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Joshi, Gaurang
- Traffic Data Analysis Using Image Processing Technique on Delhi-Gurgaon Expressway
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Authors
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1 Civil Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, IN
2 Civil Engineering Department, Indian Institute of Technology, Guwahati 781 039, IN
1 Civil Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, IN
2 Civil Engineering Department, Indian Institute of Technology, Guwahati 781 039, IN
Source
Current Science, Vol 110, No 5 (2016), Pagination: 808-822Abstract
With the advancements in video image processing system (VIPS), detection mechanism has made a significant improvement over traditional methods for traffic data analysis. Traffic on Delhi-Gurgaon expressway is heterogeneous in nature with non-lane based behaviour. Moreover, automation and instrumentation are also not implemented. In view of this, TRaffic AnalyZer and EnumeratoR (TRAZER), a VIPS was used to process video-captured data on Delhi-Gurgaon expressway to check accuracy based on traffic count, speed and lateral placement. The motivation behind using TRAZER is to evaluate its efficiency and robustness for extracting micro and macro-level traffic parameters under heterogeneous traffic conditions. To achieve this, data were extracted manually on above parameters and compared with those obtained from TRAZER. The volume count data from TRAZER generated a lesser accuracy of 60% detection under heavy traffic conditions, using default parameters. Thus, refinements were carried out in the software as part of calibration: (i) redefining maximum and minimum detection widths for each vehicle category, and (ii) selecting the optimum trap length for reducing the occlusion effect, which increased the detection percentage as well as reduced the error. After implementing these refinements, 80% of the vehicles were detected. Further, relationships between vehicle speed and its lateral placement from median across road width, at a given point were also developed. The models were developed for both aggregate (considering all vehicles) and disaggregate (vehicle category-wise) levels. The polynomial relationship was found to be best fitted function to estimate vehicle speed based on its lateral placement.Keywords
Lateral Placement, Speed, TRAZER, Video Image Processing System.Full Text
- Evaporation Estimation from Meteorological Parameters Using Multiple Linear Regression Model
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Authors
Affiliations
1 Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
1 Department of Soil and Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
Source
International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 503-507Abstract
Evaporation is one of the main elements affecting water storage and temperature in the hydrological cycle and it plays an important role in evaluation of water availability. Considering the difficulty involved in direct method of evaporation estimation and limitation of empirical methods, an attempt has been made to estimate evaporation by multiple linear regression with the aid of gamma test (GT). The data of meteorological parameters viz., average temperature (Tavg), wind speed (W), average relative humidity (Rhavg) and sunshine hours (S) were used as input parameters and evaporation was considered as output parameter. The performance of developed model was evaluated in terms of mean squared error (MSE) and correlation co-efficient (r). In developed model, MSE was found to be 1.13 and 0.92 in training and testing phase, respectively. The model demonstrated good values of correlation co-efficient, respectively as 0.91 and 0.95 for training and testing period indicating the suitability of model to estimate the evaporation.Keywords
Evaporation, Meteorological Parameters, MLR, Gamma Test.
References
- Abramson, I.S. (1982). On bandwidth variation in kernel estimates-a square ischolar_main law. Ann. Statist., 10 : 1217–1223. MR0673656
- Abtew, W. (2001). Evaporation estimation for Lake Okeechobee in South Florida. Irrigation & Drainage Engg., 127 : 140-147.
- Agalbjorn, S., Koncar, N. and Jones, A.J. (1997). A note on the gamma test.Neural Comput. Appl., 5(3) : 131–133. ISSN 0-941-0643.
- Almedeij, J. (2012). Modeling pan evaporation for Kuwait by multiple linear regression.The Scientific World J., 2012 (article id 574742), pp. 1-9.
- Alshaikh, A. (1998). Analysis of evaporation data as affected by climatic factors in arid regions. Water and land resources development and management for sustainable use. Vol. II-A. The tenth ICID Afro-Asian regional conference on irrigation and drainage, Denpasar, Bali, Indonesia, 19-26 July 1998. A-27, 9 –10.
- Anderson, M.E. and Jobson, H.E. (1982). Comparison of techniques for estimating annual lake evaporation using climatological data.Water Resour. Res., 18 : 630-636.
