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A vehicle simulation model, CO2MPAS supports the introduction of the new WLTP-based certification system for CO2 emissions in Europe. This paper investigates the possibility to use the underlying simulation methodology to accurately calculate CO2 emissions over real-world trips, thus to extend the use of the methodology beyond vehicle certification. As a reference, the analysis used measurement data obtained from four vehicles over two different routes under real-world driving conditions. The CO2 emissions were measured using portable emissions measurement systems. The formal CO2MPAS methodology and two modified versions of it that require a reduced number of input data were assessed about their capacity to predict the measured CO2 emissions. The analysis focused on the accuracy and uncertainty of the three different methodology configurations. As an additional benchmark, the analysis considered the CO2 emissions estimates obtained from the EMEP/EEA Guidebook methodology used for emissions inventorying in Europe. Results show that the basic CO2MPAS configuration demonstrates good performance in predicting CO2 emissions over on-road tests, reaching a prediction accuracy over an entire test trip of −0,3% and a standard deviation of 3,1%. The modified versions showed slightly higher biases up to 3% and uncertainties (5–7%), but remaining within reasonable limits considering the reduced number of inputs used in each case. Given its ability to predict CO2 emissions accurately on a local base, CO2MPAS could be used for the prediction of instantaneous CO2 emissions in traffic micro-simulation exercises.

Vehicle automation and connectivity bring new opportunities for safe and sustainable mobility in urban and highway networks. Such opportunities are however not directly associated with traffic flow improvements. Research on exploitation of connected and automated vehicles (CAVs) toward a more efficient traffic currently remains at a theoretical level, and/or based on simulation models with limited reliability. Furthermore, testing CAVs in the real world is still costly and very challenging from an implementation perspective. A possible alternative is to use automated robots. By designing and testing both the low- and the high-level controllers of CAVs, it is indeed possible to reach a better understanding of the challenges that future vehicles will need to face. Robotic applications can effectively test these challenges within a wide variety of research communities—for example, via robotic competitions. Along this direction, the Joint Research Centre has organized the first European robotic traffic competition for automated miniature vehicles. Each team participated with four robots and was judged based on a set of indicators that assess the collective behaviors of the vehicles. Results show the suitability of the methodology with different teams proposing completely different approaches to deal with the challenge and thus achieving different results. Future competitions may further raise awareness about the possibility of using CAVs to improve traffic and to engage with a broader community to design systems that are really capable of achieving this goal.

The study proposes a methodology for quantifying the impact of real-world heterogeneous driving behavior on vehicle energy consumption, linking instantaneous acceleration heterogeneity and CO

Many studies have highlighted the added value of incorporating vehicle dynamics into microsimulation. Such models usually focus on simulation of conventional vehicles, failing to account for the acceleration dynamics of electrified vehicles that have different power characteristics from those of internal combustion engine vehicles (ICEV). In addition, none of them have explicitly dealt with the vehicle’s deceleration characteristics. Although it is not commonly considered critical how a vehicle decelerates, unrealistic behaviors in simulations can distort both traffic flow and emissions results. The present work builds on the lightweight microsimulation free-flow acceleration (MFC) model and proposes an extension, marking the first attempt to address these research gaps. First, a comprehensive review of dynamics-based car-following (including free-flow) models is conducted. Second, the methodology of the MFC model to capture the dynamics of electrified vehicles is described. Then, the experimental setup in different dimensions is introduced for the model validation and implementation. Finally, the results of this study indicate that: (1) the acceleration and deceleration potential curves underlying the MFC model can accurately represent real dynamics of electrified vehicles tested on the chassis dynamometer; (2) smooth transitions can be guaranteed after implementing the MFC model in microsimulation; (3) when reproducing the on-road driving trajectories, the MFC model can deliver significant reductions in root mean square error (RMSE) of speed (by ∼69%) and acceleration (by ∼50%) compared with benchmarks; (4) the MFC model can accurately predict the vehicle 0–100 km/h acceleration specifications, with RMSE 49.4% and 56.8% lower than those of the Gipps model and the intelligent driver model (IDM), respectively.

