Assessment of the activity scheduling optimization method using real travel data

Bladimir Toaza () and Domokos Esztergár-Kiss ()
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Bladimir Toaza: Budapest University of Technology and Economics
Domokos Esztergár-Kiss: Budapest University of Technology and Economics

Transportation, 2025, vol. 52, issue 4, No 5, 1319-1348

Abstract: Abstract New mobility services are appearing with the support of technological developments. Part of them is related to activity scheduling of individuals and the optimization of their travel patterns. A novel method called Activity Chain Optimization (ACO) is an application of the Traveling Salesman Problem with Time Windows (TSP-TW) extended with additional assumptions about temporal and spatial flexibility of the activities, where the travelers can optimize the total travel time of their daily activity schedule. This paper aims to apply the ACO method and evaluate its performance using a real-world household survey dataset, where activity chains of up to 15 activities during a day are considered. The optimization is developed using the genetic algorithm (GA) metaheuristic with suitable parameters selected and the branch-and-bound exact algorithm. The findings demonstrate that the branch-and-bound solution exhibits superior performance for smaller activity chain sizes, while the GA outperforms computationally for activity chains with a size from nine. However, the GA found the solutions in only 2% of the time compared to the branch-and-bound method. By applying the ACO method, relevant time savings and emission reduction can be achieved for travelers, when realizing daily activities.

Keywords: Activity scheduling; Flexible activities; Heuristic algorithm; Route optimization; Travel time reduction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11116-023-10456-3

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