journal article·2025-09-23Open access

Coverage-Based Framework for Estimating Total Vehicle Travel Distance Using Point-to-Point Trajectory Data

Choongheon Yang

Applied Sciences· Volume 15· Issue 19

Abstract

Vehicle kilometers traveled (VKT) is a critical metric in transportation and environmental research. However, conventional VKT estimation approaches frequently fail to capture the complexity of route selection and spatiotemporal dynamics of individual road users. This study presents a framework for accurately estimating the total VKT using high-resolution trajectory data obtained from a commercial navigation system. To address the structural limitations of conventional origin destination matrix-based models, such as the modifiable areal unit problem, representative routes were identified based on cumulative travel distance coverage. A novel metric, coverage of estimated travel (CET), was introduced to quantify the explanatory capacity of these routes in approximating total travel distance. Representative routes were selected to maximize CET, and the resulting VKT estimates were validated against national statistical yearbook data. Robustness was further evaluated using mean absolute percentage error, correlation analysis, paired t-tests, and bootstrap-based confidence intervals. The results indicated that as few as five representative routes accounted for over 80% of the total estimated VKT, exhibiting strong agreement with the national statistics after temporal adjustment. These findings demonstrate that trajectory data can serve as a practical alternative to traditional methods, offering higher spatial resolution and enabling dynamic traffic analyses that support transportation policy and environmental planning.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Transportation Planning and Optimization · Traffic Prediction and Management Techniques · Human Mobility and Location-Based Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-Si 10223, Republic of Korea

Funding

Ministry of Land, Infrastructure, and Transport and the Korea Agency for Infrastructure Technology Advancement

References (39)

Show source-provided references
1. Korea Transport Institute (2025, August 10). Guidelines for Greenhouse Gas Emissions Estimation in the Transport Sector. Korea Transport Database (KTDB). 2020 in Korean, Available online: https://www.ktdb.go.kr/.
2. Federal Highway Administration (2017). 2017 National Household Travel Survey: Summary of Travel Trends. 2017.
3. Traffic assignment by paired alternative segments10.1016/j.trb.2009.11.004 ↗
4. Entropy weighted average method for the determination of a single representative path flow solution for the static user equilibrium traffic assignment problem10.1016/j.trb.2014.11.002 ↗
5. Modifiable areal unit problem
6. A systematic review of the modifiable areal unit problem in community food environmental research10.1007/s44212-022-00021-1 ↗
7. Using big GPS trajectory data analytics for vehicle miles traveled estimation10.1016/j.trc.2019.04.019 ↗
8. Gurram, S., Sivaraman, V., Apple, J.T., and Pinjari, A.R. (2019, January 9–12). Agent-based modeling to simulate road travel using big data from smartphone GPS: An application to the continental United States. Proceedings of the 2019 International Conference on Big Data (Big Data), Los Angeles, CA, USA.10.1109/BigData47090.2019.9006339 ↗
9. Geertman, S., Pettit, C., Goodspeed, R., and Staffans, A. (2021). Examining passenger vehicle miles traveled and carbon emissions in the Boston metropolitan area. Urban Informatics and Future Cities, Springer Nature.10.1007/978-3-030-76059-5 ↗
10. Traffic state estimation using floating car data10.1016/j.procs.2016.05.521 ↗
11. Wang, T., Huang, S., Bao, Z., Culpepper, J.S., and Arablouei, R. (2022, January 14–15). Representative routes discovery from massive trajectories. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Walter E. Washington Convention Center, Washington, DC, USA.10.1145/3534678.3539079 ↗
12. Dynamic origin-destination matrix estimation from traffic counts and automated vehicle identification data10.3141/1607-13 ↗
13. McNally, M.G. (2008). The Four-Step Model, UC Irvine Institute of Transportation Studies, Center for Activity Systems Analysis, University of California.
14. Dynamic origin-destination matrix calibration for large-scale network simulators10.1016/j.trc.2018.09.023 ↗
15. Yu, H., Zhu, S., Yang, J., Guo, Y., and Tang, T. (2021). A Bayesian method for dynamic origin-destination demand estimation synthesizing multiple sources of data. Sensors, 21.10.3390/s21154971 ↗
16. Dynamic origin-destination matrix estimation for networks operating under free-flow conditions using macroscopic flow dynamics10.1016/j.ifacol.2024.07.342 ↗
17. Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs10.1016/j.trc.2020.102747 ↗
18. Local detourness: A new phenomenon for modeling route choice and traffic assignment10.1016/j.trb.2024.103052 ↗
19. Tang, W., Chen, J., Sun, C., Wang, H., and Li, G. (2022). Traffic demand estimations considering route trajectory reconstruction in congested networks. Algorithms, 15.10.3390/a15090307 ↗
20. Trajectory data mining: A review of methods and applications
21. A survey on trajectory data mining: Techniques and applications10.1109/ACCESS.2016.2553681 ↗
22. Wang, D., Miwa, T., and Morikawa, T. (2020). Big trajectory data mining: A survey of methods, applications, and services. Sensors, 20.10.3390/s20164571 ↗
23. The path most traveled: Travel demand estimation using big data resources10.1016/j.trc.2015.04.022 ↗
24. Chong, K. (2025). Spatiotemporal influence analysis through traffic speed pattern analysis using spatial classification. Appl. Sci., 15.10.3390/app15010196 ↗
25. Determination of representative path set from vehicle trajectory samples10.1061/(ASCE)CP.1943-5487.0000528 ↗
26. Unified route representation learning for multi-modal transportation recommendation with spatiotemporal pretraining10.1007/s00778-022-00748-y ↗
27. A bi-criterion dynamic user equilibrium traffic assignment model and solution algorithm for evaluating dynamic road pricing strategies10.1016/j.trc.2007.08.002 ↗
28. Path optimization and logistics economic benefits based on sparsely sampled GPS trajectory data
29. A high-resolution trajectory data driven method for real-time evaluation of traffic safety10.1016/j.aap.2021.106503 ↗
30. Detecting anomalous trajectories and behavior patterns using hierarchical clustering from taxi GPS data10.3390/ijgi7010025 ↗
31. Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data10.1016/j.trc.2019.07.005 ↗
32. Trajectory-based vehicle energy/emissions estimation for signalized arterials using mobile sensing data10.1016/j.trd.2014.10.005 ↗
33. Trajectory data processing and mobility performance evaluation for urban traffic networks10.1177/03611981221115088 ↗
34. Bi-level clustering of vehicle trajectories for path choice set and its nested structure identification10.1016/j.trc.2022.103895 ↗
35. Kim, M., Kwak, B.L., Hou, J., and Kim, T. (2024). Robust long-term vehicle trajectory prediction using link projection and a situation-aware transformer. Sensors, 24.10.3390/s24082398 ↗
36. Origin-destination estimation using probe vehicle trajectory and link counts10.1155/2017/4341532 ↗
37. Day-to-day dynamic origin-destination flow estimation using connected vehicle trajectories and automatic vehicle identification data10.1016/j.trc.2021.103241 ↗
38. Shrinkage mamba relation network with out-of-distribution data augmentation for rotating machinery fault detection and localization under zero-faulty data10.1016/j.ymssp.2024.112145 ↗
39. Zero-faulty sample machinery fault detection via relation network with out-of-distribution data augmentation10.1016/j.engappai.2024.109753 ↗