Cyclists gaze behavior in urban space: an eye-tracking experiment on bicycle facilities.

Transcription

1 Scientific Seminar SIDT 2015 Torino Cyclists gaze behavior in urban space: an eye-tracking experiment on bicycle facilities. Alessandra Mantuano, Silvia Bernardi, Federico Rupi DICAM Department of Civil, Chemistry, Ambiental and Materials Engineering

2 Introduction The Human factor is one of the main causes of road accidents; also in cycling the visual design of facilities plays an important role in bicycle crashes. Lack of Visual Information Inconsistent design Wrong / confusing reading of the path SAFETY Excessive Visual Information Distractions Discomfort Objective & perceived hazard TRAVEL CHOICE The elements on which cyclist s visual attention focuses should be investigated in order to understand which factors really influence cyclists behavior.

3 Introduction HIGH NUMBER OF WORKS AND KNOWLEDGE about car driver s gaze and visual behavior: eye tracking applications while driving in on-road vehicles: to understand steering behavior entering a curve (Land, 1992) to determine where and how long car's drivers look (Underwood et al., 2002, Costa et. Al., 2014), to investigate attention caption and distractions under different levels of task difficulties (Lee et Triggs, 1976, Crundall et Underwood, 1998, Recarte et Nunes, 2003, Victor et al., 2005, Engstrom et al., 2005, Harbluk et al., 2007, Wang et al., 2014). Gaze behavior in cycling is still poorly documented. The first applications of head eye trackers to cycling are recent. The suitability also in bicycle riding of models applied for car driving is discussed. Some studies have been conducted to analyze: The bicycle steering (tests in controlled in-door environment) (Vansteenkiste et al., 2013) The effect of different levels of quality of bicycle paths on speed and gaze allocation (real ambient), (Vansteenkiste et al., 2014, 2015) The cyclist s attention allocation during junction negotiation (visualization of videos recorded in real environment), (Frings et al., 2014) The mental workload of elderly cyclist (simulator),(vlakveld et al., 2015).

4 Description of the experiment Equipment for the study of eye movements: Mobile eye tracking device ASL Mobile Eye-XG system Two digital high resolution cameras Lightweight glasses Portable wireless Data Transmit Unit (DTU) Data output: Laptop and two software: EyeVision and ASL Results Plus GM mouse cursor position with respect to scene image data log file (ASCII data file)

5 Description of the experiment Procedure

6 Description of the experiment Procedure Field tests have been performed in overcast weather days during Spring A cloudy sky is preferable when using Mobile Eye apparatus in outdoor applications to avoid as more as possible the loss of signal and gaze information. Participants The experiment has been performed by 8 participants, all of them had normal vision. For three of them the recorded data were excluded for the further analysis. Participant code Age Sex Bicycle use Use Purpose Confidence level Cycling Ambient Major Attentioncapturing aspect P F <1 a month Sport - Study Unfamiliar Bicycle path Route Continuity P M <1 a month Sport - Study Usual Road Speed P M >1 a week Study Recreational Usual Road Route Continuity P M >3 a week Work-Recreational Expert Bicycle path Route Continuity- Speed P M >1 a week Study - Recreational Usual Bicycle path Route Continuity- Speed Main characteristics of the participants

7 Description of the experiment Cycling route The travel route defined for the experiment includes different types of cycling environments in the urban area of Bologna, Italy. The total length is about 3 kilometers. The route was divided into seven segments, one for each type of cycling facility. Map of the field route with Mobile eye apparatus, in Bologna

8 Description of the experiment Cycling route From the surveys it results that participants rated the first segment (Masini, 1) the best for perceived global quality. Map of the field route with Mobile eye apparatus, in Bologna For this reason this segment has been chosen as a base to develop the analysis procedure for the future tests, as it provides a type facility reference of good quality level. Of the entire route only results for the first segment, Masini S1 will be described.

