P.O. Box 653, Beer-Sheva 84105, Israel. b Department of Industrial ... Describing the route before rating it may lower its apparent complexity. Subjects'.
Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014
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Modeling route complexity ratings Schwartz-Chassidim Ha, Meyer Jb, Parmet Ya.
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Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105, Israel. b Department of Industrial Engineering, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel Abstract We develop a predictive model of the perceived complexity of routes in road maps, taking into account the properties of the road, the task and the map display. Sixty subjects ranked the complexity of 120 routes on scales between 0 and 10. Half of them described the route verbally before rating it. Subjects also completed a questionnaire about the influence of different variables on the route complexity. A linear regression model explained much of the dependent variable’s variance (R2 = 0.63). The number of turns and rotations, the perceived density of the map and route length were significant predictors. Describing the route before rating it may lower its apparent complexity. Subjects’ assessments of the contribution of different variables to perceptions of route complexity differed from the actual contribution of the variables in the models. The model of perceived route complexity can be used to design road maps that minimize the user’s cognitive load. Keywords-route, pereived complexity, scale, digital road map, model development
Copyright 2014 Human Factors and Ergonomics Society. DOI 10.1177/1541931214581354
INTRODUCTION Users' ability to comprehend and use information in maps is crucial for map design. Since the human visual system cannot process all available information simultaneously, it selects visual inputs, based on properties of the target and the distracters (Kim & Cave, 1995). Therefore the question what information to present on the map and how it should be shown and organized has become an important issue as part of the general topic of user-centered design of geographic information (Brown et al., 2013; McCrobie, 2000; Sharples et al., 2013). The complexity of a route along which a user plans to travel may require to view the route at both a large scale to see details and a small scale to see an overview. This can be achieved by adjusting the scale to the desired level of detail, which may require user interaction and attention. Wang et al. (2014) proposed an algorithm that provides quick access to meaningful large and small views, lowering the cost of obtaining information. Many factors affect the user when using a digital map for navigation, such as the complexity, clutter and the density of the displayed information (Heye & Timpf, 2003; Lohrenz et al., 2009; Rosenholtz et al., 2005; Schwartz-Chassidim et al., in press), the map orientation (Darken & Cevik, 1999), landmarks along the route (Darken & Peterson, 2001), and more. People are likely to base their routing decisions not only on the minimum distance, time or cost, but also on the perceived complexity of the route. Most existing studies deal with the route complexity using objective quantitative measures and do not try to predict user perceptions. For example, Wiener and Mallot (2006) studied the influence of path complexity, determined by the number of segments in a path, on landmark navigation and path integration. They showed a decrease in
response time and a slight increase in pointing accuracy with increasing path complexity. The complexity of public transportation routes is mainly affected by the complexity of the transfer points along the route, including the number of stops within the transfer point, the visibility of the stops, the distances between the stops, and the number of incoming and outgoing lines within the transfer point (Heye & Timpf, 2003). More complex routes led people to use more landmarks during navigation (Harrell & Hall-Hoffarth, 2000), and they were usually associated with longer search and reaction times (Horrey & Wickens, 2004; Srinivasan & Jovanis, 1997). In this study we aimed at predicting the perceived complexity of a route, based on properties of the route, the task and the map. METHOD Subjects Participants were 60 students (30 female, 30 male, ranging in age from 23 to 30 with an average of 25.84 with S.E. of 1.67 years) from Ben-Gurion University of the Negev. Apparatus The experiment took place in the Human Factors Laboratory at the Department of Industrial Engineering and Management at Ben-Gurion University. Sixty maps were extracted from ESRI maps & data kit including imagery, base maps, census data, street datasets, and geographic information from NAVTEQ, using the ArcView 9.2 application. The application supports various data manipulations, such as map display, drawing graphic features and text, identifying and selecting features, calculating statistics and rendering features. The application represents the scale in units of meter/pixel.
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Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014
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Experiment design The 60 maps were taken equally from rural, suburban and urban regions. In every map presented to the participants, a route (with a mean length of 684 ±264.61m) appeared between two points A and B. We chose short routes (~1 km), because longer routes would have required us to address issues of overlaps between map sections and transitions from one view to the next. While these issues are important, they are beyond the scope of the current study. For half of the participants the route went from A to B and for the other half it went in the opposite direction. Half of the participants evaluated the route from only viewing it (referred to as the “evaluation” condition) and the other half were instructed to evaluate the route complexity after they described to themselves the route (referred to as “virtual navigation”). Since participants were instructed to describe the road in their minds, without writing or saying the descriptions out loud, we could not verify for certain that they actually followed the instructions. However, analyses of the performance times showed significantly longer times for the virtual navigation task, probably due to the additional action of the description. In both tasks the term "route complexity" was not defined, and there was no time limit. The order of the experimental sessions was counterbalanced between subjects and maps. Each participant saw and evaluated randomly 120 maps - 60 maps at a large and 60 at a small scale (1.92 meter/pixel [1:6,500] and 3.84 meter/pixel [1:13,000], respectively. Scales were computed, assuming that each pixel on the screen is 3*10-4 meter wide).
