Introducing a novel Location-Assignment Algorithm for Activity-Based Transport Models: CARLA

2025-09-19

AI Summary

To address low accuracy, large distance deviations, and neglect of locational potential in secondary activity location assignment within activity-based travel demand models, this paper proposes the CARLA algorithm. CARLA innovatively decomposes trip chains into segments and integrates spatial constraint modeling with an anchor-based recursive allocation mechanism, embedding a configurable heuristic search to jointly minimize distance deviation and incorporate geographic units’ activity potential under global optimization. Compared with existing methods, CARLA demonstrates strong robustness, multi-objective adaptability, and computational efficiency: it significantly reduces mean positional error—by up to 32% empirically—within limited computation time, markedly improves spatial realism of activity distributions, and effectively supports high-fidelity agent-based simulation and policy evaluation.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper introduces CARLA (spatially Constrained Anchor-based Recursive Location Assignment), a recursive algorithm for assigning secondary or any activity locations in activity-based travel models. CARLA minimizes distance deviations while integrating location potentials, ensuring more realistic activity distributions. The algorithm decomposes trip chains into smaller subsegments, using geometric constraints and configurable heuristics to efficiently search the solution space. Compared to a state-of-the-art relaxation-discretization approach, CARLA achieves significantly lower mean deviations, even under limited runtimes. It is robust to real-world data inconsistencies, such as infeasible distances, and can flexibly adapt to various priorities, such as emphasizing location attractiveness or distance accuracy. CARLA's versatility and efficiency make it a valuable tool for improving the spatial accuracy of activity-based travel models and agent-based transport simulations. Our implementation is available at https://github.com/tnoud/carla.

Problem

• Assigns secondary activity locations in travel models realistically
• Minimizes distance deviations while integrating location potentials
• Efficiently searches solution space using geometric constraints

Innovation

• Recursive algorithm for assigning activity locations
• Minimizes distance deviations with location potentials
• Decomposes trip chains using geometric constraints