Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China
Abstract
Overloading of road freight vehicles accelerates road damage, creates unfair competition in the transport market, and increases safety risk. There is a dearth of research on the mining of data of highway Freight Weight (FW), and this paper therefore aims to discover factors affecting road freight overloading based on highway FW data, with a view of developing strategies to mitigate such occurrences. A comprehensive sampling survey of road freight transportation was conducted in Anhui Province (China). Vehicle Characteristics (VC), Operation Mode (OM), and transportation information from a total of 3248 trucks were collected. In order to take advantage of the strengths associated with both statistical modelling techniques and non-parametric methods, a Classification And Regression Tree (CART) technique was integrated with Binary Logistic Regression (BLR) to reveal the factors affecting road freight overloading. The classification efficacy test shows that the BLR–CART method outperformed the BLR method in term of accuracy. It is also revealed that the factors affecting overloading of freight vehicles are the Type of Transportation (ToT), Rated Load (RL), OM, FW during the investigation period, interaction between RL and FW, and interaction among RL, FW, and Average Haul Distance (AHD). Road transport authorities should pay greater attention to these factors in order to improve efficiency and effectiveness of overloading inspection.
Keyword : highway transportation, overloaded trucking, sampling survey, classification and regression tree (CART), binary logistic regression (BLR), overloading inspection
How to Cite
Chen, Y., Wang, K., Zhang, Y., Luo, R., Yu, S., Shi, Q., & Hu, W. (2020). Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China. Transport, 35(3), 236-246. https://doi.org/10.3846/transport.2020.12635
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abdel-Aty, M.; Ahmed, M.; Yu, R.; Qi, S. 2012. Developing an Active Traffic Management System for I-70 in Colorado. Final Report No CDOT-2012-9. Colorado Department of Transportation, Denver, CO, US. 193 p. Available from Internet: https://www.codot.gov/programs/research/pdfs/2012/atm.pdf
Adla, A.; Frendi, M.; Benmessaoud, N. 2014. A group memory-based framework for enterprise decision support, Frontiers in Artificial Intelligence and Applications 261: 177–187. https://doi.org/10.3233/978-1-61499-399-5-177
APCD. 2006. Vehicle Overloading Study in Anhui Province P.R. China: Final Report. Anhui Provincial Communications Department (APCD), China. (in Chinese).
Bhattacharya, S.; Mishra, S. 2018. Applications of machine learning for facies and fracture prediction using Bayesian network theory and random forest: case studies from the Appalachian basin, USA, Journal of Petroleum Science and Engineering 170: 1005–1017. https://doi.org/10.1016/j.petrol.2018.06.075
Brar, L. S.; Elsayed, K. 2018. Analysis and optimization of cyclone separators with eccentric vortex finders using large eddy simulation and artificial neural network, Separation and Purification Technology 207: 269-283. https://doi.org/10.1016/j.seppur.2018.06.013
Bremner, A. P.; Taplin, R. H. 2015. Theory & methods: modified classification and regression tree splitting criteria for data with interactions, Australian & New Zealand Journal of Statistics 44(2): 169–176. https://doi.org/10.1111/1467-842X.00219
Brewer, A. M. 2000. Road rage: what, who, when, where and how?, Transport Reviews 20(1): 49–64. https://doi.org/10.1080/014416400295338
Deng, L.; Yan, W. 2018. Vehicle weight limits and overload permit checking considering the cumulative fatigue damage of bridges, Journal of Bridge Engineering 23(7): 04018045. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001267
Devlin, G. 2008. Applications and development of real-time GPS tracking systems and on-board load sensor technology for wood transport in Ireland, in COFORD Technical Workshop: Developing Cost-Effective Systems for Wood Procurement, Harvesting and Transport, 22 February 2008, Dublin, Ireland.
