Online ISSN
1751-9578
Print ISSN
1751-956X
IET Intelligent Transport Systems
Volume 6, Issue 3, September 2012
Volumes & issues:
Volume 6, Issue 3
September 2012
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- Author(s): D.O. Cualain ; M. Glavin ; E. Jones
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 223 –234
- DOI: 10.1049/iet-its.2011.0100
- Type: Article
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The increasing trend towards the use of image sensors in transportation is driven both by legislation and consumer demands for higher safety and a better driving experience. Awareness of the environment that surrounds a vehicle can make driving and manoeuvring of the vehicle much safer for all road users. The authors present an image-processing method to detect lane departures using video taken from multiple optical cameras that is specifically designed to be in accordance with proposed automotive lane departure warning standards. This multi-camera system is more robust to errors caused by lane marking occlusions, sensor failure and glare that single camera systems can suffer from. The system uses a novel lane marking segmentation algorithm in accordance with international standards for lane markings. This method does not demand the high computational requirements of inverse perspective mapping (IPM) unlike methods proposed in related research. The authors present a method for lane boundary modelling based on subtractive clustering and Kalman filtering, which is within the constraints of automotive standards. Finally, using the cameras intrinsic and extrinsic parameters, the width of the vehicle and guidelines issued by the International Organisation for Standardisation, the authors show how lane departure can be identified. Results are presented that verify the system's high detection rate and robustness to various background interference, lighting conditions and road environments. - Author(s): B.S. Anami ; V.B. Pagi ; S.M. Magi
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 235 –242
- DOI: 10.1049/iet-its.2011.0162
- Type: Article
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p.
235
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Vehicles of different types generate dissimilar sound patterns even in similar working conditions. In this study, the motorcycles are classified into bikes and scooters based on the sounds produced by them. Simple time-domain features and frequency-domain features are used for classifiers. The performances of artificial neural network, knowledge-based classifier and dynamic time warping are compared and reported. All these classifiers have shown more than 90% classification accuracy when trained with minimum 40% of the samples. - Author(s): C. Harvey and N.A. Stanton
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 243 –258
- DOI: 10.1049/iet-its.2011.0120
- Type: Article
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This study presents a case study to explore an analytic approach to the evaluation of in-vehicle information system (IVIS) usability, aimed at an early stage in product development with low demand on resources. Five methods were selected: hierarchical task analysis (HTA), multimodal critical path analysis (CPA), systematic human error reduction and prediction approach (SHERPA), heuristic analysis and layout analysis. The methods were applied in an evaluation of two IVIS interfaces: a touch screen and a remote controller. The findings showed that there was a trade-off between the objectivity of a method and consideration of the context of use: this has implications for the usefulness of analytic evaluation. An extension to the CPA method is proposed as a solution to enable more objective comparisons of IVIS, whilst accounting for context in terms of the dual-task driving environment. - Author(s): J. Chiverton
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 259 –269
- DOI: 10.1049/iet-its.2011.0138
- Type: Article
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Helmets are essential for the safety of a motorcycle rider, however, the enforcement of helmet wearing is a time-consuming labour intensive task. A system for the automatic classification and tracking of motorcycle riders with and without helmets is therefore described and tested. The system uses support vector machines trained on histograms derived from head region image data of motorcycle riders using both static photographs and individual image frames from video data. The trained classifier is incorporated into a tracking system where motorcycle riders are automatically segmented from video data using background subtraction. The heads of the riders are isolated and then classified using the trained classifier. Each motorcycle rider results in a sequence of regions in adjacent time frames called tracks. These tracks are then classified as a whole using a mean of the individual classifier results. Tests show that the classifier is able to accurately classify whether riders are wearing helmets or not on static photographs. Tests on the tracking system also demonstrate the validity and usefulness of the classification approach. - Author(s): J.M. de Fuentes ; A.I. González-Tablas ; J.L. Hernández-Ardieta ; A. Ribagorda
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 270 –281
- DOI: 10.1049/iet-its.2011.0160
- Type: Article
