Incomplete mobility trajectories can obscure the observations needed to identify important travel events and their endpoints. This study evaluates whether compact, locally deployed large language models (LLMs) can recover missing mobility information from incomplete trajectories, using high-confidence air trips as a controlled test case, and whether more explicit prompts can compensate for the loss of endpoint evidence. Using nationwide location-based services (LBS) data from the United States in January 2022 and airport reference data, thirty high-confidence air trips were converted into three incomplete-trajectory conditions by removing observations near the origin airport, the destination airport, or both. Four prompt variants and two LLMs generated 720 requests, each using a two-stage procedure to identify the flight transition segment and then select the origin and destination airports. Exact transition accuracy is 9.2%, while origin and destination accuracy are 31.4% and 10.7%, respectively. Only 1.9% of requests recover both the rule-identified transition and the reference airport pair. The most explicit prompt produced the highest observed exact transition and exact airport-pair recovery rates. The study introduces a controlled endpoint-removal framework for evaluating recovery of discrete mobility events from incomplete trajectories, allowing failures to be examined by inference stage and helping analysts decide whether recovered information should be retained, independently verified, or left unresolved.
Figures & tables
Figure 1: Framework of the study
ID
Timestamp
Latitude
Longitude
Place name
Sfacd-243rg
2022-01-07 00:10:09
37.618816
-122.37556
convenience store
Sfacd-243rg
2022-01-07 00:45:10
37.618926
-122.37671
San Francisco International Airport (SFO)
Sfacd-243rg
2022-01-07 00:49:35
37.618815
-122.37552
San Francisco International Airport (SFO)
Sfacd-243rg
2022-01-07 07:31:07
38.851436
-77.03773
Ronald Reagan Washington National Airport (DCA)
Sfacd-243rg
2022-01-07 07:33:14
38.851445
-77.03777
Ronald Reagan Washington National Airport (DCA)
Sfacd-243rg
2022-01-07 11:21:20
38.851495
-77.03787
chain coffee shop
Table 1: Sample of LBS Data
Code
Condition
Construction and intended evidence loss
E10
Origin removed
Removes observations within 20 mi of the origin endpoint during the 30 min preceding departure.
E01
Destination removed
Removes observations within 20 mi of the destination endpoint during the 30 min following arrival.
E11
Both removed
Removes the union of the origin and destination deletion sets.
Table 2: Endpoint-Removal Conditions
Figure 2: Example of an air-trip trajectory before and after endpoint removal
Code
Stage 1 instruction
Stage 2 instruction
P00
"Select the transition row that most plausibly represents an air trip."
"Select the nearest plausible origin and destination airports."
P01
"Evaluate the distance, speed, and elapsed time when selecting the transition rows."
"Select airports using endpoint-to-airport distance."
P10
"Compare all candidate rows before selecting the flight transition."
"Map the FROM endpoint to the origin and the TO endpoint to the destination."
P11
"Select transition rows after checking timing, distance, speed, and non-air alternatives."
"Select airports after verifying geographic proximity."
Table 3: Summary of the Two-Stage Prompt Instructions
Figure 3: P01 Stage-1 Full Prompt Text
Condition
Exact transition
Origin
Destination
Exact OD
Transition + OD
E10
23/240 (9.6%)
69/240 (28.8%)
33/240 (13.8%)
8/240 (3.3%)
8/240 (3.3%)
E01
19/240 (7.9%)
73/240 (30.4%)
18/240 (7.5%)
1/240 (0.4%)
1/240 (0.4%)
E11
24/240 (10.0%)
84/240 (35.0%)
26/240 (10.8%)
5/240 (2.1%)
5/240 (2.1%)
Overall
66/720 (9.2%)
226/720 (31.4%)
77/720 (10.7%)
14/720 (1.9%)
14/720 (1.9%)
Table 4: Performance by Endpoint-Removal Condition
Prompt
Exact transition
Origin
Destination
Exact OD
Transition + OD
P00
12/180 (6.7%)
53/180 (29.4%)
16/180 (8.9%)
3/180 (1.7%)
3/180 (1.7%)
P01
17/180 (9.4%)
58/180 (32.2%)
20/180 (11.1%)
2/180 (1.1%)
2/180 (1.1%)
P10
17/180 (9.4%)
54/180 (30.0%)
19/180 (10.6%)
3/180 (1.7%)
3/180 (1.7%)
P11
20/180 (11.1%)
61/180 (33.9%)
22/180 (12.2%)
6/180 (3.3%)
6/180 (3.3%)
Overall
66/720 (9.2%)
226/720 (31.4%)
77/720 (10.7%)
14/720 (1.9%)
14/720 (1.9%)
Table 5: Performance Across Prompt Variants
Condition
P00
P01
P10
P11
E10
2/60 (3.3%)
1/60 (1.7%)
1/60 (1.7%)
4/60 (6.7%)
E01
0/60 (0.0%)
0/60 (0.0%)
0/60 (0.0%)
1/60 (1.7%)
E11
1/60 (1.7%)
1/60 (1.7%)
2/60 (3.3%)
1/60 (1.7%)
Table 6: Exact-OD Recovery by Endpoint-Removal Condition and Prompt
Condition
Model
P00
P01
P10
P11
E10
Gemma
1/30 (3.3%)
1/30 (3.3%)
1/30 (3.3%)
4/30 (13.3%)
E10
Llama
1/30 (3.3%)
0/30 (0.0%)
0/30 (0.0%)
0/30 (0.0%)
E01
Gemma
0/30 (0.0%)
0/30 (0.0%)
0/30 (0.0%)
1/30 (3.3%)
E01
Llama
0/30 (0.0%)
0/30 (0.0%)
0/30 (0.0%)
