Road traffic data is important for various applications in transport studies, such as those related to safety, environmental damages, and economic evaluations. Although significant improvement in estimation accuracy has been achieved, less is known about the association of specific factors with road traffic volumes. This paper presents an investigation of the relation of various road, area, and socioeconomic characteristics with annual average daily traffic in England and Wales for four different road classes and five vehicle types. This is achieved by applying least absolute shrinkage and selection operator regression on a comprehensive set of land use, socioeconomic, public transport, and roadway variables. The output reveals that specific socioeconomic and roadway characteristics are those that are mainly associated with traffic volumes across all vehicle types and road classes. Moreover, the association of other variables with traffic volume varies, depending on the road class and vehicle type, creating space for future research. The results can support urban planning and inform policies related to transport congestion and environmental impact mitigation.
SookunA.BoojhawonR.RughooputhS. D. D. V.Assessing Greenhouse Gas and Related Air Pollutant Emissions From Road Traffic Counts: A Case Study for Mauritius. Transportation Research Part D: Transport and Environment, Vol. 32, 2014, pp. 35–47. https://doi.org/10.1016/j.trd.2014.06.005.
2.
ShuS.YangP.ZhuY.Correlation of Noise Levels and Particulate Matter Concentrations Near Two Major Freeways in Los Angeles, California. Environmental Pollution, Vol. 193, 2014, pp. 130–137. https://doi.org/10.1016/j.envpol.2014.06.025.
3.
WangT.GanA.AlluriP.Estimating Annual Average Daily Traffic for Local Roads for Highway Safety Analysis. Transportation Research Record: Journal of the Transportation Research Board, 2013. 5: 60–66.
4.
DasS.TsapakisI.Interpretable Machine Learning Approach in Estimating Traffic Volume on Low-Volume Roadways. International Journal of Transportation Science and Technology, Vol. 9, No. 1, 2020, pp. 76–88. https://doi.org/10.1016/j.ijtst.2019.09.004.
5.
LowryM.Spatial Interpolation of Traffic Counts Based on Origin-Destination Centrality. Journal of Transport Geography, Vol. 36, 2014, pp. 98–105. https://doi.org/10.1016/j.jtrangeo.2014.03.007.
6.
LuT.BuehlerR.MondscheinA.HankeyS.Designing a Bicycle and Pedestrian Traffic Monitoring Program to Estimate Annual Average Daily Traffic in a Small Rural College Town. Transportation Research Part D: Transport and Environment, Vol. 53, 2017, pp. 193–204. https://doi.org/10.1016/j.trd.2017.04.017.
7.
HankeyS.LuT.MondscheinA.BuehlerR.Spatial Models of Active Travel in Small Communities: Merging the Goals of Traffic Monitoring and Direct-Demand Modeling. Journal of Transport & Health, Vol. 7, 2017, pp. 149–159. https://doi.org/10.1016/j.jth.2017.08.009.
8.
ÇodurM. Y.TortumA.An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey. PROMET - Traffic&Transportation, Vol. 27, No. 3, 2015, pp. 217–225. https://traffic.fpz.hr/index.php/PROMTT/article/view/1551.
9.
SfyridisA.AgnolucciP.Road Emissions in London: Insights From Geographically Detailed Classification and Regression Modelling. Atmosphere, Vol. 12, No. 2, 2021, p. 188. https://www.mdpi.com/2073-4433/12/2/188.
10.
PuliafitoS. E.AllendeD.PintoS.CastesanaP.High Resolution Inventory of GHG Emissions of the Road Transport Sector in Argentina. Atmospheric Environment, Vol. 101, 2015, pp. 303–311.
11.
AprontiD.KsaibatiK.GerowK.HepnerJ. J.Estimating Traffic Volume on Wyoming Low Volume Roads Using Linear and Logistic Regression Methods. Journal of Traffic and Transportation Engineering (English Edition), Vol. 3, No. 6, 2016, pp. 493–506. https://doi.org/10.1016/j.jtte.2016.02.004.
12.
ShojaeshafieiM.DoustmohammadiM.SubediS.AndersonM.Comparison of Estimation Methodologies for Daily Traffic Count Pediction in Small and Medium Sized Communities. Nternational Journal of Traffic and Transportation Engineering, Vol. 6, No. 4, 2017, pp. 71–75.
