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Intelligent Transport Systems, IET

Issue 1 • Date March 2009

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Displaying Results 1 - 10 of 10
  • Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses

    Page(s): 1 - 9
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (454 KB)  

    Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions. View full abstract»

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  • Robust traffic sign shape recognition using geometric matching

    Page(s): 10 - 18
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (602 KB)  

    A novel approach for recognising various traffic sign shapes in outdoor environments is presented. To reduce the influence of digital noise and extract the shape of each individual traffic sign, the external boundaries of traffic signs segmented based on colour information are simplified and decomposed through discrete curve evolution whose stop stage is determined by an arc similarity measure in tangent space. The recognition of a closed candidate shape is achieved through the direct matching with templates. An optimal enclosure is generated to minimise the geometric differences between the retrieved unclosed candidate shape and templates. The experimental results justify that the proposed algorithm is translation, rotation and scaling invariant, and gives reliable shape recognition in complex traffic scenes where clustering and partial occlusion normally occur. View full abstract»

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  • Wide-angle camera technology for automotive applications: a review

    Page(s): 19 - 31
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (551 KB)  

    The development of electronic vision systems for the automotive market is a strongly growing field, driven in particular by customer demand to increase the safety of vehicles both for drivers and for other road users, including vulnerable road users (VRUs), such as pedestrians. Customer demand is matched by legislative developments in a number of key automotive markets; for example Europe, Japan and the US are in the process of introducing legislation to aid in the prevention of fatalities to VRUs, with emphasis on the use of vision systems. The authors discuss some of the factors that motivate the use of wide-angle and fish-eye camera technologies in vehicles. The authors describe the benefits of using wide-angle lens camera systems to display areas of a vehicle's surroundings that the driver would, otherwise, be unaware of (i.e. a vehicle's blind-zones). However, although wide- angle optics provide greater fields of view, they also introduce undesirable effects, such as radial distortion, tangential distortion and uneven illumination. These distortions have the potential to make objects difficult for the vehicle driver to recognise and, thus, potentially have a greater risk of accident. The authors describe some of the methods that can be employed to remove these unwanted effects, and digitally convert the distorted image to the ideal and intuitive rectilinear pin-hole model. View full abstract»

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  • Local traffic condition: improvement of a vehicle-based measurement approach

    Page(s): 32 - 41
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (457 KB)  

    Advanced driver assistance systems (ADAS) can be designed to adapt to the driver's current needs, for example, taking the traffic conditions in the immediate vicinity of the ego vehicle into account. This motivates the design of a system intended to measure the local traffic conditions in a way, which corresponds to the driver's perception of traffic conditions. A new method of measurement is introduced. It is based on automotive sensor technology. A stochastic approach is employed to optimise and evaluate the relationship between the subjective measurements made by the driver and the objective measurements presented. To this end, the drivers' subjective ratings are taken as reference. The analysis presented here is based on simulation data. The outcomes show that the design of the developed simulation environment is applicable to this work. It is shown that the objective measures correspond to the driver's subjective ratings. Thus, it is possible to estimate the driver's perception of traffic conditions using a technical measurement. The algorithm in its presented form is appropriate to give an estimation, which can be used as an input parameter for ADAS. View full abstract»

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  • Pedestrian detection using a singlemonochrome camera

    Page(s): 42 - 56
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1096 KB)  

    A car-mounted single monochrome camera-based pedestrian detection algorithm is discussed. The detection range is divided into two sub-regions, that is, the near distance range and the far distance range. Two different detection algorithms are applied in the two regions. For the near distance range, where the direction of the motion of the detected obstacle is important, a motion segmentation approach using interest points is utilised. For the far distance range where the motion of the detected obstacle is not as important, a robust and computationally efficient modified inverse perspective mapping-based obstacle detection is utilised. Finally, a low-level pedestrian-oriented segmentation algorithm, which is aimed at the depth information of the detected pedestrian candidate, is also presented. View full abstract»

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  • Map matching for intelligent speed adaptation

    Page(s): 57 - 66
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (381 KB)  

    The availability of Global Navigation Satellite Systems (GNSS) enables sophisticated vehicle guidance and advisory systems such as intelligent speed adaptation (ISA) systems. In ISA systems, it is essential to be able to position vehicles within a road network. Because digital road networks as well as GNSS positioning are often inaccurate, a technique known as map matching is needed that aims to use this inaccurate data for determining a vehicle's real road-network position. Then, knowing this position, an ISA system can compare speed with the speed limit in effect and take measures against speeding. An on-line map-matching algorithm is presented with an extensive number of weighting parameters that allow better determination of a vehicle's road network position. The algorithm uses certainty value to express its belief in the correctness of its results. The algorithm was designed and implemented to be used in the large scale ISA project 'Spar pa' farten'. Using test data and data collected from project participants, the algorithm's performance is evaluated. It is shown that the algorithm performs correctly 95% of the time and is capable of handling GNSS positioning errors in a conservative manner. View full abstract»