Connected and automated vehicles (CAVs) promise to significantly improve road traffic. To a certain extent, this situation is similar to the expectations at the end of the last century about the positive effects that the introduction of Adaptive Cruise Control (ACC) systems would have had on motorway traffic. The parallelism is interesting because ACC equipped vehicles represent the first level of vehicle automation and are now widely available on the market. In this light, studying ACC impacts can help to anticipate potential problems related to its widespread application and to avoid that AVs and CAVs will lead to the same problems. Only a few test-campaigns had been carried out studying the ACC impacts under real-world driving conditions in quantitative terms. To bridge this gap, the Joint Research Centre of the European Commission has organized a number of experimental campaigns involving several ACC-equipped vehicles to study different implications of their widespread. In this context, the present paper summarizes the outcomes of a test campaign involving 10 commercially available ACC-equipped vehicles. The test campaign has been executed in two different test-tracks of the ZalaZONE proving ground, in Hungary. The tests have been carried out at low-speeds (30–60 km/h) and have involved platoons of vehicles of different brands and different powertrains, which were tested in a variety of vehicle orders and with different settings of their ACC systems. Test results have been used to derive information about the properties of the different ACC systems, to study their string stability, to study the effect of ACC systems on traffic flow, and to draw inference about the possible implications on energy consumption and traffic safety. Results confirm the previous findings in terms of string instability of the ACC and highlight that in the present form, ACC systems will possibly lead to higher energy consumption and introduce new safety risks when their penetration in the fleet increases. However, they also highlight that the materialization of the above findings for AVs depends on the operational logic that manufacturers will adopt during the implementation phase. Therefore, results suggest that functional requirements to guarantee string stability and in general to not disrupt the normal flow of traffic should be introduced both for ACC and for any automated system that will be placed on the market in the future.

Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multiobjective co-optimization problems often overlook tradeoffs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this article proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). The high-level Pareto knowledge assists in finding a best compromise solution. The results of this article suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57%) and tracking error (by 68.94%) while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple-objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.

The European Commission’s Joint Research Centre, in line with the European Strategy for Low-Emission Mobility, has launched in 2016 the Green Driving Tool, an interactive web-based tool aiming at estimating fuel costs and CO2 emissions of individual car journeys. In parallel, it has developed U-SAVE, a routing system for fuel-efficient trip planning aiming at fuel consumption minimization and vehicle specific calibration. This paper provides a first assessment of the performance of the two tools in predicting fuel consumption and CO2 emissions over real-world trips. The analysis focused on the accuracy and uncertainty of the two tools when varying the detail of vehicle input data and of the velocity profile used in the calculation. These elements are particularly important in case of future integration of the tools with traffic simulation models where the level of detail regarding the vehicle input or the speed profile may vary. Results show that U-SAVE prediction is positively affected by the detail of vehicle specifications, while is not significantly sensitive to the detail of the velocity profile. Contrary, Green Driving didn’t show any remarkable change when varying both parameters. Overall, U-SAVE demonstrates a good performance in predicting CO2 emissions over on-road tests reaching an average prediction accuracy over an entire test trip of -4.6% and a standard deviation of 5.2%, while Green Driving exhibit higher uncertainty (on average 12%) but lower bias which ranged in the order of 0 to +3% depending on the vehicle and the test trip considered.

Adaptive Cruise Control (ACC) systems are becoming increasingly available as a standard equipment in modern commercial vehicles. Their penetration rate in the fleet is constantly increasing, as well as their use, especially under freeway conditions. At the same time, limited information is openly available on how these systems actually operate and their differences depending on the vehicle manufacturer or model. This represents an important gap because as the number of ACC vehicles on the road increases, traffic dynamics on freeways may change accordingly, and new collective phenomena, which are only marginally known at present, could emerge. Yet, as ACC systems are introduced as comfort options and their operation is entirely under the responsibility of the driver, vehicle manufacturers do not have explicit requirements to fulfill nor they have to provide any evidence about their performances. As a result, any safety implication connected to their interactions with other road users escapes any monitoring and opportunity of improvement. This work presents a set of experimental car-following campaigns, providing an overview of the behavior of commercial ACC systems under different driving conditions. Furthermore, the suggestion of a unified data structure across the different tests facilitates comparison between the different campaigns, vehicles, systems and specifications. The complete data is published as an open-access database (OpenACC), available to the research community. As more test campaigns will be carried out, OpenACC will evolve accordingly. The activity is performed in the framework of the openData policy of the European Commission Joint Research Centre with the objective to engage the whole scientific community towards a better understanding of the properties of ACC vehicles in view of anticipating their possible impacts on traffic flow and prevent possible problems connected to their widespread introduction. In this light, OpenACC, over time, also aims at becoming a reference point to study if and how the parameters of such systems need to be regulated, how homogeneously they behave, how new ACC car-following models should be designed for traffic microsimulation purposes and what are the key differences between ACC systems and human drivers., Transportation Research Part C: Emerging Technologies, 125, ISSN:0968-090X