9 Description of the experiment Two sub-segments S1.1 and S1.2 were identified, due to the change in geometric features. The different type of elements that can provide exogenous distractions along the way were listed. S1.1 S1.2 Segment code: S1.1 Segment code: S1.2 Type Bi-directional cycle path on sidewalk Type Bi-directional cycle path beside portico Lenght 250 m Width 2,4 m Lenght 400 m Width 2,00-2,75 m Sidewalk Width LS: 2 m, RS: min 2 m Sidewalk Width under portico 5,6 m Type of element Number Type of element Number Crosswalk 1 Crosswalk 3 Intersection 1 Intersection 2 Bus stop 1 Bus stop 1 Horizontal traffic sign 11 Horizontal traffic sign 16 Vertical traffic sign 3 Vertical traffic sign 11 Advertising signal 3 Advertising signal 2 Fixed obstacle 4 Fixed obstacle 2 Pavement discontinuity 26 Pavement discontinuity 27

10 Description of the experiment While cycling along this segment, participants (P1, P2, P3, P4, P5) encountered, different number of cyclists, pedestrians, cars executing maneuvers, or other obstacles. S1.1 S1.2 SEGMENT S1.1 SEGMENT S1.2 Variable elements P1 P2 P3 P4 P5 Variable elements P1 P2 P3 P4 P5 Tot. Pedestrians Tot. Pedestrians Ped. on footpath Ped. under portico Ped. on cycling lane Ped. on footpath Tot. Cyclists Tot. Cyclists Same direction Same direction Opposite direction Opposite direction Car maneuvering at Car maneuvering intersection at intersection Obstacle (parked vehicles, others) Obstacle (parked vehicles, others)

11 Data analysis: Criteria

12 Data analysis: Criteria AoI TARGET AoI TARGET S1.1 S1.2 AoI OUT AoI OUT S1.1 S1.2

13 Results and Discussion Relative frequency of fixation in AOI TARGET S11 Element P1 P2 P3 P4 P5 Ave. P1 P2 P3 P4 P5 Ave. Horiz. sign 0% 0% 27% 0% 0% 5% Advert. sign 300% 33% 167% 133% 0% 127% Crosswalk 200% 0% 200% 100% 0% 100% 100% 133% 67% 167% 0% 93% Pedestrian 156% 7% 130% 0% 200% 99% 8% 14% 32% 14% 6% 15% Cyclist 400% - - 0% - 200% 167% 250% 100% - 0% 130% Vertical sign 100% 0% 0% 33% 0% 27% 0% 27% 18% 27% 0% 14% Fix. obstacle 200% 0% 0% 0% 50% 50% 0% 0% 100% 50% 0% 30% Intersection 100% 0% 0% 100% 300% 100% 400% 50% 400% 550% 50% 290% * In bold relative frequencies of fixations on elements observed by at least three participants. Ranking of disturbance elements of Category II: S12 S1.1 1.Advertisign signs (127%) 2.Intersections (100%)&Crosswalks(100%) 3.Pedestrians (99%) S1.2 1.Intersections (290%) 2.Cyclists (130%) 3.Crosswalks (93%) 4.Pedestrians (15%) 5.Vert. signs(14%)

14 Results and Discussion Relative frequency of fixation in AOI OUT S1.1 S1.2 Element P1 P2 P3 P4 P5 Ave. Element P1 P2 P3 P4 P5 Ave. Pav.discount. 54% 0% 31% 27% 12% 25% Pav. discount. 33% 4% 48% 44% 11% 28% Horiz. sign 0% 9% 36% 18% 0% 13% Horiz. sign 25% 6% 81% 0% 0% 22% Crosswalk 0% 0% 100% 0% 0% 20% Crosswalk 233% 33% 133% 0% 0% 80% Obstacle Obstacle (parked veh.) 150% - 200% 25% 0% 94% (parked veh.) 300% 50% 0% 100% 0% 90% Intersection 0% 0% 300% 200% 100% 120% Intersection 400% 150% 500% 200% 150% 280% Bus stop 100% 0% 100% 0% 100% 60% Cyclist 33% 0% 0% - 33% 17% Pedestrian 56% 5% 63% 100% 33% 51% Pedestrian 14% 11% 19% 10% 6% 12% Car maneuv.at Car maneuv. inters. 200% % 100% at inters % 0% - 100% * In bold relative frequencies of fixations on elements observed by at least three participants. Advert. signs 100% 0% 0% 50% 50% 40% Ranking of disturbance elements of Category I: S Intersections (120%) 2. Obstacle (94%) 3. Bus stop (60%) 4. Pedestrians (51%) 5. Pav. Discont.(25%) 6. Horiz. signs (13%) 1. Intersections(280%) 2. Obstacle (90%) 3. Crosswalks (80%) S Advert. signs(40%) 5. Pav. Discont.(28%) 6. Horiz. Signs (22%) 7. Pedestrians (12%)

