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Evaluating cyclists’ perception of satisfaction using 360° videos
Transportation Research Part A 132 (2020) 205–213
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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra
Evaluating cyclists’ perception of satisfaction using 360° videos ⁎
Qiang Liua, , Riken Hommab, Kazuhisa Ikib
T
a Department of Architecture and Environment Planning, Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuoku, Kumamoto 860-8555, Japan b Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
A R T IC LE I N F O
ABS TRA CT
Keywords: Cyclists’ satisfaction 360° videos Multinomial ordered logit regression model Random parameters multinomial ordered logit regression model Sidewalks Paved shoulder
With the increase in the number of cyclists, a method to evaluate the satisfaction of cyclists has become necessary. Previous studies in Europe and America have reported several approaches to evaluate cyclists’ perception of satisfaction. This research explored an emerging technology using 360° videos to develop a method for investigating cyclists’ level of satisfaction on both sidewalks and paved shoulders in Japan. The 360° videos provide a high level of immersion compared with traditional videos. All 360° videos were filmed at sixteen different locations in Kumamoto city, Kyushu island. Participants were asked to take a video survey by viewing the 360° videos with a head-mounted display and then rating their level of satisfaction. Finally, based on the results of the video survey, both multinomial ordered logit regression model and random parameters multinomial ordered logit regression model were used to explain the relationships between cyclists’ satisfaction, traffic conditions, and road characteristics. The results show that the road characteristics variables for both sidewalks and paved shoulders have statistically significant effects on participants’ satisfaction (p < 0.05). On the other hand, traffic conditions variables did not have an impact on participants for sidewalks section. In particular, there is data available on all the variables in the model that allows planners and engineers use this method to evaluate the satisfaction of cyclists on various road segments.
1. Introduction Cycling is a common means of transport that is considered environmentally friendly and good for health (Robinson et al., 2015). With the increasing use of bicycles, improvements to the level of satisfaction experienced by cyclists on the streets should be considered (Suzuki and Yai, 2006). Previous studies have conducted in-depth research and proposed several evaluation methods for onstreet bicycle facilities (curb lane1 or bicycle path). However, the bicycle facilities and the official cycling laws in Japan are different from those of the countries in previous studies. For example, cyclists in Japan can ride on either the sidewalk (with bicycle signs) or the curb lane. While these researchers have done various studies: Kin, 2009; Kin and Imamatu, 2011; Yamanaka et al., 2001, Japanese engineers and planners do not have an approach to evaluating the satisfaction of cyclists that is widely accepted. As yet, some researchers in Europe and America have proposed several methods for obtaining evaluations of cyclists’ satisfaction and comfort, with study design methods that can be divided into field survey and video survey. Field surveys obtain real perceptions of comfort by cycling on roadways. Landis developed the bicycle level of service (BLOS) model by placing 150 participants in actual urban traffic to obtain feedback on real-time perceptions (Landis et al., 1997). Since then other studies have proposed several BLOS
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Corresponding author. E-mail addresses: [email protected] (Q. Liu), [email protected] (R. Homma), [email protected] (K. Iki). 1 The motor traffic lane nearest to the sidewalk. https://doi.org/10.1016/j.tra.2019.11.008 Received 11 December 2018; Received in revised form 26 September 2019; Accepted 9 November 2019 0965-8564/ © 2019 Elsevier Ltd. All rights reserved.
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models, which are now widely used (Bai et al., 2017; Callister and Lowry, 2013; HCM, 2000; HCM, 2010; Lowry et al., 2012). However, field investigation could be dangerous for the participants (Jensen, 2007). For the safety of participants, video surveys have been used to investigate the cyclist’s perception of satisfaction. The bicycle compatibility index (BCI) was developed by Harkey et al., who obtained the sentiments of more than 200 participants by having them watch videos recorded by a stationary camera on the roadway (Harkey et al., 1998). To develop a rural BCI (RBCI), Jones and Carlson conducted a web-based survey that had participants view video clips recorded by a camera mounted on a vehicle (Jones and Carlson, 2003). Taking the conditions in Denmark into consideration, Jensen used a preference survey that had respondents view video clips recorded by a cyclist (Jensen, 2007). However, watching ordinary videos cannot offer an immersive environment or obtain real perceptions. Real-world traffic studies can obtain realistic perspectives of cyclists, but it is dangerous for participants or difficult. Still, it is impossible to provide identical traffic situations for each cyclist in the real world. Advances in immersive technology provide opportunities for research in areas that are dangerous or difficult to study in the real world (Deb et al., 2017). Immersive technologies such as 360° videos are becoming popular and have been used in many fields including education and medicine (Harrington et al., 2018; Pieterse et al., 2018). Previous studies show that 360° videos can provide a high level of immersion that enhances a strong sense of presence and can also provide an immersive virtual environment similar to the physical environment (Fonseca and Kraus, 2016; Rupp et al., 2016; Higuera-Trujillo et al., 2017). Additionally, the acoustics would encourage participants to feel like they were in a real-world environment (Maffei et al., 2016; Tse et al., 2017). Furthermore, 360° videos can generate an immersive experience using an inexpensive, portable, and widely used device like the smartphone (Fraustino et al., 2018). Thus, 360° videos will help the researcher design a safe, immersive, and well-controlled traffic environment for each participant. The purpose of this study is to develop a method for evaluating the cyclists’ level of satisfaction on both sidewalks and paved shoulders in Japan. The researches cited earlier provided a solid methodological basis for this study. The study area is Kumamoto city, the third largest city on Kyushu Island. A video survey was conducted to investigate the cyclists’ perception of satisfaction. Participants were shown video clips recorded with a 360° camera, which produces attractive 360° 4 k panorama videos. The participants were then asked to evaluate the various road segments in the video, indicating how satisfied they felt (level of satisfaction) on sidewalk (Fig. 1a) and paved shoulder (Fig. 1b). Finally, a multinomial ordered logit regression (MOL) model was used to explain the relationships between cyclists’ satisfaction, traffic conditions, and road characteristics. A random parameters multinomial ordered logit regression (RP-MOL) model was also proposed to take into account for the unobserved factors and compared with the MOL model. The second section of this paper describes the data collection and the details of the method. Section 3 discusses the results of the video survey, while section 4 concludes and discusses the limitations.
Fig. 1. Riding environments on sidewalks and paved shoulder. 206
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Table 1 Factors and categories of site selection. No.