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270
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An adequate enforcement process is essential to make road traffic fines effective. Automatising such process is intended to provide such effectiveness. However, current enforcement practices do not achieve this goal, as they usually have weaknesses regarding the reliable identification of the offender, the immediacy of feedback after the violation and the completeness of offence description. Intelligent Transportation Systems (ITSs) technologies may be introduced to contribute to these issues. To enable such integration, a complete model of this process must be built. Based on the VERA2 model and the Spanish traffic legislation, in this work an enhanced model is proposed that identifies the stakeholders, the process entities, the data at stake and their interchanges. Its suitability to represent current enforcement systems (particularly the Spanish ESTRADA and the French CSA) is evaluated. Furthermore, based on this model, the integration of the ITS-related technologies is analysed, as well as their suitability compared with current approaches. - Author(s): K. Zhang ; Y. Sheng ; J. Li
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 282 –291
- DOI: 10.1049/iet-its.2011.0105
- Type: Article
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Considering the problem of automatic information acquisition in the field of intelligent transportation system (ITS), a new approach for detection of road traffic sign from natural scene images is proposed in this study. The adaptive colour segmentation based on pixel vector is firstly used to segment colour image into binary image and stand out traffic sign regions, which can reduce the influence of lighting conditions on image segmentation. Secondly, to improve the ability of shape identification during traffic sign detection, central projection transformation (CPT) is used to compute shape feature vectors of different candidate regions, and this shape feature is input to the probabilistic neural networks (PNN) to discriminate true traffic signs from candidates. The proposed approach is applied to many natural images. Experimental results show that the proposed method can effectively detect road traffic signs from natural scene images. - Author(s): H. Chang ; Y. Lee ; B. Yoon ; S. Baek
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 292 –305
- DOI: 10.1049/iet-its.2011.0123
- Type: Article
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292
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Short-term prediction is one of the essential elements of intelligent transportation systems (ITS). Although fine prediction methodologies have been reported, most prediction methods with current time-series data lead to inefficient predictions when current or future time-series data either exhibit fluctuations or abruptly change. In order to deal with this problem, a dynamic multi-interval traffic volume prediction model, based on the k-nearest neighbour non-parametric regression (KNN-NPR), is introduced in this study. In an empirical study with real-world data, the input parameters of the proposed model including the k-values for the nearest neighbours in the neighbourhood and the dm-values for the number of lags were optimised according to the multi-interval prediction horizon in order to immediately capture the directionality of the future states and to minimise the prediction errors. The presented model performed effectively in terms of prediction accuracy, despite multi-interval schemes, to the same degree as applications of the real ITS, even if the time-series data abruptly varied or exhibited wide fluctuations. It can clearly be seen that the proposed methodology is one of the promising system-oriented approaches in the area of multi-interval traffic flow forecasting. - Author(s): H. Yamazaki ; N. Uno ; F. Kurauchi
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 306 –317
- DOI: 10.1049/iet-its.2009.0145
- Type: Article
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This study describes a method of evaluating the level of service of road networks, based on the average travel time and travel time reliability using electronic toll collection (ETC) data. The authors focused on the variance in travel time under normal circumstances, thus, traffic accidents were removed from the database, and any effect of individual vehicle preference was excluded. They evaluated the travel time distribution based on the average travel time from ETC data for each 15-min interval. The level of service in an actual intercity highway network was analysed using the proposed method. This analysis showed that the level of service fluctuated according to the road section analysed, the month and the time of day. These findings were confirmed by the shape of the cumulative distribution and indices of average travel time and travel time reliability. Using the evaluation method described here, the analysis also confirmed the change in travel time distribution between major interchanges after the opening of a new intercity highway route. As a great change in the traffic conditions occurred, the authors analysed the relationship between traffic demand and the level of service using detector data. - Author(s): L. Jie ; H.J. van Zuylen ; Y.S. Chen ; R. Lu
