Modeling Road Link-Level Truck Travel Times Using On- and Off-Network Characteristics

Abstract

Travel times of freight trucks (or trucks) play a major role in trip planning, identification of efficient routes, and allocating resources for implementation of strategies. While research on travel time estimation models for passenger cars or traffic stream is documented in the literature, their applicability for trucks remains debatable. Truck travel is influenced by the road characteristics, surrounding land use, and demographics of an area. The focus of this research, therefore, is on estimating the truck travel times using the on-network (road) and off-network (land use and demographics) characteristics. Truck travel time data for 501 road links in North Carolina from 2019 were processed for four times of the day and two days of the week. Generalized estimating equations (GEE) were used with the average truck travel time per mile (ATTTPM) as the dependent variable. Spatial proximity (buffers of 0.25 mi, 0.50 mi, and 1 mi) and spatial weights (distance decay functions like 1/d, 1/d², and 1/d³) were explored to check the best possible approach for capturing off-network characteristics. The office (business park and administrative areas), transportation (rest areas and parking facilities), heavy commercial, and light industrial land uses have an increasing influence on the ATTTPM. The linear model developed using data from a 0.25-mi buffer is best suited to estimate the ATTTPM. Off-peak hour delivery incentives or truck priority systems on road links near these land uses can be implemented to improve truck mobility. The methodology illustrated in this research is transferable and could be used for estimating truck travel times across a region.

Keywords

data and data science road freight vehicles (trucks)truck GPS data freight systems model/modeling truck

Get full access to this article

View all access options for this article.

References

ATA Freight Forecast Projects Continued Long-Term Growth in Volumes. American Trucking Association, 2020. https://www.trucking.org/news-insights/ata-freight-forecast-projects-continued-long-term-growth-volumes. Accessed February 23, 2021.

American Trucking Association. Economics and Industry Data. ATA, 2020. https://www.trucking.org/economics-and-industry-data. Accessed March 13, 2020.

Peeta

Zhou

Zhang

Modeling and Mitigation of Car-Truck Interactions on Freeways. Transportation Research Record: Journal of the Transportation Research Board, 2004. 1899: 117-126.

Mallett

Jones

Sedor

Short

Freight Performance Measurement: Travel Time in Freight-Significant Corridors. Report # FHWA-HOP-07-071. U.S. Department of Transportation, Federal Highway Administration, Washington, D.C., 2006.

Sarvi

Heavy Commercial Vehicles-Following Behavior and Interactions With Different Vehicle Classes. Journal of Advanced Transportation, Vol. 47, No. 6, 2013, pp. 572-580. https://doi.org/10.1002/atr.182.

Kong

Guo

Yang

Analyzing the Impact of Trucks on Traffic Flow Based on an Improved Cellular Automaton Model. Discrete Dynamics in Nature and Society, Vol. 2016, 2016, p. 1236846.

Zeringue

Stich

Tian

Suitability of Fusing Vehicle Probe Data and Vessel Data to Contextualize the Multimodal Interaction Impacts on Corridor Mobility – A New Orleans Case Study. Presented at 100th Annual Meeting of the Transportation Research Board, Washington, D.C., 2021.

Chen

Skabardonis

Varaiya

Travel-Time Reliability as a Measure of Service. Transportation Research Record: Journal of the Transportation Research Board, 2003. 1855: 74-79.

Hooper

Cost of Congestion to the Trucking Industry: 2018 Update. American Transportation Research Institute (ATRI), Arlington, VA, 2018. http://atri-online.org/2018/10/18/cost-of-congestion-to-the-trucking-industry-2018-update/. Accessed November 11, 2021.

10.

McDonald

Link Travel Time Estimation Using Single GPS Equipped Probe Vehicle. Proc., IEEE 5th International Conference on Intelligent Transportation Systems Proceedings, Singapore, IEEE, New York, 2002, pp. 932-937.

11.

Lum

K. M.

Fan

H. S.

Lam

S. H.

Olszewski

Speed-Flow Modeling of Arterial Roads in Singapore. Journal of Transportation Engineering, Vol. 124, No. 3, 1998, pp. 213-222.

12.

C. F.

The Study of Vehicle Travel Time Estimation Using GPS. Master’s thesis. National Chiao Tung University, Taipei, 2001.

13.

Chien

S. I. J.

Kuchipudi

C. M.

Dynamic Travel Time Prediction With Real-Time and Historic Data. Journal of Transportation Engineering, Vol. 129, No. 6, 2003, pp. 608-616.

14.

Pulugurtha

S. S.

Mahanthi

S. S. B.

Assessing Spatial and Temporal Effects due to a Crash on a Freeway Through Traffic Simulation. Case Studies on Transport Policy, Vol. 4, No. 2, 2016, pp. 122-132.

15.

Kukkapalli

V. M.

Pulugurtha

S. S.

Modeling the Effect of a Freeway Road Construction Project on Link-Level Travel Times. Journal of Traffic and Transportation Engineering (English Edition), Vol. 8, No. 2, 2020, pp. 267-281.

16.

D’Angelo

M. P.

Al-Deek

H. M.

Wang

M. C.

Travel-Time Prediction for Freeway Corridors. Transportation Research Record: Journal of the Transportation Research Board, 1999. 1676: 184-191.

17.

Reza

R. M.

Pulugurtha

S. S.

Duddu

V. R.

ARIMA Model for Forecasting Short-Term Travel Time due to Incidents in Spatio-Temporal Context. Presented at 94th Annual Meeting of the Transportation Research Board, Washington, D.C., 2015.

18.