13.
FuM.KellyJ. A.ClinchJ. P.Estimating Annual Average Daily Traffic and Transport Emissions for a National Road Network: A Bottom-Up Methodology for Both Nationally-Aggregated and Spatially-Disaggregated Results. Journal of Transport Geography, Vol. 58, 2017, pp. 186–195. https://doi.org/10.1016/j.jtrangeo.2016.12.002.
SfyridisA.AgnolucciP.Annual Average Daily Traffic Estimation in England and Wales: An Application of Clustering and Regression Modelling. Journal of Transport Geography, Vol. 83, 2020, p. 102658. https://doi.org/10.1016/j.jtrangeo.2020.102658.
17.
GeccheleG.RossiR.GastaldiM.CapriniA.Data Mining Methods for Traffic Monitoring Data Analysis: A Case Study. Procedia - Social and Behavioral Sciences, Vol. 20, 2011, pp. 455–464. https://doi.org/10.1016/j.sbspro.2011.08.052.
18.
PunL.ZhaoP.LiuX.A Multiple Regression Approach for Traffic Flow Estimation. IEEE Access, Vol. 7, 2019, pp. 35998–36009.
19.
SelbyB.KockelmanK. M.Spatial Prediction of Traffic Levels in Unmeasured Locations: Applications of Universal Kriging and Geographically Weighted Regression. Journal of Transport Geography, Vol. 29, 2013, pp. 24–32. https://doi.org/10.1016/j.jtrangeo.2012.12.009.
20.
BrathwaiteT.VijA.WalkerJ. L.Machine Learning Meets Microeconomics: The Case of Decision Trees and Discrete Choice. arXiv Preprint arXiv:1711.04826, 2017.
21.
ParedesM.HembergE.O’ReillyU.-M.ZegrasC.Machine Learning or Discrete Choice Models for Car Ownership Demand Estimation and Prediction?Proc., 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, Italy, IEEE, New York, 2017, pp. 780–785. http://ieeexplore.ieee.org/document/8005618/.
22.
SekharC. R.MinalMadhuE.Mode Choice Analysis Using Random Forrest Decision Trees. Transportation Research Procedia, Vol. 17, 2016, pp. 644–652. https://doi.org/10.1016/j.trpro.2016.11.119.
23.
AdlerP.FalkC.FriedlerS. A.NixT.RybeckG.ScheideggerC.SmithB.VenkatasubramanianS.Auditing Black-Box Models for Indirect Influence. Knowledge and Information Systems, Vol. 54, No. 1, 2018, pp. 95–122.
24.
KrauseR. W.HuismanM.SteglichC.SniidersT. A. B.Missing Network Data a Comparison of Different Imputation Methods. Proc., IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, IEEE, New York, 2018, pp. 159–163.
LakkarajuH.KamarE.CaruanaR.LeskovecJ.Interpretable & Explorable Approximations of Black Box Models. arXiv Preprint arXiv:1707.01154, 2017.
27.
BurnsC.ThomasonJ.TanseyW.Interpreting Black Box Models via Hypothesis Testing. Proc., ACM-IMS on Foundations of Data Science Conference, Association for Computing Machinery, New York, 2020, pp. 47–57. http://arxiv.org/abs/1904.00045.
28.
ChurpekM.YuenT.WinslowC.MeltzerD.KattanM.EdelsonD.Multicenter Comparison of Machine Learning and Conventional Regression for Predicting Clinical Deterioration on the Wards. Critical Care Medicine, Vol. 44, No. 2, 2017, pp. 368–374.
29.
ZhangJ.LiZ.PuZ.XuC.Comparing Prediction Performance for Crash Injury Severity Among Various Machine Learning and Statistical Methods. IEEE Access, Vol. 6, 2018, pp. 60079–60087.
30.
EomJ. K.ParkM. S.HeoT.-Y.HuntsingerL. F.Improving the Prediction of Annual Average Daily Traffic for Nonfreeway Facilities by Applying a Spatial Statistical Method. Transportation Research Record: Journal of the Transportation Research Board, 2006. 1968: 22–29.
31.
WangX.KockelmanK.Forecasting Network Data. Transportation Research Record: Journal of the Transportation Research Board, 2009. 2105: 100–108.
32.