Site Site Site Site Site Site Site Site Site Site Site Site Site Site Site Site
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Curb lane width (m)
Sidewalk width (m)
Speed limit (km/h)
Lane number
Paved shoulder width (m)
≤6.5
≤2
≤40
Two
≤0.75
> 0.75
– – – – – – – – – – ○ ○
– – – – – – – – – –
> 6.5
>2
○ ○ ○
○ ○ ○ ○ ○ ○ ○ ○ ○
○ ○ ○ ○ ○ ○
○ ○ ○ ○
○ – – – – – –
> 40
○ ○
○ ○ ○
○ ○ ○
○ ○ ○ ○ ○ ○ ○ ○
○ ○ ○
○
– – – – – –
○
Multilane
○ ○ ○ ○ ○ ○
○ ○ ○ ○
○ ○ ○
○
○
○ ○
2. Data and methods 2.1. Data The data consists of field data and video survey data. The field data includes traffic conditions and road characteristics collected at selected sites on different streets in Kumamoto city. To ensure that the selected sites could represent the variety of road conditions, the authors developed a table (Table 1) of essential factors and categories to guide site selection. At last, a total of sixteen locations that matched Table 1 were chosen. The conditions of these sixteen locations could broadly represent the different roadway environments. The Kumamoto prefectural police provided the locations of sidewalks with bicycle signs. Traffic conditions, such as traffic volume, were captured by a camera set up on a sidewalk. The 360° video recordings used in the video survey were also captured in the same manner at the sixteen locations mentioned above. Cyclists’ perception of satisfaction were collated in the video survey data. All the participants in the video survey were randomly selected from a university and the surrounding communities in Kumamoto city. Ninety-three people were invited but four people did not complete the video survey. At last, eighty-nine people (thirty-seven women and fifty-two men) of teens to fifties years of age participated in the video survey. These participants were physically able to ride a bicycle and have normal vision. They did not report any poor physical condition at the time of video survey. 2.2. Video production All 360° videos were made during off-peak traffic hours on fair weather weekdays using a 360° camera, Ricoh Theta V. The Ricoh Theta V can produce 360° videos with a resolution of 2160p (4 K) and stereo sound. The stereo sound reinforces the immersive experience (Tse et al., 2017). The height of the camera was about 1.5 m to approximate the eye height of a cyclist (Inoue et al., 2011). Video clips were classified into a sidewalk and a paved shoulder section. In most cases, the 360° camera was set up on the left side (in the same direction with cycling) of the sidewalk (Fig. 2a). Where there were no bicycle signs on the sidewalk, it was set up on the sidewalk, but as close as possible to the paved shoulder (Fig. 2b). If there was no sidewalk, the camera was set up on the paved shoulder (Fig. 2c). A total of twenty-three video clips were presented to the participants, ten video clips filmed on the sidewalk and six video clips filmed on the paved shoulder. The rest video clips were “repeated video clips”. These “repeated video clips” were filmed at the same location but with different traffic flows, or with large trucks included. Each clip was thirty seconds in duration, with a five seconds break between clips. 2.3. Video survey The participants were asked to view the 360° videos of both sidewalks and paved shoulders and to rate their level of satisfaction for each video clip. A video survey instruction was shown to the participants before the survey began. The rating was based on answers to the question: “how satisfied would you be, as a cyclist riding on the road shown in the video?”. The satisfaction level was indicated by a five-point scale ranging from one to five, describing very dissatisfied to very satisfied. A five-point scale is a suitable tool for classifying the level of satisfaction of cyclists (Bai et al., 2017). The first step involved explaining the purpose of the survey and providing details about 360° videos. The participants were then instructed to put on the smartphone-based head-mounted display (HMD) with headphones (Fig. 3) to isolate them from the real world. They controlled the field of view by moving their head. The first video clip was a practice clip that was used to adjust focal length and pupil distance, as well as allow participants to become familiar with the virtual environment. After the participants understood the explanations, they were asked to rate their satisfaction level for 207
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Fig. 2. Three images captured by 360° camera in the left.
each video clip as described earlier. The sidewalk video section and paved shoulder video section were presented separately. If the first rating section was sidewalk video clips, then the second section was paved shoulder video clips or vice versa. The video clips in sidewalk video section and paved shoulder video section were both presented in random order. All the video clips were presented on via iPhone with a resolution of 3840 × 1920 pixels without scaling.
2.4. Regression model It is widely accepted that cyclists’ level of satisfaction should be measured by a set of variables rather than just one. For example, traffic conditions and road characteristics (Bíl et al., 2015; Lehtonen et al., 2016; Li et al., 2012). The independent continuous variables and categorical variables used in developing the model for this study are presented in Table 2. The dependent variable Yj (j = 1, 2, 3, 4, 5), cyclists’ satisfaction level on paved shoulders and sidewalks, is introduced as:
Yj = β0j + β1j x1 + β2j x2 + ⋯+βkj xk
(1)
where xk is a vector of independent variables, βkj is a regression coefficient to be estimated, and β0j is a constant parameter. In the video survey, the cyclists’ satisfaction on paved shoulders and sidewalks was defined as a discrete variable scaled from 1 to 5. Therefore, the relationship between Y and xk cannot be explained by a linear regression model. Accordingly, this study uses a discrete choice model, the well-known multinomial ordered logit regression (MOL) model to explain the relationships between cyclists’ satisfaction and a set of independent variables. In addition, for the MOL model, it is necessary to check for multicollinearity among independent variables and the parallel line assumption. The model structure of MOL is shown in Eq. (2): 208
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Fig. 3. Participant wearing the HMD to view 360° videos. Table 2 List of candidate explanatory variables. Category
