Elevated Desired Speed and Change in Desired Direction: Effects on Collective Pedestrian Flow Characteristics. Issue 1 (January 2015)

• Record Type: Journal Article
• Authors: Dias, Charitha; Sarvi, Majid; Ejtemai, Omid; Burd, Martin
• Is Part Of: Transportation research record. Volume 2490:Issue 1(2015)
• Page Start: 65
• Page End: 75
• Publication Date: 2015-01
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2490-08
• Languages: English
• ISSNs: 0361-1981

Abstract

Microscopic pedestrian simulation tools have gained increased attention and popularity in recent years. These are essential tools in planning and designing crowd-gathering places and public buildings. For increased reliability of such tools, they must be calibrated against reliable empirical data for a variety of situations. Detailed empirical studies could serve this purpose while providing insight into microscopic pedestrian flow characteristics under different conditions. A major gap in the knowledge is that no substantial research has examined the effects of elevated desired speeds and change in desired walking direction on collective behaviors of pedestrians. With experimental data collected under different walking conditions, this study evaluates the effects of elevated desired walking speed and change in desired walking direction on collective movements of pedestrians. Qualitative and quantitative empirical analyses suggest that larger deviations in microscopic and macroscopic characteristics can be expected at elevated desired speed levels compared with normal desired speed. Therefore, existing models that have been calibrated and validated for normal walking conditions may not be suitable for predicting collective behaviors when the desired walking speed is higher (e.g., evacuation, panic). Results obtained from these empirical studies could be beneficial for calibration and validation of the explanatory models so that those models could be applied to predict consequences under a wider range of situations.