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Transportation Research Procedia 34 (2018) 67–74 www.elsevier.com/locate/procedia
International Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data CollectionSimulation and its Standardization International Symposium of Transport (ISTS’18) and(IWTDCS’18) the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18)
Applicability of Virtual Reality Systems for Evaluating Pedestrians’ Applicability of Virtual Reality Systems for Evaluating Pedestrians’ Perception and Behavior Perception and Behavior a b c Miho Iryo-Asano *, Yu Hasegawa , Charitha Dias a MihoNagoya Iryo-Asano *, Yu Hasegawab, Charitha Diasc University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan a
A.T. Kearney K.K., a ARK Mori Building, East 32F, 1-12-32 Akasaka, Minato-ku, Tokyo 107-6032, Japan Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan c b The of Building, Tokyo, 4-6-1 Meguro-ku, Tokyo 153-8505, Japan A.T. Kearney K.K.,Univesity ARK Mori EastKomaba, 32F, 1-12-32 Akasaka, Minato-ku, Tokyo 107-6032, Japan b
c
The Univesity of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
Abstract Abstract In order to properly design pedestrian spaces, it is important to consider pedestrian safety and comfort. Recently, with the rapid progress of properly virtual reality technologies, objects closer to VR userssafety has become easier.Recently, Therefore,with VRthe systems In order to design(VR) pedestrian spaces, reproducing it is important to consider pedestrian and comfort. rapid are expected to be valuable tools for evaluating safety and comfort from the pedestrians’ viewpoint. In particular, VR enables users progress of virtual reality (VR) technologies, reproducing objects closer to VR users has become easier. Therefore, VR systems to interact nottoonly with ordinary vehicles but safety also with or personal mobility vehicles, VR and enables thus a wider are expected be valuable tools for evaluating andsurrounding comfort frompedestrians the pedestrians’ viewpoint. In particular, users range of applications are ordinary expected.vehicles However, to validatepedestrians VR performance have mobility so far have been limited to cases of to interact not only with butmost alsoattempts with surrounding or personal vehicles, and thus a wider pedestrians encountering ordinary vehicles. thisattempts research,topedestrians’ andhave behavioral characteristics toward other range of applications are expected. However,In most validate VR cognition performance so far have been limited to cases of pedestrians personal mobility analyzed by comparing measured VR and in real spaces. Ittoward was shown pedestrians and encountering ordinary vehicles vehicles.were In this research, pedestrians’those cognition and in behavioral characteristics other that the perception of distance and vehicles subjective danger from personal mobility vehicles are not different VR spaces. and realItspaces when pedestrians and personal mobility were analyzed by comparing those measured in VR and ininreal was shown the vehicle approaches VR participants from danger the front andpersonal back, although subjective danger tendsin to beand lessreal sensitive the that the perception of distance and subjective from mobilitythe vehicles are not different VR spaces to when lateral spaceapproaches between VR thethe personal mobility wassubjective also revealed thattends VR participants tend to keep the vehicle VRparticipants participantsand from front and back,vehicle. althoughIt the danger to be less sensitive to thea larger lateral clearanceVR thanparticipants in real spaces avoidingmobility other pedestrians. lateral space between andwhen the personal vehicle. It was also revealed that VR participants tend to keep a larger lateral clearance than in real spaces when avoiding other pedestrians. © 2018 The Authors. Published by Elsevier Ltd. © 2018 The Authors. by Elsevier Ltd. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier Ltd. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) “Peer-review under responsibility of the scientific committee of (https://creativecommons.org/licenses/by-nc-nd/4.0/) the International Symposium of Transport Simulation (ISTS’18) This is an open access article underofthe BY-NC-ND license “Peer-review under responsibility theCC scientific committee of the International Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18)” “Peer-review under responsibility the scientific committee International Symposium of Transport Simulation (ISTS’18) and the International Workshop onof Traffic Data Collection andofitsthe Standardization (IWTDCS’18)” and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18)” Keywords: Pedestrians; Virtual Reality; Perception; Avoidance Behavior Keywords: Pedestrians; Virtual Reality; Perception; Avoidance Behavior
* Corresponding author. Tel.: +81-52-788-6044; fax: +81-52-788-6205. address:author.