KimS.ParkD.HeoT.-Y.KimH.HongD.Estimating Vehicle Miles Traveled (VMT) in Urban Areas Using Regression Kriging. Journal of Advanced Transportation, Vol. 50, No. 5, 2016, pp. 769–785. https://onlinelibrary.wiley.com/doi/abs/10.1002/atr.1374.
33.
CaceresN.RomeroL. M.MoralesF. J.ReyesA.BenitezF. G.Estimating Traffic Volumes on Intercity Road Locations Using Roadway Attributes, Socioeconomic Features and Other Work-Related Activity Characteristics. Transportation, Vol. 45, No. 5, 2018, pp. 1449–1473.
34.
MohamadD.SinhaK.KuczekT.ScholerC.Annual Average Daily Traffic Prediction Model for County Roads. Transportation Research Record: Journal of the Transportation Research Board, 1998. 1617: 69–77.
35.
XiaQ.ZhaoF.ChenZ.ShenL. D.OspinaD.Estimation of Annual Average Daily Traffic for Nonstate Roads in a Florida County. Transportation Research Record: Journal of the Transportation Research Board, 1999. 1660: 32–40.
36.
SeaverW. L.ChatterjeeA.SeaverM. L.Estimation of Traffic Volume on Rural Local Roads. Transportation Research Record: Journal of the Transportation Research Board, 2000. 1719: 121–128.
37.
ZhaoF.ChungS.Contributing Factors of Annual Average Daily Traffic in a Florida County: Exploration With Geographic Information System and Regression Models. Transportation Research Record: Journal of the Transportation Research Board, 2001. 1769: 113–122.
38.
ZhaoF.ParkN.Using Geographically Weighted Regression Models to Estimate Annual Average Daily Traffic. Transportation Research Record: Journal of the Transportation Research Board, 2004. 1879: 99–107.
39.
DoustmohammadiM.AndersonM.Developing Direct Demand AADT Forecasting Models for Small and Medium Sized Urban Communities. International Journal of Traffic and Transportation Engineering, Vol. 5, No. 2, 2016, pp. 27–31.
40.
ChenP.HuS.ShenQ.LinH.XieC.Estimating Traffic Volume for Local Streets With Imbalanced Data. Transportation Research Record: Journal of the Transportation Research Board, 2019. 2673: 598–610.
41.
DiezD.BarrC.Cetinkaya-RundelM.OpenIntro Statistics, 3rd ed.OpenIntro, Boston, MA, 2012, 175 p. https://leanpub.com/os.
42.
ZoicoE.RossiA.Di FrancescoV.SepeA.OliosoD.PizziniF.FantinF., et al. Adipose Tissue Infiltration in Skeletal Muscle of Healthy Elderly Men: Relationships With Body Composition, Insulin Resistance, and Inflammation at the Systemic and Tissue Level. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, Vol. 65A, No. 3, 2010, pp. 295–299.
43.
TroyA.Morgan GroveJ.O’Neil-DunneJ.The Relationship Between Tree Canopy and Crime Rates Across an Urban-Rural Gradient in the Greater Baltimore Region. Landscape and Urban Planning, Vol. 106, No. 3, 2012, pp. 262–270. https://doi.org/10.1016/j.landurbplan.2012.03.010.
44.
TibshiraniR.Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), Vol. 58, No. 1, 1996, pp. 267–288.
45.
MusoroJ. Z.ZwindermanA. H.PuhanM. A.Ter RietG.GeskusR. B.Validation of Prediction Models Based on Lasso Regression With Multiply Imputed Data. BMC Medical Research Methodology, Vol. 14, No. 1, 2014, pp. 1–13.
46.
ChenX. (Michael)ZhangD.LiL.Multi-Model Ensemble for Short-Term Traffic Flow Prediction Under Normal and Abnormal Conditions. IET Intelligent Transport Systems, Vol. 13, No. 2, 2019, pp. 260–268. https://onlinelibrary.wiley.com/doi/10.1049/iet-its.2018.5155.
47.
Al-SahiliO.Al-DeekH.SandtA.MantzarisA. V.RogersJ. H.FarukM. O.Investigating and Modeling the Illegal U-Turn Violations at Medians of Limited Access Facilities. Transportation Research Record: Journal of the Transportation Research Board, 2018. 2672: 73–84.
