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Distributing Portable Excess Speed Detectors in AL Riyadh City

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Abstract

This study presents a mathematical approach to distribute portable excess speed detectors in urban transportation networks. This type of sensor is studied to be located in a network in order to separate most of the demand node pairs in the system resembling the well-known traffic sensor surveillance problem. However, newly, the locations are permitted to be changed introducing the dynamic form of the sensor location problem. The problem is formulated mathematically into three different location problems, namely SLP1, SLP2, and SLP3. The aim is to find the optimal number of sensors to intercept most of the daily traffic for each model objective. The proposed formulations are proven to be an NP-hard problem, and then heuristics are called for the solution. The methodology is applied to AL Riyadh city as a real case study network with 240 demand node pairs and 124 two-way streets. In the SLP1, all the demand node pairs are covered by 19% of the network’s roads, whereas SLP2 model shows the best locations for each assumed budget of sensors to purchase. The SLP2 solutions range from 24 sensors with 100% paths coverage to 1 sensor with nearly 20% of paths coverage. The SLP3 model manages to redistribute the sensors in the network while maintaining its traffic coverage efficiency. Four locations structures manage to cover all the network streets with coverage ranges between 100% and 60%. The results show the capability of providing satisfactory solutions with reasonable computing burden.

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References

  1. Lamm R, Psarianos B, Mailaender T (1999) Highway design and traffic safety engineering handbook

  2. Lee C, Hellinga B, Saccomanno F (2006) Evaluation of variable speed limits to improve traffic safety. Transp Res Part C: Emerg Technol 14:213–228

    Google Scholar 

  3. Yang CD, Najm WG (2007) Examining driver behavior using data gathered from red light photo enforcement cameras. J Saf Res 38:311–321

    Google Scholar 

  4. Maroney S, Dewar R (1987) Alternatives to enforcement in modifying the speeding behavior of drivers. Transp Res Rec 1111:121–126

    Google Scholar 

  5. Saif AA, Sammak H, Matar D, Al-Midani O (2010) Automatic monitoring and speed violation ticket system. In: Presented at the IEEE international conference on systems, man and cybernetics, Istanbul

  6. Owais M (2019) Location strategy for traffic emission remote sensing monitors to capture the violated emissions. J Adv Transp 2019

  7. Owais M, Moussa GS, Hussain KF (2020) Robust deep learning architecture for traffic flow estimation from a subset of link sensors. J Transp Eng Part A: Syst 146:04019055

    Google Scholar 

  8. Owais M, Moussa GS, Hussain KF (2019) Sensor location model for O/D estimation: multi-criteria meta-heuristics approach. Oper Res Perspect 6:100100

    MathSciNet  Google Scholar 

  9. Ye P, Wen D (2017) Optimal traffic sensor location for origin-destination estimation using a compressed sensing framework. IEEE Trans Intell Transp Syst 18:1857–1866

    Google Scholar 

  10. Zarrinmehr A, Meshkani M, Seyedabrishami S (2018) Surveillance camera location problem for route-flow observation in urban transportation networks: bi-level formulation and solution algorithm. Int J Syst Sci Oper Logist 5:327–338

    Google Scholar 

  11. Simonelli F, Marzano V, Papola A, Vitiello I (2012) A network sensor location procedure accounting for o–d matrix estimate variability. Transp Res Part B: Methodol 46:1624–1638

    Google Scholar 

  12. Yang H, Iida Y, Sasaki T (1991) An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts. Transp Res Part B: Methodol 25:351–363

    Google Scholar 

  13. Yang H, Zhou J (1998) Optimal traffic counting locations for origin–destination matrix estimation. Transp Res Part B: Methodol 32:109–126

    Google Scholar 

  14. Li B, De Moor B (2002) Dynamic identification of origin–destination matrices in the presence of incomplete observations. Transp Res Part B: Methodol 36:37–57

    Google Scholar 

  15. Ehlert A, Bell MG, Grosso S (2006) The optimisation of traffic count locations in road networks. Transp Res Part B: Methodol 40:460–479

