This paper examines the daily activity-travel behavior of undergraduate students, graduate students, faculty, and staff at a large university to better understand their activity travel demands for automobile, active transportation (AT), and transit trips. The data were derived from the first university-based activity travel diary survey [Environmentally Aware Travel Diary Survey (EnACT)] conducted in spring 2016 at Dalhousie University, Canada. Results show that students reside nearer to the campus than faculty and staff, and their trip length from home to school is significantly shorter. Zero-inflated negative binomial (ZINB) models show that if housing tenure is less than 1 year, it is more likely that the traveler will make more AT trips but fewer automobile trips. It indicates that individuals living far from the campus will produce fewer transit trips than those who live nearer. Interestingly, the mean number of AT trips per day for the overall sample is more than one trip, whereas for automobile trips and transit trips it is less than one. The behavioral analysis and empirical models provide useful insights that can be utilized to represent university populations in regional travel demand models, as well as to develop campus-based travel demand management (TDM) strategies.

Marine oil spill response operations could generate a large volume of liquid oily wastes (e.g., emulsified oil, non-emulsified oil, and wastewater) that can be up to 30 to 40 times greater than the original volume of spilled oil. Oil decanting technologies are used globally for recovering spilled oil and handling liquid wastes. Canada follows the standards set out in the MARPOL 73/78 Annex 1 International regulations in most areas, with more strict discharge requirements in certain locations. For instance, inland waters discharge should not exceed 5 ppm, and in special areas, such as the Great Lakes, the discharge standard is under 0 ppm. In the event of an oil spill, decanted seawater should be barged to shore for disposal, which significantly constrains the response capacity and efficiency of oil recovery by skimmers due to limited temporary storage space in barges and the long time and high cost of transportation. This has become one of the greatest challenges the Canadian governments and oil spill response industries are facing in Canada. Moreover, when the spill response team decides that decanting is an appropriate way to handle the spilled oil, the approval process may take a long time, which negatively impacts the spill that has already occurred. Moreover, Canada uses a 10,000-tonne planning standard for oil spill preparedness, whereas the United States uses a worst-case scenario, and Europe uses a 60,000 m³ planning standard. The existing planning threshold in Canada can cause the country to be not fully prepared when it comes to responding to a very large oil spill if one should occur. This study conducted a comprehensive review of the current Canadian oil spill response system and framework, regulations, roles and responsibilities of federal and provincial governments, existing decanting capabilities, and capacities of Canadian oil spill responders. More importantly, this study identified the gaps in the current oil spill response system and regulatory and technological barriers to oil decanting. Marine oil decanting regulations and practices in the US and selected European countries have also been reviewed to support addressing the challenges and improving the Canadian experience. It is expected that this study would help the stakeholders and professionals to better understand the oil spill response system and oil decanting status quo in Canada and facilitate Canadian governments and industries to better address the challenges in oil spill regulations and practices.

Understanding the travel behavior of individuals grouped by similar time-use activity patterns can contribute greatly to modeling regional spatial and temporal patterns of transport demand. In this paper, we present a comprehensive modeling framework to forecast and replicate individuals’ travel behavior, labeled as the Scheduler for Activities, Locations, and Travel (SALT). The prototype version of the SALT framework comprises a series of modules that employ behaviorally-based econometric, machine-learning, and data-mining techniques. The SALT model is cross-validated with 30% of the out-of-home sample survey data from the large Halifax Space Time Activity Research (STAR) household survey. Results show that the SALT scheduling model is able to assemble the travelers’ 24-hour schedules with an average 82% accuracy compared to the observed data. The proposed simulation modeling framework is useful for deeper understanding of individuals’ activity-travel decisions and may be utilized to examine sensitive policy issues such as transportation control measures and congestion-pricing.

Large universities can be considered as special trip generators requiring special Traffic Demand Management (TDM) policies. Due to their unique accessibility, mixed land-use, and welcoming environment for alternative modes, they are well placed to promote active transportation, carpooling, parking management, and shuttle buses. These have direct effects on regional traffic, as well as on the total vehicular emissions of Greenhouse Gases (GHG) and other pollutants. This paper estimates transport-related pollution emissions for university population segments at Dalhousie University, Nova Scotia, comprising undergraduate-students, graduate students, faculty members, and staff. The data used for this study are derived from the Environmentally Aware Commuter Travel Diary Survey (EnACT) conducted in spring 2016. Initially, we generate a 100% synthetic population for the entirety of university commuters. Then, we model transport-related polluting emissions, including carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxide (NO_x), particulate matter (PM₁₀ and PM_2.5), total hydrocarbon (THC), and volatile organic compounds (VOC), based on the population features and their living zones in relation to campus areas. In this study, five zones are described for emission estimation: on-campus, inner city, suburban area, inner commuter belt, and outer commuter belt. In addition, two emission scenarios are tested, which consider the impacts of changes to transit ridership and auto driving. These scenarios demonstrate how changing the primary travel mode can greatly influence emissions volume. The empirical models provide useful insights that can be utilized to improve TDM policies of university campuses, as well as to analyze environmental mitigation scenarios.

In the past three decades, activity-based travel demand models have received much consideration and seen significant progress. Activity-based models of travel demand belong to the third generation of travel demand models and are based on the concept that travel is a derived demand, originating from the need of individuals to participate in activities. Activity-based travel demand models are able to better replicate travel decisions at the individual or disaggregated level, and may therefore yield better predictions of future travel patterns. The current paper presents a comprehensive overview of recent and ongoing computational-based activity scheduling models. For comparison, a brief account of aggregated trip-generation approaches to travel demand is also presented. The advantages of activity-based travel demand modeling, and a discussion of how such modeling may be applied in practice, are provided. Finally, some of the unsolved issues and directions for further research are identified.

This study aims to investigate the mode choices and housing location choices for university population segments (undergraduate students, graduate students, faculty, and staff) using comprehensive statistical analyses and bivariate probit models. The data used in this study are derived from a web-based online survey conducted at Dalhousie University, Canada, in April 2016. Respondents who live within 1.0 km from their university campus choose to walk or bike for their commuting trips to campus. With an increase in commuting distance, the choice of using motorized vehicles and carpooling increases. Most importantly, it is found from the bivariate probit models that housing location choices and mode choices are joint decisions. The choice of living more than 1.0 km from the campus significantly reduces the choice of active transportation (AT) and significantly increases the choice of motorized vehicle for commute trips. This study provides in-depth analyses which can be further utilized for integrated housing-transportation planning and group-sensitive policies to promote sustainable commuting to campus.