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 318 –327
- DOI: 10.1049/iet-its.2010.0203
- Type: Article
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In Chinese cities, the poor performance of signalised intersections is one of the causes of urban congestion. The reasons for this have been investigated through a comparative study of the saturation flow characteristics on intersections in three Chinese and two Netherlandish cities. The analysis shows that the utilisation of the roads around Chinese intersections is 20–30% worse compared to the Netherlands intersections. The first cause is the long start lag at Chinese intersections, which is mainly brought by the presence of conflicting vehicles and pedestrians at the beginning of the green phase. The further reasons for that phenomenon are the large size of the Chinese intersections and the limited utilisation of the available space. Another cause is the driver behaviour, that is, the long and irregular time headways and sudden lane changing on the observed Chinese intersections. Chinese drivers adapt themselves to the local conditions and behave differently from Dutch drivers, giving a less efficient traffic system. The different driver behaviour in China has the consequence that most microscopic simulation programmes have to be adapted, calibrated and validated for Chinese situations. - Author(s): S. Edwards ; G. Evans ; P. Blythe ; D. Brennan ; K. Selvarajah
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 328 –335
- DOI: 10.1049/iet-its.2011.0118
- Type: Article
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Wireless technologies, in particular the fusion of fixed and mobile networks, could help deliver a safe, sustainable and robust future transport system through better collection and processing of data, and its intelligent use in a fully connected environment. In the road transport sector connected environments are termed co-operative vehicle highway systems (CVHS). CVHS can deliver a range of safety applications, real-time management, optimisation, and intelligent network design. This study provides an overview of CVHS and a description of its contribution to transport safety applications, with reference to experiments from the EU-funded EMMA, TRACKSS and SAVE ME projects. EMMA developed co-operative sensing technologies in the engine, vehicle and roadside infrastructure, demonstrating an application giving priority to emergency vehicles, and showing how sensor technology can be used with EMMA middleware between fixed infrastructure and a fast moving vehicle (130 km/h (70 mph)). TRACKSS demonstrated the fusion of information from different sensor technologies to develop a robust system for road sign detection. The fused sensors detected a sign at 130 km/h (70 mph), sufficiently early to warn the driver. SAVE ME moves fusion of wireless sensor technologies to detect natural and man-made disaster events in public transport terminals, vehicles and critical infrastructure. - Author(s): Z. Ye ; X. Shi ; C.K. Strong ; R.E. Larson
- Source: IET Intelligent Transport Systems, Volume 6, Issue 3, p. 336 –345
- DOI: 10.1049/iet-its.2011.0129
- Type: Article
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p.
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Cutting-edge technologies can make maintaining winter roadways more efficient, safer and less costly. Numerous vehicle-based technologies, including automatic vehicle location, surface temperature measuring devices, on-board freezing point and ice-presence detection systems, salinity measuring devices, visual and multi-spectral sensors and millimetre wavelength radar sensors, have been developed in recent years to achieve improvements in winter maintenance efficiency and safety. This study synthesised information obtained from a comprehensive literature review and agency surveys on the state of development and implementation of these advanced technologies. This study also identified the overall trends and barriers regarding the future use of these technologies. The information offered by this study is expected to encourage maintenance agencies to implement better winter maintenance practices with respect to providing safe, reliable winter highways in a cost-effective and environmentally responsible manner.
Multiple-camera lane departure warning system for the automotive environment
Comparative performance analysis of three classifiers for acoustic signal-based recognition of motorcycles using time- and frequency-domain features
Trade-off between context and objectivity in an analytic approach to the evaluation of in-vehicle interfaces
Helmet presence classification with motorcycle detection and tracking
Towards an automatic enforcement for speeding: enhanced model and intelligent transportation systems realisation
Automatic detection of road traffic signs from natural scene images based on pixel vector and central projected shape feature
Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences
The effect of a new intercity expressway based on travel time reliability using electronic toll collection data
Comparison of driver behaviour and saturation flow in China and the Netherlands
Wireless technology applications to enhance traveller safety
Vehicle-based sensor technologies for winter highway operations
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