Capturing trade-offs between daily scheduling choices. (June 2022)

• Record Type: Journal Article
• Authors: Pougala, Janody; Hillel, Tim; Bierlaire, Michel
• Is Part Of: Journal of choice modelling. Volume 43(2022)
• Publication Date: 2022-06
• Subjects: Activity-based modelling -- Daily scheduling behaviour -- Mobility -- Mixed-integer optimisation -- Random utility maximisation; Decision making -- Periodicals; Social choice -- Periodicals; Decision making; Social choice; Periodicals; 302.13
• DOI: 10.1016/j.jocm.2022.100354
• Languages: English
• ISSNs: 1755-5345

Abstract

Abstract: We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The fundamental behavioural principle behind our approach is that individuals schedule their day to maximise their overall derived utility from the activities they complete, according to their individual needs, constraints, and preferences. By combining multiple choices into a single optimisation problem, our framework is able to capture the complex trade-offs between scheduling decisions for multiple activities. These trade-offs could include how spending longer in one activity will reduce the time-availability for other activities or how the order of activities determines the travel-times. The implemented framework takes as input a set of considered activities, with associated locations and travel modes, and uses these to produce empirical distributions of individual schedules from which different daily schedules can be drawn. The model is illustrated using historic trip diary data from the Swiss Mobility and Transport Microcensus. The results demonstrate the ability of the proposed framework to generate complex and realistic distributions of starting time and duration for different activities within the tight time constraints. The generated schedules are then compared to the aggregate distributions from the historical data to demonstrate the feasibility and flexibility of our approach.