Variable
Description
Response variable Road characteristics
Satisfaction Curb lane width (m) Roadway width (m) Sidewalk width (m) Paved shoulder width (m) Speed limit (km/h) Lane number
Perception of satisfaction (from 1 to 5) Minimum: 2.8; maximum: 3.5; Mean: 3.11; Std.: 0.16 Minimum: 5.5; maximum: 15.8; Mean: 9.41; Std.: 3.72 Minimum: 1.5; maximum: 5.4; Mean: 3.01; Std.: 1.29 Minimum: 0.43; maximum: 2.34; Mean: 1.01; Std.: 0.61 Minimum: 40; maximum: 60; Mean: 44.54; Std.: 5.58 0 (two lane) 1 (multilane) 1 (upslope) 2 (downslope) 0 (horizontal) 1 (commercial land type) 2 (green area) 0 (residential land type) 1 (yes) 0 (no) 1 (yes) 0 (no) Vehicle traffic volume per hour with a mean: 1144.52 and a Std.: 854.76 Vehicle average speed on nearest roadside with a mean: 34.56 and a Std.: 11.14 Minimum: 0; maximum: 0.28; Mean: 0.03; Std.: 0.07
Presence of slope
Roadside land use
Presence of sidewalk Presence of greenbelt on sidewalk Traffic conditions
Traffic volume Speed (km/h) Percentage of heavy vehicles
P (Y = 1) ⎞ ln ⎛ = β01 + β11 x1 + β21 x2 + ⋯+βk1 xk ⎝ 1 − P (Y = 1) ⎠ ⎜
⎟
P (Y = 1) + P (Y = 2) ⎞ ln ⎛ = β02 + β12 x1 + β22 x2 + ⋯+βk 2 xk − 1 P (Y = 1) − P (Y = 2) ⎠ ⎝ ⎜
⎟
P (Y = 1) + P (Y = 2) + P (Y = 3) ⎞ ln ⎛ = β03 + β13 x1 + β23 x2 + ⋯+βk3 xk ⎝ 1 − P (Y = 1) − P (Y = 2) − P (Y = 3) ⎠ ⎜
⎟
P (Y = 1) + P (Y = 2) + P (Y = 3) + P (Y = 4) ⎞ ln ⎛ = β04 + β14 x1 + β24 x2 + ⋯+βk 4 xk ⎝ 1 − P (Y = 1) − P (Y = 2) − P (Y = 3) − P (Y = 4) ⎠ ⎜
⎟
209
(2)
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where P represents the probability of satisfaction level. Eq. (3) shows how to calculate the probability of the satisfaction level:
P (Y = 1|xk ) =
P (Y = j|xk ) =
exp(μ1 − ∑k βk1 xk ) 1 + exp(μ1 − ∑k βk1 xk ) exp(μj − ∑k βkj xk ) 1 + exp(μj − ∑k βkj xk )
P (Y = 5|xk ) = 1 −
−
exp(μj − 1 − ∑k βkj xk ) 1 + exp(μj − 1 − ∑k βkj xk )
exp(μ4 − ∑k βkj xk ) 1 + exp(μ4 − ∑k βkj xk )
(3)
where µj is the threshold value (or cut-off point) to be estimated. The coefficient vectors in MOL are treated as constant across observations. However, considering the unobserved factors across observations, a RP-MOL model was proposed to take into account:
βkji = βkj + ωi
(4)
where βkji is a coefficient vector that allowed to be different across observations and ωi is a randomly distributed term. We used simulated maximum likelihood procedure to estimate this model. Halton draws were used in the estimation process because of better coverage than pseudo-random draws (Sarrias, 2016; Bhat, 2003). In this study, the random parameters are assumed to follow normal distribution. The variable would be considered to vary in the observations if its mean coefficient and standard deviation were both found to be statistically significant. 3. Results Both MOL model and RP-MOL model were used for sidewalks and paved shoulder. First, the outliers were excluded by the descriptive statistics. The outliers that were the values greater than two standard deviations from the mean were not considered for further analysis. Then we checked the multicollinearity using multiple linear regression, and the results showed no multicollinearity among the independent variables for both the paved shoulder and sidewalk sections. Accordingly, all candidate variables and various combinations of selected variables were tested separately for the two facilities. The results of best-fitted MOL and RP-MOL model for two facilities are shown in Tables 3 and 4. For the sidewalk section, 5 independent variables were identified, including the width of sidewalk, the width of curb lane, the number of lanes, the roadside land use, and the presence of greenbelt on the sidewalks. According to the test of parallel lines, all the alternative models satisfied the parallel line assumption (p > 0.05). Also, the likelihood chi-square test of all the alternative models showed statistical significance (p < 0.001). The results for the MOL model and RP-MOL model of the sidewalk section are provided in Table 3. The bottom three rows of the table show the goodness-of-fit statistics included log-likelihood, Akaike information criterion (AIC), and Bayesian information criterion (BIC). The comparison results of goodness-of-fit statistics indicated that RP-MOL model provides a better fit. The variables that were selected as random parameters for sidewalks were the width of curb lane, the roadside land use (commercial), and the presence of greenbelt on the sidewalks. Table 3 Results of the MOL and RP-MOL models of sidewalk section. Variable
Fixed parameters
Random parameters
Coeff.
Sig.
Coeff.
Sig.
Width of sidewalk Width of curb lane (standard deviation of parameter distribution)
0.348*** 0.63*
0.000 0.1
0.000 0.052
Number of lanes-two lane Number of lanes-multilane Roadside land use = 0 Roadside land use = 1 (standard deviation of parameter distribution)
0a 0.72* 0a −0.863***
Roadside land use = 2 Greenbelt = 0 Greenbelt = 1 (standard deviation of parameter distribution)
0.587** 0a 0.631***
Log-likelihood AIC BIC
−611.984 1243.968 1287.837
0.846*** 1.549* (0.49***) 0a 1.598*** 0a −1.93*** (0.697*) 1.859** 0a 1.518*** (1.585***) −577.2 1180.396 1237.426
a
Reference category for the associated categorical variable. * Significant at the 0.1 level. ** Significant at the 0.05 level. *** Significant at the 0.01 level. 210
0.07 0.000 0.02 0.00
0.000 0.000 0.025 0.000
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Table 4 Results of MOL and RP-MOL model of paved shoulder section. Variable
Fixed parameters
Width of roadway (standard deviation of parameter distribution)
Random parameters
Coeff.
Sig.
Coeff.
Sig.
8.782***
0.000
14.024*** (0.182***) 0a −7.075*** 14.542*** (1.219**) 0a −7.903*** 0a 89.386 *** (1.958***) −3.518* −807.2 1640.323 1698.221
0.000
a
Presence of slope = 0 Presence of slope = 1 Presence of slope = 2 (standard deviation of parameter distribution)
0 −4.49*** 8.717***
Presence of sidewalk = 0 Presence of sidewalk = 1 Number of lanes-two lane Number of lanes-multilane (standard deviation of parameter distribution)
0a −4.385*** 0a 56.505***
Percentage of heavy vehicles Log-likelihood AIC BIC
−2.741 −844.415 1708.829 1753.365
***
0.000 0.000
0.000 0.000 0.001
0.000 0.000
0.000 0.000 0.011
a
Reference category for the associated categorical variable. * Significant at the 0.1 level. ** Significant at the 0.05 level. *** Significant at the 0.01 level.