Gilbert, G. E.; Prion, S. 2016. Making sense of methods and measurement: the chi-square test, Clinical Simulation in Nursing 12(5): 145–146. https://doi.org/10.1016/j.ecns.2015.12.013
Hamsley, A. K.; Greene, W. D.; Siry, J. P.; Mendell, B. C. 2007. Improving timber trucking performance by reducing variability of log truck weights, Southern Journal of Applied Forestry 31(1): 12–16. https://doi.org/10.1093/sjaf/31.1.12
Hang, W.; Li, X.-H.; Ju, P.; He, J. 2005. Site survey and analysis of highway trucks overloading status quo in Anhui, Journal of the Eastern Asia Society for Transportation Studies 6: 1790–1803. https://doi.org/10.11175/easts.6.1790
Hang, W.; Xie, Y.; He, J. 2013. Practices of using weigh-in-motion technology for truck weight regulation in China, Transport Policy 30: 143–152. https://doi.org/10.1016/j.tranpol.2013.09.013
Hoffman, A. J.; De Coning, A. 2014. An intelligent freight corridor overload control system, in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 8–11 October 2014, Qingdao, China, 1732–1739. https://doi.org/10.1109/ITSC.2014.6957943
Honefanger, J.; Strawhorn, J.; Athey, R.; Carson, J.; Conner, G.; Jones, D.; Kearney, T.; Nicholas, J.; Thurber, P.; Woolley, R. 2007. Commercial Motor Vehicle Size and Weight Enforcement in Europe. Report No FHWA-PL-07-002. Federal Highway Administration (FHWA), US Department of Transportation,Washington, DC, US. 104 p. Available from Internet: https://international.fhwa.dot.gov/pubs/pl07002
Hu, C.; Steingrimsson, J. A. 2018. Personalized risk prediction in clinical oncology research: applications and practical issues using survival trees and random forests, Journal of Biopharmaceutical Statistics 28(2): 333–349. https://doi.org/10.1080/10543406.2017.1377730
Iyama, Y.; Nakaura, T.; Katahira, K.; Iyama, A.; Nagayama, Y.; Oda, S.; Utsunomiya, D.; Yamashita, Y. 2017. Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI, European Radiology 27(9): 3600–3608. https://doi.org/10.1007/s00330-017-4775-2
Jacob, B.; Feypell-de La Beaumelle, V. 2010. Improving truck safety: potential of weigh-in-motion technology, IATSS Research 34(1): 9–15. https://doi.org/10.1016/j.iatssr.2010.06.003
Jacob, B.; Van Loo, H. 2008. Weigh-in-motion for enforcement in Europe, in International Conference on Heavy Vehicles HVParis 2008: Weigh-In-Motion (ICWIM 5), 19–22 May 2008, Paris, France, 25–38. https://doi.org/10.1002/9781118623305.ch1
Jihanny, J.; Subagio, B. S.; Hariyadi, E. S. 2018. The analysis of overloaded trucks in Indonesia based on weigh in motion data (east of Sumatera national road case study), MATEC Web of Conferences 147: 02006. https://doi.org/10.1051/matecconf/201814702006
Karim, M. R.; Abdullah, A. S.; Yamanaka, H.; Abdullah, A. Sh.; Ramli, R. 2013. Degree of vehicle overloading and its implication on road safety in developing countries, Civil and Environmental Research 3(12): 20–31.
