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The transportation of goods by road is crucial in Tanzania's Central and North-South Corridors. However, challenges such as truck congestion and road accidents are affecting the efficiency of these routes. Road crashes are prevalent in low- and middle-income countries, with Africa experiencing an exceptionally high rate. This study examines the opinions of Tanzanian truck drivers on the effectiveness of Advanced Driver Assistance Systems in reducing road safety risks. A discriminant analysis was conducted to assess the awareness and use of these systems among different driver experienced groups. The results highlight the importance of improving infrastructure, ensuring vehicle safety standards, providing comprehensive driver training, and integrating innovative Intelligent Transport Systems to address road safety issues. In conclusion, the study provides valuable insights for policymakers and stakeholders to strengthen road safety measures in Tanzania, facilitating smoother road freight transport operations and promoting economic growth. [ABSTRACT FROM AUTHOR]

Recently, Internet of Drones (IoD) has garnered significant attention due to its widespread applications. However, deploying IoD for area coverage poses numerous limitations and challenges. These include interference between neighboring drones, the need for directional antennas, and altitude restrictions for drones. These challenges necessitate the development of efficient solutions. This research paper presents a cooperative decision-making approach for an efficient IoDdeployment to address these challenges effectively. The primary objective of this study is to achieve an efficient IoDdeployment strategy thatmaximizes the coverage regionwhileminimizing interference between neighboring drones. In deployment problem, the interference increases as the number of deployed drones increases, resulting in bad quality of communication. On the other hand, deploying a few drones cannot satisfy the coverage demand. To accomplish this, an enhanced version of a concise population-based meta-heuristic algorithm, namely Improved Particle SwarmOptimization (IPSO), is applied. The objective function of IPSO is defined based on the coverage probability, which is primarily influenced by the characteristics of the antennas and drone altitude. A radio frequency (RF) model is derived to evaluate the coverage quality, considering both Line of Sight (LOS) and Non-Line of Sight (NLOS) down-link coverage probabilities for ground communication. It is assumed that each drone is equipped with a directional antenna to optimize coverage in a given region. Extensive simulations are conducted to assess the effectiveness of the proposed approach. Results demonstrate that the proposed method achieves maximum coverage with minimum transmission power. Furthermore, a comparison is made against Collaborative Visual Area Coverage Approach (CVACA), and a game-based approach in terms of coverage quality and convergence speed. The simulation results reveal that our approach outperforms both CVACA and the gamebased schemes in terms of coverage and convergence speed. Comparisons validate the superiority of our approach over existing methods. To assess the robustness of the proposed RFmodel, we have considered two distinct ranges of noise: range1 spanning from-120 to-90 dBm, and range2 spanning from-90 to-70 dBmfor different numbers of UAVs. In summary, this research presents a cooperative decision-making approach for efficient IoD deployment to address the challenges associated with area coverage and achieves an optimal coverage with minimal interference. [ABSTRACT FROM AUTHOR]

The traffic in developing countries presents its own specificity, notably due to the heterogeneous traffic and a weak-lane discipline. This leads to differences in driver behavior between these countries and developed countries. Knowing that the analysis of the drivers from developed countries leads the design of the majority of driver models, it is not surprising that the simulations performed using these models do not match the field data of the developing countries. This article presents a systematic review of the literature on modeling driving behaviors in the context of developing countries. The study focuses on the microsimulation approaches, and specifically on the multiagent paradigm, that are considered suitable for reproducing driving behaviors with accuracy. The major contributions from the recent literature are analyzed. Three major scientific challenges and related minor research directions are described. [ABSTRACT FROM AUTHOR]

The increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet of Drones (IoD)—a networked UAV system—has gained broad-spectrum attention for its potential applications. However, threat-prone environments, characterized by obstacles, pose a challenge to the safety of drones. One of the key challenges in IoD formation is path planning, which involves determining optimal paths for all UAVs while avoiding obstacles and other constraints. Limited battery life is another challenge that limits the operation time of UAVs. To address these issues, drones require efficient collision avoidance and energy-efficient strategies for effective path planning. This study focuses on using meta-heuristic algorithms, recognized for their robust global optimization capabilities, to solve the UAV path-planning problem. We model the path-planning problem as an optimization problem that aims to minimize energy consumption while considering the threats posed by obstacles. Through extensive simulations, this research compares the effectiveness of particle swarm optimization (PSO), improved PSO (IPSO), comprehensively improved PSO (CIPSO), the artificial bee colony (ABC), and the genetic algorithm (GA) in optimizing the IoD's path planning in obstacle-dense environments. Different performance metrics have been considered, such as path optimality, energy consumption, straight line rate (SLR), and relative percentage deviation (RPD). Moreover, a nondeterministic test is applied, and a one-way ANOVA test is obtained to validate the results for different algorithms. Results indicate IPSO's superior performance in terms of IoD formation stability, convergence speed, and path length efficiency, albeit with a longer run time compared to PSO and ABC. [ABSTRACT FROM AUTHOR]

