Review of eye-related measures of drivers’ mental workload. Procedia Manufacturing. 2015;3(7):2854-2861. DOI: 10.1016/j.promfg.2015.07.783.

Hutt S, et al. Automated gaze-based mind wandering detection during computerized learning in classrooms. User Modeling & User Adapted Interaction. 2019;(1). DOI:10.1007/s11257-019-09228-5.

Wei QL. Data envelopment analysis (DEA). Chinese Science Bulletin. 2000;45(17):1793-1808. DOI: 10.1360/csb2000-45-17-1793.

Song MH. Research on the evaluation of Beijing rail transit operation efficiency based on DEA. PhD thesis. Beijing Jiaotong University; 2017.