Reza

R. Z.

Pulugurtha

S. S.

Forecasting Short-Term Relative Changes in Travel Time on a Freeway. Case Studies on Transport Policy, Vol. 7, No. 2, 2019, pp. 205-217.

19.

Zhang

Rice

J. A.

Short-Term Travel Time Prediction. Transportation Research Part C: Emerging Technologies, Vol. 11, No. 3-4, 2003, pp. 187-210.

20.

Rice

Van Zwet

A Simple and Effective Method for Predicting Travel Times on Freeways. IEEE Transactions on Intelligent Transportation Systems, Vol. 5, No. 3, 2004, pp. 200-207.

21.

Anderson

Bell

Travel Time Estimation in Urban Road Networks. Proc., IEEE Conference on Intelligent Transportation Systems, Boston, MA, IEEE, New York, 1997, pp. 924-929.

22.

Ishak

Al-Deek

. Performance Evaluation of Short-Term Time-Series Traffic Prediction Model. Journal of Transportation Engineering, Vol. 128, No. 6, 2002, pp. 490-498.

23.

Kisgyörgy

Rilett

L. R.

Travel Time Prediction by Advanced Neural Network. Periodica Polytechnica Civil Engineering, Vol. 46, No. 1, 2002, pp. 15-32.

24.

Wei

C. H.

Lin

S. C.

Empirical Validation of Freeway Bus Travel Time Forecasting. Transportation Planning Journal, Vol. 32, 2003, pp. 651-679.

25.

Van Lint

J. W. C.

Reliable Travel Time Prediction for Freeways. PhD thesis. Delft University of Technology, Netherlands, 2004.

26.

Wei

C. H.

Lee

Development of Freeway Travel Time Forecasting Models by Integrating Different Sources of Traffic Data. IEEE Transactions on Vehicular Technology, Vol. 56, No. 6, 2007, pp. 3682-3694.

27.

Mane

A. S.

Pulugurtha

S. S.

Link-Level Travel Time Prediction Using Artificial Neural Network Models. Proc., IEEE 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, IEEE, New York, 2018, pp. 1487-1492.

28.

Badoe

D. A.

Miller

E. J.

Transportation–Land-Use Interaction: Empirical Findings in North America, and Their Implications for Modeling. Transportation Research Part D: Transport and Environment, Vol. 5, No. 4, 2000, pp. 235-263.

29.

Stead

Relationships Between Land Use, Socioeconomic Factors, and Travel Patterns in Britain. Environment and Planning B: Planning and Design, Vol. 28, No. 4, 2001, pp. 499-528.

30.

Handy

Smart Growth and the Transportation–Land Use Connection: What Does the Research Tell Us?

International Regional Science Review, Vol. 28, No. 2, 2005, pp. 146-167.

31.

Mane

A. S.

Pulugurtha

S. S.

Influence of Proximal Land Use and Network Characteristics on Link Travel Time. Journal of Urban Planning and Development, Vol. 146, No. 3, 2020, p. 04020028.

32.

Duvvuri

Pulugurtha

S. S.

Truck Travel Time Performance Measures and Their Association With On- and Off-Network Characteristics. Transportation Research Interdisciplinary Perspectives, Vol. 12, 2021, p. 100500. https://doi.org/10.1016/j.trip.2021.100500.

33.

Wang

Goodchild

A. V.

McCormack

Freeway Truck Travel Time Prediction for Freight Planning Using Truck Probe GPS Data. European Journal of Transport and Infrastructure Research, Vol. 16, No. 1, 2016, p. 3114.

34.

Environmental Protection Agency. EnviroAtlas - Percent of Developed Area. EPA, 2015. https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/Percentdevelopedarea.pdf. Accessed July 9, 2021.

35.

Kusam

P. R.

Methods to Estimate Link Level Travel Based on Spatial Effects. PhD dissertation. The University of North Carolina, Charlotte, 2011.

36.

Pulugurtha

S. S.

Agurla

Assessment of Models to Estimate Bus-Stop Level Transit Ridership Using Spatial Modeling Methods. Journal of Public Transportation, Vol. 15, No. 1, 2012, pp. 33-52.

37.

Pulugurtha

S. S.

Agurla

Geospatial Methods and Statistical Models to Estimate Pedestrian Activity at a Bus-Stop. Presented at 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.

38.

Pulugurtha

S. S.

Kusam

P. R.

Modeling Annual Average Daily Traffic With Integrated Spatial Data From Multiple Network Buffer Bandwidths. Transportation Research Record: Journal of the Transportation Research Board, 2012. 2291: 53-60.

39.

Pulugurtha

S. S.

Maradapudi

J. M. R.

Pedestrian Count Models Using Data Based on Distance Decay Affect. Presented at 92nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2013.

40.

Liang

K.-Y.

Zeger

S. L.

Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, Vol. 73, 1986, pp. 13-22.

41.

Wedderburn

R. W. M.

Quasi-Likelihood Functions, Generalized Linear Models, and the Gauss-Newton Method. Biometrika, Vol. 61, No. 3, 1974, pp. 439–447.

42.

Pan

Akaike’s Information Criterion in Generalized Estimating Equations. Biometrics, Vol. 57, 2001, pp. 120-125.

43.

Zheng

Summarizing the Goodness of Fit of Generalized Linear Models for Longitudinal Data. Statistics in Medicine, Vol. 19, No. 10, 2000, pp. 1265-1275.

44.

Hartshorn

Lamm

FHWA Freight and Land Use Handbook. Report No. FHWA-HOP-12-006. United States Federal Highway Administration, Office of Freight Management and Operations, Washington, D.C., 2012.