Traffic density estimation under heterogeneous traffic conditions using data fusion

Ramkumar, Anand; Vanajakshi, Lelitha; Subramanian, Shankar C.

doi:10.1109/ivs.2011.5940397

Cited by 37 publications

(21 citation statements)

References 8 publications

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“…However, this approach requires the density estimation procedures to be applied to the road areas manually marked beforehand. More recently, Anand et al (2011) proposed a method that also uses the Kalman filtering technique for estimating traffic density. In particular, they propose using the flow values measured from video sequences and the travel time obtained from vehicles equipped with a Global Positioning System (GPS).…”

Section: Infrastructure-based Solutions To Estimate Traffic Densitymentioning

confidence: 99%

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

Barrachina

Garrido

Fogue

et al. 2015

Wireless Pers Commun

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Road traffic is experiencing a drastic increase in recent years, thereby increasing the every day traffic congestion problems, especially in metropolitan areas. Governments are making efforts to alleviate the increasing traffic pressure, being vehicular density one of the main metrics used for assessing the road traffic conditions. However, vehicle density is highly variable in time and space, making it difficult to be estimated accurately. Currently, most of the existing vehicle density estimation approaches, such as inductive loop detectors, or traffic surveillance cameras, require very specific infrastructure to be installed on the road. In this paper, we present a novel solution to accurately estimate the density of vehicles in urban scenarios. Our proposal, that has been specially designed for Vehicular Networks, allows Intelligent Transportation Systems to continuously estimate vehicular density by accounting for the number of beacons received per Road Side Unit (RSU), and also considering the roadmap topology where the RSUs are located. Simulation results reveal that, unlike previous proposals solely based on the number of beacons received, our approach accurately estimates the vehicular density, and therefore our approach can be integrated within automatic traffic controlling systems to predict traffic jams, and thus introducing countermeasures.

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Section: Infrastructure-based Solutions To Estimate Traffic Densitymentioning

confidence: 99%

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

Barrachina

Garrido

Fogue

et al. 2015

Wireless Pers Commun

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“…In a similar study [12], the Artificial Neural Network (ANN) technique was used to build a travel time estimation model with input traffic data coming from GPSequipped intercity buses, vehicle detectors along the roadway, and the incident database. Vanajakshi et.al [13] used extended Kalman filter for fusing vehicle flow data extracted from video and travel time data from GPS equipped vehicles to estimate vehicle traffic density. In CTrack [14], vehicle movement traces were generated using cellular network based localization while correcting some systematic errors in localization using accelerometer and magnetic compass data.…”

Section: Introductionmentioning

confidence: 99%

Edge-Level Real-Time Traffic Estimation with Limited Infrastructure

Chaturvedi

Srivastava

2015

2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall)

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In developing countries like India, limited Intelligent Transportation Systems (ITS) infrastructure is available and it is difficult to generate real time traffic information for city wide or larger regions. On the other hand, cellular network is widely deployed in India covering major part of the road network. However, cellular network based positioning data have large location error (250-500 meters) and is not directly useful for edge level travel time or speed estimation. Also, penetration of GPS enabled vehicles/phones is very low in India to generate useful real time traffic information.In this simulation based study we use the map matching algorithm, proposed in [1], that operates on a series of position points having large location error to get accurate vehicle count for every edge, with time lag of about 10 minutes. Temporal extrapolation using exponential moving average is used to overcome the time lag. The vehicle count is then used to estimate edgewise vehicle flow and occupancy. Simulation results show the mean estimation error of less than 10%. Then, the edge level congestion estimation is done using volume-capacity ratio (V/C ratio) and mean error of less than 10% is observed. These results are encouraging as they show feasibility of using widely deployed cellular network for real time congestion level estimation.For edge level speed estimation, a subset of edges are chosen for infrastructure deployment using historical data of congestion levels observed on various edges in a road network. The edges with ITS infrastructure are used to learn the occupancy-speed relationship which is then spatially extrapolated to infrastructureless edges using GPS probe data to enable travel speed estimation on all the edges in the road network. The simulation results show that speed estimation with ninety percentile error of less than 15% is achievable.

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“…Other than the Kalman filtering technique, neural networks were adopted for vehicle identification and traffic density estimation using traffic videos in Ozkurt and Camci [15]. Some studies have tried to improve estimation accuracy by using data from more than one source [13,16].…”

Section: Introductionmentioning

confidence: 99%

A method for measuring accurate traffic density by aerial photography

Park

Kim

Yang

et al. 2014

J of Advced Transportation

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SUMMARYTraffic density can be accurately measured by counting the number of vehicles within 1 km; however, it is often calculated between macroscopic traffic parameters using the fundamental equation because of difficulty of observing traffic density directly in the field. Measuring density in this way may be inaccurate and may bias the analysis because the relationship between these traffic parameters can vary across the study sites. The purpose of this study is to find a method for measuring traffic density from aerial photography that is easy and accurate, and for this purpose, we investigated whether the measuring length (i.e., the length of a section of roadway from which observations of traffic are simultaneously collected) can be shorter than 1 km and yet retain the same measured traffic density. We divided an aerial photograph into several 20-m unit sections, counted the number of vehicles manually, and examined measured traffic density according to central limit theory. According to the results of this study, with the number of 20-m unit sections for observing traffic density at 15 (the measuring length is 300 m), the measured traffic density was almost the same as the density of a representative section of 1 km.

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Traffic density estimation under heterogeneous traffic conditions using data fusion

Cited by 37 publications

References 8 publications

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

Edge-Level Real-Time Traffic Estimation with Limited Infrastructure

A method for measuring accurate traffic density by aerial photography

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