Origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data

Rao, Wenming; Wu, Yao; Xia, Jingxin; Ou, Jishun; Kluger, Robert

doi:10.1016/j.trc.2018.07.002

Cited by 98 publications

(54 citation statements)

References 33 publications

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“…"ALRP" means Automatic License Plate Recognition technology, "Veh" is a Vehicle, "LCS" means the Longest Common Subsequence method, "DBSCAN" is a clustering method named "Density-Based Spatial Clustering of Applications with Noise". As shown in Figure 1, firstly, to analyze the travel behavior of a single vehicle the trips of each vehicle were extracted from ALPR data in a module using a trajectory reconstruction method [25]. Secondly, considering the repeatability of commuting vehicles, the temporal and spatial features were both extracted and selected through commuting activities analysis, specifically, the route feature was derived using the LCS (Longest Common Subsequence) method.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, with the emerging big data technologies [13], the commuting pattern at an individual level can be efficiently derived using advanced data-driven methods (e.g., machine learning) [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Various kinds of data were utilized in these data-driven based methods, including Global Positioning System (GPS) data, mobile phone call detail records (CDRs), smart card data and remote sensing imagery [14][15][16], which provide new sights for traffic control-oriented applications.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [24] used a K-means clustering algorithm to extract the commuting travel vehicles based on ALPR data. Our previous study [25] estimated the O-D patterns using vehicle trajectory data collected by ALPR devices and investigated the temporal-spatial distribution patterns of trip generation and attraction, etc. [26].…”

Section: Introductionmentioning

confidence: 99%

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Commuting Pattern Recognition Using a Systematic Cluster Framework

et al. 2020

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Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters' route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Commuting Pattern Recognition Using a Systematic Cluster Framework

et al. 2020

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“…Generally, trips are distinguished by different purposes. Vehicle trajectory is divided into multiple trips based on dwell time and dwell location [33]. In this research, the problem is discussed in freeway networks where vehicles normally keep moving.…”

Section: Origin-destination Extractionmentioning

confidence: 99%

“…Penetration rate influences estimation accuracy [32]. The second kind of data is collected by widely distributed fixed sensors, like automatic license plate recognition data [33] and Bluetooth and WiFi data [34]. Different from traditional fixed sensors, they identify each vehicle or traveler via an identification (ID), which makes trajectory reconstruction possible.…”

Section: Introductionmentioning

confidence: 99%

Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering

Zhang

Wang

Lai

et al. 2019

Journal of Advanced Transportation

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Ramp metering is an effective measure to alleviate freeway congestion. Traditional methods were mostly based on fixed-sensor data, by which origin-destination (OD) patterns cannot be directly collected. Nowadays, trajectory data are available to track vehicle movements. OD patterns can be estimated with weaker assumptions and hence closer to reality. Ramp metering can be improved with this advantage. This paper extracts OD patterns with historical trajectory data. A validation test is proposed to guarantee the sample representativeness of vehicle trajectories and then implement coordinated ramp metering based on the contribution of on-ramp traffic to downstream bottleneck sections. The contribution is determined by the OD patterns. Simulation experiments are conducted under real-life scenarios. Results show that ramp metering with trajectory data increases the throughput by another 4% compared with traditional fixed-sensor data. The advantage is more significant under heavier traffic demand, where traditional control can hardly relieve the situation; in contrast, our control manages to make congestion dissipate earlier and even prevent its forming in some sections. Penetration of trajectory data influences control effects. The minimum required penetration of 4.0% is determined by a t-test and the Pearson correlation coefficient. When penetration is less than the minimum, the correlation between the estimation and the truth significantly drops, OD estimation tends to be unreliable, and control performance becomes more sensitive. The proposed approach is effective in recurrent freeway congestion with steady OD patterns. It is ready for practice and the analysis supports the real-world application.

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Automated License Plate Recognition Process Enhancement with Convolutional Neural Network Based Detection System to Improve the Accuracy and Reliability of Vehicle Recognition

Anton

Oleh

2022

Lecture Notes on Data Engineering and Communications Technologies

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Origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data

Cited by 98 publications

References 33 publications

Commuting Pattern Recognition Using a Systematic Cluster Framework

Commuting Pattern Recognition Using a Systematic Cluster Framework

Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering

Automated License Plate Recognition Process Enhancement with Convolutional Neural Network Based Detection System to Improve the Accuracy and Reliability of Vehicle Recognition

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