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).

     

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.     

莆田显惠侯神灵及其对船员的保护

著名的莆田祥应庙前身是当地白杜方氏家族专用的祠堂.庙里供奉的神灵显惠侯,当地的百姓把他视为农业生产及生命财产的保护神,同时,他也是航海者信奉的神灵.本文作者以周尾和朱纺这两人出海之前均到该庙膜拜的典型事例,分析指出上述二人的出海经商与白杜方氏家族存在着直接关系,可能是受雇于当时既从事农业又涉猎海上贸易的白杜方氏家族,因此宋时显惠侯只是一个地方性的神灵,只对某一部分人提供某些特定的庇护,并不具有普遍意义.     

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.

     

Evaluating seaport policy alternatives: a simulation study of terminal leasing policy and system performance

The competitive characteristics of the North American seaport industry have, in theory, been causally linked to excess investment and low terminal throughput productivity. This paper empirically examines the link between seaport authority container terminal leasing policy and throughput productivity. Recent advances in inventory theory applied to service industries suggest the pooling of demand uncertainty can yield lower costs without sacrificing customer service. This concept is applied to a container seaport using simulation. The effort compares key output measures from a realistic base case model of the seaport to those generated by an alternative terminal leasing policy. It is shown that the pooling of demand in a common-user seaport reduces total vessel time in port by 17.1% with no reduction in TEU throughput. Throughput productivity is improved for the highest capacity terminals, while lower capacity terminals are made available for additional users or alternative uses.     

开足马力 快乐一夏

带上它们,再开足游艇的马力，出海。 我相信你的脸上会浮现出灿烂的笑容。 我更相信，每一个游艇玩家，从内心深处，都是一个爱玩儿的孩子。     

The effects of marine cargo delays in law and commerce

With the development of door-to-door intermodal services and the advent of just-in-time supply systems, punctual delivery of cargoes is more than ever a priority both for shippers and for shipowners. This paper is about the incidence of delay in transportation, particularly in the marine mode, and its legal and commercial consequences.

The fundamental legal obligations of marine carriers have always included delivery with dispatch. Thus the law would seem to support the commercial expectations of shippers for reliable performance. The authors discuss the nature of these obligations, investigate their adequacy under modern conditions of trade and report on a survey of Canadian shippers and consignees.

The authors conclude that there appears to be a divergence in the toleration of delay, but that few of the Canadian companies suffering unacceptable delays received, or even sought, financial compensation and none of them planned to do more. The authors speculate about their reasons for absorbing the losses incurred through unacceptable delays. The evidence from the survey is that shippers, in spite of their dissatisfaction with the resolution or outcome of incidents of unacceptable delay, are loath to seek compensation through legal means, but instead prefer to resort to a range of commercial actions.     

The opening of the Amsterdam Ring Road

This paper presents the results of a major before-and-after study carried out to establish the short term effects of the removal of a severe bottleneck in the road network around Amsterdam. An important focus in the study was on measuring changes in the timing of travel, as well as changes in route choice, mode choice, destination choice and frequency of travel. The results of the study indicated that, in the short run, there was little or no change in mode choice, nor was there significant emergence of new induced trips. On the other hand, large shifts in time of travel as well as route choice were reported, emphasising the importance of alterations to the timing and routes of existing trips when congestion is relieved, and the need to consider the benefits these bring in evaluating the impact of any road investment.     

Evaluating seaport policy alternatives: a simulation study of terminal leasing policy and system performance

The competitive characteristics of the North American seaport industry have, in theory, been causally linked to excess investment and low terminal throughput productivity. This paper empirically examines the link between seaport authority container terminal leasing policy and throughput productivity. Recent advances in inventory theory applied to service industries suggest the pooling of demand uncertainty can yield lower costs without sacrificing customer service. This concept is applied to a container seaport using simulation. The effort compares key output measures from a realistic base case model of the seaport to those generated by an alternative terminal leasing policy. It is shown that the pooling of demand in a common-user seaport reduces total vessel time in port by 17.1% with no reduction in TEU throughput. Throughput productivity is improved for the highest capacity terminals, while lower capacity terminals are made available for additional users or alternative uses.