0/30 (0.0%)
E11
Gemma
1/30 (3.3%)
0/30 (0.0%)
1/30 (3.3%)
0/30 (0.0%)
E11
Llama
0/30 (0.0%)
1/30 (3.3%)
1/30 (3.3%)
1/30 (3.3%)
Table 7: Exact-OD recovery by model, endpoint-removal condition, and prompt
Condition
S1 failure
S1 pass
S2 failure ∣ S1 pass
S2 pass ∣ S1 pass
E10
217/240 (90.4%)
23/240 (9.6%)
15/23 (65.2%)
8/23 (34.8%)
E01
221/240 (92.1%)
19/240 (7.9%)
18/19 (94.7%)
1/19 (5.3%)
E11
216/240 (90.0%)
24/240 (10.0%)
19/24 (79.2%)
5/24 (20.8%)
Overall
654/720 (90.8%)
66/720 (9.2%)
52/66 (78.8%)
14/66 (21.2%)
Table 8: Stage 1 and Stage 2 outcomes by endpoint-removal condition
Mobile device location-based service (LBS) data provide large-scale spatiotemporal observations that can help airport planners understand air travelers' mobility patterns. However, airport sightings are often missing before or after flights, making strictly rule-based detection incomplete. This study develops a two-stage framework to infer U.S. domestic air trips from large-scale LBS data under incomplete airport sightings. Stage 1 uses heuristic rules to identify high-confidence air trips whose endpoints are observed at two distinct airports. Stage 2 addresses latent air trips with a probabilistic model that infers air routes using spatial proximity, temporal compatibility, and historical flight schedule information. Using January 2022 nationwide LBS data with nearly 10 million users and 131 million sightings, results are validated against T-100 Domestic Segment route benchmarks and a Stage 1 pseudo-ground-truth reference set. The framework increases air route coverage from 10.65% under Stage 1 to 50.60% and recovers nearly all top-1,000 benchmark routes. It improves Spearman rank correlation with T-100 route shares from 0.4490 to 0.7630 and reduces Jensen-Shannon divergence from 0.2808 to 0.2263. Stage 2 also recovers 92.2% of Stage 1 reference trips. This study formalizes three endpoint observation regimes for LBS-based air trip inference (airport-observed, semi-observed, and latent) and helps airport agencies identify domestic air travel patterns missed when airport sightings are incomplete.
Hsuan-Chih Wang, Xianfeng Terry Yang
Department of Civil and Environmental Engineering, University of Maryland College Park, MD, 20742 · Maryland Transportation Institute Department of Civil and Environmental Engineering, University of Maryland College Park, MD, 20742
Human mobility generation aims to synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model-based methods excel at generating routine trajectories, they struggle to capture deviated mobility during large-scale societal events. This limitation stems from two critical gaps: (1) the absence of event-annotated mobility datasets for design and evaluation, and (2) the inability of current frameworks to reconcile competitions between users' habitual patterns and event-imposed constraints when making trajectory decisions. This work addresses these gaps with a twofold contribution. First, we construct the first event-annotated mobility dataset covering three major events: Typhoon Hagibis, COVID-19, and the Tokyo 2021 Olympics. Second, we propose ELLMob, a self-aligned LLM framework that first extracts competing rationales between habitual patterns and event constraints, based on Fuzzy-Trace Theory, and then iteratively aligns them to generate trajectories that are both habitually grounded and event-responsive. Extensive experiments show that ELLMob wins state-of-the-art baselines across all events, demonstrating its effectiveness. Our codes and datasets are available at https://github.com/deepkashiwa20/ELLMob.
Yusong Wang, Chuang Yang, Jiawei Wang +5
Institute of Science Tokyo · The University of Tokyo · The University of Osaka
Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle because tourist decisions depend heavily on context like weather and fatigue, yet models cannot generalize to unobserved scenarios. Large Language Models offer a solution by encoding commonsense knowledge about human behavior from pretraining, enabling reasoning about context-dependent decisions, while natural language representation flexibly integrates heterogeneous information. Fine-tuning on local trajectories adapts this general understanding to destination-specific patterns. We validate this approach using 566 trajectories from Wakayama Castle Park, Japan. Our fine-tuned Llama-3.1-8B achieves 49.1% next POI accuracy and maintains strong performance on undersampled scenarios like rainy days, demonstrating effective generalization. This establishes LLMs as high-fidelity behavior models for context-dependent tourist prediction, providing groundwork for counterfactual analysis of mobility interventions.
Tatsuya Amano, Hirozumi Yamaguchi
The University of Osaka, Suita, Japan · RIKEN Center for Computational Science, Kobe, Japan