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With the development of the Internet of Things and the pervasive use of internet service providers (ISPs), internet users and data have reached an unprecedented volume. However, the existence of malicious users seriously undermine user privacy and network security by distributing a large amount of unwanted traffic, such as spam, pop-up, and malware. This to some extent can be identified with the cooperation of individual users by installing anti-virus toolkits. However, users need to purchase such software at an additional cost. Therefore, unless built-in incentive mechanisms exist, rational users will choose not to install virus software. If enough network entities behave in this way, the network will be flooded with unwanted traffic. In this paper, we propose an evolutionary game theoretic incentive mechanism to promote the cooperation of individual users to curb the expansion of unwanted traffic. We propose a combined reward and punishment mechanism to further incentivize cooperative behavior. Meanwhile, the acceptance condition of our framework is analyzed and we carry out a number of simulations to evaluate the acceptance conditions of our framework.The experimental results indicate that our reward and punishment mechanism can efficiently incentivize users to adopt cooperative behavior and reduce unwanted traffic. [ABSTRACT FROM AUTHOR]

It is noted that the aircraft landing problem is one of the main tasks in the planning and regulation of air traffic flows, which requires the use of a sufficiently serious mathematical apparatus. A brief overview of the main approaches to the formulation and solution of this problem is presented. [ABSTRACT FROM AUTHOR]

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system. [ABSTRACT FROM AUTHOR]

Direction of walk of a pedestrian is vital information in various applications like visual surveillance, traffic monitoring and control, assisted living systems and automated car assistance system. Existing methods of direction of walk estimation exploit inter-frame and intra-frame features of a pedestrian frame sequence to classify the motion among predefined discrete direction classes. However, in order to achieve a robust method to estimate direction of walk, a strong analogy to justify an evident stationary or motion pattern as potential feature for direction estimation is essential. Discrete results of walk direction are famously used as it can be estimated in less time and are preferable for less precision sensitive scenario. However, the intra-frame feature that yields per-frame orientation is underutilized when the walk directions are subjected to discrete classes and hence there is a stringent need to go beyond discrete levels of direction and comment on specific walk direction angles at the same time maintaining a strong analogy to justify the potential of proposed features for direction of walk estimation. With this motivation, the article proposes a type-1 fuzzy approach over apposite inter-frame as well as intra-frame locomotion feature of pedestrian to yield precise direction of walk in terms of fuzzy directions beyond discrete levels of pedestrian walk directions. The method identifies eight directions as potential membership functions. Identified features are subjected to rule-based table for identification and removal of noisy orientation results, decision on membership function and their membership grade generation. The defuzzified result yields crisp direction of walk estimates beyond discrete levels of direction. The enhanced direction of walk estimation results is evident from qualitative comparison from existing research works and is also supported by the simulation performed over different datasets. The proposed method achieves 98.33%, 98.79%, and 100% Rank-2 balanced accuracy while applied on CASIA Dataset A, B, and NITR Conscious Walk Dataset respectively, which clearly points out its better performance than state of the art. [ABSTRACT FROM AUTHOR]

To form ergonomic driving conditions in the "driver–vehicle–road environment" system, it is proposed to use a mathematical model of automatic control theory, in which special features of traffic control are determined by the transition process described by transfer functions of the vehicle and the driver. The proposed model makes it possible to simulate vehicle speed control processes in accordance with road conditions whose impact on the driver is estimated by the complex characteristic: maximum entropy of the traffic environment. [ABSTRACT FROM AUTHOR]

In recent years, the role of vehicular networks has become increasingly important for the future of Intelligent Transportation Systems, as they are useful for providing safety, assistance to drivers, and traffic control management. Many vehicular network applications such as routing, mobility management, service discovery, and collision avoidance protocols would benefit from possessing vehicles' prior location information to improve their performance. However, the rapid mobility of vehicles and the degree of error in positioning systems create a challenging problem regarding the accuracy and efficiency of any location prediction-based model for vehicular networks. Therefore, a number of location prediction techniques has been proposed in the literature. In this paper, we study and compare the accuracy and effectiveness of the following location-based movement prediction models: Kalman filter, Extended Kalman filter (EKF), Unscented Kalman filter (UKF), and Particle filter for vehicular networks. We compare the performances of these prediction techniques with respect to different mobility models, and provide some insights on their capabilities and limitations. Our results indicate that Particle filter outperforms all other predictors with respect to location error. In addition, EKF and UKF demonstrated an increase in efficiency of more than 50% when additional measurements input were integrated with the predictors. [ABSTRACT FROM AUTHOR]

Due to the rapid topology change, frequent link failure, high vehicle mobility and hard delay constraints, the vehicular ad hoc network (VANET) became a hot research topic in this smart era. VANET has vast application areas including road congestion notification, parking, traffic management, smart transportation, road-side business advertisements. In all these applications, the messages should reach the target location within certain time limits. So, there must be efficient soft computing-enabled routing in VANET which meets these delay constraints. Two soft computing algorithms were proposed in this paper: First, a hybrid clustering algorithm is proposed which combines the geographic- and context-based clustering approaches. The hybrid clustering reduces the control overhead and traffic in the network. Second, the destination-aware routing protocol is proposed for inter-clustering routing which improves the overall packet delivery ratio and reduces the end-to-end delay. The performance of the proposed approach is compared with the well-known cluster-based approaches. The result shows that the proposed approach outperforms the existing approaches. [ABSTRACT FROM AUTHOR]

Schedule-based asynchronous duty cycling is a simple duty cycling technique that requires no synchronization, control traffic or expensive computation. Thus, it is an interesting choice for WSNs. However, most of the literature on the design of asynchronous schedules has been limited by the exaggerated restriction that the schedules present a property named rotation closure. While more flexible probabilistic schedules have been proposed, they were considered inferior under the argument that they do not provide an upper bound on the communication latency. In this paper we revisit the probabilistic schedules with the goal of showing they may be the preferred method in several scenarios. We argue that under realistic assumptions no schedule can guarantee a latency upper bound. We show that probabilistic schedules better support asymmetric duty cycle operation, while also providing unlimited duty cycle granularity and range. Finally, by means of simulation, we compare a probabilistic schedule belonging to the family of Birthday Protocols, the Birthday-Listen-and-Transmit (BLT), with traditional deterministic schedules. Results show that, on top of its other qualitative advantages, BLT achieves competitive latencies. [ABSTRACT FROM AUTHOR]

