A Kernel-Based Approach for Survey-Informed Generation of Synthetic Travel Plans

Abstract

Modeling dynamic urban traffic accurately requires travel demand data at high temporal and spatial resolution. Congestion results from overlapping and time-dependent trips that cannot be represented adequately by static demand formats such as temporally aggregated OD matrices. At the same time, many national forecasting tools estimate demand using aggregated data and simplified behavioral representations. We propose a kernel-based framework for incorporating individual-level travel survey information into the simulation of agent travel plans. Kernel similarity measures combine socio-demographic characteristics, trip attributes, and activity–location information, making it possible to compare heterogeneous objects such as travelers, trips, and zones across different feature spaces. Such an approach is interpretable and modular, making it attractive for travel-demand synthesis, where comparisons across diverse domains are required.

We demonstrate the approach in a small case study for Stockholm, using the national travel survey and a synthetic population applied in real-world transport policy analysis. The results indicate that key marginal characteristics of the survey data are reproduced in the simulated trips. Departuretime distributions, which are crucial for urban congestion analysis, were represented well despite not being explicitly encoded in the model. The activity–location component further combines survey-based activity participation with external land-use information to guide where activities are undertaken.