- Bruin, H.D. (1978). A simple model for shallow lake evaporation. Applied Meteorol., 17 : 1132-1134.
- Cahoon, E.J., Costello, T.A. and Ferguson, J.A. (1991). Estimating pan evaporation using limited meteorological observations. Agric. & Forestry Meteorol., 55 :181-190.
- Chandra, A., Shrikhande, V.J. and Kulshreshta, R. (1988). Relationship of pan evaporation with meteorological parameters. J. Indian Water Reso. Soc., 8(2) : 41- 44.
- Chiu, C.C., Cook, D.F. and Pignatiello, J.J. (1995). Radial basis function neural network for Kraft pulping forecasting. Internat. J. Ind. Eng., 2 (3) : 209–215.
- Cook, D.F. and Chiu, C.C. (1997). Predicting the internal bond strength of particleboard utilizing a radial basis function neural network. Engg. Appl. AI., 10(2) : 171–177.
- Diggle, P.J. and Hutchinson, M.F. (1989). On spline smoothing with auto-correlated errors. Australian J. Statist., 31 :166–182.
- Fan, J. and Gijbels, I. (1996). Local polynomial modeling and its applications. Chapman & Hall, London. Great lakes buoy and associated synoptic weather patterns. J. Hydro Meteorol., 3(1) : 3-12.
- Fennessey, N.M. and Vogel, R.M. (1996). Regional models of potential evaporation and reference evapotranspiration for the northeast USA. J. Hydrol., 184 : 337-354.
- Gao, X.M., Gao, X.Z., Tanskanen, J. and Ovaska, S.J. (1997). Power prediction in mobile communications systems using an optimal neural network structure. IEEE Trans. Neural Networks, 8(6) : 1446–1455.
- Hardle, W. (1990). Applied nonparametric regression. Cambridge University Press, New York.
- Hastle, T. and Loader, C. (1993). Local regression: Automatic kernel carpentry (with discussion). Statist. Sci., 8 : 120–143.
- Hoskins, J.C. and Himmelblau, D.M. (1988). Artificial neural networks models of knowledge representation in chemical engineering. Comput. Chemi. Engg., 12(9) : 881–890.
- Jones, M.C., Linton, O. and Nielsen, J.P. (1995). A simple bias reduction method for density estimation. Biometrika, 82(2) : 327–338.
- Koncar, N. (1997). Optimisation methodologies for direct inverse neurocontrol. Ph.D. Thesis, Department of computing, Imperial College of Science, Technology and Medicine, University of London.
- Murthy, S. and Gawande, S. (2006). Effect of meteorological parameters on evaporation in small reservoirs ‘Anand Sagar’ Shegaon – a case study. J. Prudushan Nirmulan, 3(2) : 52-56.
- Nottingham, Q.J. (1995). Model-robust quantile regression. Ph.D. Dissertation, Virginia.
- Robinson, P.M. (1983). Nonparametric estimators for time series. J. Time Series Anal., 4 :185–207.
- Samiuddin, M. and El-Sayyad, G. M. (1990). On nonparametric kernel density estimates.Biometrika, 77 : 865–874. MR1086696
- Senturk, K. and Kocyigit, F.O. (2010). A case study: Evaporation estimation at Oymapinar Dam. Proc., BALWOIS 2010, Balkan institute for water and environment, Montpellier, France, 1–7.
- Shrivastava, S.K., Misra, S.K., Sahu, A.K. and Bose, D. (2000). Correlation between pan evaporation and climatic parameters for Sunderbans- a case study. IE(I) Journal – AG. 81, 55-58.
- Stewart, R.B. and Rouse, W.R. (1976). A simple method for determining the evaporation from shallow lakes and ponds. Water Resour. Res., 12 : 623-627.
- Tjostheim, D. (1994). Nonlinear time series: a selective review. Scandinavian J. Statist., 21 : 97–130.
- Tsui, A.P.M., Jones, A.J. and Oliveira, A.G. (2002). The construction of smooth models usingirregular embeddings determined by a gamma test analysis. Neural Comput Appl., 10(4) : 318–329. doi:10.1007/s005210200004
- Wand, M.P. and Jones, M.C. (1995). Kernel smoothing, Chapman and Hall, London. MR1319818.
