Estimation of Road Traffic Congestion using GPS Data

Lwin, Hnin Thant; Naing, Thinn Thu

doi:10.17148/ijarcce.2015.41201

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…For predicting traffic congestion probability on user's demand, [30] collected traffic speed, direction, timestamps, and other GPS data at Yangon, Myanmar, by tracing mobile phones on vehicles. In [31], the authors collected traffic data by manually counting vehicles on high-quality digital cameras, two or four-lane highway stretches of India.…”

Section: Related Workmentioning

confidence: 99%

“…They calculated the traffic delay manually based on the traffic volume at those locations. Another work collected GPS data for traffic congestion prediction [30]. For predicting the congestion on user's demand, the authors collected traffic speed, direction, timestamps, and other GPS data by tracing mobile phones on some predefined vehicles at Yangon, Myanmar.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Traffic Congestion in Developing Countries using Google Maps Data

Pramanik¹,

Rahman²,

Anam³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…GPS data generated by in-vehicle devices describe human mobility in great detail [44,46,18,19,37] and offer an unprecedented tool to implement strategies such as reducing transport activity and congestions [10,39,56,6], improving vehicle efficiency, encouraging alternative fuels and electrification, and shifting to lower-carbon options [27,34,53,63]. Emissions from vehicles are traditionally studied with the use of two types of data: (i) measured traffic data, either coming from sensors [11], official sources [50], or household travel surveys [49]; and (ii) simulated traffic data, such as those generated with driving simulators [69], or traffic simulation models [2,54].…”

Section: Introductionmentioning

confidence: 99%

Gross polluters and vehicles' emissions reduction

Böhm,

Nanni,

Pappalardo

2021

Preprint

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Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. This study uses GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of vehicles in three European cities. We discover the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study applies to any city and may contribute to shaping the discussion on how to measure emissions with digital data. Keywords data science • human mobility • transportation • GPS data • climate change • GHG emissions • statistical analysis

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“…The study showed that the PSO algorithm outperformed all other optimisation algorithms in terms of prediction accuracy. Lwin & Naing (2015) made use of a Hidden Markov Model (HDM) for forecasting the traffic congestion using both the historical and real-time data. The system model was tested on different road segments during peak hours, and the HDM showed a promising prediction result with an average accuracy of 86%.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of a Real-Time Adaptive Prediction Algorithm for Traffic Congestion

Nadeem

Fowdur

2018

JICT

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Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicle's position to a cloud server. A control station then accesses the vehicle's position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states.

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Estimation of Road Traffic Congestion using GPS Data

Cited by 10 publications

References 8 publications

Modeling Traffic Congestion in Developing Countries using Google Maps Data

Modeling Traffic Congestion in Developing Countries using Google Maps Data

Gross polluters and vehicles' emissions reduction

Performance Analysis of a Real-Time Adaptive Prediction Algorithm for Traffic Congestion

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