Extract Traffic Information Research Articles

Statistic Supported Cooperative Creation of Training Corpora for the Extraction of Traffic Information from Microblogs

Microblogs like Twitter1 are sources for a lot of useful information. But these information are unstructured. To automatically process it, these information have to be extracted with the help of natural language processing techniques. Corresponding systems for information extraction have to be trained and validated with manual annotated data. This paper presents an approach for the cooperative creation of annotated corpora for training and validation of information extraction systems supported by statistical analyses.1https://twitter.com/

Extraction of road traffic sign information based on a vehicle‐borne mobile photogrammetric system

AbstractA new approach to automatically extracting road traffic sign information based on a vehicle‐borne mobile photogrammetric system (VMPS) is proposed in this paper. The method can be divided into four steps: (a) traffic sign detection in a single image; (b) geometric information computation in a stereopair; (c) semantic information recognition; and (d) stereoscopic information consistency verification. In order to enhance the robustness and adaptability of the approach, colour recognition probabilistic neural networks (PNNs) based on pixel vectors, together with shape‐identification PNNs based on central projected vectors, are used during the traffic sign detection and recognition steps. The proposed approach is applied to many real‐scene stereopairs taken by VMPS at different times and places. Experimental results demonstrate its feasibility and effectiveness.

Effective Urban Traffic Monitoring by Vehicular Sensor Networks

Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.

An Operational System for Estimating Road Traffic Information from Aerial Images

Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic monitoring during disaster and mass events, which is based on an airborne optical sensor system. With this system, optical image sequences are automatically examined on board an aircraft to estimate road traffic information, such as vehicle positions, velocities and driving directions. The traffic information, estimated in real time on board, is immediately downlinked to a ground station. The airborne sensor system consists of a three-head camera system, a real-time-capable GPS/INS unit, five industrial PCs and a downlink unit. The processing chain for automatic extraction of traffic information contains modules for the synchronization of image and navigation data streams, orthorectification and vehicle detection and tracking modules. The vehicle detector is based on a combination of AdaBoost and support vector machine classifiers. Vehicle tracking relies on shape-based matching operators. The processing chain is evaluated on a large number of image sequences recorded during several campaigns, and the data quality is compared to that obtained from induction loops. In summary, we can conclude that the achieved overall quality of the traffic data extracted by the airborne system is in the range of 68% and 81%. Thus, it is comparable to data obtained from stationary ground sensor networks.

A Traffic Parameters Extraction Method Using Time-Spatial Image Based on Multicameras

Based on the traffic monitoring system consisting of image processing units (IPUs), network communication, and data fusion server, this paper proposes an approach for the extraction of traffic parameters in the highway based on time-spatial image and data fusion. This system used a simple method to install cameras, which can capture the video images from the left to the right perspectives synchronously, for the sake of reducing the influence from vehicles height or width by fusing the image plane information of multicameras. Firstly, based on the synchronous and adaptive camera calibration method, images from different cameras are projected onto a same world coordinate, which is a top view picture of the same detecting region. Secondly, in order to improve the environment adaptability, an advanced method of time-spatial image is proposed to extract traffic information. At the same time, through network communication, traffic information picked up by each camera is transformed to the SDF timely. Finally, combined with probability fusion map, SDF fuses information grabbed by all cameras to improve the accuracy of traffic parameters extraction. The experimental results show that the proposed method can be implemented quickly and accurately either in crowed state or in fluent state.

A novel algorithm to identifying vehicle travel path in elevated road area based on GPS trajectory data

In recent years, the increasing development of traffic information collection technology based on floating car data has been recognized, which gives rise to the establishment of real-time traffic information dissemination system in many cities. However, the recent massive construction of urban elevated roads hinders the processing of corresponding GPS data and further extraction of traffic information (e.g., identifying the real travel path), as a result of the frequent transfer of vehicles between ground and elevated road travel. Consequently, an algorithm for identifying the travel road type (i.e., elevated or ground road) of vehicles is designed based on the vehicle traveling features, geometric and topological characteristics of the elevated road network, and a trajectory model proposed in the present study. To be specific, the proposed algorithm can detect the places where a vehicle enters, leaves or crosses under elevated roads. An experiment of 10 sample taxis in Shanghai, China was conducted, and the comparison of our results and results that are obtained from visual interpretation validates the proposed algorithm.

Traffic Flow Estimation Models Using Cellular Phone Data

Traffic volume is a parameter used to quantify demand in transportation studies, and it is commonly collected by using on-road (fixed) sensors such as inductive loops, cameras, etc. The installation of fixed sensors to cover all roads is neither practical nor economically feasible; therefore, they are only installed on a subset of links. Cellular phone tracking has been an emerging topic developed and investigated during the last few years to extract traffic information. Cellular systems provide alternative methods to detect phones in motion without the cost and coverage limitations associated with those infrastructure-based solutions. Utilizing existing cellular systems to capture traffic volume has a major advantage compared with other solutions, since it avoids new and expensive hardware installations of sensors, with a large number of cellular phones acting as probes. This paper proposes a set of models for inferring the number of vehicles moving from one cell to another by means of anonymous call data of phones. The models contain, in their functional form, terms related to the users' calling behavior and other characteristics of the phenomenon such as hourly intensity in calls and vehicles. A set of intercell boundaries with different traffic background and characteristics were selected for the field test. The experiment results show that reasonable estimates are achieved by comparison with volume measurements collected by detectors located in the same study area. The motion of phones while being involved in calls can be used as an easily accessible, fast, and low-cost alternative to deriving volume data on intercell boundaries.

