Automatic Number Plate Recognition Data Research Articles

Vehicle Trajectory Completion for Automatic Number Plate Recognition Data: A Temporal Knowledge Graph-Based Method

Vehicle trajectories represent an essential information source in intelligent transportation systems. Prior trajectory completion models based on Automatic Number Plate Recognition (ANPR) data have typically depended on vehicle re-identification results or road network information or have employed static knowledge graphs to integrate the two information sources. However, these methods have not taken into account the implicit temporal characteristics of trajectories in ANPR data and have neglected individual vehicle preferences. To address this void, this study proposes a Temporal Knowledge Graph-based Vehicle Trajectory Completion Model (TKG-VTC). The model implementation comprises three stages: first, ANPR data are converted into a temporal trajectory knowledge graph; second, knowledge representation learning is conducted using nontemporal relations, a biased temporal regularizer and multivector embeddings to embed the knowledge on the graph; and finally, the embedded results are employed to perform link prediction for incomplete trajectories, thereby restoring vehicle trajectories in ANPR data. Through model evaluation metrics and dimensionality reduction experiments, TKG-VTC is observed to demonstrate the best performance in completing trajectories when compared to TComplEx, TNTComplEx, and TeLM. This research introduces an innovative application of employing temporal knowledge graphs for trajectory reconstruction, which eliminates dependence on vehicle re-identification and road network information in previous methodologies. This is advantageous for enhancing the performance and dependability of vehicle trajectory data in intelligent transportation systems, as well as facilitating the implementation of trajectory prediction, demand analysis, and accident warning applications.

An instant discovery method for companion vehicles based on incremental and parallel calculation

In the field of intelligent transportation, the instant discovery of companions has become a research hotspot. This technique can be applied to traffic management and public security governance. This study provides an incremental and distributed approach for discovering traveling companion instantly and continuously based on a data stream of automatic number plate recognition(ANPR). First, a parallelized incremental mining algorithm is designed and implemented in Spark on the basis of traffic-monitoring streaming data. Second, an adjustable data structure DF-tree is proposed that considers the characteristics of companion vehicles with the original ANPR data stream changing dynamically. On the basis of the DF-tree, the system can discover companions without reconstructing the data tree. In addition, we introduce a time decay mechanism to satisfy the spatio-temporal constraints of companion vehicles discovery. Finally, we realize the real-time discovery of companion vehicles based on large-scale ANPR data. The proposed methods are evaluated with extensive experiments on real datasets. The experimental results show that our proposed DF-tree-based approach is faster than the existing methods for companion discovery and it can detect companion vehicles groups in real time.

Estimation Method for Road Link Travel Time Considering the Heterogeneity of Driving Styles

To solve the problem of low automatic number plate recognition (ANPR) data integrity and low completion accuracy of incomplete traffic data, which affects the quality and utilization of ANPR data, this paper proposed a model for estimating the travel time of the road link that considers the heterogeneity of the driving styles. The travel time of historical road sections in the road network was extracted from ANPR data. The driving crowd was clustered through density-based spatial clustering of applications with noise (DBSCAN) based on the time slot, the number of trips, and the travel time. To avoid the excessive data difference between different classes and the distortion of the complement data, the Lagrange interpolation method was adopted to complement the missing road link travel time within each cluster. Taking Ningbo city in China as an example, the travel time completion accuracies of the proposed method and the direct interpolation method were compared. The results show that the interpolation method considering the heterogeneity of driving styles is more sufficient to increase the completion accuracy by 37.4% compared with the direct interpolation manner. The comparison result verifies the effectiveness of the proposed method and can provide more reliable data support for the construction of the transportation system.

Dynamic graph computing: A method of finding companion vehicles from traffic streaming data

For intelligent transportation research, the detection of companion vehicle patterns aids in excavating behavioral relation between vehicles. This paper builds a dynamic license plate corpus based on streaming automatic number plate recognition data and combines the monitoring cameras character division to mine companion vehicle groups in real time. First, we establish the traffic flow graph based on vehicle trajectory data, and the improved PageRank algorithm is used to obtain the influence of the monitoring cameras. Second, the time-sliding window mechanism in Spark Streaming is applied to the streaming data, and the creation and updating of vehicle dynamic license plate corpus are completed according to the driving trajectory data. Finally, the plate-number dynamic graph computing algorithm is proposed to establish the dynamic relation graph between vehicles based on dynamic corpus and character identification of monitoring cameras. The camera characters are used as the influencing factor to correlate with the graph formed by vehicle nodes. The companion groups are obtained by real-time calculation of the trim and weight in the graph. Experimental analysis on the real license plate recognition dataset shows that the proposed model can effectively reduce the complexity of data processing and can detect companion vehicle groups in real time.

Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion

Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).

Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

Automatic number plate recognition (ANPR) systems, which have been widely equipped in many cities, produce numerous travel data for intelligent and sustainable transportation. ANPR data operate at an individual level and carry the unique identities of vehicles, which can support personalized traffic planning. However, these systems also suffer from the common problem of missing data. Different from the traditional missing cases, we focus on the problem of the loss of vehicle identities in ANPR records due to recognition failure or other environmental factors. To address the issue, we propose a heterogeneous graph embedding framework that constructs a travel heterogeneous information network (THIN) and learns the embeddings of the entities to find the best matched vehicles for the unknown records. As a result, the recovery of vehicle identities is cast as an entity alignment task on a THIN. The proposed method integrates the vehicle group entities and context relations into the THIN for capturing the spatiotemporal relationships in vehicle travel and adopts a holographic embeddings model for better fitting the network structure. Empirically, we test it with a real ANPR dataset collected from Xuancheng, China, which has a densely-distributed camera network. The experiments demonstrate the effectiveness of the proposed graph structure under different missing rates. Further, we compare it with other embedding methods and the results support the superiority of holographic embeddings.

A dynamic OD prediction approach for urban networks based on automatic number plate recognition data

OD flows provide important information for traffic management and planning. The prediction of dynamic OD matrices gives the possibility to apply anticipatory traffic management measures. In this paper, we propose an OD prediction approach based on the data obtained by Automated Number Plate Recognition (ANPR) cameras. The principal component analysis (PCA) is applied to reduce the dimension of the original OD matrices and to separate the main structure patterns from the noisier components. A state-space model is established for the main structure patterns and the structure deviations, and is incorporated in the Kalman filter framework to make predictions. We further propose three K-Nearest Neighbour (K-NN) based long-term pattern recognition approaches. The proposed approaches are validated with field ANPR data from Changsha city, P.R. China. The results show that the observed OD flows can be accurately predicted by our proposed approaches. Which prediction method performs best depends on the quality of the available data: for regular, periodic OD matrices the Kalman filter is better, for irregular OD matrices the pattern recognition that looks at different time periods in the historical data, gives better results.

Validating traffic models using large-scale Automatic Number Plate Recognition (ANPR) data

The development of reliable strategic traffic models relies on comprehensive and accurate data, but traditional survey methods are time-consuming and expensive. Manual surveys often yield small samples that require estimated expansion factors to enable the data to represent the population. Modellers have turned to new data sourced from various electronic devices to improve the reliability of the data. Automatic Number Plate Recognition (ANPR) data is one such data source that can be used to extract travel time, speed and partial origin-destination (OD) information. This study assesses ANPR data in terms of its comprehensiveness and accuracy, and shows how it can be used for the validation of strategic traffic models. Data was obtained from the Gauteng freeway system's Open Road Tolling (ORT) gantries for a period of several months. A new methodology is developed to process traffic model outputs such that they are directly comparable to the partial origin-destination outputs derived from the ANPR data. It is shown that comparing the model distribution against observed ANPR data highlights potential trip distribution issues that are not detected using standard model validation techniques.

Approach to discovering companion patterns based on traffic data stream

A companion of moving objects is an object group that move together in a period of time. Platoon companions are a generalised companion pattern, which describes a group of objects that move together for time segments, each with some minimum consecutive duration of time. This study proposes a method that can instantly discover platoon companions from a special kind of streaming traffic data, called automatic number plate recognition data. Compared to related approaches, the authors transform the companion discovery into a frequent sequence mining problem. The authors propose a data structure, platoon tree (PTree), to record discovered platoon companions. To reduce the cost of tree traversal during mining platoon companions, they utilise the last two together-moving objects of a group to update PTree. Finally, a lot of experiments have been carried out to show the efficiency and effectiveness of the proposed approach.