Free-flow movement of vehicles in microsimulation software is usually defined by a set of equations with no explicit link to the instantaneous dynamics of the vehicles. In some cases, the car and the driver are modeled in a deterministic way, producing a driving behavior, which does not resemble real measurements of car dynamics or driving style. Depending on the research topic, the interest in microsimulation is to capture traffic dynamics phenomena, such as shockwave propagation or hysterisis. Existing car-following models are designed to simulate more the traffic evolution, rather than the vehicle motion, and consequently, minimal computational complexity is a strong requirement. However, traffic-related phenomena, such as the capacity drop are influenced by the free-flow acceleration regime. Furthermore, the acceleration pattern of a vehicle plays an essential role in the estimation of the energy required during its motion, and therefore in the fuel consumption and the CO2 emissions. The present work proposes a lightweight microsimulation free-flow acceleration model (MFC) that is able to capture the vehicle acceleration dynamics accurately and consistently, it provides a link between the model and the driver and can be easily implemented and tested without raising the computational complexity. The proposed model is calibrated, validated, and compared with known car-following models on road data on a fixed route inside the Joint Research Centre of the European Commission. Finally, the MFC is assessed based on 0–100 km/h acceleration specifications of vehicles available in the market. The results prove the robustness and flexibility of the model., Transportation Research Record, 2673 (4), ISSN:0361-1981, ISSN:2169-4052

As connectivity and automation make their way in to transportation systems, they are expected to have a forceful impact, drastically changing road transportation. The introduction of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) is expected to advance safety and comfort. But, they can also affect characteristics of road networks, such as capacities, delays and efficiency. To foresee important challenges, reinforce potential benefits and reduce potential disadvantages of this new disruptive technology, its impacts should be well studied and understood before their anticipated introduction. In this paper, a microscopic simulation framework to estimate these impacts is developed. Simulation experiments are conducted for various traffic mixtures of manually driven vehicles, AVs and CAVs, different desired time headways settings and traffic demand levels, to evaluate the sensitivity of the network performance to these factors. The ring road of Antwerp is used for the case study. Thus, the results and conclusions refer to a large real-world network. The consequences of the introduction of AVS and CAVs on traffic flow and pollutant emissions are evaluated. The results show that depending on the demand, AVs introduction can have negative effects on traffic flow, while CAVs may benefit the network performance, depending on their market penetration.

The present paper discusses two fuzzy Surrogate Safety Metrics (SSMs) for rear-end collision, the Proactive Fuzzy SSM (PFS) and Critical Fuzzy SSM (CFS). The objective is to investigate their applicability for evaluating the real-time rear-end risk of collision of vehicles to support the operations of advanced driver assistance and automated vehicle functionalities (from driving assistance systems to fully automated vehicles). The proposed Fuzzy SSMs are evaluated and compared to other traditional metrics on the basis of empirical observations. To achieve this goal, an experimental campaign was organized in the AstaZero proving ground in Sweden. The campaign consisted of two main parts: a car-following experiment with five vehicles solely driven by Adaptive Cruise Control (ACC) systems and a safety critical experiment, testing the response of the Autonomous Emergency Braking (AEB) system to avoid collisions on a static target. The proposed PFS is compared with the safe distance defined by the well-known Responsibility Sensitive Safety (RSS) model, showing that it can produce meaningful results in assessing safety conditions also without the use of crisp safety thresholds (like in the case of RSS). The CFS outperformed the well-known Time-To-Collision (TTC) SSM in the a-priori identification of the cases, where the tested vehicles were not able to avoid the collision with the static target. Moreover, results show that CFS at the time of the first deceleration is correlated with the velocity of the vehicle at the time of collisions with the target.