48.
OgutuJ. O.Schulz-StreeckT.PiephoH. P.Genomic Selection Using Regularized Linear Regression Models: Ridge Regression, Lasso, Elastic Net and their Extensions. BMC Proceedings, Vol. 6, Supplement 2, 2012, pp. 1–6. http://www.biomedcentral.com/1753-6561/6/S2/S10.
49.
ZouH.HastieT.Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 67, No. 2, 2005, pp. 301–320.
50.
HastieT.TibshiraniR.FriedmanJ.Elements of Statistical Learning, 2nd ed. Springer Science+Business Media, New York, NY, Vol. 27. 2009, 745 p. https://hastie.su.domains/Papers/ESLII.pdf.
51.
MelkumovaL. E.ShatskikhS. Y.Comparing Ridge and LASSO Estimators for Data Analysis. Procedia Engineering, Vol. 201, 2017, pp. 746–755. https://doi.org/10.1016/j.proeng.2017.09.615.
52.
YangY.ZouH.A Fast Unified Algorithm for Solving Group-Lasso Penalize Learning Problems. Statistics and Computing, Vol. 25, No. 6, 2015, pp. 1129–1141. https://doi.org/10.1007/s11222-014-9498-5.
53.
WongT. T.Performance Evaluation of Classification Algorithms by k-Fold and Leave-One-Out Cross Validation. Pattern Recognition, Vol. 48, No. 9, 2015, pp. 2839–2846. https://doi.org/10.1016/j.patcog.2015.03.009.
54.
Moreno-TorresJ. G.SáezJ. A.HerreraF.Study on the Impact of Partition-Induced Dataset Shift on k-Fold Cross-Validation. IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 8, 2012, pp. 1304–1312.
55.
YadavS.ShuklaS.Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification. Proc., IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, India, IEEE, New York, 2016, pp. 78–83.
CarliR.DotoliM.EpicocoN.AngelicoB.VinciulloA.Automated Evaluation of Urban Traffic Congestion Using Bus as a Probe. Proc., IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden, IEEE, New York, October8, 2015, pp. 967–972.
GitelmanV.KorchatovA.CarmelR.Safety-Related Behaviours of E-Cyclists on Urban Streets: An Observational Study in Israel. Transportation Research Procedia, Vol. 60, 2022, pp. 609–616. https://doi.org/10.1016/j.trpro.2021.12.079.
GitelmanV.DovehE.BekhorS.The Relationship Between Free-Flow Travel Speeds, Infrastructure Characteristics and Accidents, on Single-Carriageway Roads. Transportation Research Procedia, Vol. 25, 2017, pp. 2026–2043. https://doi.org/10.1016/j.trpro.2017.05.398.
64.
GrayR. C.QuddusM. A.EvansA.Injury Severity Analysis of Accidents Involving Young Male Drivers in Great Britain. Journal of Safety Research, Vol. 39, No. 5, 2008, pp. 483–495. https://doi.org/10.1016/j.jsr.2008.07.003.
65.
ŠliupasT.The Impact of Road Parameters and the Surrounding Area on Traffic Accidents. Transport, Vol. 24, No. 1, 2009, pp. 42–47.
66.
AleA. S.Assessment of Dual Carriageway for Sustainable Socio-Economic Development in Ado Ekiti, Nigeria. International Journal of Sustainable Development & World Policy, Vol. 7, No. 1, 2019, pp. 27–36. https://archive.conscientiabeam.com/index.php/26/article/view/2056.
67.
FountasG.FonzoneA.GharaviN.RyeT.The Joint Effect of Weather and Lighting Conditions on Injury Severities of Single-Vehicle Accidents. Analytic Methods in Accident Research, Vol. 27, 2020, p. 100124. https://doi.org/10.1016/j.amar.2020.100124.
68.
SołowczukA. B.KacprzakD.Effect of Lane Narrowing Implemented on an Urban Dual Carriageway on Speed Reduction and Acoustic Environment. Buildings, Vol. 12, No. 1, 2021, p. 31.
69.
SaezM.BarcelóM. A.FarreronsM.López-CasasnovasG.The Association Between Exposure to Environmental Factors and the Occurrence of Attention-Deficit/Hyperactivity Disorder (ADHD). A Population-Based Retrospective Cohort Study. Environmental Research, Vol. 166, 2018, pp. 205–214. https://doi.org/10.1016/j.envres.2018.05.009.
