Conference Paper2017Cited by 16

Understanding Dhaka City Traffic Intensity and Traffic Expansion Using Gravity Model

Md Abu Sayed, Md Maksudur Rahman, Moinul Islam Zaber, Amin Ahsan Ali

20th International Conference On Computer and Information Technology (ICCIT 2017)

IEEE, pp. 1-6

  • Industry, Innovation and Infrastructure — Relevance: 64%
  • Sustainable Cities and Communities — Relevance: 92%

Abstract

Analysis of traffic pattern recognition and traffic congestion expansion in real time are one of the exciting and challenging tasks which help the government to build a robust and sustainable traffic management system specially in a densely populated city like Dhaka. In this paper, we analyze the traffic intensity for small areas which are also known as junction points or corridors. We describe Dhaka city traffic expansion from a congestion point by using gravity model. However, we process real-time traffic data of Dhaka city rather than depend on survey and interview. We exactly show that traffic expansion of Dhaka city exactly follows gravity model. Expansion of traffic from a congestion point spreads out rapidly to its neighbor and impact of congested point decreases as the distance increases from that congested point. This analysis will help the government making a planned urbanized Dhaka city in order to reduce traffic jam.

Keywords

  • gravity model
  • Traffic congestion
  • Traffic intensity
  • real-time traffic information
  • urban traffic management
  • Dhaka
  • transport corridors
  • traffic pattern recognition

CCDS Authors

Moinul I Zaber, PhD
Moinul I Zaber, PhD

Moinul I Zaber, PhD

  • Director, HCI Wing
  • Supervisor, DS Wing
  • Professor

    Department of Computer Science & Engineering

    University of Dhaka, Bangladesh

  • Visiting Academic

    Department of Computer Science and Technology

    University of Cambridge, United Kingdom

  • Research Fellow

    Institute of Development Studies (Sussex), United Kingdom

Published at

ICPR, HCII, JMIR

Prof. Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Prof. Amin Ahsan Ali, PhD

  • Director, AI/ML Wing
  • Supervisor, ComBio Wing
  • Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

References

  1. 1.Woo-Sung Jung, Fengzhong Wang, H. Eugene Stanley. (2008). Gravity model in the Korean highway. Europhysics Letters (EPL), 81(4), 48005[10.1209/0295-5075/81/48005]
  2. 2.Inho Hong, Woo-Sung Jung. (2016). Application of gravity model on the Korean urban bus network. Physica A Statistical Mechanics and its Applications, 462, 48–55[10.1016/j.physa.2016.06.055]
  3. 3.K.W. Ogden. (1978). The distribution of truck trips and commodity flow in urban areas: A gravity model analysis. Transportation Research, 12(2), 131–137[10.1016/0041-1647(78)90052-7]
  4. 4.WilliamR. Black. (1973). An analysis of gravity model distance exponents. Transportation, 2(3)[10.1007/bf00243358]
  5. 5.Jiang-Hai Qian, Ding-Ding Han. (2009). Gravity Model for Transportation Network Based on Optimal Expected Traffic. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 514–524[10.1007/978-3-642-02466-5_49]
  6. 6.Obuhuma Moturi. (2012). Use of GPS With Road Mapping For Traffic Analysis. University of Nairobi Research Archive (University of Nairobi)