Harvesting Real Time Traffic Information from Twitter

The flow of information on Twitter holds a wealth of information that can be extracted for specific uses. This study attempted to extract road traffic information from Twitter timelines for real time mapping. Given the worsening traffic conditions in Jakarta, a real time map of traffic information could be very beneficial for motorists as they deal with everyday road conditions. With the virtually indeterminate sources of data on Twitter, we proposed an algorithm to measure the traffic information confidence level for the real time traffic mapping system. Based on the test results of this study, the system could be used to serve its intended purpose. NOTICE: Further to an investigation the International Conference on Advances Science and Contemporary Engineering 2012, Procedia Engineering, Volume 50, has been removed by the publisher due to insufficient assurances by the programme organisers that the professional ethical codes of publishing and standards were applied consistently. Where the copyright in individual papers was transferred to the publisher, these transfers are now considered null and void, and revert back to the authors. Nonetheless this paper is available online at: https://www.researchgate.net/publication/232906167_Harvesting_Real_Time_Traffic_Information_from_Twitter SafetyLit note: We believe that the inclusion of references in this case falls under fair use. Why? 1) This article was published as open access and the journal and copyright owner is acknowledged; 2) a link to full text is provided; 3) reproduction of this reference list is relevant to a commentary on what is and is not original thought and 4) the items in this reference list are part of the data upon which an investigation are based.

Outlier Handover Location Correction of Mobile Probe

In this paper, handover location technique is employed for positioning mobile probes. In view of low location accuracy of handover location technique, an outlier handover location correction approach was presented that solved the problem in two phases, travel speed correction of outlier handover link and outlier handover location reposition. In the first phase, a two-layer data fusion model based on the definition of bridge handover link was proposed for recalculating the travel speeds of outlier handover links. In the second phase, the problem of outlier handover location reposition was solved by genetic algorithm. Based on the empirical data, absolute errors of travel speeds of handover links determined by the corrected results are analyzed and compared with results without outlier handover location correction. Evaluation results demonstrate that the proposed approach is feasible and effective, which can be used in the application of traffic information extraction.

Automatic Extraction of Traffic Flows Using TerraSAR-X Along-Track Interferometry

Spaceborne synthetic aperture radar (SAR) offers great potential for the measurement of ground traffic flows. A SAR with multiple receiving apertures aligned in flight direction repeatedly images the same ground area with a short time lag. This allows for an effective detection of moving ground objects, whose range variation translates into an interferometric phase signal between the receiving channels. The high-resolution German SAR satellite TerraSAR-X offers several ways to create multiple along-track apertures. We exploit this to demonstrate satellite-based traffic-flow measurements using along-track interferometry (ATI) and Displaced Phase Center Array techniques. In this paper, we address the usage of different TerraSAR-X ATI modes for data acquisition and describe an automatic near-real-time processing chain for the extraction of traffic information. The performance of this TerraSAR-X traffic processor is significantly driven by incorporating <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of road networks. We present examples of automatic traffic detection as well as empirical evaluations thereof using different kind of reference data.

Traffic extraction and characterisation from optical remote sensing data

AbstractThis paper presents a generic scheme to extract traffic information from both optical satellite imagery and optical airborne image sequences. The extraction is based on an explicit semantic model of traffic, from which, depending on the characteristics of the input data, different strategies for vehicle detection, vehicle queue extraction and motion estimation are derived. The model comprises different scales to exploit the scale‐dependent properties of traffic imaged by optical sensors. It is furthermore extended by context information to include knowledge about background objects as well as metadata from a road database in a consistent way. Various tests with different input data have been carried out and compared with manually acquired ground truth data of vehicles and vehicle tracks. The results show clearly the high potential of airborne and spaceborne traffic monitoring, but also indicate room for methodological improvements and exhibit some inherent sensor‐related drawbacks.

Robust Vehicle and Traffic Information Extraction for Highway Surveillance

A robust vision-based traffic monitoring system for vehicle and traffic information extraction is developed in this research. It is challenging to maintain detection robustness at all time for a highway surveillance system. There are three major problems in detecting and tracking a vehicle: (1) the moving cast shadow effect, (2) the occlusion effect, and (3) nighttime detection. For moving cast shadow elimination, a 2D joint vehicle-shadow model is employed. For occlusion detection, a multiple-camera system is used to detect occlusion so as to extract the exact location of each vehicle. For vehicle nighttime detection, a rear-view monitoring technique is proposed to maintain tracking and detection accuracy. Furthermore, we propose a method to improve the accuracy of background extraction, which usually serves as the first step in any vehicle detection processing. Experimental results are given to demonstrate that the proposed techniques are effective and efficient for vision-based highway surveillance.