Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Running economy and blood lactate accumulation in elite football players with high and low maximal aerobic power

The purpose was to determine running economy and lactate threshold among a selection of male elite football players with high and low aerobic power. Forty male elite football players from the highest Swedish division (“Allsvenskan”) participated in the study. In a test of running economy (RE) and blood lactate accumulation the participants ran four minutes each at 10, 12, 14, and 16 km•h-1 at horizontal level with one minute rest in between each four minutes interval. After the last sub-maximal speed level the participants got two minutes of rest before test of maximal oxygen uptake (VO2max). Players that had a maximal oxygen uptake lower than the average for the total population of 57.0 mL O2•kg-1•minute-1 were assigned to the low aerobic power group (LAP) (n=17). The players that had a VO2max equal to or higher than 57.0 mL O2•kg-1•minute-1 were selected for the high aerobic power group (HAP) (n=23). The VO2max was significantly different between the HAP and LAP group. The average RE, measured as oxygen uptake at 12, 14 and 16km•h-1 was significantly lower but the blood lactate concentration was significantly higher at 14 and 16 km•h-1 for theLAP group compared with the HAP group.