Volume List  / Volume 2 (4)

Article

MEASURING URBAN TRAFFIC CONGESTION – A REVIEW

DOI: 10.7708/ijtte.2012.2(4).01


2 / 4 / 286-305 Pages

Author(s)

Amudapuram Mohan Rao - Central Road Research Institute, Mathura Road, PO CRRI, New Delhi - 110 020, India -

Kalaga Ramachandra Rao - Indian Institute of Technology Delhi, Department of Civil Engineering, Hauz Khas, New Delhi - 110 016, India -


Abstract

Traffic’ congestion has been one of major issues that most metropolises are facing. It is believed that identification of congestion is the first step for selecting appropriate mitigation measures. Congestion - both in perception and in reality - impacts the movement of people. Traffic congestion wastes time, energy and causes pollution. There are broadly two factors, which effect the congestion; (a) micro-level factors (b) macro-level factors that relate to overall demand for road use. Congestion is ‘triggered’ at the ‘micro’ level (e.g. on the road), and ‘driven’ at the ‘macro’ level. The micro level factors are, for example, many people want to move at the same time, too many vehicles for limited road space. On the other side, macro level factors are e.g. land-use patžterns, car ownership trends, regional economic dynamics, etc. This paper gives an overview and presents the possible ways to identify and measure metrics for urban arterial congestion. A systematic review is carried out, based on measurement metrics such as speed, travel time/delay and volume and level of service. The review covers distinct aspects like definition; measurement criteria followed by different countries/organizations. The strengths and weaknesses of these measures are discussed. Further, a short critique of measurement criteria is presented.


Download Article

Number of downloads: 3005


Acknowledgements:

One of the authors (Amudapuram Mohan Rao) wishes to thank Dr. S. Gangopadhyay, Director of the Central Road Research Institute, New Delhi, India, for his permission to publish this paper.


References:

Aftabuzzaman, M. 2007. Measuring Traffi’c Congestion - A Critical Review. In Proceedings of the 30th Australasian Transport Research Forum. 16 p.

 

Aworemi, J.R.; Abdul-Azeez, I.A.; Oyedokun, A.J.; Adewoye, J.O. 2009. A study of the causes, effects and ameliorative measures of road traffic congestion in Lagos metropolis, European Journal of Social Sciences, 11(1): 119-128.

 

Benjamin, C.; Cassidy, M. 2002. Vehicle re-identication and travel time measurement on congested freeways, Transportation Research Part A: Policy and Practice. DOI: httžp://dx.doi.org/10.1016/S0965-8564(01)00046-5, 36(10): 899-917.

 

Bertini, R.L. 2006. You are the Traffic Jam: An Examination of Congestion Measures [CD]. In 85th Annual Meeting of the Transportation Research Board. 17 p.

 

Boarnet, M.G.; Kim, E.J.; Parkany, E. 1998. Measuring traffi’c congestion, Transportation Research Record: Journal of the Transportation Research Board. DOI: hžttp://dx.doi.org/10.3141/1634-12, 1634: 93-99.

 

Bovy, P.H.L.; Salomon, I. 2002. Congestion in Europe: measurements, patterns and policies. In Monograph Travel Behaviour: spatial patterns, congestion and modelling. 143-179.

 

Byrne, G.E.; Mulhall, S.M. 1995. Congestion management data requirement and comparisons, Transportation Research Record: Journal of the Transportation Research Board, 1499: 28-36.

 

Cambridge Systematics Inc.; Texas Transportation Institute („I). 2005. Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation. Washington, D.C.: Federal Highway Administration. 140 p.

 

Cheung, S.Y.; Coleri, S.; Dundar, B.; Ganesh, S.; Tan, C-W.; Varaiya, P. 2004. Traffi’c measurement and vehicle classification with a single magnetic sensor. Available from Internet: httžp://paleale.eecs.berkeley.edu/~varaiya/papers_ps.dir/sensors_trb_nal4.pdf.