In the paved shoulder section, 5 independent variables were identified. The independent variables included the width of roadway, the presence of slope, the presence of sidewalk, the number of lanes, and the percentage of heavy vehicles. Like the sidewalk section, the alternative models satisfied the parallel line assumption (p > 0.05) and the likelihood chi-square test showed statistical significance (p < 0.001). The results of the MOL model and RP-MOL model for the paved shoulder section are provided in Table 4. The goodness-of-fit statistics in the table also indicated that RP-MOL model provides a better model performance compared with the standard MOL model. The variables that were selected as random parameters for paved shoulder were the width of roadway, the presence of downslope, and the number of lanes (multilane). 4. Discussion and conclusion The statistically significant variables for cyclists’ satisfaction levels for the sidewalk section and the paved shoulder section were identified separately. The variables that affected the cyclists’ satisfaction levels for the sidewalk section included the width of sidewalk, the width of curb lane, the number of lanes, the roadside land use, and the presence of greenbelt on the sidewalks. For the MOL model, the width of sidewalk tended to have positive impact on cyclists’ satisfaction, because the increased width of bicycle facilities contributed to the ease of riding (Kand and Lee, 2012). The width of curb lane was found to be positively on cyclists’ satisfaction level. This finding is consistent with Sorton and Walsh (1994), and Guthrie et al. (2001). The number of lanes (multilane) also tended to have positive impact on cyclists’ satisfaction which is consistent with Jensen (2007). The roadside land use for green purpose tended to have positive impact on cyclists’ satisfaction, while the impact of roadside land use for commercial purpose was negative. This finding is consistent with the results of previous studies (Li et al., 2012; Harkey et al., 1998), but are in contrast with the results of Bai et al. (2017), who reported that roadside land use for commercial purpose positively impacted cyclists’ satisfaction. The greenbelt on the sidewalks was found to be positive because a barrier from motor vehicles makes better perceptions (Bai et al., 2017; Winters et al., 2011). It must be noted that the traffic condition variables did not have an impact on the participants, which is quite different from results with the common rating method (HCM, 2010). This might be because the sidewalk is always physically separated from the roadway and isolated from vehicular traffic. This finding is consistent with Landis et al. (1997), and Li et al. (2012). For the RP-MOL model, the width of curb lane, the roadside land use for commercial purpose, and greenbelt on the sidewalk were found to be random across observations. Both mean and standard deviation for these variables were found statistically significant, which suggested that they are likely to vary across observations. The width of curb lane is normal distribution with mean value of 1.549 and standard deviation value of 0.49, which implies that almost 100 percent of the observations resulted in positive impact on satisfaction. The roadside land use for commercial purpose was found 99.72 percent of the observations have a negative impact (mean value of 1.93 and standard deviation value of 0.697). This finding may explain why the results of roadside land use for commercial purpose in previous studies (Li et al., 2012; Harkey et al., 1998; Bai et al., 2017) we mentioned are different. The coefficient for greenbelt on the sidewalk is normal distribution with a mean value of 1.518 and standard deviation of 1.585, which suggested that for 83.09 percent of the observations resulted in positive impact and 16.91 percent in negative impact. While previous studies indicated that a barrier from motor vehicles makes better perceptions (Bai et al., 2017; Li et al., 2012; Winters et al., 2011), the greenbelt was found variance in this study. The variables that affected cyclists’ satisfaction for the paved shoulder section include the width of roadway, the presence of slope, the presence of sidewalk, the number of lanes, and the percentage of heavy vehicles. For the MOL model, the width of roadway 211
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was found to be positive on cyclists’ satisfaction level. The presence of downslope and no slope (horizontal) were positively related to cyclists’ satisfaction because they contributed to the ease of riding. These findings are consistent with Harkey et al. (1998). The presence of sidewalk was found to have negative impact. The decreases in satisfaction might be due to the height difference between the sidewalk and the curb lane, which might cause the bicycle wheel collision. The number of lanes (multilane) also tended to have positive impact on cyclists’ satisfaction which is similar to the results of sidewalk section above. The percentage of heavy vehicles was found to be negative on cyclists’ satisfaction level. This finding is consistent with Jones and Carlson (2003). However, the model for the paved shoulder section is quite surprising. The statistically significant variables did not include the width of the paved shoulder and traffic conditions variables except the percentage of heavy vehicles. The results of most of the previous studies found the width of the bicycle facility to have significant effects on satisfaction. Li et al. (2012) also indicated that in the on-street bicycle facility, contributing factors are riding space and traffic conditions. For the RP-MOL model, the width of roadway, the presence of downslope, and the width of curb lane were found to be random across observations. These variables were normally distributed and both mean and standard deviation for these variables were found statistically significant. The presence of downslope is normal distribution with mean value of 14.542 and standard deviation value of 1.219, which implies that almost 100 percent of the observations resulted in positive impact on satisfaction. The width of roadway and the number of lanes (multilane) are also normal distribution, the mean value and standard deviation value of them imply that 100 percent of the observations resulted in positive impact on satisfaction. This study provides a method that may offer reference for decision-makers or planners in Japan. They could consider using our method or variables when doing analysis or making decisions for achieving a higher cycling environment. The use of 360° videos is an emerging approach of this study when compared with field surveys and traditional video surveys. It can present a complete view of the site and its surroundings with high immersion. It also saves time and money because it does not require the participants to go to the field to take the survey. In particular, all the statistically significant variables we chose have data available. The road administrations in Japan have this data in a specific format. This method would allow planners and engineers to evaluate the satisfaction level of cyclists on various road segments and the results could be accessed on geographic information systems (GIS). We also expect growth in the data on cycling, for example, the volume of bicycles, and pavement condition. Especially data on the pavement condition, because it is an important variable affecting cyclists’ satisfaction (Landis et al., 1997). This growth in data would enable researchers to evaluate cyclists’ satisfaction more accurately. There are also some limitations to our study. First, such emerging approach perhaps need validation by a matched pairs experiment where a respondent is exposed both to the real world and the virtual environment. Then, the 360° videos can provide a virtual environment similar to the real world. However, the locations for our viewers were fixed, and viewers were limited to the frames captured by the camera. They also could not interact with the environment. Furthermore, in the 360° video environment, the participants viewed a flat video frame projected onto spherical 3D geometry. In other words, it provides a strong sense of presence but without a sense of distance. The immersive technology and 360° camera still require further development. With future advances in immersive technology and the 360° camera, there will be more worthwhile studies in the future. In addition, exposure to the immersive system could cause simulation sickness (Deb et al., 2017), which may induce discomfort and affect the rating. Thus, after the video survey, it is better to confirm the participants’ simulation sickness by simulator sickness questionnaire (SSQ) (Kennedy et al., 1993), which is a standardized method for measuring the effects of simulation sickness (Guna et al., 2018). The participants with high SSQ scores should be removed. 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s12205-012-1146-z. Kennedy, R.S., et al., 1993. Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int. J. Aviat. Psychol. 3 (3), 203–220. Kin, T., 2009. Evaluation of bicycle lane environment using the satisfaction of the bicycle users. J. City Plan. Inst. Jpn. 44 (3), 91–96 (in Japanese). Kin, T., Imamatu, R., 2011. Evaluation of bicycle lane using the bicycle compatibility checklist. In: Proceedings of Infrastructure Planning, vol. 43. pp. 1–4 (in Japanese). Landis, B.W., et al., 1997. Real-time Human perceptions toward a bicycle level of service. Transp. Res. Rec. 1578, 119–126. Lowry, M.B., et al., 2012. Using bicycle level of service to assess community-wide bikeability. Transp. Res. Rec. 2274. Lehtonen, E., et al., 2016. Evaluating bicyclists’ risk perception using video clips: comparison of frequent and infrequent city cyclists. Transp. Res. Pt. F – Traffic Psychol. Behav. 41 (2016), 195–203. Li, Z., et al., 2012. Physical environments influencing bicyclists’ perception of comfort on separated and on-street bicycle facilities. Transport. Res. Part D – Transport. Environ. 17 (3), 256–261. Maffei, L., et al., 2016. Immersive virtual reality in community planning: acoustic and visual congruence of simulated vs real world. Sust. Cities Soc. 27, 338–345. Pieterse, A.D., et al., 2018. Introducing the innovative technique of 360°virtual reality in kidney transplant education. Transpl. Immunol. 49, 5–6. https://doi.org/10. 1016/j.trim.2018.03.001. Robinson, T., et al., 2015. Utilizing Bicycle Level of and Latent Demand Score for Small City Bicycle Route Planning. Transportation Research Board, Washington DC. Rupp, M.A., et al., 2016. The effects of impressiveness and future VR expectations on subjective experiences during an educational 360 video. Human Factors and Ergonomics Society Annual Meeting. Washington DC. Sarrias, M., 2016. Discrete choice model with random parameters in R: the Rchoice package. J. Stat. Softw. 74 (10). https://doi.org/10.18637/jss.v074.i10. Sorton, A., Walsh, T., 1994. Bicycle stress level as a tool to evaluate urban and suburban bicycle compatibility. Transp. Res. Record. 1438, 17–24. Suzuki, M., Yai, T., 2006. A study of traffic safety for on-street bicycle lanes in terms of width of roads and facilities of boundary. Infrastruct. Plan. Rev. 33 (in Japanese). Tse, A. et al., 2017. Was I there? Impact of platform and headphones on 360 video immersion. In: CHI Conference Extended Abstracts on human Factors in Computing Systems. Denver, Colorado, pp. 2967–2974. Winters, M., et al., 2011. Motivators and deterrents of bicycling: comparing influences on decisions to ride. Transportation 38 (1), 153–168. Yamanaka, H., et al., 2001. Evaluation index for mixed traffic of pedestrians and cyclists considering the bicycles speed. Infrastruct. Plan. Rev. 18, 471–476 (in Japanese).
