Where Truck Drivers Stop – Application of Vehicle Tracking Data for the Identification of Rest Locations and Driving Patterns

  • Bálint Csendes KTI Institute for Transport Sciences Non Profit Ltd.
  • Gábor Albert KTI Institute for Transport Sciences Non Profit Ltd.
  • Norina Szander KTI Institute for Transport Sciences Non Profit Ltd.
  • András Munkácsy KTI Institute for Transport Sciences Non Profit Ltd.
Keywords: GNSS tracking, truck rest areas, mandatory rest time, truck traffic regulations, truck driving patterns, road freight transport


Road transport plays an essential role in freight transport throughout Europe, therefore, conditions that may hinder seamless operations in this sector require thorough consideration for evidence-based action. Critical amongst these key conditions is how, when, and where truck drivers stop, as a common set of rules strictly regulates their driving times and rest periods, which causes mandatory interruptions in the supply chains. However, approximating reliable estimations of freight traffic flows and road infrastructure usage constitutes a considerable challenge for researchers. This paper presents a robust data processing approach to designate rest area stops and to calculate the pertaining driving and rest times. Drawing on the abundance of navigation information provided by private fleet toll registration services, a comprehensive spatial-temporal truck stop database on all major rest areas along the toll road network in Hungary has been compiled. Based on the assessment and comparison of driving and rest times, driving and parking times have been analysed, including micro-scale analysis of particular rest areas. Both the methods applied and the results achieved can be of strategic interest to better understand truck driving patterns, as well as to develop targeted and cost-effective measures to streamline freight transport operations in other contexts.


Hungarian Central Statistical Office. Freight transport by mode. 2021. Available from: http://www.ksh.hu/stadat_files/sza/hu/sza0002.html [Accessed 17th May 2021].

CEDR. Trans-European Road Network, TEN-T (Roads): 2019 Performance Report. Conference of European Directors for Roads, 2020.

Magyar Közút. [Cross-sectional traffic data of Hungarian national public roads, 2019]. 2020. Available from: https://internet.kozut.hu/download/az-orszagos-kozutak-2019-evre-vonatkozo-keresztmetszeti-forgalma/ [Accessed 16th June 2021].

Regulation (EC) No 561/2006 of the European Parliament. Available from: https://eur-lex.europa.eu/legal-content/en/TXT/?uri=CELEX:32006R0561 [Accessed 16th June 2021].

Regulation (EU) 2020/1054 of the European Parliament. Available from: https://eur-lex.europa.eu/legal-contentEN/TXT/HTML/?uri=CELEX:32020R1054 [Accessed 16th June 2021].

European Commission. Sustainable and Smart Mobility Strategy – Putting European transport on track for the future. COM(2020) 789 final, 2020. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52020DC0789&from=EN [Accessed 17th May 2021].

Sipos T, Szabó Z, Török Á. Spatial econometric cross-border traffic analysis for passenger cars – Hungarian experience. Promet – Traffic&Transportation. 2021;33(2): 233-46. DOI: 10.7307/ptt.v33i2.3641

Corro KD, Akter T, Hernandez S. Comparison of overnight truck parking counts with GPS-Derived counts for truck parking facility utilization analysis. Transportation Research Record. 2019;2673(8): 377-387. DOI: 10.1177/0361198119843851

Ioannou P, de Almeida Araujo Vital F. Intelligent parking assist for trucks with prediction. National Center for Sustainable Transportation, 2018. Available from: https://escholarship.org/uc/item/7dm0x5mr

U.S. Department of Transportation. Jason’s Law Truck Parking Survey Results and Comparative Analysis. Federal Highway Administration, Office of Freight Management and Operations, 2015. Available from: https://ops.fhwa.dot.gov/freight/infrastructure/truck_parking/jasons_law/truckparkingsurvey/jasons_law.pdf [Accessed 17th May 2020].

Karam A, et al. Towards deriving freight traffic measures from truck movement data for state road planning: A proposed system framework. ISPRS International Journal of Geo-Information. 2020;9(10): 606. DOI: 10.3390/ijgi9100606

Minnesota Safety Rest Area Programs. Commercial truck usage nighttime parking demand analysis. 1998.

Iowa StateUniversity / Iowa Department of Transportation. Commercial Vehicle Parking. 1999. Available from: https://intrans.iastate.edu/app/uploads/2018/03/truckpar.pdf

Boris C, Brewster R. A comparative analysis of truck parking travel diary data. Transportation Research Record. 2018:2672(9): 242-248. DOI: 10.1177/0361198118775869

Anderson JC, Hernandez S, Jessup EL, North E. Perceived safe and adequate truck parking: A random parameters binary logit analysis of truck driver opinions in the Pacific Northwest. International Journal of Transportation Science and Technology. 2018:7(1): 89-102. DOI: 10.1016/j.ijtst.2018.01.001

Torrey IWF, Murray D. Managing critical truck parking tech memo #2: Minnesota case study – Utilizing truck GPS data to assess parking supply and demand. American Transportation Research Institute; 2017. Available from: https://truckingresearch.org/wp-content/uploads/2017/02/Managing-Critical-Truck-Parking-Tech-Memo-2-02-2017-1.pdf

Haque K, Mishra S, Paleti R, Golias MM, Sarker AA, Pujats K. Truck parking utilization analysis using GPS data. Journal of Transportation Engineering, Part A: Systems. 2017:143(9): 04017045. DOI: 10.1061/JTEPBS.0000073

Nevland EA, Gingerich K, Park PY. A data-driven systematic approach for identifying and classifying long-haul truck parking locations. Transport Policy. 2020;96: 48-59. DOI:10.1016/j.tranpol.2020.04.003

Karam A, Illemann TM, Reinau KH. GPS-derived measures of freight trucks for rest areas: A case-study based analysis. 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). DOI: 10.1109/IEEM45057.2020.9309748

Albert G. [The new national origin-destination matrices (OCF-2016) as the cornerstones of transport planning]. Közlekedéstudományi Szemle. 2017:67(5): 5-15. Hungarian.

KTI Institute for Transport Sciences NLtd. [Elaboration of the national origin-destination traffic survey and matrices (OCF-2016)]. TEN-T Analyses, Budapest, 2017. Hungarian.

Pusztai Á, Kiss I. [A methodological breakthrough - developing OD matrices of heavy goods vehicles]. Közlekedéstudományi Szemle. 2017:67(5): 44-53. Hungarian.

Parking Area Search. Available from: https://www.iru.org/apps/transpark-app [Accessed 30th June 2021].

DKV fuel station POI. Available from: https://www.dkv-mobility.com/en/fuelling/poi-navigation-data/ [Accessed 30th June 2021].

Secure bookable truck parking locations in Hungary. Available from: https://www.truckparkingeurope.com/secure-truck-parking-overview/hungary/ [Accessed 30th June 2021].

Directive 2010/40/EU. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32010L0040 [Accessed 1st Oct. 2021].

How to Cite
Csendes B, Albert G, Szander N, Munkácsy A. Where Truck Drivers Stop – Application of Vehicle Tracking Data for the Identification of Rest Locations and Driving Patterns. Promet [Internet]. 2021Dec.13 [cited 2023Jun.6];33(6):821-32. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3962