[email protected] * E-mail Corresponding Tel.: +81-52-788-6044; fax: +81-52-788-6205. E-mail address:
[email protected]
2352-1465 © 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2352-1465 © 2018 Thearticle Authors. Published by Elsevier Ltd. “Peer-review under responsibility theCC scientific committee of (https://creativecommons.org/licenses/by-nc-nd/4.0/) the International Symposium of Transport Simulation (ISTS’18) This is an open access article underofthe BY-NC-ND license and the International Workshop on Traffic Data Collection and its (IWTDCS’18)” “Peer-review under responsibility of the scientific committee of theStandardization International Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18)” 2352-1465 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) “Peer-review under responsibility of the scientific committee of the International Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18)” 10.1016/j.trpro.2018.11.015
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1. Introduction In order to design walking spaces properly, various pedestrian traffic simulators have been proposed for the purpose of evaluating traffic flow efficiency. Meanwhile, safety and comfort are also important factors when examining the quality of walking spaces, especially in shared spaces where there is a possibility of interaction with not only pedestrians but also slow mobility vehicles such as cycles, wheelchairs, personal transporters, and other newly developed personal mobility vehicles. As there are many difficulties in conducting experiments particularly to evaluate dangerous situations, there is an increasing interest in utilizing virtual reality (VR) technologies. For example, Schwebel et al. (2017) utilized a VR system for safety education for school children. Evacuation behavior was also evaluated using a VR system by Moussaïd et al. (2016). Iryo et al. (2013) and Wolinski et al. (2014) compared different pedestrian behavior models for their validation. As head-mounted displays become popular, they facilitate the representation of surrounding pedestrians and vehicles quite close to VR participants. However, several issues need to be resolved in order to confirm whether pedestrian VR tools can be reliable for the above purposes. First, if the participant feels uneasy in response to the movement of surrounding pedestrians, it might affect when the examiner attempts to evaluate other comfort factors. Thus, a pedestrian model with natural behavior in terms of human perception is needed. Second, the characteristics of subjective perceptions of participants in VR compared with those in the real world should be clarified. Third, it is necessary to evaluate the actual behavior of participants reacting to surrounding shared-space users in VR by comparison with that in the real world. There are a couple of studies on reality of virtual spaces considering the perception of distances and other factors (such as Yang et al., 2016) but most of them are about the reaction to static environment without considering other users. Deb et al. (2017) reported that the behavior of pedestrians, such as average walking speed at crosswalks, in VR matches that in real spaces (RS). However, their validation is limited only to interactions with vehicles and pedestrians or other personal mobility vehicles have not been considered. Similar to the validations conducted for driving simulators (such as Yan et al., 2008 and Schwebel et al., 2008), validations of pedestrian VR for various viewpoints are needed. This paper aims to examine the applicability of VR to pedestrian perception and behavior analysis through the improvement of pedestrian models suitable for VR, as well as the experimental comparison of VR and the real world. 2. Virtual Reality Environment for Pedestrians 2.1. VR environment A VR environment was developed with a VR software (UC-Win/Road by Forum 8 Co. Ltd.) and a head-mounted display (Oculus Rift). Subject pedestrians wore the display device and could walk freely within the laboratory, as shown in Fig. 1. The head position and orientation of the subjects were tracked by sensors and were reflected in the VR display in real time (approximately at every 0.1 s) so that the VR participant feels as if he/she is walking in the VR spaces. Two types of surrounding pedestrian behaviors in the VR were implemented: predetermined and interactive models. The former just follows the predetermined fixed trajectories, while the latter determines the acceleration vectors of pedestrians at each moment, interacting with locations, orientations, and speeds of both the VR participants and the other simulated VR pedestrians.