JianqinZ.PeiyuanQ.YingchaoD.MingyiD.FengL.A Space-Time Visualization Analysis Method for Taxi Operation in Beijing. Journal of Visual Languages and Computing, Vol. 31, 2015, pp. 1–8. https://doi.org/10.1016/j.jvlc.2015.09.002.
72.
Van ZuylenH.TaaleH.Urban Networks With Ring Roads. Transportation Research Record: Journal of the Transportation Research Board, 2004. 1984: 180–187.
73.
ChhornV.PrabnasakJ.Chompoo-InwaiC.An Evaluation of Public Truck Terminal Implementation and Truck Ban Policy in a Medium- Size City of Thailand: A Case Study of Khon Kaen City. MATEC Web of Conferences, Vol. 192, 2018, p. 02060.
74.
NugmanovaA.ArndtW.-H.HossainM. A.KimJ. R.Effectiveness of Ring Roads in Reducing Traffic Congestion in Cities for Long Run: Big Almaty Ring Road Case Study. Sustainability, Vol. 11, No. 18, 2019, p. 4973. https://www.mdpi.com/2071-1050/11/18/4973.
CaselliM.de GennaroG.TrizioA.MarzoccaL.TutinoM.Assessment of the Impact of the Vehicular Traffic on BTEX Concentration in Ring Roads in Urban Areas of Bari (Italy). Chemosphere, Vol. 81, No. 3, 2010, pp. 306–311. https://doi.org/10.1016/j.chemosphere.2010.07.033.
78.
LozhkinV.GavkalykB.LozhkinaO.EvtukovS.GinzburgG.Monitoring of Extreme Air Pollution on Ring Roads With PM2.5 Soot Particles Considering their Chemical Composition (Case Study of Saint Petersburg). Transportation Research Procedia, Vol. 50, 2020, pp. 381–388. https://doi.org/10.1016/j.trpro.2020.10.045.
79.
Baum-SnowN.BrandtL.HendersonJ. V.TurnerM. A.ZhangQ.Roads, Railroads, and Decentralization of Chinese Cities. The Review of Economics and Statistics, Vol. 99, No. 3, 2017, pp. 435–448. https://direct.mit.edu/rest/article/99/3/435-448/58455.
80.
Van NesA.The Impact of the Ring Roads on the Location Pattern of Shops in Town and City Centres. A Space Syntax Approach. Sustainability, Vol. 13, No. 7, 2021, p. 3927. https://www.mdpi.com/2071-1050/13/7/3927.
PapagiannakisA.BaraklianosI.SpyridonidouA.Urban Travel Behaviour and Household Income in Times of Economic Crisis: Challenges and Perspectives for Sustainable Mobility. Transport Policy, Vol. 65, 2018, pp. 51–60. https://doi.org/10.1016/j.tranpol.2016.12.006.
83.
e SilvaJ. A.MorencyC.GouliasK. G.Using Structural Equations Modeling to Unravel the Influence of Land Use Patterns on Travel Behavior of Workers in Montreal. Transportation Research Part A: Policy and Practice, Vol. 46, No. 8, 2012, pp. 1252–1264.
84.
AditjandraP. T.CaoX.MulleyC.Understanding Neighbourhood Design Impact on Travel Behaviour: An Application of Structural Equations Model to a British Metropolitan Data. Transportation Research Part A: Policy and Practice, Vol. 46, No. 1, 2012, pp. 22–32. https://doi.org/10.1016/j.tra.2011.09.001.
85.
DiaoM.FerreiraJ.Vehicle Miles Traveled and the Built Environment: Evidence From Vehicle Safety Inspection Data. Environment & Planning A, Vol. 46, No. 12, 2014, pp. 2991–3009.
86.
PowerA.Social Inequality, Disadvantaged Neighbourhoods and Transport Deprivation: An Assessment of the Historical Influence of Housing Policies. Journal of Transport Geography, Vol. 21, 2012, pp. 39–48. https://doi.org/10.1016/j.jtrangeo.2012.01.016.
87.