 

Choi, J.; Lee, C.; Lee, S.; Yu, J. 2007. Development of the Tra’ffic Congestion Index for Freeway Corridors in South Korea. In Proceedings of the 7th International Conference of Eastern Asia Society for Transportation Studies 2007.

 

Cottrell, W.D. 1991. Measurement of the extent and duration of tra’ffic congestion in urban areas. In Proceedings of the 61st Annual Meeting, Istitute of Transportation Engineers. 427-432.

 

Dewan, K.K.; Ahmad, I. 2007. Carpooling: A Step to Reduce Congestion (A Case Study of Delhi), Engineering Leers, 14(1): 61-66.

 

Dewees, D.N. 1978. Simulation of Traffic Congestion in Toronto, Transportation Research. DOI: httžp://dx.doi.org/10.1016/0041-1647(78)90118-1, 12(3): 153-161.

 

Dowling, R.; Skabardonis, A.; Carroll, M.; Wang, Z. 2004. Methodology for Measuring Recurrent and Nonrecurrent Traffi’c Congestion, Transportation Research Record: Journal of the Transportation Research Board. DOI: httžp://dx.doi.org/10.3141/1867-08, 1867: 60-68.

 

Downs, A. 2004. Still stuck in traffic: coping with peakhour traffic congestion. Washington, D.C.: The Brookings Institution. 455 p.

 

DRCOG. 2011. Annual Report (2010) on Traffic Congestion in the Denver Region. Available from Internet: httžp://www.drcog.org/agendas/2010%20Annual%20Report%205-5.pdf.

 

ECMT. 1999. The spread of congestion in Europe. Paris: OECD Publication Service. 237 p.

 

Hamad, K.; Kikuchi, S. 2002. Developing a Measure of Traffic Congestion: Fuzzy Inference Approach, Transportation Research Record: Journal of the Transportation Research Board. DOI: httžp://dx.doi.org/10.3141/1802-10, 1802: 77-85.

 

Hao, Y.; Tian-dong, X.; Li-jun, S. 2007. Analysis and control of recurrent traffi’c congestion on urban expressway. In Proceedings of the 1st International Conference on Transportation Engineering, Southwest Jiaotong University, Chengdu, China. DOI: hžttp://dx.doi.org/10.1061/40932(246)102.

 

Hao, Y.; Wang, W.; Sun, L.; Xu, T.D. 2008. Research on spatial-temporal features of urban freeway congestion. In Proceedings of the 1st International Symposium on Transportation and Development Innovative Best Practices, Beijing, China. DOI: hžttp://dx.doi.org/10.1061/40961(319)31.

 

HCM. 1985. Highway Capacity Manual. Washington, D.C.: TRB, National Research Council.

 

Hinz, S.; Meyer, F.; Eineder, M.; Bamler, M. 2007. Traffi’c monitoring with spaceborne SAR-theory, simulations, and experiments, Computer Vision and Image Understanding. DOI: http://dx.doi.org/10.1016/j.cviu.2006.09.008, 106(2-3): 231-244.

 

Hongsakham, W.; Pažttara-atikom, W.; Peachavanish, R. 2008. Estimating road traffic congestion from cellular handoff information using cell-based neural networks and k-means clustering. In Proceedings of the 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. DOI: httžp://dx.doi.org/10.1109/ECTICON.2008.4600361. 13-16.

 

Ishida, H.; Furuya, H.; Kai, S.H; Okamoto, S. 2003. Travel speed and tra’ffic congestions recognition on expressways, Journal of the Eastern Asia Society for Transportation Studies, 5: 1881-1892.

 

Ishizaka, T.; Fukuda, A.; Narupiti, S. 2005. Evaluation of probe vehicle system by using micro simulation model and cost analysis, Journal of the Eastern Asia Society for Transportation Studies, 6: 2502-2514.

 

Kockelman, K. 2004. Traffi’c congestion. In Monograph Handbook of transportation engineering. 32 p.