Fig. 1. VR environment. Left: the participant waring the head-mounted display, right: example of VR view.
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2.2. Modification of pedestrian model for virtual reality The performance of the interactive pedestrian model strongly influences the subjects’ sense of reality. In this study, the modified social force model of Specification II by Johannson et al. (2007) was used as the base model, because it contains the anticipation factor necessary for improved representation of pedestrian maneuvers (Iryo et al, 2013). This model describes the acceleration of pedestrian i at time t as the summation of driving forces to the destination, repulsive (or social) forces from other pedestrians, and a random term as the following equation similar to the original social force model (Helbing and Molnar, 1995).
dvi (t ) vi0 ei − vi = + dt τi
∑f
j ( ≠i )
ij (t ) +
∑f k
ik (t ) +
ξi (t )
(1)
vi is the velocity of pedestrian i; τ i is the relaxation time; vi0 and ei are the desired speed and directions of i, respectively; f ij (t ) and f ik (t ) are the repulsive forces that pedestrian i receives from the surrounding pedestrian j and obstacles k; and ξ i (t ) is the random term which represents fluctuations.
where,
Specification II of Johannson’s model considers the impact of the relative velocity on the repulsive forces. The repulsive forces can be expressed by
𝑓𝑓𝑓𝑓⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 �𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 � = 𝑤𝑤𝑤𝑤�𝜑𝜑𝜑𝜑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 �. 𝑔𝑔𝑔𝑔⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 �𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 �
(2)
where, 𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ,𝑡𝑡𝑡𝑡 is the displacement vector between individual i and j; 𝜑𝜑𝜑𝜑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ,𝑡𝑡𝑡𝑡 is the angle between the normalized distance vector and direction of the motion of the considered pedestrian i; and g is the force shown as in Equation (3), taking the distance and the relative speed from pedestrian j into account.
𝑔𝑔𝑔𝑔⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 �𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 � = 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 �−
𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 �𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 � + �𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝑦𝑦𝑦𝑦⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 � 1 𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝑦𝑦𝑦𝑦⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 . � + � �. 𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 2𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 2 �𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 � �𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − 𝑦𝑦𝑦𝑦⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 �
(3)
where 𝑦𝑦𝑦𝑦⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = (𝑣𝑣𝑣𝑣⃗𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑣𝑣⃗𝑖𝑖𝑖𝑖 )Δ𝑡𝑡𝑡𝑡 and 𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖 and 𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 are parameters. bij is the adjusted distance between pedestrians i and j, considering the relative speed of the pedestrians as follows. ∆t is the forecasting time set as 0.5 s. 2
2 2𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = ���𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 � + �𝑑𝑑𝑑𝑑⃗𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 − (𝑣𝑣𝑣𝑣⃗𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑣𝑣⃗𝑖𝑖𝑖𝑖 )Δ𝑡𝑡𝑡𝑡�� − �(𝑣𝑣𝑣𝑣⃗𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑣𝑣⃗𝑖𝑖𝑖𝑖 )Δ𝑡𝑡𝑡𝑡�
w is the weight of anisotropic factor described in the following equation using parameter 𝜆𝜆𝜆𝜆𝑖𝑖𝑖𝑖 (0 ≤ 𝜆𝜆𝜆𝜆𝑖𝑖𝑖𝑖 ≤ 1):
𝑤𝑤𝑤𝑤�𝜑𝜑𝜑𝜑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 � = �𝜆𝜆𝜆𝜆𝑖𝑖𝑖𝑖 + (1 − 𝜆𝜆𝜆𝜆𝑖𝑖𝑖𝑖 )
1 + cos(𝜑𝜑𝜑𝜑𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 ) � 2
(4)
(5)
Although the anticipation factor of Johansson’s model enables the accurate representation of actual behavior, there is still a need for adjustment to represent more “realistic” behavior from the subject’s viewpoint. First, the model calculates acceleration by only considering the current state. Therefore, the pedestrians' velocities fluctuate frequently and are influenced strongly by the slight movement of participants. The effect becomes remarkable especially when the pedestrians are close to the VR participant and are affected by the higher repulsive force from the participant, which reduces the sense of reality. The second point to be considered is body orientation. Pedestrians often change the velocity orientation while maintaining their body orientation. Thus, if the body and velocity orientations are the same, subjects may feel strange during experiments. In order to resolve the first issue, a simple forecasting method for the participant’s movement as well as a real-time smoothing method were applied. The basic strategy is to deal with the pedestrian locations calculated by the social