Kim, J.-T.; Heo, S.-H.; Lee, J. S.; Choi, M.-J.; Choi, K.-H.; Nam, T.-S.; Lee, S.-H.; Park, M.-S.; Kim, B. C.; Kim, M.-K.; Cho, K.-H. 2015. Aspirin resistance in the acute stages of acute ischemic stroke is associated with the development of new ischemic lesions, PLoS ONE 10(4): e0120743. https://doi.org/10.1371/journal.pone.0120743
Lei, Y.; Nollen, N.; Ahluwahlia, J.; Yu, Q.; Mayo, M. S. 2015. An application in identifying high-risk populations in alternative tobacco product use utilizing logistic regression and CART: a heuristic comparison, BMC Public Health 15: 341. https://doi.org/10.1186/s12889-015-1582-z
Li, J.; Zhou, J.; Hu, Z. 2009. Safety analysis of overloaded truck for transportation, in R. Liu, J. Zhang, C. Gua (Eds.). Logistics: the Emerging Frontiers of Transportation and Development in China, 4067–4073. https://doi.org/10.1061/40996(330)595
Li, Z.; Wang, W.; Chen, R.; Liu, P. 2014. Conditional inference tree-based analysis of hazardous traffic conditions for rear-end and sideswipe collisions with implications for control strategies on freeways, IET Intelligent Transport Systems 8(6): 509–518. https://doi.org/10.1049/iet-its.2012.0203
Liu, P.; Mu, D.; Gong, D. 2017. Eliminating overload trucking via a modal shift to achieve intercity freight sustainability: a system dynamics approach, Sustainability 9(3): 398. https://doi.org/10.3390/su9030398
Mahmoudabadi, A.; Abolghasem, A. 2013. Application of chaos theory in trucks’ overloading enforcement, Journal of Engineering 2013: 245293. https://doi.org/10.1155/2013/245293
Mannering, F. L.; Bhat, C. R. 2014. Analytic methods in accident research: methodological frontier and future directions, Analytic Methods in Accident Research 1: 1–22. https://doi.org/10.1016/j.amar.2013.09.001
McDonnell, K.; Devlin, G.; Lyons, J.; Russell, F.; Mortimer, D. 2008. Assessment of GPS tracking devices and associated software suitable for real time monitoring of timber haulage trucks, in COFORD Annual Report 2008. National COuncil for FOrest Research and Development (COFORD), Dublin, Ireland. 53–54.
Menard, S. 2001. Applied Logistic Regression Analysis. SAGE Publications. 128 p.
Moreno-Quintero, E.; Fowkes, T.; Watling, D. 2013. Modelling planner–carrier interactions in road freight transport: Optimisation of road maintenance costs via overloading control, Transportation Research Part E: Logistics and Transportation Review 50: 68–83. https://doi.org/10.1016/j.tre.2012.11.001
MoT PRoC. 2018. Statistic Bulletin of Development of Transportation Industry in 2017. Ministry of Transport of People’s Republic of China (MoT PRoC). Available from Internet: http://www.mot.gov.cn (in Chinese).
MoT PRoC. 2016. 2016. Regulations on Highway Management for Oversized Transport Vehicles. Ministry of Transport of People’s Republic of China (MoT PRoC). Available from Internet: http://www.mot.gov.cn (in Chinese).
Mujalli, R. O.; De Oña, J. 2013. Injury severity models for motor vehicle accidents: a review, Proceedings of the Institution of Civil Engineers – Transport 166(5): 255–270. https://doi.org/10.1680/tran.11.00026
Nandi, A.; Shakoor, A. 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses, Engineering Geology 110(1–2): 11–20. https://doi.org/10.1016/j.enggeo.2009.10.001
Nesa, N.; Ghosh, T.; Banerjee, I. 2018. iGRM: improved grey relational model and its ensembles for occupancy sensing in internet of things applications, ACM Transactions on Knowledge Discovery from Data 12(4): 47. https://doi.org/10.1145/3186268
Nishida, N.; Tanaka, M.; Hayashi, N.; Nagata, H.; Takeshita, T.; Nakayama, K.; Morimoto, K.; Shizukuishi, S. 2005. Determination of smoking and obesity as periodontitis risks using the classification and regression tree method, Journal of Periodontology 76(6): 923–928. https://doi.org/10.1902/jop.2005.76.6.923
Pais, J. C.; Amorim, S. I. R.; Minhoto, M. J. C. 2013. Impact of traffic overload on road pavement performance, Journal of Transportation Engineering 139(9): 873–879. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000571
