Enhancing Human Mobility Prediction with Spatially Aware LLM-based Multi-Agent Systems

Shangyu Lou; Ziqi Cui

cs.SISep 13, 2026

Enhancing Human Mobility Prediction with Spatially Aware LLM-based Multi-Agent Systems

Authors: Shangyu Lou, Ziqi Cui

Organizations: University of California & Santa Barbara & San Diego State University California, USA · Politecnico di Milano Milan, Italy

Abstract

Predicting a user's next POI is a task in human mobility modeling, yet LLM-based approaches focus on semantic reasoning from previous mobility records, while neglecting real-world spatial context. However, human mobility is inherently shaped by spatial cognition, including geographic distance and neighborhood context. This issue is further compounded by prior evidence that LLMs often struggle with spatial reasoning tasks, including distance estimation and geographically biased prediction. To address these limitations, we propose our framework, a multi-agent LLM framework that decomposes next-POI prediction into three stages: Firstly, a Pattern Extraction Agent that captures temporal and categorical mobility patterns from trajectory history; Secondly, a Spatial Reasoning Agent that structures candidate activity choices by combining behavioral preferences with real-world spatial constraints, including geographic distance, road network distance, and neighborhood affiliation; and Thirdly, a Decision Synthesis Agent that integrates behavioral patterns and spatial reasoning for final prediction. Experiments on the NYC benchmark dataset with two LLM backbones show improvements over baseline methods, with up to 493% Hit@1 improvement and 37% relative improvement in Hit@5. Ablations show that combining neighborhood affiliation with distance-based features generally outperforms distance-only settings, and that the Spatial Reasoning Agent plays a crucial role in final prediction by integrating behavioral preferences with real-world spatial constraints, especially for smaller models. Overall, the results highlight the importance of spatial reasoning in mobility prediction. Accurate next-POI prediction requires combining behavioral patterns with explicit real-world spatial constraints, and multi-agent decomposition provides an effective structure for organizing these forms of context.

Figures & tables

Figure 1. Overview of the our framework framework. Agent 1 (Pattern Extraction) extracts behavioral patterns from trajectory history; Agent 2 (Spatial Reasoning) performs spatial analysis over candidate POIs using pre-computed geographic distance, road network distance, and neighborhood affiliation; Agent 3 (Decision Synthesis) synthesizes both outputs for final prediction. Architecture diagram showing three agents in a sequential pipeline with spatial feature pre-computation.

	Qwen-7B			GPT-20B
Method	H@1	H@5	NDCG@5	H@1	H@5	NDCG@5
LLM-ZS	.047 $\pm$ .025	.160 $\pm$ .016	.105 $\pm$ .018	.045 $\pm$ .006	.156 $\pm$ .026	.107 $\pm$ .015
LLM-Mob	.043 $\pm$ .020	.163 $\pm$ .026	.107 $\pm$ .020	.060 $\pm$ .044	.155 $\pm$ .020	.113 $\pm$ .010
LLM-Move	.147 $\pm$ .025	.270 $\pm$ .037	.205 $\pm$ .031	.235 $\pm$ .043	.396 $\pm$ .006	.303 $\pm$ .011
Single-LLM	.213 $\pm$ .025	.383 $\pm$ .035	.305 $\pm$ .033	.214 $\pm$ .025	.369 $\pm$ .009	.285 $\pm$ .018
our framework (ours)	.209 $\pm$ .023	.487 $\pm$ .047	.357 $\pm$ .039	.267 $\pm$ .044	.504 $\pm$ .018	.387 $\pm$ .036

Table 1. Main results comparing our framework with baselines (mean

\pm

std over three seeds).

	Qwen-7B			GPT-20B
Spatial Features	H@1	H@5	NDCG@5	H@1	H@5	NDCG@5
Dist	.190 $\pm$ .028	.473 $\pm$ .019	.338 $\pm$ .019	.232 $\pm$ .031	.480 $\pm$ .046	.357 $\pm$ .044
Road	.188 $\pm$ .048	.442 $\pm$ .048	.317 $\pm$ .050	.237 $\pm$ .008	.470 $\pm$ .050	.351 $\pm$ .030
Neigh	.174 $\pm$ .010	.426 $\pm$ .053	.309 $\pm$ .034	.208 $\pm$ .017	.495 $\pm$ .010	.357 $\pm$ .011
Dist+Road	.194 $\pm$ .019	.446 $\pm$ .006	.329 $\pm$ .015	.212 $\pm$ .021	.495 $\pm$ .021	.354 $\pm$ .010
Dist+Neigh	.203 $\pm$ .032	.477 $\pm$ .040	.348 $\pm$ .031	.253 $\pm$ .008	.487 $\pm$ .031	.374 $\pm$ .023

Table 2. Spatial feature ablation for the Spatial Reasoning Agent (mean

\pm

std over three seeds).

	Qwen-7B			GPT-20B
Configuration	H@1	H@5	NDCG@5	H@1	H@5	NDCG@5
Full (A1+A2+A3)	.209 $\pm$ .023	.487 $\pm$ .047	.357 $\pm$ .039	.267 $\pm$ .044	.504 $\pm$ .018	.387 $\pm$ .036
Skip Agent 2	.175 $\pm$ .056	.329 $\pm$ .024	.257 $\pm$ .040	.246 $\pm$ .033	.464 $\pm$ .020	.354 $\pm$ .015

Table 3. Agent ablation study (mean

\pm

std over three seeds).

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