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This paper aims to develop a new approach to investigating the relationships between people's perceptions and physical components of sidewalk environments. A psychological survey composed of semantic differential items was administered to 112 participants in order to assess their perceptions of 20 sidewalk environments in Iksan city, South Korea. A field survey of the selected sidewalks was conducted to survey the physical components of the sidewalk environments. Because conventional statistical methods are not appropriate owing to the qualitative data, small sample size, and uncertainty, a new approach based on an artificial intelligence technique—rough sets theory—is applied to deal with the collected data. The application of the rough sets theory outputs the most important attributes of people's perceptions, minimal attribute sets without redundancy, and a series of decision rules that represent the relationships between perceptions and physical components of sidewalk environments. The analytical approach helps to understand better people's perceptions to sidewalk environments in a small city and then to establish a useful and constructive ground of discussion for walking environment design and management.

Traffic accidents at signalized intersections are influenced by many environmental factors, including site layout, traffic volume, and signal operation. However, most traffic accidents are attributable to driver behavior in response to the environment, because responses to the environment vary with drivers’ individual characteristics, as observed in the concept of a dilemma zone (DZ) at intersection approaches. For DZ protection, the installation of traffic signals closer to the stop line (i.e., before the intersection) has been proposed in South Korea. Although such proposals have been implemented experimentally, the actual effect of this measure on driver behavior has not yet been investigated. A discrete choice model is used to identify the factors that influence drivers’ crossing and stopping behaviors at signalized intersections, under the assumption that traffic safety is at risk if the influence of the signal location is ignored. Two analyses are presented to assess the impact of signal location on driver behavior at intersections. Violations of stop line crossing behavior and remaining time to stop line are examined, and two types of driver behavior models are presented that address the effects of signal location and other factors. The results consistently reveal that traffic signal location affects drivers’ stopping behavior, suggesting that traffic signal location can jeopardize road safety and therefore should be considered in intersection design.

The aim of this paper is to offer a contribution to the study of the relationship between the physical elements of a sidewalk and people's perception of it by applying the rough sets approach. In previous studies in this context statistical methods such as variance analysis and regression analysis are the most used techniques, and these suffer from the limitations of large sample sizes and rigorous statistical assumptions. However, the rough sets approach, as a nonparametric method of artificial intelligence, can handle this problem. In this study, twenty civil-engineering students and twenty noncivil-engineering students took part in an image preference survey assessing photographs of sidewalks. Then the rough sets approach was conducted by a four-step procedure to analyze the survey results. Finally, the output of the rough sets approach is used to express people's perception of good and bad features of sidewalk space. The results will hopefully help planners to make effective decisions about sidewalk features.

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