Pillay, K.; Bosman, J. 2001. Heavy vehicle overload in the city of Tshwane, in 20th Annual South African Transport Conference “Meeting the Transport Challenges in Southern Africa”, 16–20 July 2001, South Africa, 1–9. Available from Internet: https://repository.up.ac.za/bitstream/handle/2263/7924/1c5.pdf
Ramos, H. M.; Ollero, J.; Suárez-Llorens, A. 2017. A new explanatory index for evaluating the binary logistic regression based on the sensitivity of the estimated model, Statistics & Probability Letters 120: 135–140. https://doi.org/10.1016/j.spl.2016.08.022
Rys, D.; Judycki, J.; Jaskula, P. 2016. Analysis of effect of overloaded vehicles on fatigue life of flexible pavements based on weigh in motion (WIM) data, International Journal of Pavement Engineering 17(8): 716–726. https://doi.org/10.1080/10298436.2015.1019493
Stanfill, C.; Waltz, D. 1986. Toward memory-based reasoning, Communications of the ACM 29(12): 1213–1228. https://doi.org/10.1145/7902.7906
Titi, H. H.; Coley, N. J.; Latifi, V. 2018. Evaluation of pavement performance due to overload single-trip permit truck traffic in Wisconsin, Advances in Civil Engineering 2018: 1070653. https://doi.org/10.1155/2018/1070653
Torres Martínez, A. J.; Oliete Josa, S.; Magrinyà, F.; Gauthier, J.-M. 2018. Cost-effectiveness of enforcing axle-load regulations: the Douala-N’Djamena corridor in Sub-Saharan Africa, Transportation Research Part A: Policy and Practice 107: 216–228. https://doi.org/10.1016/j.tra.2017.11.016
Trzciński, G.; Moskalik, T.; Wojtan, R. 2018. Total weight and axle loads of truck units in the transport of timber depending on the timber cargo, Forests 9(4): 164. https://doi.org/10.3390/f9040164
Trzciński, G.; Moskalik, T.; Wojtan, R.; Tymendorf, Ł. 2017. Zmienność ładunków i masy całkowitej zestawów wywozowych przy transporcie drewna, Sylwan 161(12): 1026−1034. (in Polish). https://doi.org/10.26202/sylwan.2017090
Van Loo, H.; Henny, R. 2005. REMOVE: Requirements for enforcement of overloaded vehicles in Europe, in Proceedings of International Conference on Weigh-in-Motion, 20–23 February 2005, Taipei, Taiwan, 1–9.
Wang, L.; Li, Q.; Yu, Y.; Liu, J. 2018. Region compatibility based stability assessment for decision trees, Expert Systems with Applications 105: 112–128. https://doi.org/10.1016/j.eswa.2018.03.036
Wang, Y.; Priestley, J. L. 2017. Binary classification on past due of service accounts using logistic regression and decision tree, in Grey Literature from PhD Candidates. Paper No 4. Kennesaw State University, US. Available from Internet: https://digitalcommons.kennesaw.edu/dataphdgreylit/4/
Wu, D.; Jian, M.; Wei, F. 2012. Research on the highway freight overload supervision based on game theory, in ICLEM 2012: Logistics for Sustained Economic Development – Technology and Management for Efficiency, 8–10 October 2012, Chengdu, China, 681–686. https://doi.org/10.1061/9780784412602.0106
Xu, C.; Wang, W.; Liu, P.; Zhang, F. 2015. Development of a real-time crash risk prediction model incorporating the various crash mechanisms across different traffic states, Traffic Injury Prevention 16(1): 28–35. https://doi.org/10.1080/15389588.2014.909036
Yassenn, O. M.; Hafez, M. A.; Endut, I. R.; Bin Baharom, I. B.; Ab Wahab, M. Y. 2011. Overloading at the northern part of the Malaysian expressway, in 2011 IEEE Colloquium on Humanities, Science and Engineering, 5–6 December 2011, Penang, Malaysia, 76–81. https://doi.org/10.1109/CHUSER.2011.6163839
You, J.; Wang, J.; Fang, S.; Guo, J. 2017. An optimized real-time crash prediction model on freeway with over-sampling techniques based on support vector machine, Journal of Intelligent & Fuzzy Systems 33(1): 555–562. https://doi.org/10.3233/JIFS-162155
Zhang, H.; Lu, Y.; Shi, F.; Zhu, D. 2012a. Overloaded vehicle choice behavior analysis based on nested logit model, Journal of Transportation Systems Engineering and Information Technology 12(6): 113–118. https://doi.org/10.1016/S1570-6672(11)60238-9
Zhang, W.; Zhang, Y.-M.; Wei, L.; Duan, X.; Chen, J. H. 2012b. Influence of vehicle overloading on service life of highway, Journal of Traffic and Transportation Engineering 12(6): 82–88. (in Chinese).