- Yakowitz, S. (1987). Nearest-neighbor methods for time series analysis. J. Time Series Anal., 8 : 235–247.
- Examining Traffic Flow Parameters at Merging Section on High-Speed Urban Roads in India
Abstract Views :254 |
PDF Views:95
Authors
Affiliations
1 Civil Engineering Department, Muzaffarpur Institute of Technology, Muzaffarpur 842 003, IN
2 Civil Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, IN
1 Civil Engineering Department, Muzaffarpur Institute of Technology, Muzaffarpur 842 003, IN
2 Civil Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, IN
Source
Current Science, Vol 117, No 1 (2019), Pagination: 94-103Abstract
This study analyses micro-level and macro-level traffic parameters at merging location on high-speed urban roads in India. For this purpose, representative merging section was selected on Delhi–Gurgaon expressway. Speeds on different lanes at merging section were found to gradually decrease from median side lane to lane closer to merging point. At low and moderate flow conditions, most of the vehicles avoid moving in lanes closer to merging point in the main traffic stream. This is done to reduce the crash as well as to avoid competing for the same road space. Speed versus flow relationship developed for basic roadway section and merging section shows the difference in capacity by almost 8% due to merging operation. Moreover, stream speed at the merging is found to be the function of main stream flow and entry flow. The findings from the study would be useful in operational analysis of merging sections on high-speed urban roads in India.Keywords
Heterogeneous Traffic, Merging Section, Time Headway, Speed Distribution, Speed-flow Relationship.Full Text
References
- Elefteriadou, L., Roess, R. P. and McShane, W. R., Probabilistic nature of breakdown at freeway merge junctions. Trans. Res. Rec., 1995, 1484(1484), 80–89.
- Kerner, B. and Rehborn, H., Experimental properties of complexity in traffic flow. Phys. Rev. E, 1996, 53(5), R4275–R4278; https://doi.org/10.1103/PhysRevE.53.R4275.
- Kerner, B. S. and Rehborn, H., Experimental properties of phase transitions in traffic flow. Phys. Rev. Lett., 1997, 79, 4030–4033; https://doi.org/10.1103/PhysRevLett.79.4030.
- Kondyli, A. and Elefteriadou, L., Modeling driver behavior at freeway-ramp merges. Transp. Res. Rec.: J. Trans. Res. Board, 2011, 2249(2249), 29–37; https://doi.org/10.3141/2249-05.
- Sarvi, M. and Kuwahara, M., Microsimulation of freeway ramp merging processes under congested traffic conditions. IEEE Trans. Intel. Trans. Syst., 2007, 8(3), 470–479; https://doi.org/10.1109/ TITS.2007.895305.
- Sarvi, M. and Kuwahara, M., Using ITS to improve the capacity of freeway merging sections by transferring freight vehicles. IEEE Trans. Intell. Transp. Syst., 2008, 9(4), 580–588; https://doi.org/10.1109/TITS.2008.2006812.
- Weng, J., Xue, S. and Yan, X., Modeling vehicle merging behavior in work zone merging areas during the merging implementation period. IEEE Trans. Intell. Trans. Syst., 2016, 17(4), 917– 925; https://doi.org/10.1109/TITS.2015.2477335.
- AASHTO, A policy on geometric design of highways and streets. Washington, DC, 2011.
- Kumar, P. Arkatkar, S., Joshi, G. and Dhamaniya, A., New Methodology for Estimating PCU On Multi Lane Urban Roads Under Mixed Traffic Scenario Based on Area Occupancy 96th Annual TRB meeting Proceedings, Washington, DC, 2017.
- Drake, J. S., Schofer, J. L. and May Jr, A. D., A statistical analysis of speed-density hypotheses. Highway Res. Rec., 1967, 154, 112– 117.
- Rakha, H. A., Validation of Van Aerde’s simplified steady-state car-following and traffic stream model. Trans. Lett.: Int. J. Transp. Res., 2009, 1(3), 227–244; https://doi.org/10.3328/Tl.2009.01.03.227-244.