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Contents lists available at ScienceDirect
Transportation Research Part A journal homepage: www.elsevier.com/locate/tra
Evaluating cyclists’ perception of satisfaction using 360° videos ⁎
Qiang Liua, , Riken Hommab, Kazuhisa Ikib
T
a Department of Architecture and Environment Planning, Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuoku, Kumamoto 860-8555, Japan b Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
A R T IC LE I N F O
ABS TRA CT
Keywords: Cyclists’ satisfaction 360° videos Multinomial ordered logit regression model Random parameters multinomial ordered logit regression model Sidewalks Paved shoulder
With the increase in the number of cyclists, a method to evaluate the satisfaction of cyclists has become necessary. Previous studies in Europe and America have reported several approaches to evaluate cyclists’ perception of satisfaction. This research explored an emerging technology using 360° videos to develop a method for investigating cyclists’ level of satisfaction on both sidewalks and paved shoulders in Japan. The 360° videos provide a high level of immersion compared with traditional videos. All 360° videos were filmed at sixteen different locations in Kumamoto city, Kyushu island. Participants were asked to take a video survey by viewing the 360° videos with a head-mounted display and then rating their level of satisfaction. Finally, based on the results of the video survey, both multinomial ordered logit regression model and random parameters multinomial ordered logit regression model were used to explain the relationships between cyclists’ satisfaction, traffic conditions, and road characteristics. The results show that the road characteristics variables for both sidewalks and paved shoulders have statistically significant effects on participants’ satisfaction (p < 0.05). On the other hand, traffic conditions variables did not have an impact on participants for sidewalks section. In particular, there is data available on all the variables in the model that allows planners and engineers use this method to evaluate the satisfaction of cyclists on various road segments.
1. Introduction Cycling is a common means of transport that is considered environmentally friendly and good for health (Robinson et al., 2015). With the increasing use of bicycles, improvements to the level of satisfaction experienced by cyclists on the streets should be considered (Suzuki and Yai, 2006). Previous studies have conducted in-depth research and proposed several evaluation methods for onstreet bicycle facilities (curb lane1 or bicycle path). However, the bicycle facilities and the official cycling laws in Japan are different from those of the countries in previous studies. For example, cyclists in Japan can ride on either the sidewalk (with bicycle signs) or the curb lane. While these researchers have done various studies: Kin, 2009; Kin and Imamatu, 2011; Yamanaka et al., 2001, Japanese engineers and planners do not have an approach to evaluating the satisfaction of cyclists that is widely accepted. As yet, some researchers in Europe and America have proposed several methods for obtaining evaluations of cyclists’ satisfaction and comfort, with study design methods that can be divided into field survey and video survey. Field surveys obtain real perceptions of comfort by cycling on roadways. Landis developed the bicycle level of service (BLOS) model by placing 150 participants in actual urban traffic to obtain feedback on real-time perceptions (Landis et al., 1997). Since then other studies have proposed several BLOS
⁎
Corresponding author. E-mail addresses: [email protected] (Q. Liu), [email protected] (R. Homma), [email protected] (K. Iki). 1 The motor traffic lane nearest to the sidewalk. https://doi.org/10.1016/j.tra.2019.11.008 Received 11 December 2018; Received in revised form 26 September 2019; Accepted 9 November 2019 0965-8564/ © 2019 Elsevier Ltd. All rights reserved.
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models, which are now widely used (Bai et al., 2017; Callister and Lowry, 2013; HCM, 2000; HCM, 2010; Lowry et al., 2012). However, field investigation could be dangerous for the participants (Jensen, 2007). For the safety of participants, video surveys have been used to investigate the cyclist’s perception of satisfaction. The bicycle compatibility index (BCI) was developed by Harkey et al., who obtained the sentiments of more than 200 participants by having them watch videos recorded by a stationary camera on the roadway (Harkey et al., 1998). To develop a rural BCI (RBCI), Jones and Carlson conducted a web-based survey that had participants view video clips recorded by a camera mounted on a vehicle (Jones and Carlson, 2003). Taking the conditions in Denmark into consideration, Jensen used a preference survey that had respondents view video clips recorded by a cyclist (Jensen, 2007). However, watching ordinary videos cannot offer an immersive environment or obtain real perceptions. Real-world traffic studies can obtain realistic perspectives of cyclists, but it is dangerous for participants or difficult. Still, it is impossible to provide identical traffic situations for each cyclist in the real world. Advances in immersive technology provide opportunities for research in areas that are dangerous or difficult to study in the real world (Deb et al., 2017). Immersive technologies such as 360° videos are becoming popular and have been used in many fields including education and medicine (Harrington et al., 2018; Pieterse et al., 2018). Previous studies show that 360° videos can provide a high level of immersion that enhances a strong sense of presence and can also provide an immersive virtual environment similar to the physical environment (Fonseca and Kraus, 2016; Rupp et al., 2016; Higuera-Trujillo et al., 2017). Additionally, the acoustics would encourage participants to feel like they were in a real-world environment (Maffei et al., 2016; Tse et al., 2017). Furthermore, 360° videos can generate an immersive experience using an inexpensive, portable, and widely used device like the smartphone (Fraustino et al., 2018). Thus, 360° videos will help the researcher design a safe, immersive, and well-controlled traffic environment for each participant. The purpose of this study is to develop a method for evaluating the cyclists’ level of satisfaction on both sidewalks and paved shoulders in Japan. The researches cited earlier provided a solid methodological basis for this study. The study area is Kumamoto city, the third largest city on Kyushu Island. A video survey was conducted to investigate the cyclists’ perception of satisfaction. Participants were shown video clips recorded with a 360° camera, which produces attractive 360° 4 k panorama videos. The participants were then asked to evaluate the various road segments in the video, indicating how satisfied they felt (level of satisfaction) on sidewalk (Fig. 1a) and paved shoulder (Fig. 1b). Finally, a multinomial ordered logit regression (MOL) model was used to explain the relationships between cyclists’ satisfaction, traffic conditions, and road characteristics. A random parameters multinomial ordered logit regression (RP-MOL) model was also proposed to take into account for the unobserved factors and compared with the MOL model. The second section of this paper describes the data collection and the details of the method. Section 3 discusses the results of the video survey, while section 4 concludes and discusses the limitations.
