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Analysis of the Determinants of Service Headway Variability at Tollbooths Under Mixed Traffic Scenario in Emerging Countries


Affiliations
1 Department of Civil Engineering, NIT Campus P. O., Kozhikode 673 601, India
2 Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India
 

This study makes an effort to model service-headway distribution at manually operated toll plazas (MTC) under mixed traffic conditions. To identify the most suitable probability distribution among the selected candidate distributions, Kolmogorov–Smirnov, Ander-son–Darling, and chi-squared tests were performed. It was found that the generalized extreme value (GEV) was the most suited distribution for modelling service-headway distribution at tollbooths. The results show that GEV distribution parameters can capture possi-ble variations in service headway at tollbooths under MTC reasonably well. The study results can also be used for capacity and level-of-service estimation and the development of warrants for converting MTC to electronic lanes.

Keywords

Generalized Extreme Value, Mixed Traffic Conditions, Probability Distribution, Service Headway, Shape Factor, Tollbooth.
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  • Al-Ghamdi, A. S., Analysis of time headways on urban roads: a case study from Riyadh. J. Transp. Syst. ASCE, 2001, 127, 289–294.

  • Arasan, V. and Koshi, R., Headway distribution of heterogeneous traffic on urban arterials. J. Inst. Eng., 2003, 84, 210–215.

  • Chandra, S. and Kumar, R., Headway modeling under mixed traffic on urban roads. Road Transp. Res., 2001, 10, 61–79.

  • Dey, P. P. and Chandra, S., Desired time gap and time headway in steady-state car-following on two-lane roads. J. Transp. Eng., 2009, 135, 687–693.

  • Katti, B. and Pathak, R., A study on headway distribution models for the urban road sections under mixed traffic conditions. Highway Research Bulletin 26, Indian Roads Congress. New Delhi, 1985, pp. 1–31.

  • Ramanayya, T., Highway capacity under mixed traffic conditions. Traf. Eng. Control, 1988, 29, 284–287.

  • Rossi, R., Gastaldi, M. and Pascucci, F., Gamma-GQM time headway model: endogenous effects in rural two-lane two-way roads. Proc. – Soc. Behav. Sci., 2014, 111, 859–868.

  • Panichpapiboon, S., Time-headway distributions on an expressway: case of Bangkok. J. Transp. Eng. ASCE, 2015, 141, 1–8.

  • Yun, M. and Huang, L., Distribution characteristics of headway at weaving section of signalized intersection upstream. International Conference on Transportation Engineering, ASCE, 2015, 1346– 1353.

  • Tan, J., Li, L., Li, Z. and Zhang, Y., Distribution models for start-up lost time and effective departure flow rate. Transp. Res. Part A, 2013, 51, 1–11.

  • Woo, H. T. and Hoel, L. A., Toll plaza capacity and level of service. Transp. Res. Rec.: J. Transp. Res. Board, 1991, 1320, 119–127.

  • Zarrillo, M. L. and Radwan, A. E., Methodology SHAKER and the capacity analysis of five toll plazas. J. Transp. Eng. ASCE, 2009, 135, 83–93.

  • Osborne, R. P., Implementing Toll Plaza Analysis into Freeplan, University of Florida, USA, 2012.

  • Liu, X., Yun, M. and Yang, X., Lane capacity estimation and level of service evaluation for freeway toll plazas based on transaction data. In 97th TRB Annual Meeting, Transportation Research Board, Washington, DC, USA, 2018, pp. 1–8.

  • Navandar, Y. V., Dhamaniya, A., Patel, D. A. and Chandra, S., Traffic flow analysis at manual tollbooth operation under mixed traffic conditions. J. Transp. Eng. Part A, ASCE, 2019, 145(6), 1–17.

  • Jang, J., Park, C., Kim, B. and Choi, N., Modeling of time headway distribution on suburban arterial: case study from South Korea. Procedia – Soc. Behav. Sci., 2011, 16, 240–247.

  • Zhang, G., Wang, Y., Wei, H. and Chen, Y., Examining headway distribution models with urban freeway loop event data. Transp. Res. Rec.: J. Transp. Res. Board, 2007, 1999, 141–149.

  • Chen, D. and Xiaozheng, H., An evaluation method for toll policy in transportation. International Conference on E-Business and E-Government, 2010, pp. 618–621.

  • Riccardo, R. and Massimiliano, G., An empirical analysis of vehicle time headways on rural two-lane two-way roads. Procedia. – Soc. Behav. Sci., 2012, 54, 865–874.

