Optimization of drying performance considering driving conditions

Compressed air can be used as an energy source for brake systems in medium-heavy and heavy-duty commercial vehicles. The moisture in compressed air, which is due to high temperature and humidity, can be eliminated by using an air dryer. In this paper, drying performance data for a cartridge were obtained and used to develop a drying performance program, to predict the moisture and relative humidity in the air tanks of vehicles. The on-load time, off-load time, air flow, duty cycle, humidity and dew point temperature were calculated according to air consumption. The validity of the program was verified, and it was shown to be able to predict humidity changes in the air tank. The air tank capacity was increased from 100 to 130 to reduce the duty cycle. Therefore, the regeneration rate decreased from 18% to 15%, but the dew point depression temperature (ΔT) remained above 30°C. The duty cycle decreased from 50% to 43%, and the total operation time and power consumption of the air compressor were reduced. In conclusion, fuel savings were obtained by changing the parameters to optimize the system.     

A route guidance system considering travel time unreliability

Under a stochastic roadway, drivers need a route guidance system incorporating travel time variability. To recommend a customized path depending on the trip purpose and the driver’s risk-taking behavior, various path ranking methods have been developed. Unlike those methods, our proposed disutility method can easily incorporate a target arrival time in the ranking process by measuring how late the travel is and by penalizing it depending on the severity of lateness. In addition, the disutility-based route guidance system can properly address travel time unreliability that causes unacceptable disruptions to the driver’s schedule (i.e., unexpected long delay). We compare the disutility-based path ranking method with other ranking methods, the percentile travel time, the mean excess travel time, and the on-time arrival probability. We show that the disutility has stronger discriminating power and requires less solution space to find an optimal path. The most important advantage is that it can estimate a driver’s risk-taking behavior for each trip purpose by using the discrete choice analysis. We construct a simulation framework to acquire the travel time data on a hypothetical roadway. We analyze the data and show how various ranking methods recommend a customized path. Using the data, we show the advantage of the disutiltiy method over the other methods, which is generating a customized path with respect to the target arrival time by properly penalizing the travel time lateness.     

Classification modeling approach for vehicle dynamics model‐integrated traffic simulation assessing surrogate safety

To assess safety impacts of untried traffic control strategies, an earlier study developed a vehicle dynamics model‐integrated (i.e., VISSIM‐CarSim‐SSAM) simulation approach and evaluated its performance using surrogate safety measures. Although the study found that the integrated simulation approach was a superior alternative to existing approaches in assessing surrogate safety, the computation time required for the implementation of the integrated simulation approach prevents it from using it in practice. Thus, this study developed and evaluated two types of models that could replace the integrated simulation approach with much faster computation time, feasible for real‐time implementation. The two models are as follows: (i) a statistical model (i.e., logit model) and (ii) a nonparametric approach (i.e., artificial neural network). The logit model and the neural network model were developed and trained on the basis of three simulation data sets obtained from the VISSIM‐CarSim‐SSAM integrated simulation approach, and their performances were compared in terms of the prediction accuracy. These two models were evaluated using six new simulation data sets. The results indicated that the neural network approach showing 97.7% prediction accuracy was superior to the logit model with 85.9% prediction accuracy. In addition, the correlation analysis results between the traffic conflicts obtained from the neural network approach and the actual traffic crash data collected in the field indicated a statistically significant relationship (i.e., 0.68 correlation coefficient) between them. This correlation strength is higher than that of the VISSIM only (i.e., the state of practice) simulation approach. The study results indicated that the neural network approach is not only a time‐efficient way to implementing the VISSIM‐CarSim‐SSAM integrated simulation but also a superior alternative in assessing surrogate safety. Copyright © 2014 John Wiley & Sons, Ltd.