Fig. 1. Riding environments on sidewalks and paved shoulder. 206
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Table 1 Factors and categories of site selection. No.
Site Site Site Site Site Site Site Site Site Site Site Site Site Site Site Site
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Curb lane width (m)
Sidewalk width (m)
Speed limit (km/h)
Lane number
Paved shoulder width (m)
≤6.5
≤2
≤40
Two
≤0.75
> 0.75
– – – – – – – – – – ○ ○
– – – – – – – – – –
> 6.5
>2
○ ○ ○
○ ○ ○ ○ ○ ○ ○ ○ ○
○ ○ ○ ○ ○ ○
○ ○ ○ ○
○ – – – – – –
> 40
○ ○
○ ○ ○
○ ○ ○
○ ○ ○ ○ ○ ○ ○ ○
○ ○ ○
○
– – – – – –
○
Multilane
○ ○ ○ ○ ○ ○
○ ○ ○ ○
○ ○ ○
○
○
○ ○
2. Data and methods 2.1. Data The data consists of field data and video survey data. The field data includes traffic conditions and road characteristics collected at selected sites on different streets in Kumamoto city. To ensure that the selected sites could represent the variety of road conditions, the authors developed a table (Table 1) of essential factors and categories to guide site selection. At last, a total of sixteen locations that matched Table 1 were chosen. The conditions of these sixteen locations could broadly represent the different roadway environments. The Kumamoto prefectural police provided the locations of sidewalks with bicycle signs. Traffic conditions, such as traffic volume, were captured by a camera set up on a sidewalk. The 360° video recordings used in the video survey were also captured in the same manner at the sixteen locations mentioned above. Cyclists’ perception of satisfaction were collated in the video survey data. All the participants in the video survey were randomly selected from a university and the surrounding communities in Kumamoto city. Ninety-three people were invited but four people did not complete the video survey. At last, eighty-nine people (thirty-seven women and fifty-two men) of teens to fifties years of age participated in the video survey. These participants were physically able to ride a bicycle and have normal vision. They did not report any poor physical condition at the time of video survey. 2.2. Video production All 360° videos were made during off-peak traffic hours on fair weather weekdays using a 360° camera, Ricoh Theta V. The Ricoh Theta V can produce 360° videos with a resolution of 2160p (4 K) and stereo sound. The stereo sound reinforces the immersive experience (Tse et al., 2017). The height of the camera was about 1.5 m to approximate the eye height of a cyclist (Inoue et al., 2011). Video clips were classified into a sidewalk and a paved shoulder section. In most cases, the 360° camera was set up on the left side (in the same direction with cycling) of the sidewalk (Fig. 2a). Where there were no bicycle signs on the sidewalk, it was set up on the sidewalk, but as close as possible to the paved shoulder (Fig. 2b). If there was no sidewalk, the camera was set up on the paved shoulder (Fig. 2c). A total of twenty-three video clips were presented to the participants, ten video clips filmed on the sidewalk and six video clips filmed on the paved shoulder. The rest video clips were “repeated video clips”. These “repeated video clips” were filmed at the same location but with different traffic flows, or with large trucks included. Each clip was thirty seconds in duration, with a five seconds break between clips. 2.3. Video survey The participants were asked to view the 360° videos of both sidewalks and paved shoulders and to rate their level of satisfaction for each video clip. A video survey instruction was shown to the participants before the survey began. The rating was based on answers to the question: “how satisfied would you be, as a cyclist riding on the road shown in the video?”. The satisfaction level was indicated by a five-point scale ranging from one to five, describing very dissatisfied to very satisfied. A five-point scale is a suitable tool for classifying the level of satisfaction of cyclists (Bai et al., 2017). The first step involved explaining the purpose of the survey and providing details about 360° videos. The participants were then instructed to put on the smartphone-based head-mounted display (HMD) with headphones (Fig. 3) to isolate them from the real world. They controlled the field of view by moving their head. The first video clip was a practice clip that was used to adjust focal length and pupil distance, as well as allow participants to become familiar with the virtual environment. After the participants understood the explanations, they were asked to rate their satisfaction level for 207
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Fig. 2. Three images captured by 360° camera in the left.
each video clip as described earlier. The sidewalk video section and paved shoulder video section were presented separately. If the first rating section was sidewalk video clips, then the second section was paved shoulder video clips or vice versa. The video clips in sidewalk video section and paved shoulder video section were both presented in random order. All the video clips were presented on via iPhone with a resolution of 3840 × 1920 pixels without scaling.