Mechanistic-empirical pavement design guide (MEPDG) or Pavement ME Design requires traffic inputs in three levels based on the availability of data and the importance of the project. The availability of site-specific (Level 1) traffic data is generally limited due to expensive cost and the accuracy of Level 3 average is unsatisfactory, and therefore the intermedium level (Level 2) traffic data need to be developed. Using multiple years of weigh-in-motion (WIM) and vehicle classification data from Oklahoma, this paper starts with a comprehensive check of the traffic data quality, and generates site-specific traffic inputs that are required in Pavement ME Design. Cluster analysis is then applied to develop homogeneous groups for each traffic input. Subsequently, decision tree model and multinomial regression model are developed for the selection of appropriate traffic clusters under given site design conditions. In addition, a case study is included to evaluate the variations of pavement performance at various levels of traffic inputs. [ABSTRACT FROM AUTHOR]

This paper presents a new method to solve the scheduling problem of adaptive traffic signal control at intersection. The method involves recursive least-squares temporal difference (RLS-TD(λ)) learning that is integrated into approximate dynamic programming. The learning mechanism of RLS-TD(λ) is to make an adaptation of linear function approximation by updating its parameters based on environmental feedback. This study investigates the method implementation after modeling a traffic dynamic system at intersection in discrete time. In the model, different traffic control schemes regarding signal phase sequence are considered, especially the defined adaptive phase sequence (APS). By simulating traffic scenarios, RLS-TD(λ) is superior to TD(λ) for updating functional parameters in the approximation, and APS outperforms other conventional control schemes on reducing traffic delay. By comparing with other traffic signal control algorithms, the proposed algorithm yields satisfying results in terms of traffic delay and computation time. [ABSTRACT FROM AUTHOR]

We study a double-ended queue consisting of two classes of customers. Whenever there is a pair of customers from both classes, they are matched and leave the system. The matching is instantaneous following the first-come-first-match principle. If a customer cannot be matched immediately, he/she will stay in a queue. We also assume customers are impatient with generally distributed patience times. Under suitable heavy traffic conditions, we establish simple linear asymptotic relationships between the diffusion-scaled queue length process and the diffusion-scaled offered waiting time processes and show that the diffusion-scaled queue length process converges weakly to a diffusion process that admits a unique stationary distribution. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things facilitates various dimensionalities in a home, industrial and business applications. The integration of sensors and smart objects with accessible infrastructure makes the efficient data processing and decreases the human resource, operating time. The novel smart traffic control framework is proposed using local traffic smart server and remote cloud server to improve the traffic signal processing time that reduces the vehicles' waiting time, congestion and pollution at the roadway intersection. This approach is also capturing the vehicle's transition that is used to track high-speed vehicles. Optimized regression algorithm is proposed to collect multi-path data and compute single-point nifty decision using waiting vehicle density at four-direction roadway intersection. The sensor data is processed using regression algorithm and take the decision to change the lamp onset. The case study implementation has been demonstrated for four-direction roadway by considering the existing infrastructure with Arduino Uno kit and evaluated the smart traffic framework by comparing with normal traffic system. The results prove that proposed framework smooth the progress of hassle-free travel by reducing the waiting time for the green lamp onset, and also can be use the recorded vehicles images to track the high-speed vehicles. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of vehicles has given rise to the traffic jam, which is a major problem these days. The effect of traffic jam also effects the operation of ambulance. To avoid the traffic jam for the ambulance, a new idea is developed. In this paper we propose a new concept to avoid traffic jam for the ambulance and thus saving the life of an individual. At first the ambulance is detected and the information about the arrival of the ambulance is sent to the next station. The ambulance will make siren only when it carries patient inside, so by detecting both the ambulance siren and the image taken from the acquisition device, the information is sent to the next point which is on the way to hospital. Announcement of the arrival of the ambulance is done. The CCTV camera is monitoring each vehicle on that road. If any vehicle doesn't abide by the announcement done, the image of the vehicle will be captured, and then by using the acquired image the number plate will be tracked and will be detected automatically by image processing. The vehicle information is sent to the police station, to take the necessary action. Thus the traffic control can be done and the way is cleared for the ambulance and life is saved. The processing is done using MATLAB and is verified. [ABSTRACT FROM AUTHOR]

In this contemporary world, mobile ad-hoc networks emerges as an imperative field in computer science. An application of MANET is vehicular networks (VANET) which gives its maximum coverage towards real time actions like vehicular traffic control etc. A mobile ad-hoc network is a network where nodes are movable in state, infrastructure-less, self-configuring and self-repairing from the disconnected links between the nodes. The network topology changes according to the nodes' factors. Due to lack of monitoring, any node can join the network topology based on RF level or distance coverage. Any unauthorised node enters the network and involves in communication without any legalized acceptance from the network. In case of any unusual entry of the node will results in degrade in the network efficiency factors. Anyhow it will ruin the entire network performance and makes to split the topology which causes overhead to start from the initial process. In this paper, we combined one or more attacks by deploying one or more suspicious nodes into the network. By analysing the results from simulation various performance factors are compared based on several test cases. An authorised agent node is placed in the network and issues secret key value to the mobile nodes. Analysis form the results are calculated using regression and deviation method. This paper also gives the analysis of various combinatorial ways of deployed attacks in the network. [ABSTRACT FROM AUTHOR]

With the increasing number of vehicles in large- and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands. [ABSTRACT FROM AUTHOR]