    Google Scholar 

  16. Castillo E, Conejo AJ, Menéndez JM, Jiménez P (2008) The observability problem in traffic network models. Comput-Aid Civ Infrastruct Eng 23:208–222

    Google Scholar 

  17. Habiballah IO, Irving MR (2001) Observability analysis for state estimation using linear programming. IEE Proc Gen Transm Distrib 148:142–145

    Google Scholar 

  18. Nucera RR, Gilles ML (1991) Observability analysis: a new topological algorithm. IEEE Trans Power Syst 6:466–475

    Google Scholar 

  19. Monticelli A, Wu FF (1985) Network observability: identification of observable islands and measurement placement. IEEE Trans Power Appar Syst 1:1035–1041

    Google Scholar 

  20. Wu FF, Monticelli A (1985) Network observability: theory. IEEE Trans Power Appar Syst 1:1042–1048

    Google Scholar 

  21. Monticelli A (2000) Electric power system state estimation. Proc IEEE 88:262–282

    Google Scholar 

  22. Castillo E, Conejo A, Pruneda R, Solares C (2005) State estimation observability based on the null space of the measurement Jacobian matrix. IEEE Trans Power Syst 20:1656–1658

    Google Scholar 

  23. Shirmohammadi H, Hadadi F (2017) Application of fuzzy logic for evaluation of resilient modulus performance of stone mastic asphalt. J Theor Appl Inf Technol 95:1

    Google Scholar 

  24. Shirmohammadi H, Hadadi F (2019) Optimizing total delay and average queue length based on the fuzzy logic controller in urban intersections. Int J Supply Oper Manag 6:142–158

    Google Scholar 

  25. Shirmohammadi H, Najib AS, Hadadi F (2018) Identification of road critical segments using wavelet theory and multi-criteria decision-making method. European Transport–Trasporti Europei

  26. Van Zuylen HJ, Willumsen LG (1980) The most likely trip matrix estimated from traffic counts. Transp Res Part B: Methodol 14:281–293

    Google Scholar 

  27. Bell MG (1991) The estimation of origin-destination matrices by constrained generalised least squares. Transp Res Part B: Methodol 25:13–22

    MathSciNet  Google Scholar 

  28. Cascetta E, Nguyen S (1988) A unified framework for estimating or updating origin/destination matrices from traffic counts. Transp Res Part B: Methodol 22:437–455

    MathSciNet  Google Scholar 

  29. Cascetta E (1984) Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator. Transp Res Part B: Methodol 18:289–299

    Google Scholar 

  30. Maher M (1983) Inferences on trip matrices from observations on link volumes: a Bayesian statistical approach. Transp Res Part B: Methodol 17:435–447

    MathSciNet  Google Scholar 

  31. Florian M, Chen Y (1995) A coordinate descent method for the Bi-level O–D matrix adjustment problem. Int Trans Oper Res 2:165–179

    MATH  Google Scholar 

  32. Cascetta E, Postorino MN (2001) Fixed point approaches to the estimation of O/D matrices using traffic counts on congested networks. Transp Sci 35:134–147

    MATH  Google Scholar 

  33. Lam W, Lo H (1990) Accuracy of OD estimates from traffic counts. Traffic Eng Control 31:358–367

    Google Scholar 

  34. Chootinan P, Chen A, Yang H (2005) A bi-objective traffic counting location problem for origin-destination trip table estimation. Transportmetrica 1:65–80

    Google Scholar 

  35. Bianco L, Confessore G, Reverberi P (2001) A network based model for traffic sensor location with implications on O/D matrix estimates. Transp Sci 35:50–60

    MATH  Google Scholar 

  36. Bianco L, Confessore G, Gentili M (2006) Combinatorial aspects of the sensor location problem. Ann Oper Res 144:201–234

    MathSciNet  MATH  Google Scholar 

  37. Fu C, Zhu N, Ma S (2017) A stochastic program approach for path reconstruction oriented sensor location model. Transp Res Part B: Methodol 102:210–237

    Google Scholar 

  38. Fu C, Zhu N, Ling S, Ma S, Huang Y (2016) Heterogeneous sensor location model for path reconstruction. Transp Res Part B: Methodol 91:77–97