  • Dubey, S. K., Ponnu, B. and Arkatkar, S. S., Time gap modeling using mixture distributions under mixed traffic conditions. J. Transp. Syst. Eng. Inf. Technol., 2013, 12, 72–84.

  • Zhang, G. and Wang, Y., A Gaussian kernel-based approach for modeling vehicle headway distributions. Transp. Sci., 2013, 48, 206–216.

  • Dong, S., Wang, H., Hurwitz, D., Zhang, G. and Shi, J., Nonparametric modeling of vehicle-type-specific headway distribution in freeway work zones. J. Transp. Eng. ASCE, 2015, 141, 05015004.

  • Kumar, P., Arkatkar, S. and Joshi, G., Examining traffic flow parameters at merging section on high-speed urban roads in India. Curr. Sci., 2019, 117, 94–103.

  • Mondal, S. and Gupta, A., Assessment of vehicles headway during queue dissipation at signal-controlled intersection under mixed traffic. Curr. Sci., 2019, 116, 437–444.

  • Mathwave, EasyFit Software Manual, Version 5.4, 2010; https://www.mathwave.com/downloads.

  • Mahapatra, G. and Maurya, A. K., Dynamic parameters of vehicles under heterogeneous traffic stream with non-lane discipline: an experimental study. J. Traff. Transp. Eng. (English Ed.), 2018, 5, 386–405.

  • Espinoza Mondragón, J., Jiménez García, J. A., Medina Flores, J. M., Vázquez López, J. A. and Téllez Vázquez, S., Experiments simulation and design to set traffic lights’ operation rules. Transp. Policy, 2018, 67, 2–12.

  • Khraibani, R., de Palma, A., Picard, N. and Kaysi, I., A new evaluation and decision making framework investigating the eliminationby-aspects model in the context of transportation projects’ investment choices. Transp. Policy, 2016, 48, 67–81.

  • Ghasemi, S. H., Jalayer, M., Mahdi, P.-R., Nowak, A. S. and Zhou, H., State-of-the-art model to evaluate space headway based on reliability analysis. J. Transp. Eng. ASCE, 2016, 142, 1–9.

  • Chandra, R. and Mujumdar, P. P., Quantification of uncertainty in spatial return levels of urban precipitation extremes. J. Hydrol. Eng. ASCE, 2017, 23, 2–14.

  • Chepuri, A., Ramakrishnan, J., Arkatkar, S., Joshi, G. and Pulugurtha, S. S., Examining travel time reliability-based performance indicators for bus routes using GPS-based bus trajectory data in India. J. Transp. Eng. Part A, ASCE, 2018, 144(5), 04018012.

  • Bari, C., Navandar, Y. and Dhamaniya, A., Service time variation analysis at manually operated toll plazas under mixed traffic conditions in India. J. East Asia Soc. Transp. Stud., 2019, 13, 331–350.

  • Indian Roads Congress, Manual of Specifications and Standards for Four Laning of Highways through Public Private Partnership, IRC SP: 84, 2014.

  • Teena, N. V., Sanil Kumar, V., Sudheesh, K. and Sajeev, R., Statistical analysis on extreme wave height. Nat. Hazards, 2012, 64, 223–236.

  • Friederichs, P. and Thorarinsdottir, T. L., Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction. Environmetrics, 2012, 23, 579–594.


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  • Analysis of the Determinants of Service Headway Variability at Tollbooths Under Mixed Traffic Scenario in Emerging Countries

Abstract Views: 359  |  PDF Views: 137

Authors

Yogeshwar V. Navandar
Department of Civil Engineering, NIT Campus P. O., Kozhikode 673 601, India
Chintaman Santosh Bari
Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India
Ashish Dhamaniya
Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India
Shriniwas S. Arkatkar
Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India
D. A. Patel
Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat 395 007, India

Abstract


This study makes an effort to model service-headway distribution at manually operated toll plazas (MTC) under mixed traffic conditions. To identify the most suitable probability distribution among the selected candidate distributions, Kolmogorov–Smirnov, Ander-son–Darling, and chi-squared tests were performed. It was found that the generalized extreme value (GEV) was the most suited distribution for modelling service-headway distribution at tollbooths. The results show that GEV distribution parameters can capture possi-ble variations in service headway at tollbooths under MTC reasonably well. The study results can also be used for capacity and level-of-service estimation and the development of warrants for converting MTC to electronic lanes.

Keywords


Generalized Extreme Value, Mixed Traffic Conditions, Probability Distribution, Service Headway, Shape Factor, Tollbooth.

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DOI: https://doi.org/10.18520/cs%2Fv121%2Fi1%2F148-160