In view of the disturbance of the emergency signal preemption to the normal traffic flow, a scientific and reasonable signal transition optimization model was proposed in this paper, in which fair and efficiency are both considered. Since the emergency signal preemption may lead to longer vehicle queuing and greater vehicle delay at intersections, the difference of queue length and the vehicle delay were selected as the objectives taking into account the fair benefit, and a multi-objective signal recovery transition optimization model for emergency rescue was established based on the power function method. The queue length was calculated by the 95% queue length model of the SYNCHRO, and the average vehicle delay was calculated with HCM2000 delay model; furthermore, parameters’ weight in the model was calibrated with the variation coefficient method, and model was solved by genetic algorithm. Moreover, power function method was used to score the signal recovery transition schemes of emergency traffic. Finally, based on the survey data from the typical route network in the Lion Mountain area of Suzhou City, the model proposed in this study was compared with three classical smooth transition schemes (immediate transition scheme, two-cycle transition scheme and three-cycle transition scheme) at the scenario of evening peak. Simulation results showed that the model in this paper can reduce the vehicle delay and queue length compared with the above three smooth transition schemes; the average reduction ratio of queue length delay was 13.82%, and this number of queue length was 13.65%, from which we can conclude that the model proposed in this has better performance than other three classical transition schemes in emergency signal transition applicability. [ABSTRACT FROM AUTHOR]

We study a single server queueing model with admission control and retrials. In the heavy traffic limit, the main queue and retrial queue lengths jointly converge to a degenerate two-dimensional diffusion process. When this model is considered with holding and rejection costs, formal limits lead to a free boundary curve that determines a threshold on the main queue length as a function of the retrial queue length, above which arrivals must be rejected. However, it is known to be a notoriously difficult problem to characterize this curve. We aim instead at optimizing the threshold on the main queue length independently of the retrial queue length. Our main result shows that in the small and large retrial rate limits, this problem is governed by the Harrison-Taksar free boundary problem, which is a Bellman equation in which the free boundary consists of a single point. We derive the asymptotically optimal buffer size in these two extreme cases, as the scaling parameter and the retrial rate approach their limits. [ABSTRACT FROM AUTHOR]

Arrival processes to service systems often display fluctuations that are larger than anticipated under the Poisson assumption, a phenomenon that is referred to as overdispersion. Motivated by this, we analyze a class of discrete-time stochastic models for which we derive heavy-traffic approximations that are scalable in the system size. Subsequently, we show how this leads to novel capacity sizing rules that acknowledge the presence of overdispersion. This, in turn, leads to robust approximations for performance characteristics of systems that are of moderate size and/or may not operate in heavy traffic. [ABSTRACT FROM AUTHOR]

The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: (1) the elapsing of a predefined time interval, or, (2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3× compared to ECMP while packet loss is decreased up to 2×. [ABSTRACT FROM AUTHOR]

Traffic surveillance is an important issue in intelligent transportation systems. Efficient and accurate vehicle detection is one challenging problem for complex urban traffic surveillance. As such, this paper proposes a new vehicle detection method using spatial relationship GMM for daytime and nighttime based on a high-resolution camera. First, the vehicle is treated as an object composed of multiple components, including the license plate, rear lamps and headlights. These components are localized using their distinctive color, texture, and region feature. Deriving plate color converting model, plate hypothesis score calculation and cascade plate refining were accomplished for license plate localization. Multi-threshold segmentation and connected component analysis are accomplished for rear lamps localization. Frame difference and geometric features similarity analysis are accomplished for headlights localization. After that, the detected components are taken to construct the spatial relationship using GMM. Finally, similar probability measures of the model and the GMM, including GMM of plate and rear lamp, GMM of both rear lamps and GMM of both headlights are adopted to localize vehicle. Experiments in practical urban scenarios are carried out under daytime and nighttime. It can be shown that our method can adapt to the partial occlusion and various lighting conditions well, meanwhile it has a fast detection speed. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is a common problem that affects many cities around the world. In this paper, an actual urban traffic simulation model (AUTM) for traffic congestion predicting and avoiding is proposed, which includes three key components: the map and transfer (MT) conversion method, the optimized spatial evolution rules, and a congestion-avoidance routing algorithm. Three key techniques are combined in our proposed model: (1) The MT conversion method is proposed to get actual urban cellular spaces, which apply the optimized spatial evolution rules to simulate the vehicular dynamics better. (2) AUTM is proposed for simulating traffic congestion and predicting the effect of adding overpasses and roadblocks. (3) The congestion-avoidance routing algorithm is proposed for vehicles to dynamically update their routes toward their destinations, which can achieve traffic optimization in urban simulations. This paper presents the results of applying this novel model to a large-scale real-world case in different urban traffic congestion situations. Extensive experimental simulations in various actual cities have been carried out. Our results in the extreme case are encouraging: The prediction accuracy of traffic congestions is almost 89%, and the variance of prediction road density is less than 0.15. [ABSTRACT FROM AUTHOR]

Pavement resurfacing is a maintenance activity that is undertaken to enhance the service life of pavement. This pavement resurfacing activity involves laying a new layer of asphalt concrete over existing pavement after certain time. Due to engineering factors, economic variables and uncertainty in forecasting, the pavement resurfacing decision process is a complicated activity. Various optimization approaches currently used are simplified models for finding optimal frequency and resurfacing intensity within pavement maintenance framework. In this research, artificial bee colony algorithm is proposed to solve this pavement resurfacing optimization problem. This algorithm mimics the collective behaviour of bees while searching for nectar. In this approach, various scenarios are generated, optimality of each case is evaluated, and the information thus generated is used in subsequent evaluation until global optimality is reached. The effectiveness of proposed method is demonstrated through a numerical example. The solution obtained is similar to the exact solution reported in literature. The results indicate optimal resurfacing values can be obtained with little computational effort with proposed approach. The main advantage of proposed algorithm is removal of specification of trigger values for maintenance decision. [ABSTRACT FROM AUTHOR]