2.4. Regression model It is widely accepted that cyclists’ level of satisfaction should be measured by a set of variables rather than just one. For example, traffic conditions and road characteristics (Bíl et al., 2015; Lehtonen et al., 2016; Li et al., 2012). The independent continuous variables and categorical variables used in developing the model for this study are presented in Table 2. The dependent variable Yj (j = 1, 2, 3, 4, 5), cyclists’ satisfaction level on paved shoulders and sidewalks, is introduced as:
Yj = β0j + β1j x1 + β2j x2 + ⋯+βkj xk
(1)
where xk is a vector of independent variables, βkj is a regression coefficient to be estimated, and β0j is a constant parameter. In the video survey, the cyclists’ satisfaction on paved shoulders and sidewalks was defined as a discrete variable scaled from 1 to 5. Therefore, the relationship between Y and xk cannot be explained by a linear regression model. Accordingly, this study uses a discrete choice model, the well-known multinomial ordered logit regression (MOL) model to explain the relationships between cyclists’ satisfaction and a set of independent variables. In addition, for the MOL model, it is necessary to check for multicollinearity among independent variables and the parallel line assumption. The model structure of MOL is shown in Eq. (2): 208
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Fig. 3. Participant wearing the HMD to view 360° videos. Table 2 List of candidate explanatory variables. Category
Variable
Description
Response variable Road characteristics
Satisfaction Curb lane width (m) Roadway width (m) Sidewalk width (m) Paved shoulder width (m) Speed limit (km/h) Lane number
Perception of satisfaction (from 1 to 5) Minimum: 2.8; maximum: 3.5; Mean: 3.11; Std.: 0.16 Minimum: 5.5; maximum: 15.8; Mean: 9.41; Std.: 3.72 Minimum: 1.5; maximum: 5.4; Mean: 3.01; Std.: 1.29 Minimum: 0.43; maximum: 2.34; Mean: 1.01; Std.: 0.61 Minimum: 40; maximum: 60; Mean: 44.54; Std.: 5.58 0 (two lane) 1 (multilane) 1 (upslope) 2 (downslope) 0 (horizontal) 1 (commercial land type) 2 (green area) 0 (residential land type) 1 (yes) 0 (no) 1 (yes) 0 (no) Vehicle traffic volume per hour with a mean: 1144.52 and a Std.: 854.76 Vehicle average speed on nearest roadside with a mean: 34.56 and a Std.: 11.14 Minimum: 0; maximum: 0.28; Mean: 0.03; Std.: 0.07
Presence of slope
Roadside land use
Presence of sidewalk Presence of greenbelt on sidewalk Traffic conditions
Traffic volume Speed (km/h) Percentage of heavy vehicles
P (Y = 1) ⎞ ln ⎛ = β01 + β11 x1 + β21 x2 + ⋯+βk1 xk ⎝ 1 − P (Y = 1) ⎠ ⎜
⎟
P (Y = 1) + P (Y = 2) ⎞ ln ⎛ = β02 + β12 x1 + β22 x2 + ⋯+βk 2 xk − 1 P (Y = 1) − P (Y = 2) ⎠ ⎝ ⎜
⎟
P (Y = 1) + P (Y = 2) + P (Y = 3) ⎞ ln ⎛ = β03 + β13 x1 + β23 x2 + ⋯+βk3 xk ⎝ 1 − P (Y = 1) − P (Y = 2) − P (Y = 3) ⎠ ⎜
⎟
P (Y = 1) + P (Y = 2) + P (Y = 3) + P (Y = 4) ⎞ ln ⎛ = β04 + β14 x1 + β24 x2 + ⋯+βk 4 xk ⎝ 1 − P (Y = 1) − P (Y = 2) − P (Y = 3) − P (Y = 4) ⎠ ⎜
⎟
209
(2)
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where P represents the probability of satisfaction level. Eq. (3) shows how to calculate the probability of the satisfaction level:
P (Y = 1|xk ) =
P (Y = j|xk ) =
exp(μ1 − ∑k βk1 xk ) 1 + exp(μ1 − ∑k βk1 xk ) exp(μj − ∑k βkj xk ) 1 + exp(μj − ∑k βkj xk )
P (Y = 5|xk ) = 1 −
−
exp(μj − 1 − ∑k βkj xk ) 1 + exp(μj − 1 − ∑k βkj xk )
exp(μ4 − ∑k βkj xk ) 1 + exp(μ4 − ∑k βkj xk )
(3)
where µj is the threshold value (or cut-off point) to be estimated. The coefficient vectors in MOL are treated as constant across observations. However, considering the unobserved factors across observations, a RP-MOL model was proposed to take into account:
βkji = βkj + ωi
(4)
where βkji is a coefficient vector that allowed to be different across observations and ωi is a randomly distributed term. We used simulated maximum likelihood procedure to estimate this model. Halton draws were used in the estimation process because of better coverage than pseudo-random draws (Sarrias, 2016; Bhat, 2003). In this study, the random parameters are assumed to follow normal distribution. The variable would be considered to vary in the observations if its mean coefficient and standard deviation were both found to be statistically significant. 3. Results Both MOL model and RP-MOL model were used for sidewalks and paved shoulder. First, the outliers were excluded by the descriptive statistics. The outliers that were the values greater than two standard deviations from the mean were not considered for further analysis. Then we checked the multicollinearity using multiple linear regression, and the results showed no multicollinearity among the independent variables for both the paved shoulder and sidewalk sections. Accordingly, all candidate variables and various combinations of selected variables were tested separately for the two facilities. The results of best-fitted MOL and RP-MOL model for two facilities are shown in Tables 3 and 4. For the sidewalk section, 5 independent variables were identified, including the width of sidewalk, the width of curb lane, the number of lanes, the roadside land use, and the presence of greenbelt on the sidewalks. According to the test of parallel lines, all the alternative models satisfied the parallel line assumption (p > 0.05). Also, the likelihood chi-square test of all the alternative models showed statistical significance (p < 0.001). The results for the MOL model and RP-MOL model of the sidewalk section are provided in Table 3. The bottom three rows of the table show the goodness-of-fit statistics included log-likelihood, Akaike information criterion (AIC), and Bayesian information criterion (BIC). The comparison results of goodness-of-fit statistics indicated that RP-MOL model provides a better fit. The variables that were selected as random parameters for sidewalks were the width of curb lane, the roadside land use (commercial), and the presence of greenbelt on the sidewalks. Table 3 Results of the MOL and RP-MOL models of sidewalk section. Variable
Fixed parameters
Random parameters
Coeff.
Sig.
Coeff.
Sig.
Width of sidewalk Width of curb lane (standard deviation of parameter distribution)
0.348*** 0.63*
0.000 0.1
0.000 0.052
Number of lanes-two lane Number of lanes-multilane Roadside land use = 0 Roadside land use = 1 (standard deviation of parameter distribution)
0a 0.72* 0a −0.863***
Roadside land use = 2 Greenbelt = 0 Greenbelt = 1 (standard deviation of parameter distribution)
0.587** 0a 0.631***
Log-likelihood AIC BIC
−611.984 1243.968 1287.837
0.846*** 1.549* (0.49***) 0a 1.598*** 0a −1.93*** (0.697*) 1.859** 0a 1.518*** (1.585***) −577.2 1180.396 1237.426
a
Reference category for the associated categorical variable. * Significant at the 0.1 level. ** Significant at the 0.05 level. *** Significant at the 0.01 level. 210
0.07 0.000 0.02 0.00
0.000 0.000 0.025 0.000
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Table 4 Results of MOL and RP-MOL model of paved shoulder section. Variable
Fixed parameters
Width of roadway (standard deviation of parameter distribution)
Random parameters
Coeff.
Sig.
Coeff.
Sig.
8.782***
0.000
14.024*** (0.182***) 0a −7.075*** 14.542*** (1.219**) 0a −7.903*** 0a 89.386 *** (1.958***) −3.518* −807.2 1640.323 1698.221
0.000
a
Presence of slope = 0 Presence of slope = 1 Presence of slope = 2 (standard deviation of parameter distribution)
0 −4.49*** 8.717***
Presence of sidewalk = 0 Presence of sidewalk = 1 Number of lanes-two lane Number of lanes-multilane (standard deviation of parameter distribution)
0a −4.385*** 0a 56.505***
Percentage of heavy vehicles Log-likelihood AIC BIC
−2.741 −844.415 1708.829 1753.365
***
0.000 0.000
0.000 0.000 0.001
0.000 0.000
0.000 0.000 0.011
a
Reference category for the associated categorical variable. * Significant at the 0.1 level. ** Significant at the 0.05 level. *** Significant at the 0.01 level.