Adaptive cruise control (ACC) systems are standard equipment in many commercially available vehicles. They are considered the first step of automation, and their market penetration rate is expected to rise, along with the interest of researchers worldwide to assess their impact in relation to traffic flow and stability. These properties are currently discussed mainly through microsimulation studies and empirical observations, with the first being the most common. Experimental observations can draw safer conclusions about the behavior of such systems, but the literature is limited. In this work, an experimental campaign with five vehicles equipped with ACC was conducted at the proving ground of AstaZero in Sweden to improve understanding on the properties of ACC systems and their functionality under real driving conditions. The main parameters under investigation are the response time of controllers, the available time headway settings, and the stability of the car-platoon. The results show that the response time range for the controllers is between 1.7 and 2.5 s, significantly longer than the values reported in the literature. The range of the time headway settings was found to be quite broad. Finally, a dataset of perturbations on a variety of equilibrium speeds of the car-platoon and of variable magnitudes was created. Results clearly highlight the instability of the car-platoon. Instability is also displayed even for slight perturbations derived by variability in the road gradient. Numerical differentiation on the altitude shows a negative correlation with the speed trajectory of the leading vehicle., Transportation Research Record, 2674 (4), ISSN:0361-1981, ISSN:2169-4052

Road traffic congestion is the result of various phenomena often of random nature and not directly observable with empirical experiments. This makes it difficult to clearly understand the empirically observed traffic instabilities. The vehicles’ acceleration/deceleration patterns are known to trigger instabilities in the traffic flow under congestion. It has been empirically observed that free-flow pockets or voids may arise when there is a difference in the speeds and the spacing between the follower and the leader increases. During these moments, the trajectory is dictated mainly by the characteristics of the vehicle and the behaviour of the driver and not by the interactions with the leader. Voids have been identified as triggers for instabilities in both macro and micro level, which influence traffic externalities such as fuel consumption and emissions. In the literature, such behaviour is usually reproduced by injecting noise to the results of car-following models in order to create fluctuations in the instantaneous vehicles’ acceleration. This paper proposes a novel car-following approach that takes as input the driver and the vehicle characteristics and explicitly reproduces the impact of the vehicle dynamics and the driver’s behaviour by adopting the Microsimulation Free-flow aCceleration (MFC) model. The congested part of the model corresponds to the Lagrangian discretization of the LWR model and guarantees a full consistency at the macroscopic scale with congested waves propagating accordingly to the first-order traffic flow theory. By introducing naturalistic variation in the driving styles (timid and aggressive drivers) and the vehicle characteristics (specification from different vehicle models), the proposed model can reproduce realistic traffic flow oscillations, similar to those observed empirically. An advantage of the proposed model is that it does not require the injection of any noise in the instantaneous vehicle accelerations. The proposed methodology has been tested by studying a) the traffic flow oscillations produced by the model in a one-lane road uphill simulation scenario, b) the ability of the model to reproduce car-following instabilities observed in three car-following trajectory datasets and c) the ability of the model to produce realistic fuel consumption estimates. The results prove the robustness of the proposed model and the ability to describe traffic flow oscillations as a consequence of the combination of driving style and vehicle’s technical specifications.

Concerns over energy efficiency and greenhouse gas (GHG) emissions are driving research investments into advanced propulsion technologies. Plug-in hybrid electric vehicles (PHEVs) can provide a bridge that connects transport electrification to renewable bioenergy sources such as ethanol. However, it remains unclear how this pathway can simultaneously address economic, energy and environmental goals. To tackle this challenge, the present study explores, for the first time, the multiobjective optimal sizing of PHEVs powered by low-carbon sources of electricity and ethanol-gasoline blend. The empirical ethanol-gasoline blend model is incorporated into the PHEV simulation whose relevant parameters are validated using laboratory data from the European Commission – Joint Research Centre. We develop a full picture of the use-phase well-to-wheel (WTW) GHG emissions from ethanol, gasoline and grid electricity and their energy consumptions. Consequently, market-oriented PHEV sizing solutions are provided as per the power utility generation portfolio and automobile fuel properties of the target region. The results indicate that better performances of the PHEV, regarding GHG emissions and energy consumption, are associated with larger battery size and smaller engine displacement but result in a higher cost-to-power ratio. Specifically, for E25-fuelled PHEVs in markets with world average electricity carbon intensity, every 1.0 USD/kW increase in cost-to-power ratio leads to savings of 1.6 MJ energy consumption and 1.7 g CO2-eq/km WTW GHG emissions. Moreover, a clear benefit of using E25 in the hybrid propulsion system is identified, where the energy consumption and GHG emissions can be reduced by 5.9% and 12.3%, respectively.