This study’s aim was to enhance the maneuverability safety in the coordination of active rear steering and differential braking control for untripped rollover prevention, which performs a panic lane change maneuver to bypass the obstacle encountered in the path. In avoiding rollover accidents, there are several guidance preventions, such as to secure the vehicle from the intention of the driver and to position the vehicle in the actual lane. A crosswind effect is also found to be a crucial factor since this may cause other accidents. Therefore, there is a need to monitor the driver’s actual path and maintaining the stability of the vehicle along the desired path in order to avoid rollover accidents. We extended the analysis of Yakub and Mori (2015) [1], by suggesting an explicit model of predictive control, which includes an active rear steering and braking control for each wheel. Our main focus was on the general trade-off between rollover prevention and path tracking. The effectiveness of the explicit control model invented for this study was measured and validated by the simulation results for a heavy vehicle proposed in this research. [ABSTRACT FROM AUTHOR]

Different memories of traffic scenes affect a driver’s visual search, driving behavior and traffic safety. The aim of the study was to examine how drivers’ memory changes during daily repetitive stimuli from traffic scenes, to provide a guide of driving times which can characterize drivers’ familiarity, and to explore whether there is a difference in memory characteristics between male drivers and female drivers. A driving simulator was used to collect data. Twenty licensed drivers were recruited to perform the experiments. The Driver’s Memory Scale for Traffic Scenes was designed to measure a driver’s memory. The negative exponential model was selected to model drivers’ memory of daily stimuli in traffic scenes. The saturation value of the driver’s memory for the traffic scene, i.e., the capacity of memory, and the memory growth coefficient were proposed to characterize drivers’ memory. The Mann-Whitney U test was used to conduct the significance tests. The results show that the average capacity and the average growth coefficient of drivers’ memory for the traffic scene were 0.86 and 0.54, respectively. Drivers’ memory increased over time, and rapid initial growth was followed by an asymptotic stability after a certain period of time, called steady time. The average steady time of drivers’ memory for the traffic scene was 7.6 times. The results also show that there was no statistically significant difference in the capacity, the growth coefficient, and the steady time of drivers’ memory for the traffic scene between male drivers and female drivers. The findings provide a theoretical reference for the related research on road familiarity and driving behavior. The findings also have potential applications in the familiarity training of a route for fixed route drivers and the familiarity training of search and rescue routes for firefighters. [ABSTRACT FROM AUTHOR]

While climatic effects on species biogeographic distributions are well documented, less mobile species must compensate for climate change in situ via behavioral plasticity. Despite this being a critical mechanism, behavioral plasticity is rarely modeled explicitly. Here, we use novel accelerometer and active-RFID transponder technology to quantify weather-driven modification of activity, mechanical energy expenditure, and ranging behavior, using the European badger as a model species. We then examine how these behaviors could respond to future climate change. From multi-model inference, activity was promoted significantly by a quadratic relationship with temperature, but inhibited by a quadratic relationship with humidity, and the amount of solar radiation. Drier conditions also encouraged more movement. Modeled against IPCC SRES low and high emissions climate change scenarios, milder and drier conditions projected for the next century would likely produce a change to badgers’ current phenology, with elevated levels of activity being maintained into the winter. This increased activity could necessitate up to a 15% increase in energy expenditure. Furthermore, conditions projected under the high emissions scenario may also lead to substantially increased movement, with implications for road traffic mortality rates. We contend that behavioral adaptation must be better incorporated into conservation strategies, versus the assumption of non-adaptive failure. [ABSTRACT FROM AUTHOR]

We consider a two-node tandem queueing network in which the upstream queue is M/G/1 and each job reuses its upstream service requirement when moving to the downstream queue. Both servers employ the first-in-first-out policy. We investigate the amount of work in the second queue at certain embedded arrival time points, namely when the upstream queue has just emptied. We focus on the case of infinite-variance service times and obtain a heavy traffic process limit for the embedded Markov chain. [ABSTRACT FROM AUTHOR]

Map matching combining vehicle Global Positioning System (GPS) with road attribute information has a significant impact on the traffic route guidance. In the current work, an advanced topological map matching algorithm based on the D-S (Dempster-Shafer) theory is proposed to improve the application for the high-density road network. Data cleaning is carried out to delete the apparent errors of original data and to reduce the data sources. Matching features of the GPS point stated four factors, namely the road speed limit, heading, proximity and spatial correlation. These factors are in line with the vehicle travel characteristics. The D-S theory is applied to dynamically estimate the weight of each factor and further to calculate the fusion result in order to increase the proposed algorithm flexibility. An optimization method based on trajectory shape matching is also proposed to filter the candidate point set to enhance the algorithm accuracy. The results established that the data cleaning can eliminate the invalid data up to more than 5% of the collected data; where using the four factors can guarantee the basic demand of the data matching, while the D-S theory and shape matching can effectively improve the matching accuracy. The proposed system achieved an overall algorithm matching accuracy up to 96.5%. [ABSTRACT FROM AUTHOR]

This paper introduces a multi-agent approach to adjust traffic lights based on traffic situation in order to reduce average delay time. In the traffic model, lights of each intersection are controlled by an autonomous agent. Since decision of each agent affects neighbor agents, this approach creates a classical non-stationary environment. Thus, each agent not only needs to learn from the past experience but also has to consider decision of neighbors to overcome dynamic changes of the traffic network. Fuzzy Q-learning and Game theory are employed to make policy based on previous experiences and decision of neighbor agents. Simulation results illustrate the advantage of the proposed method over fixed time, fuzzy, Q-learning and fuzzy Q-learning control methods. [ABSTRACT FROM AUTHOR]

As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD. [ABSTRACT FROM AUTHOR]

