Background: The explosive growth of social media has rendered them powerful communication channels. User generated content is an important source of inspiration and influence among web friends, it generates new activities and consequently affects mobility decisions. Whether to visit a place, or how to get to a place of interest are decisions that can be triggered through people's interactions on social media. Objective: The main objective of this paper is to investigate the influence of social media use on activity planning and travel arrangements for women and men. Methods: An online survey was conducted to examine the social media use and the impact of the shared content for women and men, on the phase before any activity in an urban environment. Inferential statistics were applied to detect gender differences in a sample size comprised of 804 respondents. Result: The significant results showed that the variables gender and social media use for activity planning and travel arrangements are associated with each other. Results have also indicated that the influence of reviews and ratings, photos/ videos and proposed transport mode on activity planning is gender dependent. Photos/ videos influence more often both women (m=3.47) and men (m=3.00) than reviews and ratings (m=3.21 for women and 2.94 for men). Both these contents influence more than proposed transport mode (m=2.62 and 2.37 for women and men).
Conclusion:The analysis of the results indicated that before an activity, both women and men tend to use majorly social media for activity planning and travel arrangements, while photos/videos influence women's decisions more often than men.Travel arrangements of the majority of respondents would be influenced by a post of a designated account related to transport. Finally, social media use affects travel arrangements of both women and men more before performing an activity rather than during.
This article analyzes the use of Bluetooth-based travel times, for Automatic Incident Detection (AID) purposes. Automatic incident messages were derived for simulated data through the use of an AID algorithm, which was developed by Technical University of Munich (TUM). A Vissim model of a 15 kilometre section of A9 motorway in Germany was set up, where different scenarios of traffic situation, incidents and detector layout were introduced and travel times were generated, processed and then run through the TUM algorithm. The performance measures Detection Rate (DR), False Alarm Rate (FAR) and Mean Time To Detect (MTTD) were used for the analysis of the incident messages' quality of the simulated data and compared for every incident scenario. Local data were also generated in the Vissim model and used by VKDiff algorithm for incident detection. A comparison of the quality of the incident messages of both TUM and VKDiff algorithm was conducted.
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