Zhao, Y.; Tan, Y.; Zhou, C. 2012. Determination of axle load spectra based on percentage of overloaded trucks for mechanistic-empirical pavement design, Road Materials and Pavement Design 13(4): 850–863. https://doi.org/10.1080/14680629.2012.735796
Zhou, L. J. 2014. Economic analysis and governance countermeasure of overload and out-of-gauge on the highway, Advanced Materials Research 989–994: 5124–5127. https://doi.org/10.4028/www.scientific.net/AMR.989-994.5124
Zuo, W.; Yuan, H.; Shang, Y.; Liu, Y.; Chen, T. 2016. Calculation of a health index of oil-paper transformers insulation with binary logistic regression, Mathematical Problems in Engineering 2016: 6069784. https://doi.org/10.1155/2016/6069784
Adla, A.; Frendi, M.; Benmessaoud, N. 2014. A group memory-based framework for enterprise decision support, Frontiers in Artificial Intelligence and Applications 261: 177–187. https://doi.org/10.3233/978-1-61499-399-5-177
APCD. 2006. Vehicle Overloading Study in Anhui Province P.R. China: Final Report. Anhui Provincial Communications Department (APCD), China. (in Chinese).
Bhattacharya, S.; Mishra, S. 2018. Applications of machine learning for facies and fracture prediction using Bayesian network theory and random forest: case studies from the Appalachian basin, USA, Journal of Petroleum Science and Engineering 170: 1005–1017. https://doi.org/10.1016/j.petrol.2018.06.075
Brar, L. S.; Elsayed, K. 2018. Analysis and optimization of cyclone separators with eccentric vortex finders using large eddy simulation and artificial neural network, Separation and Purification Technology 207: 269-283. https://doi.org/10.1016/j.seppur.2018.06.013
Bremner, A. P.; Taplin, R. H. 2015. Theory & methods: modified classification and regression tree splitting criteria for data with interactions, Australian & New Zealand Journal of Statistics 44(2): 169–176. https://doi.org/10.1111/1467-842X.00219
Brewer, A. M. 2000. Road rage: what, who, when, where and how?, Transport Reviews 20(1): 49–64. https://doi.org/10.1080/014416400295338
Deng, L.; Yan, W. 2018. Vehicle weight limits and overload permit checking considering the cumulative fatigue damage of bridges, Journal of Bridge Engineering 23(7): 04018045. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001267
Devlin, G. 2008. Applications and development of real-time GPS tracking systems and on-board load sensor technology for wood transport in Ireland, in COFORD Technical Workshop: Developing Cost-Effective Systems for Wood Procurement, Harvesting and Transport, 22 February 2008, Dublin, Ireland.
Gilbert, G. E.; Prion, S. 2016. Making sense of methods and measurement: the chi-square test, Clinical Simulation in Nursing 12(5): 145–146. https://doi.org/10.1016/j.ecns.2015.12.013
Hamsley, A. K.; Greene, W. D.; Siry, J. P.; Mendell, B. C. 2007. Improving timber trucking performance by reducing variability of log truck weights, Southern Journal of Applied Forestry 31(1): 12–16. https://doi.org/10.1093/sjaf/31.1.12
Hang, W.; Li, X.-H.; Ju, P.; He, J. 2005. Site survey and analysis of highway trucks overloading status quo in Anhui, Journal of the Eastern Asia Society for Transportation Studies 6: 1790–1803. https://doi.org/10.11175/easts.6.1790
Hang, W.; Xie, Y.; He, J. 2013. Practices of using weigh-in-motion technology for truck weight regulation in China, Transport Policy 30: 143–152. https://doi.org/10.1016/j.tranpol.2013.09.013
Hoffman, A. J.; De Coning, A. 2014. An intelligent freight corridor overload control system, in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 8–11 October 2014, Qingdao, China, 1732–1739. https://doi.org/10.1109/ITSC.2014.6957943