Some studies have estimated fatality and injury rates for bus occupants, but data was aggregated at the country level and made no distinction between bus types. Also, injured pedestrians and cyclists, as a result of bus travel, were overlooked. We compared injury rates for car and city bus occupants on specific urban major roads, as well as the cyclist and pedestrian injuries associated with car and bus travel. We selected ten bus routes along major urban arterials (in Montreal, Canada). Passenger-kilometers traveled were estimated from vehicle counts at intersections (2002-2010) and from bus passenger counts (2008). Police accident reports (2001-2010) provided injury data for all modes. Injury rates associated with car and bus travel were calculated for vehicle occupants, pedestrians, and cyclists. Injury rate ratios were also computed. The safety benefits of bus travel, defined as the number of vehicle occupant, cyclist, and pedestrian injuries saved, were estimated for each route. Overall, for all ten routes, the ratio between car and bus occupant injury rates is 3.7 (95% CI [3.4, 4.0]). The rates of pedestrian and cyclist injuries per hundred million passenger-kilometers are also significantly greater for car travel than that for bus travel: 4.1 (95% CI [3.5, 4.9]) times greater for pedestrian injuries; 5.3 (95% CI [3.8, 7.6]) times greater for cyclist injuries. Similar results were observed for fatally and severely injured vehicle occupants, cyclists, and pedestrians. At the route level, the safety benefits of bus travel increase with the difference in injury rate associated with car and bus travel but also with the amount of passenger-kilometers by bus. Results show that city bus is a safer mode than car, for vehicle occupants but also for cyclists and pedestrians traveling along these bus routes. The safety benefits of bus travel greatly vary across urban routes; this spatial variation is most likely linked to environmental factors. Understanding the safety benefits of public transit for specific transport routes is likely to provide valuable information for mobilizing city and transportation planners. [ABSTRACT FROM AUTHOR]

In recent years, the number of motor vehicles in the country has risen rapidly. Traffic demand has continued to grow, while land resources, capital and energy have become increasingly tense. The development trend of urban transport system is not optimistic. Especially in the face of major events, vehicle evacuation suddenly increased, which is a serious planning for the evacuation of a serious challenge. Reasonable and accurate implementation of the emergency evacuation plan is to make the evacuation time at least, to minimize the major traffic congestion protection. Among them, route selection and traffic flow distribution are the core contents of emergency evacuation plan. In this paper, traffic emergency evacuation of major activities is taken as the research object, and the state vector of each road is introduced into the navigation system according to the linking analysis algorithm thought. The network model of evacuation route selection is established by using the theory of spatial diversity and the theory of minimum cost maximum flow, and an empirical analysis is made on the road network. Based on the research, the emergency evacuation plan for large-scale activities is proposed, and the theory and method system of emergency evacuation are enriched, which can help with formulating more reasonable and effective traffic management policies according to the characteristics of emergency evacuation, meanwhile we provide decision-making basis for urban emergency evacuation transportation planning and management. An experimental evaluation is also conducted with the real data from city of Beijing, in aspects of effectiveness and efficacy. [ABSTRACT FROM AUTHOR]

Introduction: The time headway of vehicles is of fundamental importance in traffic engineering applications like capacity, level-of-service and safety studies. Further , the performance of traffic simulation depends on inputs into the simulation process and 'accurate vehicle generation' is critical in this context. Thus, it is important to define headway distribution pattern for the purpose of analyzing traffic and subsequently, taking infrastructure related decisions. In so far, majority of the researches on this subject are based on homogeneous traffic and effects of mixed traffic especially on two-lane roads are yet to be culminated. The present study, thus, aimed at investigating headway distributions on such roads under mixed traffic situation. Methods: Field study was conducted on two-lane highways in India that exhibits heterogeneity in its traffic composition. Contestant headway distribution models were evaluated and four distribution functions namely, log-logistic, lognormal, Pearson 5 and Pearson 6 were considered while modeling the headway data. The appropriate models were selected using a methodology based on K-S test and subsequent field validation. Results: Log-logistic distribution was found appropriate at moderate flow whereas, at congested state of flow it was Pearson 5. However, at unstable flow nearing capacity, both, following and non-following components of headways were observed to follow different distributional characteristics. Nomographs are developed for calculating the headway probabilities at different flow levels considering the appropriate distribution models. 'Probability of headway less than't's' increases with the flow rate and rate of such increase is considerably high for headways 7.5 s or more. This attributes to the fact that at heavy flow more vehicles are entrapped inside platoons and they move in following with shorter headways. Further, a comparison was made between the headway probabilities obtained in the current study and road segments that exhibit more or less homogeneous traffic. It was found that at moderate flow level proportion of shorter headways are considerably high under mixed traffic. Conclusions: The present paper demonstrates the effect of mixed traffic on distribution of time headways of two-lane roads. Presence of slower vehicles in such traffic leads to frequent formation of platoons, thereby, increases the risk taking behavior of drivers' while overtaking. As a result, proportion of shorter headways increases resulting in highly skewed observations. Thus, the study proposed Log-logistic distribution under medium flow since it can model events which have 'increased initial rate' and Pearson 5 under heavy flow to model 'highly skewed data'. The model outputs were accordingly compared with other studies and were found to explain the mixed traffic characteristics satisfactorily. The present study, thus, creates a starting point of further initiatives aimed at establishing a robust method of modeling headways on two-lane roads with mixed traffic. [ABSTRACT FROM AUTHOR]

As vehicular networking has recently been developed and commercialized, vehicular cloud computing has received much attention in various research areas, such as intelligent transportation systems and vehicular ad hoc networks. An efficient intersection traffic control using vehicular cloud computing is one of the key research topics in intelligent transportation systems. To efficiently deal with intersection traffic control via vehicle-to-vehicle communications, we design a distributed mutual exclusion algorithm that does not rely on broadcast, which introduces communication overheads; instead, our algorithm use point-to-point messages sent between the vehicles to keep network traffic load lower. In our algorithmic design, to pass an intersection, the lead vehicle on a lane must get permissions from a subset of other vehicles and its following vehicles on the same lane can follow the lead vehicle without permissions unlike the previous research. To evaluate the performance of our distributed mutual exclusion algorithm, we conduct extensive experiments. The results show that our algorithmic design is both effective and efficient. [ABSTRACT FROM AUTHOR]