15 Results and Discussion Quantifications of workload and visual task demand level, associated to higher or lower quality of the cycling environment. S1.1 S1.2 TARGET AREA P1 P2 P3 P4 P5 Ave. P1 P2 P3 P4 P5 Ave. Number of fixations Fix. on total fix. (%) 61% 22% 48% 39% 46% 43% 36% 63% 45% 55% 35% 47% Time fix. on tot. fix. Time (%) 63% 22% 62% 44% 43% 47% 43% 62% 50% 60% 36% 50% Mean fixation duration (s) St. Dev (s) Fix. in CORRECT Target area on total fixations (%) 26% 0% 33% 42% 42% 29% 45% 23% 55% 53% 26% 40% S1.1 S1.2 OUT OF TARGET P1 P2 P3 P4 P5 Ave. P1 P2 P3 P4 P5 Ave. Number of fixations Fix. on total fix. (%) 39% 78% 52% 61% 54% 57% 64% 37% 55% 45% 65% 53% Time fix. on tot. fix. Time (%) 37% 78% 38% 56% 57% 53% 57% 38% 50% 40% 64% 50% Mean fixation duration (s) St. Dev (s) For most of the participants the fixation in the correct region of the TARGET area is higher for segment S1.2 than for segment S1.1 The average % of gaze location during fixation passes from 29% to 40%.

16 Results and Discussion Quantifications of workload and visual task demand level, associated to higher or lower quality of the cycling evironment. S1.1 S1.2 TARGET AREA P1 P2 P3 P4 P5 Ave. P1 P2 P3 P4 P5 Ave. Number of fixations Fix. on total fix. (%) 61% 22% 48% 39% 46% 43% 36% 63% 45% 55% 35% 47% Time fix. on tot. fix. Time (%) 63% 22% 62% 44% 43% 47% 43% 62% 50% 60% 36% 50% Mean fixation duration (s) St. Dev (s) Fix. in CORRECT Target area on total fixations (%) 26% 0% 33% 42% 42% 29% 45% 23% 55% 53% 26% 40% S1.1 S1.2 OUT OF TARGET P1 P2 P3 P4 P5 Ave. P1 P2 P3 P4 P5 Ave. Number of fixations Fix. on total fix. (%) 39% 78% 52% 61% 54% 57% 64% 37% 55% 45% 65% 53% Time fix. on tot. fix. Time (%) 37% 78% 38% 56% 57% 53% 57% 38% 50% 40% 64% 50% Mean fixation duration (s) St. Dev (s) All values of fixations in the correct area do not exceed 50%, in both situation. There seems to be an equilibrium between distractions and attention on the target. This proportion of fixations could be defined as an optimal condition of visual workload while cycling: indeed the overall path achieved the best quality score in the participants evaluation.

17 Conclusions and future developments The aim of this work was to present the first tests performed in a real urban environment to investigate cyclists gaze behavior, using an eye movement tracking device. Measures based on fixations have been used to classify the elements that capture cyclists attention the most, by evaluating relative frequencies of fixations. Interruptions and discontinuities of the path (intersections and crosswalks) seem to be the elements requiring more attention. While cycling on cycling lanes adjacent to sidewalks the attention allocation to these elements is lower: the presence of pedestrians represent an exogenous distraction that can increase cycling risk. Fixation percentage time and avarage fixation duration have been assumed as an indicator of visual workload. A higher segregation of cyclist s lead to determine a lower visual information complexity and a lower processing time (< fixation duration) required by the cyclist. It seems that an equilibrium between distractions and attention on the target could be defined as an optimal condition of visual workload while cycling.

18 Conclusions and future developments The number of participants will be extended, in order to provide a more consistent statistical analysis. Though, for tests employing eye movement tracking devices in cycling applications results are obtained for small data samples. Extention of the analysis to the other segments of the defined route. Definition of the most suitable measure of workload and its thresholds, it could provide a new tool to quantify a cycle path s quality. While in car driving definitions and measures of cognitive workload are numerous, in cycling further discussion is still needed.

19 Alessandra Mantuano Ph.D Student Silvia Bernardi Ph.D Student Federico Rupi Associate Professor DICAM Department of Civil, Chemistry, Ambiental and Materials Engineering