Dionysios D. Kehagias

Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction

The exploration of the potential correlations of traffic conditions between roads in large urban networks, which is of profound importance for achieving accurate traffic prediction, often implies high computational complexity due to the implicated network topology. Hence, focal methods are required for dealing with the urban network complexity, reducing the performance requirements that are associated to the classical network search techniques (e.g., Breadth First Search). This paper introduces a graph-theory-based technique for managing spatial dependence between roads of the same network. In particular, after representing the traffic network as a graph, the local neighbors of each road are extracted using Breadth First Search graph traversal algorithm and a lower complexity variant of it. A Pearson product-moment correlation-coefficient-based metric is applied on the selected graph nodes for a prescribed number of level sets of neighbors. In order to evaluate the impact of the new method to the traffic prediction accuracy achieved, the most correlated roads are used to build a STARIMA model, taking also into account the possible time delays of traffic conditions between the interrelated roads. The proposed technique is benchmarked using traffic data from two different cities: Berlin, Germany, and Thessaloniki, Greece. Benchmark results not only indicate significant improvement on the computational time required for calculating traffic correlation metric values but also reveal that a different variant works better in different network topologies, after comparison to third-party approaches.

Investigating the effect of global metrics in travel time forecasting

The effect of traffic in routing, either for individuals or fleets, becomes more and more noticeable as the social, economical, and the ecological effects that it has, seem to be crucial. Forecasting travel times is an interesting, yet challenging problem, which if taken into careful consideration, could have a positive impact on the effectiveness of Intelligent Transportation Systems. Upon analyzing the problem and describing its variances, this paper compares different methodologies on traffic prediction, along with analyzing the effect of metrics, such as Principal Component Analysis and Cross Correlation, when interpreting traffic data. We evaluate known literature methods along with a new prototype algorithmic variation of STARIMA, based on the use of global Coefficient of Determination, against two diverse datasets. The benchmarking results, which are promising, are discussed with respect to the distinct characteristics of the two datasets.

An ontology-based mechanism for automatic categorization of web services

The addition of semantic information into Web services (WS) results in more accurate search and retrieval in service registries. The key issue to facilitate organization of services, taking into account their semantics, is the development of automatic mechanisms that generate appropriate mappings between Web service elements and their semantics‐enabled counterparts. In this paper, we introduce an ontology‐based mechanism for automatic semantic categorization of WS and their structural components. The presented approach, as opposed to similar ones, takes into account the lexicographic, structural, and data type characteristics of WS. Moreover, a software tool that implements the proposed service categorization mechanism is presented, and a benchmark process is executed that reveals outstanding performance of the developed mechanism in comparison with a relevant state‐of‐the‐art approach. Copyright

Evaluating the Effect of Time Series Segmentation on STARIMA-Based Traffic Prediction Model

As the interest for developing intelligent transportation systems increases, the necessity for effective traffic prediction techniques becomes profound. Urban short-term traffic prediction has proven to be an interesting yet challenging task. The goal is to forecast the values of appropriate traffic descriptors such as average travel time or speed, for one or more time intervals in the future. In this paper a novel and efficient short-term traffic prediction approach based on time series analysis is provided. Our idea is to split traffic time series into segments (that represent different traffic trends) and use different time series models on the different segments of the series. The proposed method was evaluated using historical GPS traffic data from the city of Berlin, Germany covering a total period of two weeks. The results show smaller traffic prediction error, in terms of travel time, with respect to two basic time series analysis techniques in the relevant literature.

Static Analysis-Based Approaches for Secure Software Development

Software security is a matter of major concern for software development enterprises that wish to deliver highly secure software products to their customers. Static analysis is considered one of the most effective mechanisms for adding security to software products. The multitude of static analysis tools that are available provide a large number of raw results that may contain security-relevant information, which may be useful for the production of secure software. Several mechanisms that can facilitate the production of both secure and reliable software applications have been proposed over the years. In this paper, two such mechanisms, particularly the vulnerability prediction models (VPMs) and the optimum checkpoint recommendation (OCR) mechanisms, are theoretically examined, while their potential improvement by using static analysis is also investigated. In particular, we review the most significant contributions regarding these mechanisms, identify their most important open issues, and propose directions for future research, emphasizing on the potential adoption of static analysis for addressing the identified open issues. Hence, this paper can act as a reference for researchers that wish to contribute in these subfields, in order to gain solid understanding of the existing solutions and their open issues that require further research.