Cited By

  1. 1.Shubing Shan, Buyang Cao, Zhiqiang Wu. (2019). Forecasting the Short-Term Electricity Consumption of Building Using a Novel Ensemble Model. IEEE Access, 7, 88093–88106[10.1109/access.2019.2925740]
  2. 2.Md Abu Sayed, Ahmed H. Anwar, Christopher Kiekintveld, Charles Kamhoua. (2023). Honeypot Allocation for Cyber Deception in Dynamic Tactical Networks: A Game Theoretic Approach. Lecture notes in computer science, 195–214[10.1007/978-3-031-50670-3_10]
  3. 3.Mohammed Ziaul Haider, Rabeya Sultana Papri. (2021). Cost of traffic congestion in Dhaka Metropolitan City. Public Transport, 13(2), 287–299[10.1007/s12469-021-00270-4]
  4. 4.Naoto Minakawa, Kiyoshi Izumi, Hiroki Sakaji. (2022). Bilateral Trade Flow Prediction by Gravity-informed Graph Auto-encoder. IEEE International Conference on Big Data (BigData), 2327–2332[10.1109/bigdata55660.2022.10021066]
  5. 5.Shahriar Iqbal Raj, A.S.M Julker Naem. (2022). Socioeconomic impact of the first Mass Rapid Transit (MRT) in Dhaka. [10.5821/siiu.11977]
  6. 6.Md Maksudur Rahman, S M Ariful Hoque, Moinul Islam Zaber. (2018). Understanding Real Time Traffic Characteristics of Urban Zones Using GPS Data: A Computational Study on Dhaka City. International Conference on Informatics, Electronics & Vision (ICIEV), 514–519[10.1109/iciev.2018.8640961]
  7. 7.Durhan Saliev, Georgi Mladenov, Plamen Petkov. (2025). Research and Analysis of Traffic Intensity on a Street with High Traffic Load: Case Study of the City of Sofia. International Scientific Conference TechSys — Engineering, Technology and Systems (TechSys), 37[10.3390/engproc2025100037]
  8. 8.Nahida Shaulin, Azmaine Faeique. (2023). Role of Traffic Police in Managing Traffic in Dhaka City: Challenges and The Ways Forward. Indonesian Journal of Social Research (IJSR), 5(2), 132–141[10.30997/ijsr.v5i2.322]
  9. 9.Ahmed Mahir Sultan Rumi, Hasan Nahiyan Nobel, A. H. M. Osama Haque, Abu Humayed Azim Fahmid, Tarik Reza Toha, Nouf Alaloula, Najla Abdulrahman Al-Nabhan, Abdulaziz Abdulrahman S. Alnamlah, A. B. M. Alim Al Islam. (2025). A Tale of Side Friction Elements in Dhaka City: From Modeling Real Cases to Revealing Impacts Through Event-Based Simulation. IEEE Access, 13, 37344–37360[10.1109/access.2025.3544511]
  10. 10.Md Abu Sayed, Ahmed Hemida, Christopher Kiekintveld, Charles Kamhoua. (2024). Strategic Honeypot Allocation in Dynamic Networks: A Game-Theoretic Approach for Enhanced Cybersecurity. Research Square[10.21203/rs.3.rs-3960163/v1]
  11. 11.Mayra Virginia Carvajal Castillo, José Luis Argueta Mayorga. (2025). Análisis de Movilidad Urbana con Datos Geoespaciales en Tiempo Real y el Modelo de Gravedad. Ciencia Latina Revista Científica Multidisciplinar, 9(1), 11882–11912[10.37811/cl_rcm.v9i1.16764]
  12. 12.Naoto Minakawa, Kiyoshi Izumi, Yuri Murayama, Hiroki Sakaji. (2024). Firm Default Prediction by GNN with Gravity-Model Informed Neighbor Node Sampling. The Review of Socionetwork Strategies, 18(2), 303–328[10.1007/s12626-024-00170-6]
  13. 13.Md. Mushfiqur Rahman, Nahian Muhtasim Zahin, Kazi Raiyan Mahmud, Md. Azmaeen Bin Ansar. (2020). Automated Intersection Management with MiniZinc. International Conference on Sustainable Technologies for Industry 5.0 (STI), 50, 1–6[10.1109/sti50764.2020.9350408]
  14. 14.Moinul Zaber, Amin Ali, Md Abu Sayed, Farhad Bhuiyan, Samiul Islam, Nibras Rakib. (2018). Scrutiny of Electricity Billing and Supply Data as a Probable Proxy for Economic Activities: A Comprehensive Analysis of Power Consumption of Dhaka, Bangladesh. SSRN Electronic Journal[10.2139/ssrn.3142594]
  15. 15.Sourav Saha, Abu Bakar Mohammad Abdullah, Monosij Kanti Sarker, Sabrina Islam, Mohammad Rejwan Uddin, Mahady Hasan. (2025). IoT-based Smart Traffic Management System Using Tripwire Laser. International Conference on Sustainable Technologies for Industry 5.0 (STI), 1–6[10.1109/sti69347.2025.11367570]
  16. 16.Shubing Shan, Buyang Cao, Zhiqiang Wu. (2019). The Predictor for Urban Buildings’ Hourly Electricity Consumption. IOP Conference Series Earth and Environmental Science, 322(1), 012007[10.1088/1755-1315/322/1/012007]