Honefanger, J.; Strawhorn, J.; Athey, R.; Carson, J.; Conner, G.; Jones, D.; Kearney, T.; Nicholas, J.; Thurber, P.; Woolley, R. 2007. Commercial Motor Vehicle Size and Weight Enforcement in Europe. Report No FHWA-PL-07-002. Federal Highway Administration (FHWA), US Department of Transportation,Washington, DC, US. 104 p. Available from Internet: https://international.fhwa.dot.gov/pubs/pl07002
Hu, C.; Steingrimsson, J. A. 2018. Personalized risk prediction in clinical oncology research: applications and practical issues using survival trees and random forests, Journal of Biopharmaceutical Statistics 28(2): 333–349. https://doi.org/10.1080/10543406.2017.1377730
Iyama, Y.; Nakaura, T.; Katahira, K.; Iyama, A.; Nagayama, Y.; Oda, S.; Utsunomiya, D.; Yamashita, Y. 2017. Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI, European Radiology 27(9): 3600–3608. https://doi.org/10.1007/s00330-017-4775-2
Jacob, B.; Feypell-de La Beaumelle, V. 2010. Improving truck safety: potential of weigh-in-motion technology, IATSS Research 34(1): 9–15. https://doi.org/10.1016/j.iatssr.2010.06.003
Jacob, B.; Van Loo, H. 2008. Weigh-in-motion for enforcement in Europe, in International Conference on Heavy Vehicles HVParis 2008: Weigh-In-Motion (ICWIM 5), 19–22 May 2008, Paris, France, 25–38. https://doi.org/10.1002/9781118623305.ch1
Jihanny, J.; Subagio, B. S.; Hariyadi, E. S. 2018. The analysis of overloaded trucks in Indonesia based on weigh in motion data (east of Sumatera national road case study), MATEC Web of Conferences 147: 02006. https://doi.org/10.1051/matecconf/201814702006
Karim, M. R.; Abdullah, A. S.; Yamanaka, H.; Abdullah, A. Sh.; Ramli, R. 2013. Degree of vehicle overloading and its implication on road safety in developing countries, Civil and Environmental Research 3(12): 20–31.
Kim, J.-T.; Heo, S.-H.; Lee, J. S.; Choi, M.-J.; Choi, K.-H.; Nam, T.-S.; Lee, S.-H.; Park, M.-S.; Kim, B. C.; Kim, M.-K.; Cho, K.-H. 2015. Aspirin resistance in the acute stages of acute ischemic stroke is associated with the development of new ischemic lesions, PLoS ONE 10(4): e0120743. https://doi.org/10.1371/journal.pone.0120743
Lei, Y.; Nollen, N.; Ahluwahlia, J.; Yu, Q.; Mayo, M. S. 2015. An application in identifying high-risk populations in alternative tobacco product use utilizing logistic regression and CART: a heuristic comparison, BMC Public Health 15: 341. https://doi.org/10.1186/s12889-015-1582-z
Li, J.; Zhou, J.; Hu, Z. 2009. Safety analysis of overloaded truck for transportation, in R. Liu, J. Zhang, C. Gua (Eds.). Logistics: the Emerging Frontiers of Transportation and Development in China, 4067–4073. https://doi.org/10.1061/40996(330)595
Li, Z.; Wang, W.; Chen, R.; Liu, P. 2014. Conditional inference tree-based analysis of hazardous traffic conditions for rear-end and sideswipe collisions with implications for control strategies on freeways, IET Intelligent Transport Systems 8(6): 509–518. https://doi.org/10.1049/iet-its.2012.0203
Liu, P.; Mu, D.; Gong, D. 2017. Eliminating overload trucking via a modal shift to achieve intercity freight sustainability: a system dynamics approach, Sustainability 9(3): 398. https://doi.org/10.3390/su9030398
Mahmoudabadi, A.; Abolghasem, A. 2013. Application of chaos theory in trucks’ overloading enforcement, Journal of Engineering 2013: 245293. https://doi.org/10.1155/2013/245293
Mannering, F. L.; Bhat, C. R. 2014. Analytic methods in accident research: methodological frontier and future directions, Analytic Methods in Accident Research 1: 1–22. https://doi.org/10.1016/j.amar.2013.09.001
McDonnell, K.; Devlin, G.; Lyons, J.; Russell, F.; Mortimer, D. 2008. Assessment of GPS tracking devices and associated software suitable for real time monitoring of timber haulage trucks, in COFORD Annual Report 2008. National COuncil for FOrest Research and Development (COFORD), Dublin, Ireland. 53–54.
