The identification of activity locations in con- tinuous GPS trajectories is an essential preliminary step in obtaining person trip data and for activity-based trans- portation demand forecasting. In this research, a ...The identification of activity locations in con- tinuous GPS trajectories is an essential preliminary step in obtaining person trip data and for activity-based trans- portation demand forecasting. In this research, a two-step methodology for identifying activity stop locations is pro- posed. In the first step, an improved density-based spatial clustering of applications with noise （DBSCAN） algorithm identifies stop points and moving points; then in the second step, the support vector machines （SVMs） method distin- guishes activity stops from non-activity stops among the identified stop points. A time sequence constraint and a direction change constraint are applied as improvements to DBSCAN （yielding an improved algorithm known as C-DBSCAN）. Then three major features are extracted for use in the SVMs method： stop duration, mean distance to the centroid of a cluster of points at a stop location, and the shorter of distances from current location to home and to the workplace. The proposed methodology was tested using GPS data collected from mobile phones in the Nagoya area of Japan. The C-DBSCAN algorithm achieves an accuracy of 90 % in identifying stop points in the first step, while the SVMs method is 96 % accurate in distin- guishing the locations of activity stops from non-activity stops in the second step. Compared to other variants of DBSCAN used to identify activity locations from GPS trajectories, this two-step method is generally superior.展开更多
The standard ordered response model (SORM) is a common disaggregate approach with ordered outcomes in which the effects of various exogenous attributes are assumed constant across ordinal choices. In this study, an in...The standard ordered response model (SORM) is a common disaggregate approach with ordered outcomes in which the effects of various exogenous attributes are assumed constant across ordinal choices. In this study, an innovative latent class based generalized ordered response model (LC-GORM) is formulated and used to assess the effects of various factors on respondents’ choice behavior with respect to congestion charge proposal for Jakarta, Indonesia. The proposed model probabilistically assigns respondents into selfish and altruistic class memberships (latently) based on their knowledge of the proposed scheme and their specific attributes. Aiming to capture observable preference heterogeneity across ordinal choices and allow the thresholds to be varied across observations, we parameterize the thresholds as a linear function of the exogenous variables for each ordinal preference. Using stated preference data collected in Jakarta in December 2013, we incorporate the influence of a comprehensive set of explanatory variables into four categories: charges, latent variables related to respondent’s psychological motivations, mobility attributes and socio-demographic characteristics. Empirical results obviously verify the existence of preference heterogeneity across outcomes. The findings confirm that the altruistic class are more sensitive with respect to acceptance of the scheme, while the selfish class are more sensitive with respect to rejection. The key factors influencing public acceptability include the charge level and respondent variables such as car dependency, awareness of the problem of cars in society, frequency of visits to the city center and frequency of private mode usage.展开更多
基金supported by Grant-in-Aid for Scientific Research(No.25630215 and 26220906)from the Ministry of Education,Culture,Sports,Science,and Technology,Japanthe Japan Society for the Promotion of Science
文摘The identification of activity locations in con- tinuous GPS trajectories is an essential preliminary step in obtaining person trip data and for activity-based trans- portation demand forecasting. In this research, a two-step methodology for identifying activity stop locations is pro- posed. In the first step, an improved density-based spatial clustering of applications with noise （DBSCAN） algorithm identifies stop points and moving points; then in the second step, the support vector machines （SVMs） method distin- guishes activity stops from non-activity stops among the identified stop points. A time sequence constraint and a direction change constraint are applied as improvements to DBSCAN （yielding an improved algorithm known as C-DBSCAN）. Then three major features are extracted for use in the SVMs method： stop duration, mean distance to the centroid of a cluster of points at a stop location, and the shorter of distances from current location to home and to the workplace. The proposed methodology was tested using GPS data collected from mobile phones in the Nagoya area of Japan. The C-DBSCAN algorithm achieves an accuracy of 90 % in identifying stop points in the first step, while the SVMs method is 96 % accurate in distin- guishing the locations of activity stops from non-activity stops in the second step. Compared to other variants of DBSCAN used to identify activity locations from GPS trajectories, this two-step method is generally superior.
文摘The standard ordered response model (SORM) is a common disaggregate approach with ordered outcomes in which the effects of various exogenous attributes are assumed constant across ordinal choices. In this study, an innovative latent class based generalized ordered response model (LC-GORM) is formulated and used to assess the effects of various factors on respondents’ choice behavior with respect to congestion charge proposal for Jakarta, Indonesia. The proposed model probabilistically assigns respondents into selfish and altruistic class memberships (latently) based on their knowledge of the proposed scheme and their specific attributes. Aiming to capture observable preference heterogeneity across ordinal choices and allow the thresholds to be varied across observations, we parameterize the thresholds as a linear function of the exogenous variables for each ordinal preference. Using stated preference data collected in Jakarta in December 2013, we incorporate the influence of a comprehensive set of explanatory variables into four categories: charges, latent variables related to respondent’s psychological motivations, mobility attributes and socio-demographic characteristics. Empirical results obviously verify the existence of preference heterogeneity across outcomes. The findings confirm that the altruistic class are more sensitive with respect to acceptance of the scheme, while the selfish class are more sensitive with respect to rejection. The key factors influencing public acceptability include the charge level and respondent variables such as car dependency, awareness of the problem of cars in society, frequency of visits to the city center and frequency of private mode usage.