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force model and the locations drawn in the VR space separately. The locations of pedestrians were recorded during the past α seconds and the locations in the future α seconds were forecasted by the social force model. The average of the locations was drawn in the VR space. The procedure is outlined below. In this experiment, α was set as 0.2 s according to the opinions of examinees in the pre-experiments. • Step 1: Based on the trajectory record of the subject in the past α seconds, his/her location and velocity until α seconds later are extrapolated. • Step 2: Using this trajectory, the trajectories of all simulated pedestrians till α seconds later are calculated by social force model. • Step 3: The arithmetic average of the locations from α seconds before to α seconds later for each pedestrian is determined. These values are used as the pedestrian locations in the VR space. For the improvement of the second point, the body orientation is given based on the average of the past and future velocity orientations. Using this VR environment, two types of experiments were conducted. The first one is aimed at understanding people’s perception; the second is aimed at analyzing the behavior of pedestrians. Details of the experiments and the results will be described in the following sections. 3. Standing experiments for perception analysis 3.1. Settings of the experiment The purpose of this experiment is to compare the perceptions of pedestrians toward approaching objects in virtual and real environments. A Segway-like personal mobility vehicle (Robstep type M1, hereafter PMV) was used as the approaching object since the vehicle is easy to control both at high and low speeds, while other shared-space users are not; therefore, it enables us to create various scenarios even in RS. The participants were asked to stand facing different directions (i.e., front, side, and back) relative to the approaching direction of the PMV, as shown in the Fig. 2. They were also instructed not to move their body, face, and eyes during the experiments. The lateral distance between the path of the PMV rider and the participants (distance l1 in Fig. 2(a)) was set as either 0.6 m, 0.8 m, or 1 m, and PMV was run at the speed of 6 km/h. The scenarios were designed by the combination of participants’ orientation and the distance to the PMV path.
l2
l1
(e)
Fig. 2. Explanation of scenario components and snapshots during experiments: (a) lateral distance between pedestrians and PMV; (b) pedestrians’ orientations; (c) a snapshot during a "front" scenario; (d) a snapshot during a "back" scenario; (e) a snapshot of a VR “front” scenario.
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The participants experienced the same scenario both in VR and in RS. Thirty-two participants joined the experiment and they experienced each scenario twice. In this experiment, a predetermined path was used for PMV movement in the VR system. The motor sound of the approaching PMV was recorded at the location of the participants in RS and played in VR so that the participants can listen to the same sound in both cases. The participants performed the following tasks during the experiment: • Task 1 (spatial perception): the participants were instructed to hold a hand switch and turn it on the moment they think that the distance between the PMV and themselves (l2) is 1 m. • Task 2 (subjective perception): after each run, the participants scored their subjective danger (from -3: not dangerous to +3: dangerous) and answered whether their timing in turning on the switch was appropriate or not. Most of the participants were not familiar with the PMV or the other Segway-like vehicles. Thus, all participants experienced firstly the RS scenarios and then VR scenarios. The order of the RS scenarios is randomly given to each participant and the order of the VR scenarios followed the same as the RS scenarios. Before both experiments, training scenarios were given to participants. All of the participants were beginners of the VR system. 3.2. Comparison of perceptions in virtual reality and real space environments
Distance (m)
Fig. 3 depicts the average of the measured distances l2, comparing between the VR and RS experiments. The number of samples of all cases was 64. The error bars of the graph show the standard errors. Overall, the participants tend to turn on the switch earlier than the PMV’s arrival at a distance of 1 m, both in RS and VR. The differences in the average distance between RS and VR cases are not significant when the participants faced the front and side of the approaching PMV. In the case of the “back” scenario in VR, the participants pressed the switch much sooner than in RS.