Menard, S. 2001. Applied Logistic Regression Analysis. SAGE Publications. 128 p.
Moreno-Quintero, E.; Fowkes, T.; Watling, D. 2013. Modelling planner–carrier interactions in road freight transport: Optimisation of road maintenance costs via overloading control, Transportation Research Part E: Logistics and Transportation Review 50: 68–83. https://doi.org/10.1016/j.tre.2012.11.001
MoT PRoC. 2018. Statistic Bulletin of Development of Transportation Industry in 2017. Ministry of Transport of People’s Republic of China (MoT PRoC). Available from Internet: http://www.mot.gov.cn (in Chinese).
MoT PRoC. 2016. 2016. Regulations on Highway Management for Oversized Transport Vehicles. Ministry of Transport of People’s Republic of China (MoT PRoC). Available from Internet: http://www.mot.gov.cn (in Chinese).
Mujalli, R. O.; De Oña, J. 2013. Injury severity models for motor vehicle accidents: a review, Proceedings of the Institution of Civil Engineers – Transport 166(5): 255–270. https://doi.org/10.1680/tran.11.00026
Nandi, A.; Shakoor, A. 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses, Engineering Geology 110(1–2): 11–20. https://doi.org/10.1016/j.enggeo.2009.10.001
Nesa, N.; Ghosh, T.; Banerjee, I. 2018. iGRM: improved grey relational model and its ensembles for occupancy sensing in internet of things applications, ACM Transactions on Knowledge Discovery from Data 12(4): 47. https://doi.org/10.1145/3186268
Nishida, N.; Tanaka, M.; Hayashi, N.; Nagata, H.; Takeshita, T.; Nakayama, K.; Morimoto, K.; Shizukuishi, S. 2005. Determination of smoking and obesity as periodontitis risks using the classification and regression tree method, Journal of Periodontology 76(6): 923–928. https://doi.org/10.1902/jop.2005.76.6.923
Pais, J. C.; Amorim, S. I. R.; Minhoto, M. J. C. 2013. Impact of traffic overload on road pavement performance, Journal of Transportation Engineering 139(9): 873–879. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000571
Pillay, K.; Bosman, J. 2001. Heavy vehicle overload in the city of Tshwane, in 20th Annual South African Transport Conference “Meeting the Transport Challenges in Southern Africa”, 16–20 July 2001, South Africa, 1–9. Available from Internet: https://repository.up.ac.za/bitstream/handle/2263/7924/1c5.pdf
Ramos, H. M.; Ollero, J.; Suárez-Llorens, A. 2017. A new explanatory index for evaluating the binary logistic regression based on the sensitivity of the estimated model, Statistics & Probability Letters 120: 135–140. https://doi.org/10.1016/j.spl.2016.08.022
Rys, D.; Judycki, J.; Jaskula, P. 2016. Analysis of effect of overloaded vehicles on fatigue life of flexible pavements based on weigh in motion (WIM) data, International Journal of Pavement Engineering 17(8): 716–726. https://doi.org/10.1080/10298436.2015.1019493
Stanfill, C.; Waltz, D. 1986. Toward memory-based reasoning, Communications of the ACM 29(12): 1213–1228. https://doi.org/10.1145/7902.7906
Titi, H. H.; Coley, N. J.; Latifi, V. 2018. Evaluation of pavement performance due to overload single-trip permit truck traffic in Wisconsin, Advances in Civil Engineering 2018: 1070653. https://doi.org/10.1155/2018/1070653
Torres Martínez, A. J.; Oliete Josa, S.; Magrinyà, F.; Gauthier, J.-M. 2018. Cost-effectiveness of enforcing axle-load regulations: the Douala-N’Djamena corridor in Sub-Saharan Africa, Transportation Research Part A: Policy and Practice 107: 216–228. https://doi.org/10.1016/j.tra.2017.11.016
Trzciński, G.; Moskalik, T.; Wojtan, R. 2018. Total weight and axle loads of truck units in the transport of timber depending on the timber cargo, Forests 9(4): 164. https://doi.org/10.3390/f9040164
Trzciński, G.; Moskalik, T.; Wojtan, R.; Tymendorf, Ł. 2017. Zmienność ładunków i masy całkowitej zestawów wywozowych przy transporcie drewna, Sylwan 161(12): 1026−1034. (in Polish). https://doi.org/10.26202/sylwan.2017090
Van Loo, H.; Henny, R. 2005. REMOVE: Requirements for enforcement of overloaded vehicles in Europe, in Proceedings of International Conference on Weigh-in-Motion, 20–23 February 2005, Taipei, Taiwan, 1–9.