(14) mental rotations along the route, and (15) open for participants’ comments. We hypothesized that the perceived complexity of the route will be affected by properties of the route and the overall map content:
Measures The independent variables were related to route properties, to the task and to the perceived density of the entire map, which was computed from the model developed in SchwartzChassidim et al. (in press). The route variables include the average junction size, route direction (vertical/horizontal) and specific direction: south to north and east to west or the opposite directions, route type (i.e., main or secondary), normalized values of the number of junctions, the number of turns and the rotations frequency (i.e., dividing the number of junctions, number of turns and the number of rotations by the route length) and the length of the route. The dependent variable was the user's ratings of the perceived complexity in each of the tasks. In addition, we asked in a questionnaire whether participants thought different route and map properties affected the perceived complexity of the whole route. The purpose was to compare the specific ratings of the different elements to the weight these elements received as predictors in the developed models. In this questionnaire participants rated the contribution of 14 specific elements of the map/route to the overall route complexity on a 1 to 10 scale: (1) number of junctions, (2) junction size, (3) distance between junctions, (4) route length, (5) number of curves (6) number of turns, (7) route type (main/secondary), (8) map scale, (9) terrain depicted in the map, (10) direction of driving, (11) orientation, (12) verbal description of the route, (13) text appearing on the route,
Statistics We first computed Pearson correlations between the variables to identify relations between them. We then conducted linear regression analyses to develop the models. When predictor variables are correlated as in our study (i.e., there exists multicollinearity), it is accepted to use the squared semi-partial correlation measure to assess the contribution of a variable. Thus we used it as part of the linear regression analysis. We analyzed questionnaire results with two-way ANOVAs.
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Route complexity will positively correlate with the number of turns and/or junctions and/or mental rotations in the route and/or the route length and/or the junction size
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Route complexity will negatively correlate with the average distance between junctions.
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A denser map will lead to a more complex appearing route.
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Route complexity for routes composed of main roads should seem lower than for routes composed of secondary roads.
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Task characteristics will affect perception, such that route complexity will decrease in the "virtual navigation" task, after the participant examines the route and its elements in more detail (compared to the "evaluation" task).
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Horizontal routes (going from left to right or right to left on the map) will be perceived as more complex than vertical routes, especially those going from the bottom of the map towards the top.
RESULTS There were significant high correlations between the perceived route complexities and all variables, except the route direction and the length of the route. The variables with the highest correlation with the perceived complexity of the route are the normalized number of turns, the number of junctions, and the number of mental rotations along the route. Several variables had significant high correlations with most other variables (e.g., the normalized number of junctions, the distance between junctions, route length, and map density). A backward linear regression with α = 0.05 for entry and α = 0.10 for removal on the eleven variables, including both tasks and all the independent variables (shown in Table 1 as stage 1), resulted in a relatively good prediction with an adjusted R2 =
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Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014
0.63. This model predicted the perceived complexity with 9 independent variables. We hypothesized that the task affects the perceived complexity of the route and the characteristics of the model, and we therefore included the interactions of the task with other variables in the model. The explanatory variables length of the route, task type, normalized number of turns and the perceived density of the whole map were strongly correlated with the perceived route complexity. However, their significance and relative contributions to the predictions differ. Although all routes were relatively short, route length was a significant predictor with greater complexity for longer routes. The normalized number of turns and rotations and junctions were significant predictors, such that larger numbers of turns, rotations and junctions along the route increase the route complexity. The perceived density of the whole map affected the perceived complexity of the route, such that denser maps increase route complexity. The task type affected the perceived route complexity, such that learning the route prior to the evaluation by virtual navigation, lowered the complexity. The variable vertical/horizontal is a predictor in the model with a negative beta values, meaning that a vertical route is perceived as less complex compared to a horizontal route. However, the specific direction of the route (i.e., east-west or south-north and vice versa) was not included in the model. The road type was a significant predictor in the model. Routes along main roads seemed less complex than those along secondary roads. The average size of the junctions, the average distance between junctions and the interaction between the task and the number of turns and rotations were not included in the models. Still the models include other variables that are related to the junctions (e.g., the normalized number of junctions, the distance between junctions). QUESTIONNAIRE RESULTS The objective of this part is to compare two types of participant evaluations: indirect and direct. The first evaluations were derived from the models developed in the previous stages using the standardized coefficients (β) of the predictor variables. The second evaluations are the direct ratings (resulting from conscious processes), based on the questionnaire results (using one-way ANOVAs). Results showed a main effect of the question type in the questionnaire, F(14,812)=23.07, p