Front Front Front
Back
Back
Back
Side
Side
Side
Note: ** means the averages of VR and RS are different at 1% significance level. Fig. 3. Actual distance between the PMV rider and the participant when the switch is turned on.
Fig. 4 depicts the average of the subjective danger evaluation by participants. It was found that the longer the lateral distance l1, the lower the subjective danger scores for all directions, both in RS and VR. In the front and back scenarios, the absolute values of the scores in VR tend to be lower than in RS, although the significance of the differences was not observed in all cases. Meanwhile, in the side scenario, the scores in VR were significantly higher than those in RS in all scenarios. Fig. 5 shows the percentage of trials in which the participants answered that they were not able to press the switch at the appropriate time. In RS, the percentage in the front and side scenarios are low and becomes higher in the back scenarios. In VR, both the side and back scenarios have significantly higher percentages than in RS scenarios. It was also found that the percentage of the side scenarios decreases rapidly as the lateral distance from PMV increases. The above comparisons provide interesting insights. At first, the spatial cognition of back scenarios in VR differs from that in RS. In the back scenarios, VR participants cannot use their sense of sight and fully depend on sound. Therefore, the results were mainly affected by the reproducibility of noise in VR. Although the PMV body in VR can emit the sound of the motor, the diffusion mechanism of the sound was not considered in detail in the VR system.
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Percentage of Trials (%)
Not dangerous
Dangerous
72 6
Front
Front
Front
Back
Back
Back
Side
Side
Side
Front
Front
Front
Back
Back
Back
Side
Side
Side
Note: * and ** mean the averages of VR and RS are different at 5% and 1% significance levels, respectively. Fig. 4. Average of subjective danger scores.
Fig. 5. Percentage of trials in which participants consider the timing of pressing the switch as appropriate.
The perceptions of danger in the front and back scenarios in VR were not different from those in RS. In the side scenarios, VR participants tended to feel more in danger than in RS. The reason for this is because of the difference between the range of vision in VR and RS. In VR, the view is limited to 110° while the binocular vision area of humans in RS is 124°, and even more than 180° can be seen by one eye. Therefore, owing to the differences in this vision range, VR participants might feel that the PMV suddenly appears in front of them and this is why they felt more danger. The participants' answers that they felt the timings of pressing the switch were not correct in the side scenarios further support this implication as the participants had to react to the sudden appearance of the PMV. 4. Comparison of Behavior in Virtual Reality and Real Environments 4.1. Settings of the experiment The purpose of the second experiment is to evaluate the behavioral characteristics of pedestrians in VR. In this experiment, pedestrians were allowed to walk in the VR spaces, avoiding surrounding virtual pedestrians. The participant was asked to walk a few meters along a sidewalk, as shown in Fig. 6. The view of the VR is shown in the right hand side of Fig. 1. All simulated pedestrians were generated at the same time so that the density of pedestrians could be kept as 0.1 person/m2 or 0.3 persons/m2. The VR participant starts to walk following an instruction, a few seconds after the simulated pedestrians start walking. The simulated pedestrians moved based on the model shown in Section 2.2, thus they themselves have a function to avoid VR participant. Meanwhile, the VR participant also tried to avoid the simulated pedestrians. Therefore as a result, an interactive avoidance behavior between human and virtual pedestrians is observed. All participants were beginner of the VR system again, and thus training scenarios were given before the experiment. For the comparison of RS data, the observed data obtained by Asano et al. (2013) at the west crosswalk of the Sasashima intersection in Nagoya, Japan, was utilized. This data contains trajectories of each pedestrian at the signalized crosswalk extracted from video images using semi-automated image processing system. In that system, the locations of pedestrians are manually tracked at every 0.5 seconds and the coordinates are transferred to world coordinates by projective transformation. Then, Kalman smoothing method is applied to obtain VR Experiment Room Sensors
VR Scenario Sidewalk
Simulated pedestrians
3.0m VR participant
2m – 4m
More than 10 m
Fig. 6. Geometric settings of VR experiment.