Using automatic number plate recognition data to investigate the regularity of vehicle arrivals

This paper uses automatically-recorded vehicle number plate data from a network of 22 cameras in Dorset, UK, to investigate the extent to which regular trip making can be determined using the regularity of individual vehicle arrival times across the same sites and time intervals over extended periods of several months and illustrates how a cohort of recognised regular vehicles may provide indicative evidence of traffic delays. Regularity was defined based on minimum numbers of observations over a given period and with specified maximum values of standard deviation in arrival time, with sensitivity to different values being tested. It was found that around one-fifth of all vehicles were regular during the morning peak where the definition required at least 30 observations out of 210 working days and with a standard deviation in arrival time of no more than ten minutes; significantly fewer vehicles were found to be regular in the afternoon peak. The turnover, or churn, of regular vehicles was found to be considerable, with only one-tenth of defined regular vehicles being continuously regular throughout the period and with identified pools of regular drivers halving in size every three months, as vehicles ceased to be regular and where the pool was not updated. This suggests that any database of regular drivers should be updated at least quarterly to ensure that new regular vehicles are included and that old ones are discarded. These findings may have inferences for traffic information systems tailored for different driver groups according to assumed levels of network knowledge.

Non-parametric estimation of route travel time distributions from low-frequency floating car data

The paper develops a non-parametric method for route travel time distribution estimation using low-frequency floating car data (FCD). While most previous work has focused on link travel time estimation, the method uses FCD observations for estimating the travel time distribution on a route. Potential biases associated with the use of sparse FCD are identified. The method involves a number of steps to reduce the impact of these biases. For evaluation purposes, a case study is used to estimate route travel times from taxi FCD in Stockholm, Sweden. Estimates are compared to observed travel times for routes equipped with Automatic Number Plate Recognition (ANPR) cameras with promising results. As vehicles collecting FCD (in this case, taxis) may not be a representative sample of the overall vehicle fleet and driver population, the ANPR data along several routes are also used to assess and correct for this bias. The method is computationally efficient, scalable, and supports real time applications with large data sets through a proposed distributed implementation.

VAPA: Vehicle Activity Patterns Analysis based on Automatic Number Plate Recognition System Data

With the explosive growth and wide spread of traffic cameras deployed on the road networks, the amount of Automatic Number-Plate Recognition (ANPR) data captured daily by traffic cameras is very substantial. In this paper, we apply data-mining techniques to discovering vehicle activity patterns from ANPR data. We propose some quantitative indicators of vehicle trace features, and vehicle activity classification method based on the feature of vehicle trace. Evaluations base on collected ANPR records show the capability and efficiency of the proposed approach.

Using automatic number plate recognition technology to observe drivers' headway preferences

ABSTRACTThe paper investigates whether more meaningful interpretation of time headways could be achieved when vehicle identities are taken into account as opposed to the conventional headway studies with no vehicle identification. The idea of spotting those vehicles that are in close car‐following fashion, through automatic number plate recognition (ANPR) technology, was the initial motive of the research. With the use of a set of ANPR data collected in the Republic of Ireland, a number of empirical observations were made, which, in turn, attracted further attention. It was found that the distribution of headways with respect to time was more skewed to the right compared with the overall distribution of all data that had no particular reference to the vehicle identities. It was also interesting to show preliminarily that the ratio of standard deviation to the mean of the time headways (when vehicle identities are taken into consideration) was smaller than that of the headways obtained from the overall population without any number plate identities. It is therefore highlighted that there is need for research to explore the issue further from both points of view of academic studies and enforcement bodies. Copyright © 2012 John Wiley & Sons, Ltd.

Short‐term prediction of motorway travel time using ANPR and loop data

AbstractTravel time is a good operational measure of the effectiveness of transportation systems. The ability to accurately predict motorway and arterial travel times is a critical component for many intelligent transportation systems (ITS) applications. Advanced traffic data collection systems using inductive loop detectors and video cameras have been installed, particularly for motorway networks. An inductive loop can provide traffic flow at its location. Video cameras with image‐processing software, e.g. Automatic Number Plate Recognition (ANPR) software, are able to provide travel time of a road section. This research developed a dynamic linear model (DLM) model to forecast short‐term travel time using both loop and ANPR data. The DLM approach was tested on three motorway sections in southern England. Overall, the model produced good prediction results, albeit large prediction errors occurred at congested traffic conditions due to the dynamic nature of traffic. This result indicated advantages of use of the both data sources. Copyright © 2008 John Wiley & Sons, Ltd.