By Yueguo Chen, Wolf-Tilo Balke, Jianliang Xu, Wei Xu, Peiquan Jin, Xin Lin, Tiffany Tang, Eenjun Hwang
This booklet constitutes the refereed court cases of five workshops of the fifteenth overseas convention on Web-Age details administration, WAIM 2014, held in Macau, China, June 16-18, 2014.
The 38 revised complete papers are prepared in topical sections at the five following workshops: moment overseas Workshop on Emergency administration in enormous info Age, BigEM 2014; moment overseas Workshop on giant information administration on rising undefined, HardBD 2014; foreign Workshop on facts administration for Next-Generation Location-based providers, DaNoS 2014; overseas Workshop on Human elements of constructing thoughts in Social Ubiquitous Networking atmosphere, HRSUME 2014; overseas Workshop on tremendous facts platforms and providers, BIDASYS 2014.
Read Online or Download Web-Age Information Management: WAIM 2014 International Workshops: BigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Macau, China, June 16-18, 2014, Revised Selected Papers PDF
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Extra resources for Web-Age Information Management: WAIM 2014 International Workshops: BigEM, HardBD, DaNoS, HRSUNE, BIDASYS, Macau, China, June 16-18, 2014, Revised Selected Papers
Example text
Because τ and θ have a small range of changes, and VM n is usually only affected by the road, the only parameters that needs to be calibrated are An, Bn and b nÀ1 . These are the three parameters that form a vector and is used to describe driving B behavior: È É ^ nÀ1 ; dn;t ¼ An ; Bn ; B ð5Þ Because the driving behavior could be very diverse, in normal states the data set dn,t is not exclusive. The set of driving behaviors that could arise in the standard state is defined as DN ¼ fdn;t g. The set needs to input all of the data sets of driving behaviors which appeared in the normal situation, forming a spatiotemporal database for normal driving behaviors, as Table 1 has shown.
In the car-following model, Sn describes the vehicle’s motion in a micro state. This paper is based on the Gipps’ model [8], whose form is as follows: s vn 1=2 Ã mn ¼ min mn þ 2:5An s 1 À M ; ÀBn þ h ; 2 Vn ð4Þ 1=2 ! 2 s vnÀ1 þ B2n þ h þBn 2gn À svn þ ^ 2 BnÀ1 where τ is the reaction time, θ is the safety margin time, VM n is the speed limit of vehicle b nÀ1 is the n, An is the largest acceleration of vehicle n, Bn is the actual braking, and B perceived braking. A Cross-Simulation Method for Large-Scale Traffic Evacuation with Big Data 17 Through the data in sn,t , the Gipps’ model would be able to calculate the new velocity of the car v*n in the next time step sn,t+τ.
In emergencies the distribution of driving behaviors are different from those in normal situations. 18 S. Yuan et al. If the above stands correct, the outcome of the process of Acquisition DN could be adapted into the Analysis process, which is divided into three steps: Step 1: Categorize the spatiotemporal data of driving behaviors using time. For example, using thirty minutes as an interval to categorize the data. ^ nÀ1 g, divide the space of driving Step 2: Set tolerance rate Dd ¼ fDAn ; DBn ; DB behavior by Dd into multiple blocks Dm;t .