VR Participant 3.5m
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smoothed location, velocity and acceleration of each pedestrian. The length of the crosswalk is 30 m and trajectories of 10 m in the middle of the crosswalk were used. Since pedestrians may rush into the crosswalks at the end of green time, pedestrians who started to cross more than 10 s after the onset of pedestrian green were excluded from the analysis. The pedestrian density at the Sasashima intersection was at most 0.3 persons/m2. 4.2. Results of behavior analysis
Cumulative percentage
Firstly, the average speeds of participants in the VR experiment were measured. The average maximum speed was at most 0.7 m/s in the VR experiment, which is much lower than the average walking speed in the real world (approximately 1.3 m/s at Sasashima intersection). This may be because the participants were aware of the limited size of the experiment room and that the head-mounted display is connected to a wire; thus, they were more careful while walking in VR than in RS conditions. Fig. 7 shows the comparison of the lateral distance from other pedestrians when they avoid each other. The lateral distance is measured based on the center of the human body. The lateral distance distributions in VR and RS are similar, while the variation in RS is larger. This is because in VR the walking spaces are limited in the walkable areas, and so the degree of freedom of pedestrian behavior in VR is restricted. It should be noted that smaller lateral distances are observed more frequently in RS. The possible reason is that participants in VR cannot see their own body and cannot measure the distance between others and their own body. Furthermore, their field of view is limited and cannot clearly see the pedestrians just beside of them. Because of these features of VR, they try to take greater safety margins than in RS when they interact with other pedestrians. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
VR (n=503) Real spaces (n=86)
0
0.5 1 1.5 Lateral distance of avoidance (m)
2
Fig. 7. Lateral distance distribution when avoiding opposing pedestrians.
4.3. Calibration results of pedestrian behavior model Assuming that the pedestrian follows the behavior of Johansson’s Specification II model, the model parameters were estimated using VR experimental data and RS observation data at Sasashima crosswalk. A similar approach to Dias et al. (2017) using the cross entropy method was applied for the calibration. As the average speed of pedestrians in VR is quite low, the desired speed in VR was set as 0.7 m/s. For the Sasashima crosswalk, the desired speed of 1.35 m/s was given considering the average free flow speed. Table 1 shows the calibration results. For reference, Johannson’s original model parameters are also listed. The parameter λ estimated by VR is quite higher than with the ones estimated by RS and the original model. This means that the impact of anisotropy is less in VR, and that the participants treat the surrounding pedestrians in front or at the side almost equally. Meanwhile, the parameters A and B are smaller than the original but almost the same as Sasashima case; thus, the force they receive from the same distance is less in VR and in Sasashima than the original. This may be because the density level of this study was lower than the original and pedestrians did not need to strongly react to others. The combination of parameters indicates that pedestrians in VR receive less forces from pedestrians in front and relatively higher forces from the side. These characteristics explain the larger lateral margins in VR scenarios compared to RS discussed in Section 4.2.
Miho Iryo-Asano et al. / Transportation Research Procedia 34 (2018) 67–74 Miho Iryo-Asano, Yu Hasegawa and Charitha Dias / Transportation Research Procedia 00 (2018) 000–000
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Table. 1. Calibration results of Johannson’s model using VR data.