An Ontology-Based Framework for Web Service Integration and Delivery to Mobility Impaired Users

This paper describes an ontology-based framework whose purpose is to collect content from various existing Web services in order to fulfill the information needs of mobility impaired users, while they are planning a trip, moving from a city to another, or performing home control activities during the trip. In order to access the user-requested content the tool is equipped with semantic Web service search and discovery mechanisms. The service alignment tool, which is part of the presented framework, enables different service providers to map their Web services against a set of ontologies in order to support the discovery and invocation of services.

Mining Traffic Data for Road Incidents Detection

Tackling urban road congestion by means of ITS technologies, involves a number of key challenges. One such challenge is related to the accurate detection of traffic incidents in urban networks for more efficient traffic management. This paper introduces a classification approach that achieves accurate detection of road traffic incidents, based on data retrieved from inductive-loop detectors. In the core of the proposed approach lies a more efficient feature extraction technique, based on the dynamic characteristics of data corresponding to those vehicles that are involved in incidents. Our work observes how dynamic aspects of measured data can be exploited for extracting features that result in measurable improvement of the incident detection rate by the application of a Support Vector Machines classification approach. The latter is known to be one of the most precise solutions that have been widely applied up to now for dealing with relevant incident detection problems. In this paper we conduct appropriate experimental evaluation, including comparison to a set of well known techniques, for assessing the impact of the proposed feature selection technique on the accuracy of the detection rate. The evaluation results show that our approach manages to realize a more accurate and faster incident detection mechanism, although it has a low impact on the improvement of the false alarm rate. We also describe how the proposed approach can be applied to a generic context of ITS for efficient urban traffic management.

A Semantic Web service-oriented application for mobility impaired users

This paper presents a service-oriented application whose purpose is to assist various activities of mobility impaired people as they move from one location to another. Such activities include trip planning, route guidance, e-working, e-learning, search for points of interest and social events among others. A number of registered web services provide all the required content and information that are displayed on the end-user device. Existing services are marked up by a number of domain ontologies about the information needs of mobility impaired users, in order to enable ontology-based search and retrieval of services. A service alignment tool has been developed to facilitate the markup process on behalf of service providers. After describing the architecture of the overall application and the involved mechanisms, we demonstrate a number of use cases that are enabled by the presented technologies.

A Multi-objective Data Mining Approach for Road Traffic Prediction

Road traffic prediction for the efficient traffic control has lately been in the focus of the research community, as it can solve significant urban issues, such as city evacuation plans, increased concentration of CO2 emissions and delays caused by extended traffic jams. The current paper proposes a novel approach for multi-variate data mining from past traffic data (i.e. average speed values per road), so as to dynamically detect all significant correlations between the road network components (i.e. the segments of the roads) by mapping the latter onto a low dimensional embedding. Multiple traffic-related features (e.g. speed correlation, spatial proximity, phase difference, etc.) are utilized in a multi-objective optimization framework, producing all Pareto-optimal embeddings, each one corresponding to a different trade-off between the objectives. The operator is provided with the option to interactively select among these Pareto-optimal solutions, so as to explore the most descriptive sets of road influences. The proposed method has been evaluated on real traffic data, while the evaluation of the forecasting performance of the multi-objective approach exhibited accuracy improvement with respect single-objective approaches.

Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model.

One of the most challenging goals of the modern Intelligent Transportation Systems comprises the accurate and real-time short-term traffic prediction. The achievement of this goal becomes even more critical when the presence of atypical traffic conditions is concerned. In this paper, we propose a novel hybrid technique for short-term traffic prediction under both typical and atypical conditions. An Automatic Incident Detection (AID) algorithm, based on Support Vector Machines (SVM), is utilized to check for the presence of an atypical event (e.g. traffic accident). If such an event occurs, the k-Nearest Neighbors (k-NN) non-parametric regression model is used for traffic prediction. Otherwise, the Autoregressive Integrated Moving Average (ARIMA) parametric model is activated for the same purpose. In order to evaluate the performance of the proposed model, we use open real world traffic data from Caltrans Performance Measurement System (PeMS). We compare the proposed model with the unitary k-NN and ARIMA models, which represent the most commonly used non-parametric and parametric traffic prediction models. Preliminary results show that the proposed model achieves larger accuracy under both typical and atypical traffic conditions.