In the paved shoulder section, 5 independent variables were identified. The independent variables included the width of roadway, the presence of slope, the presence of sidewalk, the number of lanes, and the percentage of heavy vehicles. Like the sidewalk section, the alternative models satisfied the parallel line assumption (p > 0.05) and the likelihood chi-square test showed statistical significance (p < 0.001). The results of the MOL model and RP-MOL model for the paved shoulder section are provided in Table 4. The goodness-of-fit statistics in the table also indicated that RP-MOL model provides a better model performance compared with the standard MOL model. The variables that were selected as random parameters for paved shoulder were the width of roadway, the presence of downslope, and the number of lanes (multilane). 4. Discussion and conclusion The statistically significant variables for cyclists’ satisfaction levels for the sidewalk section and the paved shoulder section were identified separately. The variables that affected the cyclists’ satisfaction levels for the sidewalk section included the width of sidewalk, the width of curb lane, the number of lanes, the roadside land use, and the presence of greenbelt on the sidewalks. For the MOL model, the width of sidewalk tended to have positive impact on cyclists’ satisfaction, because the increased width of bicycle facilities contributed to the ease of riding (Kand and Lee, 2012). The width of curb lane was found to be positively on cyclists’ satisfaction level. This finding is consistent with Sorton and Walsh (1994), and Guthrie et al. (2001). The number of lanes (multilane) also tended to have positive impact on cyclists’ satisfaction which is consistent with Jensen (2007). The roadside land use for green purpose tended to have positive impact on cyclists’ satisfaction, while the impact of roadside land use for commercial purpose was negative. This finding is consistent with the results of previous studies (Li et al., 2012; Harkey et al., 1998), but are in contrast with the results of Bai et al. (2017), who reported that roadside land use for commercial purpose positively impacted cyclists’ satisfaction. The greenbelt on the sidewalks was found to be positive because a barrier from motor vehicles makes better perceptions (Bai et al., 2017; Winters et al., 2011). It must be noted that the traffic condition variables did not have an impact on the participants, which is quite different from results with the common rating method (HCM, 2010). This might be because the sidewalk is always physically separated from the roadway and isolated from vehicular traffic. This finding is consistent with Landis et al. (1997), and Li et al. (2012). For the RP-MOL model, the width of curb lane, the roadside land use for commercial purpose, and greenbelt on the sidewalk were found to be random across observations. Both mean and standard deviation for these variables were found statistically significant, which suggested that they are likely to vary across observations. The width of curb lane is normal distribution with mean value of 1.549 and standard deviation value of 0.49, which implies that almost 100 percent of the observations resulted in positive impact on satisfaction. The roadside land use for commercial purpose was found 99.72 percent of the observations have a negative impact (mean value of 1.93 and standard deviation value of 0.697). This finding may explain why the results of roadside land use for commercial purpose in previous studies (Li et al., 2012; Harkey et al., 1998; Bai et al., 2017) we mentioned are different. The coefficient for greenbelt on the sidewalk is normal distribution with a mean value of 1.518 and standard deviation of 1.585, which suggested that for 83.09 percent of the observations resulted in positive impact and 16.91 percent in negative impact. While previous studies indicated that a barrier from motor vehicles makes better perceptions (Bai et al., 2017; Li et al., 2012; Winters et al., 2011), the greenbelt was found variance in this study. The variables that affected cyclists’ satisfaction for the paved shoulder section include the width of roadway, the presence of slope, the presence of sidewalk, the number of lanes, and the percentage of heavy vehicles. For the MOL model, the width of roadway 211
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was found to be positive on cyclists’ satisfaction level. The presence of downslope and no slope (horizontal) were positively related to cyclists’ satisfaction because they contributed to the ease of riding. These findings are consistent with Harkey et al. (1998). The presence of sidewalk was found to have negative impact. The decreases in satisfaction might be due to the height difference between the sidewalk and the curb lane, which might cause the bicycle wheel collision. The number of lanes (multilane) also tended to have positive impact on cyclists’ satisfaction which is similar to the results of sidewalk section above. The percentage of heavy vehicles was found to be negative on cyclists’ satisfaction level. This finding is consistent with Jones and Carlson (2003). However, the model for the paved shoulder section is quite surprising. The statistically significant variables did not include the width of the paved shoulder and traffic conditions variables except the percentage of heavy vehicles. The results of most of the previous studies found the width of the bicycle facility to have significant effects on satisfaction. Li et al. (2012) also indicated that in the on-street bicycle facility, contributing factors are riding space and traffic conditions. For the RP-MOL model, the width of roadway, the presence of downslope, and the width of curb lane were found to be random across observations. These variables were normally distributed and both mean and standard deviation for these variables were found statistically significant. The presence of downslope is normal distribution with mean value of 14.542 and standard deviation value of 1.219, which implies that almost 100 percent of the observations resulted in positive impact on satisfaction. The width of roadway and the number of lanes (multilane) are also normal distribution, the mean value and standard deviation value of them imply that 100 percent of the observations resulted in positive impact on satisfaction. This study provides a method that may offer reference for decision-makers or planners in Japan. They could consider using our method or variables when doing analysis or making decisions for achieving a higher cycling environment. The use of 360° videos is an emerging approach of this study when compared with field surveys and traditional video surveys. It can present a complete view of the site and its surroundings with high immersion. It also saves time and money because it does not require the participants to go to the field to take the survey. In particular, all the statistically significant variables we chose have data available. The road administrations in Japan have this data in a specific format. This method would allow planners and engineers to evaluate the satisfaction level of cyclists on various road segments and the results could be accessed on geographic information systems (GIS). We also expect growth in the data on cycling, for example, the volume of bicycles, and pavement condition. Especially data on the pavement condition, because it is an important variable affecting cyclists’ satisfaction (Landis et al., 1997). This growth in data would enable researchers to evaluate cyclists’ satisfaction more accurately. There are also some limitations to our study. First, such emerging approach perhaps need validation by a matched pairs experiment where a respondent is exposed both to the real world and the virtual environment. Then, the 360° videos can provide a virtual environment similar to the real world. However, the locations for our viewers were fixed, and viewers were limited to the frames captured by the camera. They also could not interact with the environment. Furthermore, in the 360° video environment, the participants viewed a flat video frame projected onto spherical 3D geometry. In other words, it provides a strong sense of presence but without a sense of distance. The immersive technology and 360° camera still require further development. With future advances in immersive technology and the 360° camera, there will be more worthwhile studies in the future. In addition, exposure to the immersive system could cause simulation sickness (Deb et al., 2017), which may induce discomfort and affect the rating. Thus, after the video survey, it is better to confirm the participants’ simulation sickness by simulator sickness questionnaire (SSQ) (Kennedy et al., 1993), which is a standardized method for measuring the effects of simulation sickness (Guna et al., 2018). The participants with high SSQ scores should be removed. 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