Parameters A B λ
Estimated value using Estimated value using RS Reference parameters estimated by VR data data at Sasashima Johannson et al. (2007) 0.018 0.011 0.04 2.35 2.29 3.22 0.89 0.29 0.06
5. Conclusions In this paper, pedestrians’ cognitions and behaviors in VR and RS were compared. In the first experiment, participants similarly recognized the distance between the PMV and themselves in VR and in RS when the PMV approached them from the front or at the back. In the side scenario, the participants felt more danger in VR than in RS. In the second experiment, the pedestrian speed and avoidance behavior were examined. The results imply that participants tend to have more lateral margins when avoiding others. The results of both experiments indicate that limitations in the field of view of the display may affect the feelings of pedestrians, especially when surroundings come from the side, and make pedestrians keep more margins for avoidance behavior. More general and comprehensive analysis are needed to figure out the quantitative relationship of perception and behavior in VR and in RS, though this limitation can be solved if displays with wider view will be available in the future. It should be noted that the conditions of VR data in the second experiment and those of RS data are different, and the results need to be carefully examined. However, the results do not contradict those of the first experiment and they provide important suggestions for planning VR experiments. In future work, more detailed comparisons are required in the behavioral analysis using data under similar conditions. Acknowledgements This research is supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 16K14317. References Asano, M., Alhajyaseen, W.K.M., Nakamura, H. and Zhang, X. (2013) Stochastic approach for modeling pedestrian crossing behavior after the onset of pedestrian flashing green signal indication, Proceedings of the 13th World Conference on Transportation Research, Rio de Janeiro, Brazil. Deb, S., Carruth, D.W., Sween, R., Strawderman, L. and Garrison, T.M. (2017). Efficacy of virtual reality in pedestrian safety research, Applied Ergonomics, 65, 1-12. Dias, C., Iryo-Asano, M., Shimono, K. and Nakano, K. (2017) Calibration of a Social Force-based Shared Space Model for Personal Mobility Vehicle and Pedestrian Mixed Traffic, 96th Transportation Research Board Annual Meeting, Washington, D.C. Helbing, D. and Molnar, P. (1995). Social force model for pedestrian dynamics. Physical review E, 51(5), 4282. Iryo, T., Asano, M., Odani, S., & Izumi, S. (2013). Examining factors of walking disutility for microscopic pedestrian model–A virtual reality approach. Procedia-Social and Behavioral Sciences, 80, 940-959. Johansson, A., Helbing, D., & Shukla, P. K. (2007). Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Advances in Complex Systems, 10(2), 271-288. Moussaïd, M., Kapadia, M., Thrash, T., Sumner, R. W., Gross, M., Helbing, D., & Hölscher, C. (2016). Crowd behaviour during high-stress evacuations in an immersive virtual environment. Journal of The Royal Society Interface, 13(122), 20160414. Schwebel, D.C., Gaines, J. and Severson, J. (2008). Validation of virtual reality as a tool to understand and prevent child pedestrian injury, Accident Analysis & Prevention, 40 (4), 1394-1400. Schwebel, D.C., Wu, Y., Li, P., Severson, J., He, Y., Xiang, H., Hu, G. (2017). Evaluating Smartphone-Based Virtual Reality to Improve Chinese Schoolchildren’s Pedestrian Safety: A Nonrandomized Trial, Journal of Pediatric Psychology, 1-12. Wolinski, D., Guy, S.J., Olivier, A.H., Lin, M., Manocha, D., Pettre, J. (2014). Paramter Estimation and Comparative Evaluation of Crowd Simulations, Eurographics, 33(2), pp.303-312. Yan, X., Abdel-Aty, M., Radwan, E., Wang, X. and Chilakapati, P. (2008). Validating a driving simulator using surrogate safety measures, Accident Analysis & Prevention, 40(1), 274-288. Yang, U., Kim, N.G. and Kim, K.H. (2017). Perception adjustment for egocentric moving distance between real space and virtual space with seeclosed-type HMD. In SIGGRAPH Asia 2017 Posters Article No. 23.