Advancement in technology causes the rise in smart systems. City authorities want to make their cities smarter by making an intelligent decision at real time without the involvement of humans. Monitoring and controlling city traffic is one of the major challenges faced by the authorities. These days, city traffic is monitored by static network cameras deployed on few places of highways. Most of the vehicles are also equipped with cameras to store the videos as a black box. However, monitoring and controlling city traffic by using these thousands of cameras produce an overwhelming amount of high-speed videos, which is challenging to process at real time. Therefore, in this paper, we proposed a system to control city traffic by identifying illegal traffic behaviour, such as illegal U-turn, through continuous monitoring of city traffic. The continuous city traffic is monitored by the network static cameras placed on the road as well as by all the vehicles' cameras. An architecture is proposed to handle high-speed vast volume of real-time videos efficiently. For that, the two-level parallelism is achieved with the combination of Hadoop and graphics processing unit (GPU) while processing each frame using parallel environment of Hadoop and each block of a frame using GPU. MapReduce Hadoop programming paradigm is not suitable for real-time processing. Thus, we proposed a parameter calculation algorithm that is equivalent to MapReduce mechanism for image processing while dividing the images/frames into fixed-size blocks. We analyzed the city road traffic, which is collected by static cameras placed on various roads and also by vehicles' cameras while running on the road. Later, the illegal traffic behaviour are recognized, e.g. illegal U-turn, drunken drive, zig-zag drive, over-speed, etc. Finally, the efficiency of the designed system and algorithms are tested by considering the overall running time and system's throughput with respect to video duration as well as the number of frames. The findings indicate that the proposed architecture with GPU-based algorithm over the Hadoop system perform extraordinary. [ABSTRACT FROM AUTHOR]

The ramp entrance connects the main road and the ramp and is the merging zone for vehicles from main road and ramp. As ramp entrance is considered to be the most accident-prone area, this paper focuses on the early warning to avoid collision of vehicles for the ramp entrance on freeway. Firstly, we studied the relation between the vehicle speed and collision risk by analyzing the vehicle trajectories. Secondly, the risk grades were defined based on the lag of the time when vehicles enter into the merging zone from the main road and the ramp. Finally, the risk grade was released by the information board and vehicle terminal to make driver aware of it, so that the vehicles can pass the merging zone in sequence rather than collide with each other. Theoretical analysis and experiment results show that the proposed method could realize early warning for the ramp entrance, reducing the possible traffic accidents. [ABSTRACT FROM AUTHOR]

This paper consider the coordination problem for the boundaries of control polygons in large-scale network structures; coordination is performed among different types of network partitions into such polygons. We define conditions ensuring smaller control costs in the case where the polygon boundaries of one partition type coincide with those of another partition type. The solution of this class of problems is topical for the analysis of traffic control and infrastructure maintenance in transport networks (particularly, railway networks). [ABSTRACT FROM AUTHOR]

This paper considers the problem of moving object evasion from detection in the plane. Detection is performed by a system of heterogenous observers that consists of one sensor and a group of detectors. We establish the optimal evasion law in explicit form for the case when the level of transmitted signal has power-type dependence on the evader's speed. The paths to evade the system of observers and penetrate through the system are constructed. [ABSTRACT FROM AUTHOR]

Recently, with increased interest in high levels of automated driving systems such as automatic lane change system, the need for reliable assessment methods of driver acceptance has arisen. Because the acceptance depends on the individual, the assessment of the acceptance can only be based on an individual's personal attitude, expectations, and experiences. Accordingly, subjective evaluation methods have mostly been utilized to assess the acceptance of newly developed advanced driver assistance systems. In this study, an investigation of the effects of vehicle dynamic behavior and the traffic environment on driver acceptance is conducted to provide an objective evaluation method of driver acceptance for an automatic lane change system. In order to conduct the investigation, a specific experimental program is designed and a massive database, including information on interaction behaviors between drivers, a vehicle and the traffic environment is constructed with a selected group of 19 drivers. Then, 21 parameters and their descriptive statistics for an objective evaluation index are presented to illustrate the analysis results. The results of this research can be important not only for an objective evaluation of the acceptance, but can also be expanded to suggest design criteria for control of advanced and automated driving assistance systems. [ABSTRACT FROM AUTHOR]

Purpose: Freight vehicle parking facilities at large urban freight traffic generators, such as urban retail malls, are often characterized by a high volume of vehicle arrivals and a poor parking supply infrastructure. Recurrent congestion of freight parking facilities generates environmental (e.g. pollution), economic (e.g. delays in deliveries) and social (e.g. traffic) negative externalities. Solutions aimed at either improving or better managing the existing parking infrastructure rely heavily on data and data-driven models to predict their impact and guide their implementation. In the current work, we provide a quantitative study of the parking supply and freight vehicle drivers' parking behaviour at urban retail malls. Methods: We use as case studies two typical urban retail malls located in Singapore, and collect detailed data on freight vehicles delivering or picking up goods at these malls. Insights from this data collection effort are relayed as data stories. We first describe the parking facility at a mall as a queueing system, where freight vehicles are the agents and their decisions are the parking location choice and the parking duration. Results: Using the data collected, we analyse (i) the arrival rates of vehicles at the observed malls, (ii) the empirical distribution of parking durations at the loading bays, (iii) the factors that influence the parking duration, (iv) the empirical distribution of waiting times spent by freight vehicle queueing to access the loading bay, and (v) the driver parking location choices and how this choice is influenced by system congestion. Conclusions: This characterisation of freight driver behaviour and parking facility system performance enables one to understand current challenges, and begin to explore the feasibility of freight parking and loading bay management solutions. [ABSTRACT FROM AUTHOR]

