Traffic Pattern Analysis from GPS Data: A Case Study of Dhaka City

Rahman, Mustafizur; Shuvo, M M Mahmudunnabi; Zaber, Moinul; Ali, Amin Ahsan

doi:10.1109/conecct.2018.8482371

Cited by 12 publications

(10 citation statements)

References 1 publication

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“…A variety of classical machine learning algorithms and neural network models have been applied on urban traffic data to forecast congestion. Md Maksudur Rahman et al [9] recorded the traffic blockage over days of the week and hours of the week and detected factors that cause congestion in Dhaka city. The authors investigated the influence of the number of road intersections, market places and having rickshaw free roads on the traffic intensity.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic congestion prediction based on Estimated Time of Arrival

Zafar

Haq

2020

PLoS ONE

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With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper, we elaborate on our traffic prediction application, which is based on traffic data collected through Google Map API. Our application is a desktop-based application that predicts traffic congestion state using Estimated Time of Arrival (ETA). In addition to ETA, the prediction system takes into account various features such as weather, time period, special conditions, holidays, etc. The label of the classifier is identified as one of the five traffic states i.e. smooth, slightly congested, congested, highly congested or blockage. The results demonstrate that the random forest classification algorithm has the highest prediction accuracy of 92 percent followed by XGBoost and KNN respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Traffic congestion prediction based on Estimated Time of Arrival

Zafar

Haq

2020

PLoS ONE

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“…Roy et al collected traffic volume data manually in peak periods of the morning(9:00-10:00 AM) and collected geometric and control data for four intersections of Dhaka city and simulated the data using VISSIM optimization of existing traffic signals [24]. For identifying the traffic density patterns of Dhaka across different roads, [25] used GPS data for 11769 road segments. Furthermore, In [26], another group collected GPS data from a telecommunication company of Bangladesh who has vehicles with GPS tracking devices.…”

Section: Related Workmentioning

confidence: 99%

“…Another group collected traffic data by manually counting the vehicles at Science Laboratory Intersection in peak periods of the morning (9:00 -10:00 AM) [24]. Some researchers collected traffic data using GPS for different purposes, such as GPS data from several taxis for 11769 road segments [25]. Salma et al collected GPS data from a telecommunication company of Bangladesh with vehicles with GPS tracking devices [26].…”

Section: Introductionmentioning

confidence: 99%

Modeling Traffic Congestion in Developing Countries using Google Maps Data

Pramanik¹,

Rahman²,

Anam³

et al. 2020

Preprint

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Traffic congestion research is on the rise, thanks to urbanization, economic growth, and industrialization. Developed countries invest a lot of research money in collecting traffic data using Radio Frequency Identification (RFID), loop detectors, speed sensors, high-end traffic light, and GPS. However, these processes are expensive, infeasible, and non-scalable for developing countries with numerous non-motorized vehicles, proliferated ride-sharing services, and frequent pedestrians. This paper proposes a novel approach to collect traffic data from Google Map's traffic layer with minimal cost. We have implemented widely used models such as Historical Averages (HA), Support Vector Regression (SVR), Support Vector Regression with Graph (SVR-Graph), Auto-Regressive Integrated Moving Average (ARIMA) to show the efficacy of the collected traffic data in forecasting future congestion. We show that even with these simple models, we could predict the traffic congestion ahead of time. We also demonstrate that the traffic patterns are significantly different between weekdays and weekends.

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“…Like our proposed system, any other traffic system that aims to reduce traffic delay needs to have access to clean and accurate data for performing efficiently. Several innovative approaches to collect traffic data using GPS data [15], real-time traffic data from Google Map [16], gravity model [17], artificial neural network [18] were proposed which showed some excellent results.…”

Section: Related Workmentioning

confidence: 99%

Automated Intersection Management with MiniZinc

Rahman,

Zahin,

Mahmud

et al. 2020

Preprint

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Ill-managed intersections are the primary reasons behind the increasing traffic problem in urban areas, leading to nonoptimal traffic-flow and unnecessary deadlocks. In this paper, we propose an automated intersection management system that extracts data from a well-defined grid of sensors and optimizes traffic flow by controlling traffic signals. The data extraction mechanism is independent of the optimization algorithm and this paper primarily emphasizes on the later one. We have used MiniZinc [1] modeling language to define our system as a constraint satisfaction problem which can be solved using any offthe-shelf solver. The proposed system performs much better than the systems currently in-use. Our system reduces mean waiting time and standard deviation of waiting time of vehicles, and avoids deadlocks.

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Traffic Pattern Analysis from GPS Data: A Case Study of Dhaka City

Cited by 12 publications

References 1 publication

Traffic congestion prediction based on Estimated Time of Arrival

Traffic congestion prediction based on Estimated Time of Arrival

Modeling Traffic Congestion in Developing Countries using Google Maps Data

Automated Intersection Management with MiniZinc

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