 

Lam, W.H.K.; Tam, M.L. 1997. Why standard modelling and evaluation procedures are inadequate for assessing traffi’c congestion measures, Transport Policy. DOI: httžp://dx.doi.org/10.1016/S0967-070X(97)00023-1, 4(4): 217-223.

 

Levinson, H.S; Lomax, T. 1996. Development of travel time congestion index, Transportation Research Record: Journal of the Transportation Research Board. DOI: httžp://dx.doi.org/10.3141/1564-01, 1564: 1-10.

 

Lindley, J.A. 1987. Urban freeway congestion: quantication of the problem and effectiveness of potential solutions, Institute of Transportation Engineers Journal, 57(1): 27-32.

 

Lomax, S.T.T.; Turner, S.; Shunk, G.; Levinson, H.S.; Praž, R.H.; Bay, P.N.; Douglas, G.B. 1997. Quantifying congestion, Volume 1, NCHRP Final Report 398. Washington, D.C.: Transportation Research Board. 108 p.

 

Long Chen, L.L.; Huang, X.; Huang, J. 2008. A traffi’c congestion estimation approach from video using timespatial imagery. In Proceedings of the First International Conference on Intelligent Networks and Intelligent Systems. DOI: httžp://dx.doi.org/10.1109/ICINIS.2008.182, 465-469.

 

Medley, S.B.; Demetsky, M.J. 2003. Development of Congestion Performance Measures Using Its Information, Final Report. Charložesville: Virginia Transportation Research Council. 43 p.

 

Merugu, D.; Prabhakar, B.; Rama, N.S. 2009. An incentive mechanism for decongesting the roads: a pilot program in Bangalore. In Proceedings of the NetEcon ‘09, ACM Workshop on the Economics of Networked Systems. 6 p.

 

Miller, M.A.; Li, K. 1994. An investigation of the costs of roadway traffic congestion: a preparatory step for IVHS benefits’ evaluation. Berkley: Institute of Transport Studies, University of California. 53 p.

 

OECD. 2006. Managing Urban Traffic Congestion, OECD report. Available from Internet: http://www. internationaltransportforum.org/Pub/pdf/07Congestion.pdf.

 

Owusu, J.; Afukaar, F.; Prah, B.E.K. 2006. Towards Improving Road Traffi’c Data Collection: The Use of GPS/ GIS. In Procedeengs of the 5th FIG Regional Conference Accra. 11 p.

 

Palubinskas, G.; Kurz, F.; Reinartz, P. 2008. Detection of traffi’c congestion in optical remote sensing imagery. In Procedeengs of the IEEE International Geoscience and Remote Sensing Symposium. DOI: http://dx.doi.org/10.1109/IGARSS.2008.4779019, 2: II-426-II-429.

 

Pattžara-atikom, W.; Pongpaibool, P.; Thajchayapong, S. 2006. Estimating Road Tra’ffic Congestion Using vehicle Velocity. In Proceedings of the 6th Intertional Conference on ITS Telecommunications. DOI: httžp://dx.doi.org/10.1109/ITST.2006.288722, 1001-1004.

 

Pucher, J.; Korattyswaropam, N.; Mittal, N.; Ittyerah, N. 2005. Urban transport crisis in India, Transport Policy. DOI: httžp://dx.doi.org/10.1016/j.tranpol.2005.02.008, 12(3): 185-198.

 

Rao, K.R.; Rao, A.M. 2009. Application of GPS for Traffi’c Studies, Indian Urban Transport Journal, 8(1): 44-55.

 

Robert, R.J.A.; Theodore, F.E. 2002. Contrasting timebased and distance-based measures for quantifying traffi’c congestion levels, Transportation Research Record: Journal of the Transportation Research Board. DOI: httžp://dx.doi.org/10.3141/1817-18, 1817: 143-148.

 

Rosenbloom, S. 1978. Peak-period traffi’c congestion: a state-of-art analysis and evaluation of effective solution, Transportation, 7(2): 167-191.

 

Rothenberg, M.J. 1985. Urban congestion in the United States-what does the future hold, Institute of Transportation Engineers Journal, 55(7): 22-39.