Introduction: The planning of transport infrastructures in France has been marked by three major developments: the European reform concerning the opening of rail transport to competition, the privatisation of semi-public motorway concession-holding companies, and the use of public-private partnership contracts. This article proposes examining the following research question: how to plan a series of infrastructure projects within an uncertain context? The methodology: (i) analyses whether the objectives (traffic, travel time, safety, cost, socio-economic profitability, financial profitability) of the 12 motorway and HSL projects built since the 1990s have been attained with the new planning procedure (such as risk management methods, financial risk assessment, impact assessment, public consultation) (ii) compares the additional costs and delays for three HSL projects according to their type of financing and whether or not they have used these planning procedures. Results: The analysis shows that traffic levels are lower than forecasts for 8 projects. The cost is greater than the forecasts for all infrastructures. The socio-economic profitability and the financial profitability are also lower than forecasts for approximately two-thirds of the projects. Conclusion: The social, political, institutional and environmental risks are generally well identified and treated by the client, no matter whether public or private.. The risk of additional cost is less well covered. The traffic risk remains a topical issue. Anticipating risks is liable to produce other risks, such as overcosts or opposition. It seems necessary to deepen the traffic forecast studies and to develop the multi criteria analysis method that can take account of the different points of view from the consultation. [ABSTRACT FROM AUTHOR]

Purpose: Road traffic incidents cause delay, affect public safety and the environment. The CEDR PRIMA project aims to extend practical guidance for traffic managers in pro-active Traffic Incident Management (TIM) techniques to reduce the impacts and associated costs of incidents. Methods: The paper describes modelling methods used in the project for assessing the effect of different management techniques on incident duration and travel delay under various scenarios, including collision, adverse weather, heavy vehicle breakdown and other obstruction, assuming various management strategies and generic impacts of novel technologies. Macroscopic simulations of 178 variations of 13 basic scenarios have been performed using a flexible and computationally efficient macroscopic queue model, results being verified by simulation using a velocity-based Cell Transmission Model (CTM-v). Results: The results of the two modelling methods are broadly consistent. While delays estimated by the two methods can differ by up to 20%, this is small compared to the factor of 30 range of modelled delays caused by incidents, depending on their nature and circumstances, and is not sufficient to affect general conclusions. Under the peak traffic conditions assumed, the most important factor affecting delay is whether running lanes can be kept open, but quick clearance of carriageway is not always feasible. Conclusions: Comparison of two very different modelling methods confirms their consistency within the context of highly scenario-dependent results, giving confidence in the results. Future research and data needs include further validation of the models, potential application to traffic flow and conflict prediction and incident prevention, and more complete and consistent recording of incident timelines and impacts. [ABSTRACT FROM AUTHOR]

The use of modeling and simulation aids in deriving many decisions related to transportation planning and traffic operations. Representing the real systems via simulation allows exploring system behavior in an articulated way, which is often impossible in the real world. In this paper, a simulation-based framework is presented to evaluate the impact of congestion charging on daily activity plans of the individuals. Personal decision to accept the congestion charges is evaluated by comparing the value of time with congestion charge. Value of time varies throughout the day depending upon the time pressure at any moment exerted by preceding and succeeding activities. Time pressure during an activity increases if available time for that activity is insufficient to attain the perceived utility. Daily activities in the schedules are modeled using bell-shaped marginal utility that results in sigmoid utility. A model is presented which derives the activity-specific parameters of the marginal utility function for the specific individual. To examine value of time of each person, the congestion charging is applied where personal willingness-to-pay is determined by comparing the ratios of cost to utility for original and adapted schedules. A large-scaled microsimulation of the modeled framework is used to simulate the whole population, which is created by FEATHERS, an operational activity-based model for Flanders, Belgium. The results of the simulation show that the number of individuals who avoid the congestion charges by adapting their schedules is almost three times the number of those who agree to pay it. The proposed framework can be useful to evaluate the tradeoff between value of time and costs where flexibility in selection of time defines the variability in cost. [ABSTRACT FROM AUTHOR]

Path computation in a network is dependent on the network's processes and resource usage pattern. While distributed traffic control methods improve the scalability of a system, their topology and link state conditions may influence the sub-optimal path computation. Herein, we present Pathfinder, an application-aware distributed path computation model. The proposed model framework can improve path computation functions through software-defined network controls. In the paper, we first analyse the key issues in distributed path computation functions and then present Pathfinder's system architecture, followed by its design principles and orchestration environment. Furthermore, we evaluate our system's performance by comparing it with FreeFlow and Prune-Dijk techniques. Our results demonstrate that Pathfinder outperforms these two techniques and delivers significant improvement in the system's resource utilisation behaviour. [ABSTRACT FROM AUTHOR]

In the field of self-driving technology, the stability and comfort of the intelligent vehicle are the focus of attention. The paper applies cognitive psychology theory to the research of driving behavior and analyzes the behavior mechanism about the driver's operation. Through applying the theory of hierarchical analysis, we take the safety and comfort of intelligent vehicle as the breakthrough point. And then we took the data of human drivers' perception behavior as the training set and did regression analysis using the method of regression analysis of machine learning according to the charts of the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision. At last we established linear and nonlinear regression models (including the logarithmic model) for the training set. The change in thinking is the first novelty of this paper. Last but not least important, we verified the accuracy of the model through the comparison of different regression analysis. Eventually, it turned out that using logarithmic relationship to express the relationship between the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision is better than other models. In the aspect of application, we adopted the technology of multi-sensor fusion and transformed the acquired data from radar, navigation and image to log-polar coordinates, which makes us greatly simplify information when dealing with massive data problems from different sensors. This approach can not only reduce the complexity of the server's processing but also drives the development of intelligent vehicle in information computing. We also make this model applied in the intelligent driver's cognitive interactive debugging program, which can better explain and understand the intelligent driving behavior and improved the safety of intelligent vehicle to some extent. [ABSTRACT FROM AUTHOR]

