Current Science

Open Access Open Access  Restricted Access Subscription Access

A Comparative Study on Application of Time Series Analysis for Traffic Forecasting in India: Prospects and Limitations


Affiliations
1 Zachry Department of Civil Engineering, Texas A&M University, College Station 77840, TX, United States
2 Department of Agricultural Economics, Texas A&M University, College Station 77843, TX, United States
3 Department of Civil Engineering, National Institute of Technology, Surat 395 007, India
4 Department of Civil Engineering, Birla Institute of Technology and Science, Pilani 333 031, India
 

Modelling of growth trend and improvement in forecasting techniques for vehicular population has always been and will continue to be of paramount importance for any major infrastructure development initiatives in the transportation engineering sector. Although many traditional as well as some advanced methods are in vogue for this process of estimation, there has been a continuous quest for improving on the accuracy of different methods. Time-series (TS) analysis technique has been in use for short-term forecasting in the fields of finance and economics, and has been investigated here for its prospective use in traffic engineering. Towards this end, results obtained from two other traditional approaches, namely trend line analysis and econometric analysis, have also been collated, underlining the better results obtained from TS analysis. A regression model has been developed for predicting fatality rate and its results have been compared with those from TS analysis. Based on the incentive provided by reduced errors obtained from using increasing number of data points for model-building, forecasting has been done for the year 2021 using time-series modelling. With most of the datasets used and locations analysed for forecasting, the TS analysis technique has been found to be a useful tool for prediction, resulting in lower estimation errors for almost all the cases considered. It has also been inferred that the proximity of the forecasting window to the sample dataset has a noticeable effect on the accuracy of time-series forecasting, in addition to the amount of data used for analysis.

Keywords

Regression Model, Time-Series Analysis, Traffic Forecasting, Transportation Engineering.
User
Notifications
Font Size

  • Dhingra, S. L. et al., Application of time series techniques for forecasting truck traffic attracted by the Bombay metropolitan region. J. Adv. Transp., 1993, 27(3), 227–249.

  • Matas, A. et al., Demand forecasting in the evaluation of projects. Working Paper in Economic Evaluation of Transportation Projects, Centro de Estudios y Experimentación de Obras Públicas (CEDEX), 2009, pp. 1–31.

  • Skamris, M. K. and Flyvbjerg, B., Inaccuracy of traffic forecasts and cost estimates on large transport projects. Transp. Policy, 1997, 4(3), 141–146.

  • Cervero, R., Are induced traffic studies inducing bad investments? ACCESS, 2003, 22, 22–27.

  • Cervero, R. and Hansen, M., Induced travel demand and induced road investment: a simultaneous equation analysis. J. Transp. Econ. Policy, 2002, 36(3), 469–490.

  • Hymel, K. M. et al., Induced demand and rebound effects in road transport. Transp. Res. Board, Methodol., 2010, 44(10), 1220–1241.

  • Ramsey, S., Of mice and elephants. ITE J., 2005, 75(9), 38.

  • Clark, S., Traffic prediction using multivariate nonparametric regression. J. Transp. Eng., ASCE, 2003, 129(2), 161–168.

  • Kadiyali, L. R., Road transport demand forecast for 2000 AD. J. Indian Roads Congress, 1987, 48(3), 353–432.

  • Kadiyali, L. R. and Shashikala, T. V., Road transport demand forecast for 2000 AD revisited and demand forecast for 2021. J. Indian Roads Congress, 2009, 557, 235–237.

  • Project: Feasibility for 6-laning of NH-2 from Delhi–Agra project on DBFO pattern under NHDP Phase V, Consulting Engineering Services, New Delhi, India, October 2007, chapter 3.

  • Bhar, L. M. and Sharma, V. K., Time-series analysis. Indian Agricultural Statistics Research Institute, New Delhi, 2005, pp. 1–15.

  • Nihan, N. L. and Holmesland, K. O., Use of Box and Jenkins time series technique in traffic forecasting. Transportation, 1980, 9(2), 125–143.

  • Oswald, R. K. et al., Traffic flow forecasting using approximate nearest neighbor nonparametric Regression. Research Report No. UVACTS-15-13-7, Center for Transportation Studies at the University of Virginia, USA, 2001.

  • Gazis, D. and Knapp, C., On-line estimation of traffic densities from time series of traffic and speed data. Transp. Sci., 1971, 5(3), 283–301.

  • Levin, M. and Tsao, Y., On forecasting freeway occupancies and volumes. Transp. Res. Rec., 1980, 773, 47–49.

  • Ahmed, M. S. and Cook, A. R., Analysis of freeway traffic timeseries data by using Box–Jenkins techniques. Transp. Res. Rec., 1982, 722, 1–9.

  • Okutani, I. and Stephanedes, Y., Dynamic prediction of traffic volume through Kalman filtering theory. Transp. Res., Part B, 1984, 18(1), 1–11.

  • Moorthy, C. K. and Ratcliffe, B. G., Short term traffic forecasting using time series methods. Transp. Plann. Technol., 1988, 12(1), 45–56.