 

Roy, S.; Sen, R.; Kulkarni, S.; Kulkarni, P.; Raman, B.; Singh, L. 2011. Wireless Across Road: RF based Road Traffic Congestion Detection. In Proceedings of the 5th Annual Workshop on Wireless Systems: Advanced Research and Development. DOI: http://dx.doi.org/10.1109/COMSNETS.2011.5716525, 1-6.

 

Schrank, D.; Lomax, T. 2005. The 2005 Annual Urban Mobility Report. Texas: Texas Transportation Institute. 91 p.

 

Sen, R.; Sevani, S.; Sharma, P.; Koradia, Z.; Raman, B. 2009. Challenges in communication assisted road transportation systems for developing regions. In Proceedings of the 3rd ACM Workshop on Networked Systems for Developing Regions. 6 p.

 

Skabardonis, A.; Varaiya, P.; Pežy, K.F. 2003. Measuring recurrent and non-recurrent traffic congestion, Transportation Research Record: Journal of the Transportation Research Board. DOI: httžp://dx.doi.org/10.3141/1856-12, 1856: 118-124.

 

Stathopoulos, A.; Karlaftis, M.G. 2002. Modeling Duration of Urban Traffic Congestion, Journal of Transportation Engineering. DOI: http://dx.doi.org/10.1061/(ASCE)0733-947X(2002)128:6(587), 128(6): 587-590.

 

Sun, W.; Zhengyu, D.; Xue, Y. 2009. Experimental Features of Urban Freeway Traffi’c Flow. In Proceedings of the International Conference on Transportation Engineering. DOI: http://dx.doi.org/10.1061/41039(345)592, 3590-3595.

 

Taylor, M.A.P.; Woolley, J.E.; Zito, R. 2000. Integration of the global positioning system and geographical information systems for traffic congestion studies, Transportation Research Part C: Emerging Technologies. DOI: hžttp://dx.doi.org/10.1016/S0968-090X(00)00015-2, 8(1-6): 257-285.

 

Thurgood, G.S. 1995. Development of freeway congestion index using an instrumented vehicle, Transportation Research Record: Journal of the Transportation Research Board, 1494: 21-29.

 

Transportation Planning Rule (TPR) Oregon. 1991. Statewide Congestion Overview for Oregon. Available from Internet: httžp://cms.oregon.gov/ODOT/TD/TP/docs/cm_hers/overview0204.pdf.

 

Turner, S.M. 1992. Examination of indicators of congestion level, Transportation Research Record: Journal of the Transportation Research Board, 1360: 150-157.

 

Varaiya, P. 2001. Freeway Performance Measurement System: Final Report. Available from Internet: httžp://www.path.berkeley.edu/PATH/Publications/PDF/PWP/2001/PWP-2001-01.pdf.

 

Victoria Transport Policy Institute (VTPI). 2005. Congestion reduction strategies: identifying and evaluating strategies to reduce congestion. Available from Internet: httžp://www.vtpi.org/tdm/tdm96.htm.

 

Vuchic, V.R.; Kikuchi, S. 1994. The bus transit system: its underutilized potential, Report DOT-T-94-29, Washington, D.C.: Federal Transit Administration. 82 p.

 

Weisbrod, G.; Vary, D.; Treyz, G. 2001. Economic Implications of congestion, NCHRP Report 463. Washington, D.C.: Transportation Research Board. 47 p.

 

Zhang, S.; Gang, R. 2009. Quantitative Analysis Model of Urban Tra’ffic Congestion State. In Proceedings of the 2nd International Conference on Transportation Engineering. DOI: hžttp://dx.doi.org/10.1061/41039(345)174, 1051-1056.

 

Zhengyu, D.; Liu, L; Sun, W. 2009. Traffi’c Congestion Analysis of Shanghai Road Network Based On Floating Car Data. In Proceedings of the 2nd International Conference on Transportation Engineering. DOI: http://dx.doi.org/10.1061/41039(345)450, 2731-2736.


Quoted IJTTE Works



Related Keywords