Decreasing road transport's harmful effects on environment and health and reducing road accidents are major policy priorities. A variety of technologies could drastically improve air quality, reduce energy consumption and CO2 emissions of road vehicles: in this respect, a prominent trend leverages Electric Vehicles (EVs), supported by improved performance and energy efficiency through connectivity and automation. A noteworthy research question in the transition from Internal Combustion Engine Vehicles (ICEVs) to the alternative technologies, is to understand how Intelligent Transport Systems and other traffic-related measures can contribute to the reduction of fuel consumption and greenhouse gas emissions. In fact, a widely acknowledged tenet assumes that congestion removal or mitigation in presence of ICEVs implies also a reduction of transport-related externalities. This paper explores whether this effect still holds for EVs, by performing an analysis of energy consumption over different vehicle trajectories, under both congested and free-flow conditions. Calculations are carried out using two vehicle simulators: the VT-CPEM (Virginia Tech Comprehensive Power-based Energy consumption model) model for EVs and the CO2MPAS (CO2 model for Passenger and commercial vehicle Simulation) vehicle simulator for the ICEVs, for both electric and conventional cases passengers and freight/commercial powertrains have been analysed. Results are presented on real and simulated data related to four powertrain-vehicle combinations, in terms of general trends of energy/fuel consumption versus speed. Interestingly, results show that, differently from ICEVs, the relationship between congestion and energy consumption underlying EVs can change with higher energy consumption connected to an increased average traffic speed.

The interest on the impact of vehicle automation and connectivity in the future road transport networks is very high, both from a research and a policy perspective. Results in the literature show that many of the anticipated advantages of connected and automated vehicles or automated vehicles without connectivity (CAVs and AVs respectively) on congestion and energy consumption are questionable. Some studies provide quantitative answers to the above questions through microsimulation but they systematically ignore the realistic simulation of vehicle dynamics, driver behaviour or instantaneous emissions estimates, mostly due to the overall increased complexity of the transport systems and the need for low computational demand on large-scale simulations. However, recent studies question the capability of common car-following models to produce realistic vehicle dynamics or driving behaviour, which directly impacts emissions estimations as well. This work presents a microsimulation study that contributes on the topic, using a scenario-based approach to give insights regarding the impact of CAVs and AVs on the evolution of emissions over a highway network. The motivation here is to answer whether the different driving behaviours produce significant differences in emissions during rush hours, and how significant is the impact of detailed vehicle dynamics simulation and instantaneous emissions in the outcome. The status of the network is assessed in terms of flow and speed. Furthermore, emissions are estimated using both the average-speed EMEP/EEA fuel consumption factors and a generic version of the European Commission's CO2MPAS model that provides instantaneous fuel consumption estimates. The simulation results of this work show that AVs can deteriorate the status of the network, and that connectivity is the key for improved traffic flow. Emissions-wise, the AVs have the highest fuel consumption per km travelled among other types, while CAVs only marginally lower the overall consumption of human-driven vehicles. For the same traffic demand, the total emissions for different vehicle types remain at comparable levels.

Connectivity, coupled with vehicle automation, will disrupt the transport system as we know it today. In parallel, new mobility trends are increasingly emerging, such as Mobility as a Service (MaaS) and shared mobility models. The socio-economic and environmental impacts from these different (but entangled) trends could be overwhelmingly positive (e.g. reducing accidents, congestion, emissions). However, if the transition from the current status to the future mobility system is not done in a thoughtful way, it could lead to negative outcomes. This paper analyses future scenarios in consideration of these trends and discusses requirements for the future road transport system management, proposing the novel Coordinated Automated Road Transport (C-ART) concept for traffic flow and system management in highly automated and connected traffic. In future scenarios (especially in the event of substantial increases in travel demand), traffic management would need to play a decisive role in enabling a safe and efficient mobility, with increased control possibilities offered by a C-ART system.

