Daily activity and travel sequences of students,faculty and staff at a large Canadian university

This paper examines the activity engagement, sequencing and timing of activities for student, faculty and staff commuter groups at the largest university in the Maritime Provinces of Canada. The daily activity patterns of all university community groups are modeled using the classification and regression tree classifier algorithm. The data used for this study are derived from the Environmentally Aware Travel Diary Survey (EnACT) conducted in spring 2016 at Dalhousie University, Nova Scotia. Results show that there are significant differences in activity and travel behavior between university population segments and the general population in the region, and between campus groups. For example, students participate in more recreation activities compared to faculty and staff. They also take more trips to and from campus, and are more flexible in their scheduling of trips. The insights gained from this study will provide helpful information for promoting sustainability across university campuses, and for development of campus-based travel demand management strategies.     

A time-use activity-pattern recognition model for activity-based travel demand modeling

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).

     

Trip chaining propensity and tour mode choice of out-of-home workers: evidence from a mid-sized Canadian city

Suburban development patterns, flexible work hours, and increasing participation in out-of-home activities are making the travel patterns of individuals more complex, and complex trip chaining could be a major barrier to the shift from drive-alone to public transport. This study introduces a cohort-based approach to analyse trip tour behaviors, in order to better understand and model their relationships to socio-demographics, trip attributes, and land use patterns. Specifically, it employs worker population cohorts with homogenous activity patterns to explore differences and similarities in tour frequency, trip chaining, and tour mode choices, all of which are required for travel demand modeling. The paper shows how modeling of these important tour variables may be improved, for integration into an activity-based modeling framework. Using data from the Space–Time Activity Research (STAR) survey for Halifax, Canada, five clusters of workers were identified from their activity travel patterns. These were labeled as extended workers, 8 to 4 workers, shorter work-day workers, 7 to 3 workers, and 9 to 5 workers. The number of home-based tours per day for all clusters were modeled using a Poisson regression model. Trip chaining was then modeled using an Ordered Probit model, and tour mode choice was modeled using a Multinomial logit (MNL) model. Statistical analysis showed that socio-demographic characteristics and tour attributes are significant predictors of travel behavior, consistent with existing literature. Urban form characteristics also have a significant influence on non-workers’ travel behavior and tour complexity. The findings of this study will assist in the future evaluation of transportation projects, and in land-use policymaking.

     

A numerical transport model for predicting the distributions of Cd, Cu, Ni, Pb and Zn in the southern North Sea: the sensitivity of model results to the uncertainties in the magnitudes of metal inputs

A new transport model for metals (named NOSTRADAMUS) has been developed to predict concentrations and distributions of Cd, Cu, Ni, Pb and Zn in the southern North Sea. NOSTRADAMUS is comprised of components for water, inorganic and organic suspended particulate matter transport; a primary production module contributes to the latter component. Metal exchange between dissolved (water) and total suspended particulate matter (inorganic + organic) phases is driven by distribution coefficients. Transport is based on an existent 2-D vertically integrated model, incorporating a 35 × 35 km grid. NOSTRADAMUS is largely driven by data obtained during the Natural Environment Research Council North Sea Project (NERC NSP). The sensitivity of model predictions to uncertainties in the magnitudes of metal inputs has been tested. Results are reported for a winter period (January 1989) when plankton production was low. Simulated ranges in concentrations in regions influenced by the largest inflows, i.e. the NE English coast and the Southern Bight, are similar to the ranges in the errors of the concentrations estimated at the northern and southern open sea boundaries of the model. Inclusion of uncertainties with respect to atmospheric (up to ± 54%) and riverine (± 30%) inputs makes little difference to the calculated concentrations of both dissolved and particulate fractions within the southern North Sea. When all the errors associated with the inputs are included there is good agreement between computed and observed concentrations, and that for dissolved and particulate Cd, Cu and Zn, and dissolved Ni and Pb, many of the observations fall within, or are close to, the range of values generated by the model. For particulate Pb, model simulations predict concentrations of the right order, but do not reproduce the large scatter in actual concentrations, with simulated concentrations showing a bias towards lower values compared to those observed. A factor which could have contributed to observed concentrations, and which is not included in the model, is considered to be a substantial benthic input of dissolved lead during this winter period, coupled to a rapid and extensive scavenging of the dissolved lead to particles. Significant reductions in riverine and aeolian inputs of total Cd and Cu of 70% and 50%, respectively, consistent with aims of North Sea Conferences, are predicted to lead to minor decreases (~ 10%) in water column concentrations of dissolved and particulate Cd and Cu, except near river sources, where maximum reductions of ~ 30–40% may occur.