  • Stamatiadis, C. and Taylor, W., Travel time predictions for dynamic route guidance with a recursive adaptive algorithm. In Paper Presented at the 73rd Annual Meeting of Transportation Research Board, Washington, DC, USA, 1994.

  • Hamed, M. M., Al-Masaeid, H. R. and Said, Z. M. B., Short-term prediction of traffic volume in urban arterials. J. Transp. Eng., ASCE, 1995, 121(3), 249–254.

  • Chang, J. L. and Miaou, S. P., Real-time prediction of traffic flows using dynamic generalized linear models. Transp. Res. Rec., 1999, 1678, 168–178.

  • D’Angelo, M., Al-Deek, H. and Wang, M., Travel time prediction for freeway corridors. Transp. Res. Rec., 1999, 1676, 184–191.

  • Lee, S. and Fambro, D., Application of the subset ARIMA model for short-term freeway traffic volume forecasting. Transp. Res. Rec., 1999, 1678, 179–188.

  • Williams, B. M., Multivariate vehicular traffic flow prediction: an evaluation of ARIMAX modeling. In Paper Presented at the 80th Annual Meeting of Transportation Research Board, Washington DC, USA, 2001.

  • Ishak, S. and Al-Deek, H., Performance evaluation of a short-term time-series traffic prediction model. J. Transp. Eng., ASCE, 2002, 128(6), 490–498.

  • Tang, Y. F. and Lam, W. H. K., Annual average daily traffic forecasts in Hong Kong. J. East Asia Soc. Transp. Stud., 2001, 4(3), 145–158.

  • Smith, B. L., Williams, B. M. and Oswald, R. K., Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C, 2002, 10(4), 303–321.

  • Tang, Y. F., Lam, W. H. K. and Pan, L. P., Comparison of four modeling techniques for short-term AADT forecasting in Hong Kong. J. Transp. Eng. ASCE, 2003, 129(3), 223–329.

  • Chandra, R. S. and Al-Deek, H., Cross correlation analysis and multivariate prediction of spatial time series of freeway traffic speeds. Transp. Res. Rec., 2008, 2061, 64–76.

  • Jha, K. et al., Modeling growth trend and forecasting techniques for vehicular population in India. Int. J. Traffic Transp. Eng., 2013, 3(2), 139–158.

  • Box, G. E. P. and Jenkins, G. M., Time Series Analysis: Forecasting and Control, 1976, Holden-Day, San Francisco.

  • Performance Measurement System Database, California Department of Transportation, USA; http://www.pems.dot.ca.gov (accessed on 29 March 2012)

  • Pankratz, A., Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, 1983, John Wiley, New York.

  • Naylor, T. H., Seaks, T. G. and Wichevn, D. W., Box–Jenkins methods: an alternative to economic forecasting. Int. Stat. Rev., 1972, 40(2), 123–137.

  • Nelson, C. R., Applied Time Series Analysis: For Managerial Forecasting, Holden-Day, San Francisco, 1973, pp. 139–169.

  • Vishwas, M. et al., Some issues pertaining to sustainability of road transport operations, road construction and maintenance in India over the next twenty years. J. Indian Roads Cong., 2012, 73(2), 135–158.


Abstract Views: 397

PDF Views: 154




  • A Comparative Study on Application of Time Series Analysis for Traffic Forecasting in India: Prospects and Limitations

Abstract Views: 397  |  PDF Views: 154

Authors

Kartikeya Jha
Zachry Department of Civil Engineering, Texas A&M University, College Station 77840, TX, United States
Nishita Sinha
Department of Agricultural Economics, Texas A&M University, College Station 77843, TX, United States
Shriniwas S. Arkatkar
Department of Civil Engineering, National Institute of Technology, Surat 395 007, India
Ashoke K. Sarkar
Department of Civil Engineering, Birla Institute of Technology and Science, Pilani 333 031, India

Abstract


Modelling of growth trend and improvement in forecasting techniques for vehicular population has always been and will continue to be of paramount importance for any major infrastructure development initiatives in the transportation engineering sector. Although many traditional as well as some advanced methods are in vogue for this process of estimation, there has been a continuous quest for improving on the accuracy of different methods. Time-series (TS) analysis technique has been in use for short-term forecasting in the fields of finance and economics, and has been investigated here for its prospective use in traffic engineering. Towards this end, results obtained from two other traditional approaches, namely trend line analysis and econometric analysis, have also been collated, underlining the better results obtained from TS analysis. A regression model has been developed for predicting fatality rate and its results have been compared with those from TS analysis. Based on the incentive provided by reduced errors obtained from using increasing number of data points for model-building, forecasting has been done for the year 2021 using time-series modelling. With most of the datasets used and locations analysed for forecasting, the TS analysis technique has been found to be a useful tool for prediction, resulting in lower estimation errors for almost all the cases considered. It has also been inferred that the proximity of the forecasting window to the sample dataset has a noticeable effect on the accuracy of time-series forecasting, in addition to the amount of data used for analysis.

Keywords


Regression Model, Time-Series Analysis, Traffic Forecasting, Transportation Engineering.

References





DOI: https://doi.org/10.18520/cs%2Fv110%2Fi3%2F373-385