http://iet.metastore.ingenta.com
1887

Mapping of truck traffic in New Jersey using weigh-in-motion data

Mapping of truck traffic in New Jersey using weigh-in-motion data

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study presents an innovative hierarchical Bayesian model for mapping of county level truck traffic in New Jersey. First, the model is estimated using truck counts. Then, using overweight truck counts from weigh-in-motion data as the response variable, the model is re-estimated. The goal in using the overweight trucks in the spatial model is to demonstrate the importance of representing their spatial variation due to their impact on the life of the roadway network elements. Finally, truck count maps are developed based on modelling results to visualise the effects of spatial covariates. The results of the study indicate that the most influential covariate for the truck traffic is the length of interstate roadways, followed by employment and population. The developed truck count maps can help transportation professionals on identifying and ranking the locations at an aggregate level, which requires closer attention.

References

    1. 1)
      • 1. Miaou, S.-P., Song, J.J., Mallick, B.K.: ‘Roadway traffic crash mapping: a space-time modeling approach’, J. Transp. Stat., 2003, 6, (1), pp. 3357.
    2. 2)
      • 2. Waller, L.A., Gotway, C.A.: ‘Applied spatial statistics for public health data’ (John Wiley & Sons, Inc., Hoboken, NJ, 2004).
    3. 3)
      • 3. MacNab, Y.C.: ‘Bayesian spatial and ecological models for small-area accident and injury analysis’, Accident Anal. Prev., 2004, 36, pp. 10191028.
    4. 4)
      • 4. Aguero-Valverde, J., Jovanis, P.P.: ‘Spatial analysis of fatal and injury crashes in Pennsylvania’, Accident Anal. Prev., 2006, 38, pp. 618625.
    5. 5)
      • 5. Quddus, M.A.: ‘Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data’, Accident Anal. Prev., 2008, 40, pp. 14861497.
    6. 6)
      • 6. Huang, H., Abdel-Aty, M.A., Darwiche, A.L.: ‘County-level crash risk analysis in Florida’, Transp. Res. Rec., J. Transp. Res. Board, 2010, 2148, pp. 2737.
    7. 7)
      • 7. Demiroluk, S., Ozbay, K.: ‘Spatial analysis of county level crash risk in new jersey using severity based hierarchical Bayesian models’. 93rd Annual Meeting of the Transportation Research Board, Washington, DC, 2014.
    8. 8)
      • 8. Faghri, A., Hua, J.: ‘Roadway seasonal classification using neural network’, J. Comput. Civ. Eng., 1995, 9, (1), pp. 209215.
    9. 9)
      • 9. Lam, W., Xu, J.: ‘Estimation of AADT from short period counts in Hong-Kong a comparison between neural network method and regression analysis’, J. Adv. Transp., 2000, 34, (2), pp. 249268.
    10. 10)
      • 10. Lingras, P., Sharma, S.C., Osborne, P., et al: ‘Traffic volume time-series analysis according to the type of road use’, Comput.-Aided Civ. Infrastruct. Eng., 2000, 15, (5), pp. 365373.
    11. 11)
      • 11. Sharma, S.C., Lingras, P., Xu, F., et al: ‘Application of neural networks to estimate AADT on low-volume roads’, J. Transp. Eng., 2001, 127, (5), pp. 426432.
    12. 12)
      • 12. Mohamad, D., Sinha, K.C., Kuczek, T., et al: ‘Annual average daily traffic prediction model for county roads’, Transp. Res. Rec., 1998, 1617, pp. 6977.
    13. 13)
      • 13. Xia, Q., Zhao, F., Chen, Z., et al: ‘Estimation of annual average daily traffic for nonstate roads in Florida county’, Transp. Res. Rec., 1999, 1660, pp. 3240.
    14. 14)
      • 14. Zhao, F., Park, N.: ‘Using geographically weighted regression models to estimate annual average daily traffic’, Transp. Res. Rec., 2004, 1879, pp. 99107.
    15. 15)
      • 15. Noland, R.B.: ‘Traffic fatalities and injuries: the effect of changes in infrastructure and other trends’, Accident Anal. Prev., 2003, 35, pp. 599611.
    16. 16)
      • 16. Fridstrom, L., Ingebrigtsen, S.: ‘An aggregate accident model based on pooled, regional time-series data’, Accident Anal. Prev., 1991, 23, (5), pp. 363378.
    17. 17)
      • 17. Karlaftis, M.G., Tarko, A.P.: ‘Heterogeneity considerations in accident modeling’, Accident Anal. Prev., 1998, 30, (4), pp. 425433.
    18. 18)