In the next decades, road transport will undergo a deep transformation with the advent of Connected and Automated Vehicles (CAVs), which are about to drastically change the way we commute. CAVs promise to increase productivity and comfort and to facilitate a greater inclusion in mobility of specific user groups, which may eventually lead to increased travel demand. Together, CAVs will enable the full potential of self-driving technology and they will completely merge over time. The complexity of transportation systems is high and therefore, efficient tools for the assessment of this disruptive change are important. The objective of this paper is to evaluate the behavior of automation and/or connectivity in vehicles under realistic traffic conditions and provide preliminary indicative results aiming to assess the efficiency of the corresponding technologies in terms of traffic congestions and network capacity. In the present work, the case-study of the ring road of Antwerp was used along with traffic demand generated based on real traffic counts. Preliminary results show that, ceteris paribus, automation alone is less probable to have positive impacts on traffic conditions. The safety constraints that AVs will be designed to fulfill are in fact likely to generate vehicle which are as cautious (if not more cautious), than human drivers. In this picture, the significant step ahead can be brought by connectivity. Vehicles able to communicate in a seamless and secure way can theoretically see an effective reduction in time headways and reaction time with a consequent positive effect on network capacity and thus road congestion until the traffic demand stays constant. This effect is made more evident as their penetration rate over the entire network increases. Preliminary results presented in this paper seem to confirm the effectiveness of the strategy put forward by the European Commission to consider connectivity, cooperation and automation as deeply intertwined aspects of the future transformation in the transportation sector.

In the next decades, road transport will undergo a deep transformation with the advent of connected and automated vehicles (CAVs), which promise to drastically change the way we commute. CAVs hold significant potential to positively affect traffic flows, air pollution, energy use, productivity, comfort, and mobility. On the other hand, there is an increasing number of sources and reports highlighting potential problems that may arise with CAVs, such as, conservative driving (relaxed thresholds), problematic interaction with human-driven vehicles (inability to take decisions based on eye contact or body language) and increased traffic demand. Therefore, it is of high importance to assess vehicle automated functionalities in a case-study simulation. The scope of this paper is to present some preliminary results regarding the impact assessment of cooperative adaptive cruise control (CACC) on the case-study of the ring road of Antwerp, which is responsible for almost 50% of the traffic and pollution of the city. Scenarios with various penetration rates and traffic demands were simulated showing that coordination of vehicles may be needed to significantly reduce traffic congestion and energy use.

This paper propose a method to infer the itinerary followed by goods transported in shipping containers. The final application can have a positive impact in risk analysis of transported cargo performed by authorities. Our work relies on Container Status Message as source of information, which is often characterized to contain ambiguous, incomplete, imprecise and redundant data since it is generated from heterogenous sources. We propose a method, based on conditional random fields, in order to obtain sequences of messages characterizing containerized goods trajectories from large and noisy data sets. The experiments, which were performed with a set of container status messages from different carriers, suggest that CRF provides a high accuracy for estimating the cargo itineraries., JRC.G.3-Maritime affairs

Word segmentation is a crucial step for segmentation-free document analysis systems and is used for creating an index based on word matching. In this paper, we propose a novel methodology for word segmentation in historical and degraded machine-printed documents. The proposed technique faces problems such as having text of different size, having text and non-text areas lying very near and having non-straight and warped text lines. It is based on: (i) a dynamic run length smoothing algorithm that helps grouping together homogeneous text regions, (ii) noise and punctuation marks removal as well as on obstacle detection in order to facilitate the segmentation process and (iv) a draft text line estimation procedure that guides the final word segmentation result. After testing on numerous historical and degraded machine-printed documents, it has turned out that our methodology performs better compared to current state-of-the-art word segmentation techniques for historical and degraded machine-printed documents.

A new technique for solving jigsaw puzzles is proposed, which takes advantage of both geometrical and color features. It is considered that an image is being divided into a number of pieces (sub-images). The proposed technique is based on extraction of a set of boundary characteristic points and on a Kohonen self-organized feature map (KSOFM) color reduction technique. For each characteristic point a set of color and geometrical features are extracted. The technique compares these sets of features and decides whether two sub-images match or not. When a matching pair has been found, a corrective procedure is applied in order for these sub-images to fit exactly. Next, the proposed technique creates a new sub-image, which consists of the two matched sub-images. The whole matching procedure is being repeated until only one sub-image remains or no more matching sub-images can be found.

The proposed technique deals with jigsaw puzzles and takes advantage of both geometrical and color features. It is considered that an image is being divided into pieces. The shape of these pieces is not predefined, yet the background’s color is. The whole method concerns a recurrent algorithm, which initially, finds the most important corner points around the contour of a piece, afterwards performs color segmentation with a Kohonen’s SOFM based technique and finally uses a comparing routine. This routine is based on the corner points found before. It compares a set of angles, the color of the image around the region of the corner points, the color of the contour and finally compares sequences of points by calculating the Euclidean distance of luminance between them. At a final stage the method decides which pieces match. If the result is not satisfying, the algorithm is being repeated with new adaptive modified parameter values as far as the corner points and the color segmentation is concerned.