Formulation and solution for calibrating boundedly rational activity-travel assignment:An exploratory study

Parameter calibration of the traffic assignment models is vital to travel demand analysis and management.As an extension of the conventional traffic assignment,boundedly rational activity-travel assignment(BR-ATA)combines activity-based modeling and traffic assignment endogenously and can capture the interdependencies between high dimensional choice facets along the activity-travel patterns.The inclusion of multiple episodes of activity participation and bounded rationality behavior enlarges the choice space and poses a challenge for calibrating the BR-ATA models.In virtue of the multi-state supernetwork,this exploratory study formulates the BRATA calibration as an optimization problem and analyzes the influence of the two additional components on the calibration problem.Considering the temporal dimension,we also propose a dynamic formulation of the BR-ATA calibration problem.The simultaneous perturbation stochastic approximation algorithm is adopted to solve the proposed calibration problems.Numerical examples are presented to calibrate the activity-based travel demand for illustrations.The results demonstrate the feasibility of the solution method and show that the parameter characterizing the bounded rationality behavior has a significant effect on the convergence of the calibration solutions.

Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach

With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model(HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people’s eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activitytravel patterns of working adults in Hong Kong, two distinctive groups of balanced(38.4%) and work-oriented(61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people’s behavior in urban mobility research.