AmrA.Spatial Inertia and Separation in a Hierarchy-Driven Structure: Four Case Studies of Flow Continuity Along Ring Roads. Urban, Planning and Transport Research, Vol. 8, No. 1, 2020, pp. 171–195. https://doi.org/10.1080/21650020.2020.1843527.
88.
ZhaoP.LiS.Restraining Transport Inequality in Growing Cities: Can Spatial Planning Play a Role?International Journal of Sustainable Transportation, Vol. 10, No. 10, 2016, pp. 947–959. https://doi.org/10.1080/15568318.2016.1191693.
89.
SaifM. A.ZefrehM. M.TorokA.Public Transport Accessibility: A Literature Review. Periodica Polytechnica Transportation Engineering, Vol. 47, No. 1, 2018, pp. 36–43. https://pp.bme.hu/tr/article/view/12072.
90.
OwenD.HogarthT.GreenA. E.Skills, Transport and Economic Development: Evidence From a Rural Area in England. Journal of Transport Geography, Vol. 21, 2012, pp. 80–92. https://doi.org/10.1016/j.jtrangeo.2012.01.015.
91.
BasagañaX.Triguero-MasM.AgisD.PérezN.RecheC.AlastueyA.QuerolX.Effect of Public Transport Strikes on Air Pollution Levels in Barcelona (Spain). Science of the Total Environment, Vol. 610–611, 2018, pp. 1076–1082. https://doi.org/10.1016/j.scitotenv.2017.07.263.
92.
BauernschusterS.HenerT.RainerH.When Labor Disputes Bring Cities to a Standstill: The Impact of Public Transit Strikes on Traffic, Accidents, Air Pollution, and Health. American Economic Journal. Economic Policy, Vol. 9, No. 1, 2017, pp. 1–37.
93.
BeckerF.AxhausenK. W.Literature Review on Surveys Investigating the Acceptance of Automated Vehicles. Transportation, Vol. 44, No. 6, 2017, pp. 1293–1306.
94.
KwonS. B.NamgungH. G.JeongW.ParkD.EomJ. K.Transient Variation of Aerosol Size Distribution in an Underground Subway Station. Environmental Monitoring and Assessment, Vol. 188, No. 6, 2016, pp. 1–11. https://doi.org/10.1007/s10661-016-5373-5.
95.
SerianiS.FernándezR. Planning Guidelines for Metro–Bus Interchanges by Means of a Pedestrian Microsimulation Model. Transportation Planning and Technology, Vol. 38, No. 5, 2015, pp. 569–583. https://doi.org/10.1080/03081060.2015.1039235.
96.
KangL.ZhuX.SunH.WuJ.GaoZ.HuB.Last Train Timetabling Optimization and Bus Bridging Service Management in Urban Railway Transit Networks. Omega, Vol. 84, 2019, pp. 31–44. https://doi.org/10.1016/j.omega.2018.04.003.
97.
HongS. P.MinY. H.ParkM. J.KimK. M.OhS. M.Precise Estimation of Connections of Metro Passengers From Smart Card Data. Transportation, Vol. 43, No. 5, 2016, pp. 749–769. https://doi.org/10.1007/s11116-015-9617-y.
98.
BentoA.CropperM. L.MobarakA. M.VinhaK.The Effects of Urban Spatial Structure on Travel Demand in the United States. The Review of Economics and Statistics, Vol. 87, No. 3, 2005, pp. 466–478.
99.
FriedmanB.GordonS.PeersJ.Effect of Neotraditional Neighborhood Design on Travel Characteristics. Transportation Research Record: Journal of the Transportation Research Board, 1994. 1466: 63–70. http://onlinepubs.trb.org/Onlinepubs/trr/1994/1466/1466-009.pdf.
100.
RAND. Motorcycles and Congestion: The Effect of Modal Split. Full Summary. Department for Transport (With Support From Transport for London). London, 2004. http://mwigan.com/mrw/ewExternalFiles/04 0800 wsp dft tfl Motorcycles and congestion final summary report.pdf.
101.
WongK. I.An Analysis of Car and Motorcycle Ownership in Macao. International Journal of Sustainable Transportation, Vol. 7, No. 3, 2013, pp. 204–225.
HoerlA. E.KennardR.W.Ridge Regression: Applications to Nonorthogonal Problems. Technometrics, Vol. 12, No. 1, 1970, pp. 69–82.
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