The mechanical performance of epoxy asphalt concrete has been well reported in existing studies. This study investigated the surface characteristics of asphalt concrete, including the permeability, friction coefficient, moisture damage resistance and surface texture. The permeability test, dynamic friction test, Cantabro test, Hamburg test and laser texture scanning test were carried out for such investigations. Test results show that the permeability of the epoxy asphalt concrete was very low, which is desirable for protecting the steel deck. The friction coefficient can satisfy the requirement for steel deck bridge according to the Pendulum Number. However, it was found that the skid resistance could be a concern when the vehicle speed is higher than a certain level according to the dynamic friction test. The results suggested to control the traffic speed when epoxy asphalt concrete is applied as steel bridge pavement. In terms of the raveling and stripping, it was found that the epoxy asphalt mixture has an excellent anti-stripping performance based on the Cantabro test and Hamburg wheel tracking test results. The yield point was not reached after 20,000 loading cycles at 60 °C in the immersed Hamburg wheel tracking test. The mean profile depth of the epoxy asphalt concrete is 0.305 mm, much lower than conventional asphalt concrete indicating a very fine surface texture. [ABSTRACT FROM AUTHOR]

The objective of this research is to investigate the pavement response of road-bridge transition section without slab under impact load caused by vehicle bumping. The three-dimensional (3D) finite element models (FEM) of road-bridge transition section without slab were developed to simulate the response under impact load. The influence of different parameters (damping ratio, step height, vehicle speed as well as axle load) on the pavement response was investigated. Results indicated that an obvious increase of pavement response was observed with the step height and axle load rising. On the contrary, the rise of vehicle speed and damping ratio led to the decrease of pavement response. In addition, the pavement under impact load exhibited larger response as compared to under static load. Therefore, the phenomenon that static load was considered only in the traditional pavement structure is defective. Finally, the correction coefficients of design index for highway asphalt pavement were proposed to consider the influence of impact load. [ABSTRACT FROM AUTHOR]

In China, the research on asphalt pavement deterioration was hindered by a lack of comprehensive investigations on field data collection of pavement performance within its service life. The problem of traditional methods that yield inadequate data can be addressed using a combination of the Pavement Mechanistic-Empirical (PME) system and matter-element analysis (MEA). Measured data for the rutting of a road section in the Shaanxi province was employed as the validation data in this study. The input parameter for PME was localized, and a decay model based on the predicted results of PME was established by the MEA method. The results from PME indicate that the predicted results of rutting show a ''lagging" characteristic when compared with the measured data. Some optimization measures are employed to process the predicted results of PME so as to better fit the measured data. The predicted data for rutting resistance can be considered essential data for the MEA method to investigate the decay tendency of the rutting of asphalt pavement in its design life. A case study indicates that the rutting resistance of asphalt pavement will deteriorate from excellent to good, good to medium, medium to inferior, and inferior to bad in 13.8 months, 32.5 months, 43.8 months, and 55.2 months, respectively. The combination of PME and MEA proves to be appropriate to evaluate rutting potential in project level pavements. Follow-up research can be carried out on the developing trend of pavement damage, so as to better determine the maintenance time for a chosen road section. [ABSTRACT FROM AUTHOR]

Smart cities involve the provision of advanced services for road traffic users. Vehicular ad hoc networks (VANETs) are a promising communication technology in this regard. Preservation of privacy is crucial in these services to foster their acceptance. Previous approaches have mainly focused on PKI-based or ID-based cryptography. However, these works have not fully addressed the minimum information disclosure principle. Thus, questions such as how to prove that a driver is a neighbour of a given zone, without actually disclosing his identity or real address, remain unaddressed. A set of techniques, referred to as Attribute-Based Credentials (ABCs), have been proposed to address this need in traditional computation scenarios. In this paper, we explore the use of ABCs in the vehicular context. For this purpose, we focus on a set of use cases from European Telecommunications Standards Institute (ETSI) Basic Set of Applications, specially appropriate for the early development of smart cities. We assess which ABC techniques are suitable for this scenario, focusing on three representative ones-Idemix, U-Prove and VANET-updated Persiano systems. Our experimental results show that they are feasible in VANETs considering state-of-the-art technologies, and that Idemix is the most promising technique for most of the considered use cases. [ABSTRACT FROM AUTHOR]

Uneven workload distributions in teams can lead to suboptimal team performance. This paper therefore describes the design of a virtual assistant that supports workload harmonization in teams by measuring workload, informing team members about their own and other team members' workload, and supporting team members in the redistribution of workload. The virtual assistant was developed in the context of train traffic control according to the situated Cognitive Engineering (sCE) methodology, which was extended to allow for a value sensitive design process. More specifically, the values 'insight,' 'helping others' and 'privacy' were explicitly accounted for throughout the design of the virtual assistant. A prototype of the virtual assistant was evaluated positively in a focus group. Thereby, the contribution of the paper is twofold. First, an improvement in a human-centered development methodology-sCE with values-is described and its use is demonstrated in an actual design case. Second, a novel, positively evaluated solution for workload harmonization in teams is presented. [ABSTRACT FROM AUTHOR]

There has been an increase in the amount and complexity of traffic on Dutch inland waterways. Traffic management shifted from local traffic control to corridor traffic management. Due to these changes, operators in nautical traffic management centers experience difficulty to gain and maintain sufficient situation awareness. In our previous research, three groups of deficiencies of current situation awareness support have been identified. To overcome these deficiencies, a theoretical framework for information engineering user interface concepts was developed and used for designing three interface concepts. A coherent, an integrated and a context-dependent adaptable user interface were designed and implemented as testable prototypes. To support the process of information engineering, we combined user-centered design techniques with the formalism of set theory, directed graphs and semantic networks. From a methodological point of view, these proved to be useful both in conceptualization and implementation of the concepts. The usability of these concepts was tested in a simulator environment. The results show that the implemented prototypes represent an important improvement compared to the traditional interface solutions currently used by nautical traffic management operators. However, it was recognized that the coherent user interface implementation still suffered from a number of deficiencies. The context-dependent adaptable user interface was evaluated most positively. [ABSTRACT FROM AUTHOR]