Cruising for parking: New empirical evidence and influential factors on cruising time

The goal of this study is to explore the perceptions and behaviors of drivers who cruise for parking. We conducted surveys with drivers in Brisbane, Australia, to understand potential factors that influence drivers’ cruising behavior. This study reveals that errors in drivers’ perception of parking cost are one of the leading factors encouraging drivers to cruise for on-street parking. Drivers are not necessarily well informed about parking costs, even when they claim to be familiar with these costs. The survey also reveals that the more informed drivers are about the local traffic and parking conditions, the less likely they are to cruise for extended periods of time. This finding demonstrates the value of traffic and parking information to effectively mitigate cruising for parking. The interview results also demonstrate that the on-street parking premium (i.e., accessibility or convenience factor) could be much larger than our common assumptions and a significant contributor to increased cruising time. Finally, this study introduces the sunk cruising cost and its potential impact on cruising time. Our hypothesis is that the effect of the sunk cost may manifest in a greater tendency for drivers to continue cruising because the time spent cruising is simply unrecoverable past expenditure. The survey data supports our hypothesis, and with findings on the drivers’ misperception about parking cost and the familiarity factor, this result highlights the value of accurate and timely parking cost and availability of information to drivers to tackle the cruising-for-parking issue

Information Management in the Built Environment

This chapter sought to answer the questions: Why is there a reluctance to share a common data environment and to collaborate in bringing together data that can be analysed and used for the efficient operation of the facility? What are the inhibitors to the wide-ranging collection of data and its use as information in the construction industry? What are the preconditions necessary for a change in such working practices and attitudes and that will enable integrated information management and data collection? We suggested answers throughout the chapter and summarise our response as follows. The construction and greater built environment suffers from a lack of integrated information that can enable better decision-making for all parties involved across different projects. There are many reasons that can create this phenomenon but lack of business alignment and benefit realisation are immediate concerns

Prior storm experience moderates water surge perception and risk

Background How accurately do people perceive extreme water speeds and how does their perception affect perceived risk? Prior research has focused on the characteristics of moving water that can reduce human stability or balance. The current research presents the first experiment on people's perceptions of risk and moving water at different speeds and depths. Methods Using a randomized within-person 2 (water depth: 0.45, 0.90 m) ×3 (water speed: 0.4, 0.8, 1.2 m/s) experiment, we immersed 76 people in moving water and asked them to estimate water speed and the risk they felt. Results Multilevel modeling showed that people increasingly overestimated water speeds as actual water speeds increased or as water depth increased. Water speed perceptions mediated the direct positive relationship between actual water speeds and perceptions of risk; the faster the moving water, the greater the perceived risk. Participants' prior experience with rip currents and tropical cyclones moderated the strength of the actual–perceived water speed relationship; consequently, mediation was stronger for people who had experienced no rip currents or fewer storms. Conclusions These findings provide a clearer understanding of water speed and risk perception, which may help communicate the risks associated with anticipated floods and tropical cyclones

Simple effects: Wind perception as a function of actual wind speed.

<p><i>Note. N</i>s = 76 participants, 454 observations (2 data points missing due to procedural error).</p>*<p><i>p</i><.05.</p

Wind Speed Perception and Risk

<div><h3>Background</h3><p>How accurately do people perceive extreme wind speeds and how does that perception affect the perceived risk? Prior research on human–wind interaction has focused on comfort levels in urban settings or knock-down thresholds. No systematic experimental research has attempted to assess people's ability to estimate extreme wind speeds and perceptions of their associated risks.</p> <h3>Method</h3><p>We exposed 76 people to 10, 20, 30, 40, 50, and 60 mph (4.5, 8.9, 13.4, 17.9, 22.3, and 26.8 m/s) winds in randomized orders and asked them to estimate wind speed and the corresponding risk they felt.</p> <h3>Results</h3><p>Multilevel modeling showed that people were accurate at lower wind speeds but overestimated wind speeds at higher levels. Wind speed perceptions mediated the direct relationship between actual wind speeds and perceptions of risk (i.e., the greater the perceived wind speed, the greater the perceived risk). The number of tropical cyclones people had experienced moderated the strength of the actual–perceived wind speed relationship; consequently, mediation was stronger for people who had experienced fewer storms.</p> <h3>Conclusion</h3><p>These findings provide a clearer understanding of wind and risk perception, which can aid development of public policy solutions toward communicating the severity and risks associated with natural disasters.</p> </div

Multilevel modeling results for perceived wind speed as a function of actual wind speed.

<p>Thin gray lines represent individual predicted curves for 76 participants. Thick black line represents the average curve. Thin black line represents a one-to-one relationship.</p

Perceived wind speed as a function of actual wind speed: Simple slopes.

<p>Examples of simple slopes tangent to the average curve (thick solid line) at 20 (dotted line) and 50 (dashed line) mph (8.9 and 22.3 m/s). Slopes are shown in reference to a one-to-one relationship (thin solid line).</p

Descriptive statistics for wind and risk perceptions by actual wind speed.

<p><i>Note.</i> Nesting <i>not</i> taken into account for this table; data averaged across persons rather than examining data within persons. Skew. = Skewness. Exc. Kurt. = Excess Kurtosis. <i>r</i> = correlation between wind perceptions and risk perceptions. <i>N</i>s = 76 participants, 454 observations (2 data points missing due to procedural error).</p>*<p><i>p</i><.05.</p

Perceived wind speed as a function of actual wind speed and number of storms experienced.

<p>Perceived wind speed as a function of actual wind speed and number of storms experienced.</p

Perceived wind speed as a function of actual wind speed.

<p>Point estimates and 95% confidence intervals are shown for the average slope for each wind speed tested.</p