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  • Self-adapting traffic flow status forecasts using clustering

    Page(s): 67 - 76
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (655 KB)  

    A key problem for the efficient use of stationary traffic prediction models is that for adaptation to new data they require human-made re-calibration with a new database. So far, there has been a lack of knowledge of how to develop a practical prediction model that would learn while working online. Anyone providing real-time traffic information and making forecasts of the traffic situation could benefit from such models. The aim of the study is to develop a method to make a self-adapting short-term prediction model for the status of traffic flow. The principles for such a model are described. The method is based on self-organising map and the model is implemented on a highway in the Helsinki Metropolitan Area. Specifically, the structure of the model makes it possible for the model to learn by itself without the need to save all the data into databases. Consequently, long-term online use of the model makes fewer demands on computers. The results indicated that the self-adapting principle improved the performance of the model. The principles of the model can also be applied in other locations. View full abstract»

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  • Estimation of travel time using fuzzy clustering method

    Page(s): 77 - 86
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (346 KB)  

    A methodology to estimate overall travel time from individual travel time measurements within a time window is presented. To better handle data with complex outlier generation mechanisms, fuzzy clustering techniques have been used to represent relationships between individual travel time data collected within a measuring time window. The data set is considered to be a fuzzy set to which each data point belongs at some degrees of membership. This allows transitions from the main body of data to extreme data points to be treated in a smooth and fuzzy fashion. Two algorithms have been developed based on dasiapointdasia and dasialinedasia fuzzy cluster prototypes. Iterative procedures have been developed to calculate the fuzzy cluster centre and the fuzzy line. A novel estimation method based on time projection of a fuzzy line has been proposed. The method has the advantage of being robust by using a wide time window and the timeliness by employing time projection in resolving the most recent travel time estimation. Unlike deterministic approaches where hard thresholds need to be specified in order to exclude outliers, the proposed methods estimate travel times using all available data and, thus, can be applied in a wide variety of scenarios without fine tuning of the threshold. View full abstract»

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  • Handover location accuracy for travel time estimation in GSM and UMTS

    Page(s): 87 - 94
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (422 KB)  

    Field measurements from the GSM and UMTS networks are analysed in a road traffic information context. The measurements indicate a potentially large improvement using UMTS signalling data compared with GSM regarding handover location accuracy. These improvements can be used to generate real-time traffic information with higher quality and extend the geographic usage area for cellular-based travel time estimation systems. The results confirm previous reports indicating that the technology has a large potential in GSM and also show that the potential might be even larger and more flexible using UMTS. Assuming that non-vehicle terminals can be filtered out, that vehicles are tracked to the correct route and that handovers can be predicted correctly, a conclusion from the experiments is that the handover location accuracy in both GSM and UMTS will be sufficient to estimate useful travel times, also in urban environments. In a real system, these tasks are typically very challenging, especially in an urban environment. Further, it is reasonably established that the location error will be minor for the data obtained from UMTS. View full abstract»

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  • Collection and use of environmental data for transport management: a view from local authorities

    Page(s): 95 - 101
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (127 KB)  

    Research undertaken by the Transportation Research Group at the University of Southampton is discussed, which studies the views, needs and requirements of local authorities as users of sensor grids for environmental monitoring. The study was undertaken through a combination of literature review and workshop activity. Representatives from local authorities were invited to participate in a workshop discussion. It was agreed that more detailed and comprehensive environmental data will be useful for transport planning, network management and traveller information provision. The detailed data can be used to provide a sound understanding of local environmental situations and to identify specific problem areas and times. For local authorities, monitoring noise level and air pollutants which have direct impact on human health such as PM10, PM2.5 and NOx is more important than monitoring green house emission. Historic data on air pollution will be essential for local transport plans focusing on reduction of road-transport-related pollution. Real time environmental data will be needed to be integrated with existing traffic control and traveller information systems. Information on areas surrounding schools and hospitals are most desirable. However, there was a concern regarding negative information disseminated to the public. View full abstract»

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Aims & Scope

IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of intelligent transport systems and infrastructures.

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