An approach to urban traffic condition estimation by aggregating GPS data

Ma, Qinglu; Zou, Zhengrong; Ullah, Saleem

doi:10.1007/s10586-017-1262-0

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Most of the researchers [10][11][12] who studied the traffic congestion are based on data collected from GPS or sensors. While some researchers try to collect data from video based on various methods, some of them are summarized below.…”

Section: Related Workmentioning

confidence: 99%

A Way to Automatically Generate Lane Level Traffic Data from Video in the Intersections

Yang

Ali

Ding

et al. 2021

Journal of Advanced Transportation

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Lane level traffic data such as average waiting time and flow data at each turn direction not only enable navigation systems to provide users with more detailed and finer-grained information; it can also pave the way for future traffic congestion prediction. Although few studies considered extracting traffic flow data from a video at the lane level, it is not clear how many vehicles required turn left in fine-grained lanes during a fixed time. Many previous works focus on applying sensor data instead to videos to extract traffic flow. However, the reversible lanes and various shooting angles obstruct the progress of constructing a traffic data collection system. A framework is proposed to get these data in the intersection directly from a video and solve the problem of vehicle occlusion based on the delayed matching model. First, the different direction lanes are detected automatically by clustering trajectory data which are generated by tracking each vehicle. Experiments are conducted on urban intersections to show that our method can generate these traffic data effectively.

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Section: Related Workmentioning

confidence: 99%

A Way to Automatically Generate Lane Level Traffic Data from Video in the Intersections

Yang

Ali

Ding

et al. 2021

Journal of Advanced Transportation

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“…Adequate credentials for trafc management control, trafc volume, average travel speed, and space occupancy could comprehensively refect the road trafc operation [38]. Te average travel speed is the most direct refection of the trafc state [39]. In this paper, the above three are selected as trafc state identifcation indicators, calculated and characterized using a three-order tensor model.…”

Section: Research On Evaluation Indicatorsmentioning

confidence: 99%

Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach

Zhao

Rui

2023

Journal of Advanced Transportation

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Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability.

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“…Several research and commercial projects have been proposed (a good review can be found in [44]). Other works have also explored the use of GPS data streams [33], [34], [45], [46]. Smartphone applications and GSP data streams have been mainly used for average speed and travel time estimation, but, to the best of our knowledge, not for traffic flow estimation.…”

Section: State-of-the-art and Proposed Framework A Review Of Ubimentioning

confidence: 99%

“…In this paper we only present the new proposed data-driven model of traffic state estimation [31] and we assume that the aggregated speed data are given as input. Issues related to the development of sensing architectures relying on aggregated GPS-Data sources and their use for the implementation of practical traffic state estimation solutions in the context of smart cities are out the scope of this paper and have been addressed in many previous research works, such as [32], [33], [34] and [3]. More recent insight about the use of GPS data for city-wide traffic state estimation and monitoring in general can be found in [35], [36].…”

Section: Introductionmentioning

confidence: 99%

Toward a Cost-Effective Motorway Traffic State Estimation From Sparse Speed and GPS Data

et al. 2021

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In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only. The model is devised to implement a cost-effective framework that aggregates heterogeneous sources of vehicles' GPS and speed measurements to infer traffic flow using a novel triplet system called Conditionally Gaussian Observed Markov Fuzzy Switching Systems (CGOMFSM). Unlike its hard counterpart, CGOMFSM allows for a transient and gradual representation of traffic state transition and hence improves the estimation performance using a tractable scheme. The potential of the proposed model is illustrated through an application to the problem of traffic incident detection, particularly sporadic traffic congestion caused by unexpected road conditions. The performance of the proposed model is assessed using real traffic datasets from England highways. A simulation of traffic in the city of Salalah in Oman was conducted to evaluate the efficacy of the CGOMFSM-based traffic estimation and incident detection schemes with different penetration rates.

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An approach to urban traffic condition estimation by aggregating GPS data

Cited by 8 publications

References 17 publications

A Way to Automatically Generate Lane Level Traffic Data from Video in the Intersections

A Way to Automatically Generate Lane Level Traffic Data from Video in the Intersections

Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach

Toward a Cost-Effective Motorway Traffic State Estimation From Sparse Speed and GPS Data

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