      • 18. Amoros, E., Martin, J.L., Laumon, B.: ‘Comparison of road crashes incidence and severity between some French counties’, Accident Anal. Prev., 2003, 35, (4), pp. 537547.
    19. 19)
      • 19. Noland, R.B., Oh, L.: ‘The effect of infrastructure and demographic change on traffic-related fatalities and crashes: a case study of Illinois county-level data’, Accident Anal. Prev., 2004, 36, (4), pp. 525532.
    20. 20)
      • 20. Abdel-Aty, M.A., Radwan, A.E.: ‘Modeling traffic accident occurrence and involvement’, Accident Anal. Prev., 2000, 32, (5), pp. 633642.
    21. 21)
      • 21. Lee, J., Mannering, F.: ‘Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis’, Accident Anal. Prev., 2002, 34, (2), pp. 149161.
    22. 22)
      • 22. Lord, D., Mannering, F.: ‘The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives’, Transp. Res. A, 2010, 44, (5), pp. 291305.
    23. 23)
      • 23. Louch, H., Robert, W., Gurenich, D., et al: ‘Asset management implementation strategy’. Research report, NJ-2009-005, New Jersey Department of Transportation, 2009.
    24. 24)
      • 24. ‘New Jersey Department of Transportation, Roadway Information and Traffic Monitoring System Program’. Available at http://www.state.nj.us/transportation/refdata/roadway/truckwt.shtm, 16 accessed June 2017.
    25. 25)
      • 25. ‘FHWA. Traffic Monitoring Guide, U.S. Department of Transportation, Federal Highway Administration’. Available at https://www.fhwa.dot.gov/policyinformation/tmguide, accessed 16 June 2017.
    26. 26)
      • 26. Ozbay, K., Nassif, H., Demiroluk, S.: ‘NJDOT state-wide large truck monitoring program: data collection, processing, and reporting (ASSISTME-WIM)’. Draft research report, New Jersey Department of Transportation, 2016.
    27. 27)
      • 27. ‘Department of Labor and Workforce Development, the State of New Jersey’. Available at http://lwd.dol.state.nj.us/labor/lpa/dmograph/Demographics_Index.html, accessed 16 June 2017.
    28. 28)
      • 28. Besag, J.: ‘Spatial interaction and the statistical analysis of lattice systems’, J. R. Stat. Soc., B, 1974, 36, pp. 192236.
    29. 29)
      • 29. Bernardinelli, L., Clayton, D., Montomoli, C.: ‘Bayesian estimates of disease maps: how important are priors?’, Stat. Med., 1995, 14, pp. 24112431.
    30. 30)
      • 30. Spiegelhalter, D.J., Best, N., Carlin, B.P., et al: ‘Bayesian measures of model complexity and fit’, J. R. Stat. Soc., B, 2002, 64, (4), pp. 583639.
    31. 31)
      • 31. Spiegelhalter, D., Thomas, A., Best, N., et al: ‘WINBUGS user manual version 1.4’ (MRC Biostatistics Unit, Cambridge, UK). Available athttps://www.mrc-bsu.cam.ac.uk/wp-content/uploads/manual14.pdf, accessed 12 February 2017.
    32. 32)
      • 32. Gelfand, A.E., Sahu, S.K., Carlin, B.P.: ‘Efficient parameterizations for normal linear mixed models’, Biometrika, 1995, 82, pp. 479488.
    33. 33)
      • 33. Hills, S.E., Smith, A.F.M.: ‘Parameterization issues in Bayesian inference’, in Bernardo, J.M., Berger, J.O., Dawid, A.P., Smith, A.F.M. (Eds.): ‘Bayesian statistics 4’ (Oxford University Press, Oxford, UK, 1992), pp. 227246.
    34. 34)
      • 34. Liu, C., Rubin, D.B., Wu, Y.N.: ‘Parameter expansion to accelerate EM: the PX-EM algorithm’, Biometrika, 1998, 85, (4), pp. 755770.
    35. 35)
      • 35. Nassif, H., Ozbay, K., Wang, H., et al: ‘Impact of freight on highway infrastructure in New Jersey’. FHWA-NJ-2016-04, New Jersey Department of Transportation, Trenton, NJ, 2016.
    36. 36)
      • 36. ‘Superload Online Permitting System, NJDOT at GotPermits’. Available at http://nj.gotpermits.com, accessed 26 June 2017.
    37. 37)
      • 37. ‘New Jersey Department of Transportation, New Jersey's Long-Range Transportation Plan: The 2030 Plan’. Available at http://www.state.nj.us/transportation/works/njchoices/pdf/2030plan.pdf, accessed 2 April 2018.
    38. 38)
      • 38. Ozbay, K., Jawad, D., Parker, N., et al: ‘Life cycle cost analysis: state-of the art vs state-of-practice’, J. Transp. Res. Rec., 2004, 1864, pp. 6271.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.0055
Loading

Related content

content/journals/10.1049/iet-its.2018.0055
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address