A Two-Stage Hybrid Framework for Inferring Air Trips from Mobile Location-based Service (LBS) Data under Incomplete Airport Sightings

Hsuan-Chih Wang; Xianfeng Terry Yang

cs.AIOct 17, 2026

A Two-Stage Hybrid Framework for Inferring Air Trips from Mobile Location-based Service (LBS) Data under Incomplete Airport Sightings

Authors: Hsuan-Chih Wang, Xianfeng Terry Yang

Organizations: 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

Abstract

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.

Figures & tables

Figure 1: Three endpoint-sighting regimes in LBS-based air-trip inference and their corresponding modeling strategies.

Figure 2: Overview of the proposed two-stage hybrid inference framework

Variables
$u$	An anonymized mobile-device user.
$e_{u}$	Candidate air-trip segment for user $u$ .
$x_{o}$ , $x_{d}$	Observed pre-transition and post-transition sighting locations.
$t_{o}$ , $t_{d}$	Timestamps associated with $x_{o}$ and $x_{d}$ , respectively.
$O$	Observed spatial state, $O=(x_{o},x_{d})$ .
$\phi$ , $\lambda$	Latitude and longitude of a location sighting.

Table 1: List of Key Notations

ID	Timestamp	Latitude	Longitude	Place name
Sfacd-243rg	2022-01-07 00:10:09	26.069201	-80.14125	convenient store
Sfacd-243rg	2022-01-07 00:45:10	26.069323	-80.14297	Fort Lauderdale-Hollywood International Airport
Sfacd-243rg	2022-01-07 00:49:35	26.069323	-80.14297	Fort Lauderdale-Hollywood International Airport
Sfacd-243rg	2022-01-07 07:31:07	29.986477	-95.34981	Houston - George Bush Intercontinental Airport (IAH)
Sfacd-243rg	2022-01-07 07:33:14	29.986478	-95.34982	Houston - George Bush Intercontinental Airport (IAH)
Sfacd-243rg	2022-01-07 11:21:20	29.986563	-95.34812	chain coffee shop

Table 2: Sample of Mobile Device Location Data

Parameter	Symbol	Value
Maximum search radius	$\xi$	50 miles
Origin/destination supply weight	$\alpha$	1.0
Distance friction weight	$\beta$	1.0
Temporal tolerance	$\sigma$	120 min
Cruise speed	$V_{\mathrm{c}}$	500 mph
Time buffer	$T_{\mathrm{b}}$	120 min

Table 3: Parameter settings used in the probabilistic inference model

Model	Identified unique air routes	Actual air routes from T-100 data	Coverage
Stage 1 only	995	9,339	10.65%
Proposed framework	4,726	9,339	50.60%

Table 4: Route-level detection performance against T-100 Domestic Segment data

Air route group	Stage 1	Proposed framework
Top 20 routes	19 / 20 (95.00%)	20 / 20 (100.00%)
Top 50 routes	46 / 50 (92.00%)	48 / 50 (96.00%)
Top 100 routes	88 / 100 (88.00%)	98 / 100 (98.00%)
Top 500 routes	357 / 500 (71.40%)	486 / 500 (97.20%)
Top 1,000 routes	556 / 1,000 (55.60%)	973 / 1,000 (97.40%)
All recovered air routes	995 / 9,339 (10.65%)	4,726 / 9,339 (50.60%)

Table 5: Top-

N

route coverage against T-100 Domestic Segment data

Metric	Stage 1	Proposed framework
Spearman correlation	0.4490	0.7630
Share MAE	0.0123%	0.0117%
JS divergence	0.2808	0.2263

Table 6: Route-share distribution metrics against T-100 Domestic Segment data

Figure 3: Recovery of the historical top-1,000 domestic air-route network

Metric	Value
Stage 1 unique air-trip count	2,073
Stage 2 unique air-trip count	305,563
Overlapped unique air-trip count	1,911
Stage 1 trips missed by Stage 2	162
Stage 2-only trips	303,490
Coverage of Stage 1 trips by Stage 2	92.2%

Table 7: Internal consistency of Stage 2 against Stage 1 pseudo-ground-truth reference set

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Oct 16, 2026J/K move · Enter open · S save

Can Compact Open-Weight Large Language Models Recover Mobility Events from Incomplete Trajectories? A Controlled Evaluation Using Air Trips

Hsuan-Chih Wang, Xianfeng Terry Yang

Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, 20742 · Maryland Transportation Institute

Trajectory Inference LLM Evaluation

Open paper AskPDF arXiv

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.

Abstract

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