Wang, L.; Li, Q.; Yu, Y.; Liu, J. 2018. Region compatibility based stability assessment for decision trees, Expert Systems with Applications 105: 112–128. https://doi.org/10.1016/j.eswa.2018.03.036
Wang, Y.; Priestley, J. L. 2017. Binary classification on past due of service accounts using logistic regression and decision tree, in Grey Literature from PhD Candidates. Paper No 4. Kennesaw State University, US. Available from Internet: https://digitalcommons.kennesaw.edu/dataphdgreylit/4/
Wu, D.; Jian, M.; Wei, F. 2012. Research on the highway freight overload supervision based on game theory, in ICLEM 2012: Logistics for Sustained Economic Development – Technology and Management for Efficiency, 8–10 October 2012, Chengdu, China, 681–686. https://doi.org/10.1061/9780784412602.0106
Xu, C.; Wang, W.; Liu, P.; Zhang, F. 2015. Development of a real-time crash risk prediction model incorporating the various crash mechanisms across different traffic states, Traffic Injury Prevention 16(1): 28–35. https://doi.org/10.1080/15389588.2014.909036
Yassenn, O. M.; Hafez, M. A.; Endut, I. R.; Bin Baharom, I. B.; Ab Wahab, M. Y. 2011. Overloading at the northern part of the Malaysian expressway, in 2011 IEEE Colloquium on Humanities, Science and Engineering, 5–6 December 2011, Penang, Malaysia, 76–81. https://doi.org/10.1109/CHUSER.2011.6163839
You, J.; Wang, J.; Fang, S.; Guo, J. 2017. An optimized real-time crash prediction model on freeway with over-sampling techniques based on support vector machine, Journal of Intelligent & Fuzzy Systems 33(1): 555–562. https://doi.org/10.3233/JIFS-162155
Zhang, H.; Lu, Y.; Shi, F.; Zhu, D. 2012a. Overloaded vehicle choice behavior analysis based on nested logit model, Journal of Transportation Systems Engineering and Information Technology 12(6): 113–118. https://doi.org/10.1016/S1570-6672(11)60238-9
Zhang, W.; Zhang, Y.-M.; Wei, L.; Duan, X.; Chen, J. H. 2012b. Influence of vehicle overloading on service life of highway, Journal of Traffic and Transportation Engineering 12(6): 82–88. (in Chinese).
Zhao, Y.; Tan, Y.; Zhou, C. 2012. Determination of axle load spectra based on percentage of overloaded trucks for mechanistic-empirical pavement design, Road Materials and Pavement Design 13(4): 850–863. https://doi.org/10.1080/14680629.2012.735796
Zhou, L. J. 2014. Economic analysis and governance countermeasure of overload and out-of-gauge on the highway, Advanced Materials Research 989–994: 5124–5127. https://doi.org/10.4028/www.scientific.net/AMR.989-994.5124
Zuo, W.; Yuan, H.; Shang, Y.; Liu, Y.; Chen, T. 2016. Calculation of a health index of oil-paper transformers insulation with binary logistic regression, Mathematical Problems in Engineering 2016: 6069784. https://doi.org/10.1155/2016/6069784