This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activity-based travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov–Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT).
Under the Alternative Motor Fuels Act (AMFA), vehicles that run on ethanol, methanol, or natural gas get extra credits in the calculation of Corporate Average Fuel Economy (CAFE). This paper uses hedonic techniques to examine the effect of production of alternative-fuel vehicles (AFVs) on the implicit price of fuel economy. This study finds that, after AFVs came to market, the marginal value of fuel economy from companies producing them decreased. This finding suggests that manufacturers who produced AFVs were willing to offer a lower price for fuel economy, because automakers had an additional way to achieve fuel economy standards beyond improving the fuel efficiency of conventional cars. These findings bolster the argument that a major role of the AMFA credit for AFVs is to allow automakers to increase their production of fuel-inefficient vehicles. 相似文献
In this paper, we present a Finite pointset method (FPM) for the numerical simulation of free surface flow around a ship in calm water. It is a Lagrangian and meshless particle scheme which is applied to the projection method for the incompressible governing equations. This requires the solution of Poisson problems in each time step, so a moving least squares (MLS) interpolants is used for the spatial derivatives in order to discretize the Poisson equation with pressure-Dirichlet condition of free surface flow in meshless structure. Meanwhile, an additional problem of the periodic particle locations redistribution in the present approach is still handled by MLS interpolants. With the proposed FPM technique, problems associated with the free surface flow around a ship are circumvented. A verification of numerical modeling is made using the Wigley hull and the validity of the proposed methodology is examined by comparing the detail of wave profile and wave-making resistance with Series 60 model. The results demonstrate that FPM is able to perform efficient and stable simulations of free surface flow around a ship. 相似文献