Measurement and analysis of heterogeneous road transport parameters using Smart Traffic Analyzer and SUMO Simulator:An experimental approach

被引：1

作者：

Ravindran, Santhiya ^{[1
]}

Balachandran, Gurukarthik Babu ^{[1
]}

David, Prince Winston ^{[1
]}

机构：

[1] Kamaraj Coll Engn & Technol, Dept Elect & Elect Engn, Madurai 625701, Tamil Nadu, India

来源：

MEASUREMENT | 2025年 / 242卷

关键词：

Intelligent transportation systems; Open street map; Measurement; Smart traffic analyzer; SUMO; Traffic behavior; VEHICLE DETECTION; DATA-COLLECTION; CLASSIFICATION; TIME; STATES; MODEL;

D O I：

10.1016/j.measurement.2024.116233

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The main objective of research work is to study the characteristics of heterogeneous road transport environment & compare its dynamic parameters with the performance metrics measurement in terms of error rate & accuracy for vehicle count and classification using Smart Traffic Analyzer (STA) and SUMO Traffic Simulator. SUMO GUI is a simple tool for microscopic traffic simulation and helps to obtain vehicle dynamic parameters in the easiest way. Experimental research is also carried out by capturing live traffic video at study area of four way intersection road and analyzed through STA. The outcome results from SUMO and STA explicit overall accuracy of 96.63 %, and 95.62 % for vehicle count with the error rate of 3.35% & 4.37%. Similarly for vehicle classification, it provides the overall accuracy of 97.21% and 83.01% with the error rate of 2.78% & 16.97% respectively.

引用

页数：13

共 62 条

[1] A probabilistic estimation of traffic congestion using Bayesian network [J].

Afrin, Tanzina ;

Yodo, Nita .

MEASUREMENT, 2021, 174

[2]

[Anonymous], 2020, Sumo Documentation

[3] Blockchain For Intelligent Transport System [J].

Balasubramaniam, Anandkumar ;

Gul, Malik Junaid Jami ;

Menon, Varun G. ;

Paul, Anand .

IETE TECHNICAL REVIEW, 2021, 38 (04) :438-449

[4] Estimation of regional trip length distributions for the calibration of the aggregated network traffic models [J].

Batista, S. F. A. ;

Leclercq, Ludovic ;

Geroliminis, Nikolas .

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2019, 122 :192-217

[5] SS-ITS: secure scalable intelligent transportation systems [J].

Belhadi, Asma ;

Djenouri, Youcef ;

Srivastava, Gautam ;

Lin, Jerry Chun-Wei .

JOURNAL OF SUPERCOMPUTING, 2021, 77 (07) :7253-7269

[6] A recurrent neural network for urban long-term traffic flow forecasting [J].

Belhadi, Asma ;

Djenouri, Youcef ;

Djenouri, Djamel ;

Lin, Jerry Chun-Wei .

APPLIED INTELLIGENCE, 2020, 50 (10) :3252-3265

[7] A novel approach to traffic modelling based on road parameters, weather conditions and GPS data using feedforward neural networks [J].

Betkier, Igor ;

Oszczypa, Mateusz .

EXPERT SYSTEMS WITH APPLICATIONS, 2024, 245

[8] Generating Trips and Assigning Route to a SUMO Network Through the Origin-Destination Matrix: A Case Study of Mobility Routing Model for VANETs [J].

Bhatia, Tarandeep Kaur ;

Ramachandran, Ramkumar Ketti ;

Doss, Robin ;

Pan, Lei .

IETE TECHNICAL REVIEW, 2022, 39 (04) :903-917

[9] A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance [J].

Calhan, Ali .

SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2015, 40 (02) :351-367

[10] Real-time traffic signal control with swarm optimization methods [J].

Celtek, Seyit Alperen ;

Durdu, Akif ;

Ali, Muzamil Eltejani Mohammed .

MEASUREMENT, 2020, 166

← 1 2 3 4 5 6 7 →