    Google Scholar 

  39. Hu S-R, Peeta S, Liou H-T (2016) Integrated determination of network origin-destination trip matrix and heterogeneous sensor selection and location strategy. IEEE Trans Intell Transp Syst 17:195–205

    Google Scholar 

  40. Antoniou C, Barceló J, Breen M, Bullejos M, Casas J, Cipriani E et al (2016) Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: design, demonstration and validation. Transp Res Part C: Emerg Technol 66:79–98

    Google Scholar 

  41. Hadavi M, Shafahi Y (2016) Vehicle identification sensor models for origin–destination estimation. Transp Res Part B: Methodol 89:82–106

    Google Scholar 

  42. Gentili M, Mirchandani P (2011) Survey of models to locate sensors to estimate traffic flows. Transp Res Rec: J Transp Res Board 1:108–116

    Google Scholar 

  43. Gentili M, Mirchandani P (2012) Locating sensors on traffic networks: models, challenges and research opportunities. Transp Res Part C: Emerg Technol 24:227–255

    Google Scholar 

  44. Yoshino M, Haruyama S, Nakagawa M (2008) High-accuracy positioning system using visible LED lights and image sensor. In: 2008 IEEE radio and wireless symposium, Orlando, FL, 2008, pp. 439–442

  45. Viti F, Verbeke W, Tampère CM (2008) Sensor locations for reliable travel time prediction and dynamic management of traffic networks. Transp Res Rec 2049:103–110

    Google Scholar 

  46. Liu HX, Danczyk A (2009) Optimal sensor locations for freeway bottleneck identification. Comput-Aid Civ Infrastruct Eng 24:535–550

    Google Scholar 

  47. Owais M (2015) Issues related to transit network design problem. Int J Comput Appl 975:8887

    Google Scholar 

  48. Owais M, Moussa G, Abbas Y, El-Shabrawy M (2014) Simple and effective solution methodology for transit network design problem. Int J Comput Appl 89:32–40

    Google Scholar 

  49. Owais M, Osman MK, Moussa G (2016) Multi-objective transit route network design as set covering problem. IEEE Trans Intell Transp Syst 17:670–679

    Google Scholar 

  50. Owais M, Osman MK (2018) Complete hierarchical multi-objective genetic algorithm for transit network design problem. Expert Syst Appl 114:143–154

    Google Scholar 

  51. Owais M, Hassan T (2018) Incorporating dynamic bus stop simulation into static transit assignment models. Int J Civ Eng 16:67–77

    Google Scholar 

  52. Owais M, Abulwafa O, Abbas YA (2000) When to decide to convert a roundabout to a signalized intersection: simulation approach for case studies in Jeddah and Al-Madinah. In: Arab J Sci Eng (In Press)

  53. Owais M, Alshehri A (2020) Pareto optimal path generation algorithm in stochastic transportation networks. IEEE Access 8:58970–58981

    Google Scholar 

  54. Al-Saleh F, Al-Harshan G (2008) Measurements of radiation level in petroleum products and wastes in Riyadh City Refinery. J Environ Radioact 99:1026–1031

    Google Scholar 

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Authors and Affiliations

  1. Civil Engineering Department, Faculty of Engineering, Assiut University, Assiut, 71515, Egypt

    Mahmoud Owais, Mohamed El deeb & Youssef Ali Abbas

  2. Department of Civil and Environmental Engineering, College of Engineering, Majmaah University, Majmaah, 11952, Kingdom of Saudi Arabia

    Mahmoud Owais

Authors
  1. Mahmoud Owais
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  2. Mohamed El deeb
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  3. Youssef Ali Abbas
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Corresponding author

Correspondence to Mahmoud Owais.

Appendix

Appendix

See Table 3.

Table 3 AL Riyadh network links characteristics
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Owais, M., El deeb, M. & Abbas, Y.A. Distributing Portable Excess Speed Detectors in AL Riyadh City. Int J Civ Eng 18, 1301–1314 (2020). https://doi.org/10.1007/s40999-020-00537-0

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  • DOI: https://doi.org/10.1007/s40999-020-00537-0

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