With the aim to enhance prediction accuracy for nonlinear time series, this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation (SNA) function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model's sensitivity to hyper-parameters. The Strategy was implemented in a two-state reconstruction process by first inputting the time series data to the model separately. Once, the time data passes through the reservoirs and is activated by the SNA activation function, the new state for the reservoirs is created. The state is input to the next layer, and the concatenate states module saves. Pairs of states are selected from the activated multi-layer reservoirs and input into the state reconstruction module. Multiple input states are transformed through the state reconstruction module and finally saved to the concatenate state module. Two evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy. [ABSTRACT FROM AUTHOR]

Vehicle ad hoc networks (VANETs) have gradually emerged to enhance transportation information, entertainment, safety, and other services. However, such infrastructures have certain limitations, causing intermittent network disconnection. Further, in urban areas, terrain heights act as obstacles and hinder or attenuate transmitted signals. In this study, we propose a dynamic 3D internet of drones collaborative communication approach for efficient VANET-assistance (3DIoDAV) by integrating the IoD network and VANET to support terrestrial communication. We model IoD locations as an optimization problem to optimize the IoD nodes in three-dimensional terrain. Improved particle swarm optimization is used to optimally deploy IoD nodes in 3D terrain for minimizing the number of isolated vehicles. The proposed approach considers the terrain profile influence on communication. Therefore, we propose a 3D propagation model for efficient IoD-to-vehicle (IoD2V) communication in 3D space. Experiments are performed based on the received signal from ground vehicles to examine the performance of the proposed model and the 3DIoDAV approach. Simulation results show different behaviors of IoD nodes in two-dimensional (2D) and 3D scenarios. Comparison with 2D VANET-assisted and IoDAV approaches demonstrates the proposed 3DIoDAV approach's ability to detect terrain obstacles, which guarantees the dispatching of IoD nodes into the most appropriate locations in 3D space, thereby minimizing the impact of terrain obstacles on communication. [ABSTRACT FROM AUTHOR]

Driving styles reflect the performance and the ability of drivers to drive in a safe and protective manner. As some of them would possibly result into harmful behaviors, the recognition of these styles continue to attract intensive investigations from the transportation community. In spite of the current promising results, the existing approaches did not yet address the management of simultaneous driving behaviors that are exhibited by a driver during the same commute. They did not also explicitly investigate the legal implication of these driving styles. To this end, we argue that intelligent collaborative solutions could adequately handle the constantly changing traffic environment, prevent aberrant driving behaviors, classify driving styles, and identify the right road traffic policies to apply at the right time to the right driver. Therefore, we are proposing a new intelligent divide-and-conquer approach that aims to process concurrent driver's driving behaviors and identify the related driving styles, accordingly. Our solution relies on a four-layer Multi-Agent System (MAS) architecture, where intelligent agents execute injection, filtering, action, and feedback processing steps to ultimately generate personalized recommendations and feedback to drivers. For the sake of illustration, we collected driving data about braking and acceleration behaviors via our dedicated mobile app AWARIDE. We successfully classified the driving styles into aggressive, normal, and conservative. We also successfully identified the transitions between these styles. [ABSTRACT FROM AUTHOR]

Traffic accidents are continuously rising in many countries all over the world. Violation of road safety regulations and traffic laws is one of major reason to this increasing trend. Existing intelligent transportation systems (ITS) to date are accurately enforcing safe municipal speed limits using enforcement technology solutions. The systems for automated traffic enforcement emerged in the last decade. Automated enforcement technologies provide opportunity to remotely capture images of drivers violating laws. RADAR/LIDAR-based tools are used to determine the speed of vehicles. Choosing the right speed detection technology is an area of significant concern for transportation agencies. This paper presents a comparison review of the use of LIDAR technologies vs. RADAR technologies in traffic enforcement systems. The current trend suggests an inclination toward LIDAR technology because of many advantages, particularly its precise identification of violators; especially, this functionality is being required in case of new travel modes (such e-bikes and e-scooty) emerged in urban areas. However, the objectives and available resources of enforcement programs are key factors that may result in growth in relation to demand for both types of technologies. [ABSTRACT FROM AUTHOR]

Although the pick-up/drop-off (PUDO) strategy in carpooling offers the convenience of short-distance walking for passengers during boarding and disembarking, there is a noticeable hesitancy among commuters to adopt this travel method, despite its numerous benefits. Here, this paper establishes a tripartite evolutionary game theory (EGT) model to verify the evolutionary stability of choosing the PUDO strategy of drivers and passengers and offering subsidies strategy of carpooling platforms in carpooling system. The model presented in this paper serves as a valuable tool for assessing the dissemination and implementation of PUDO strategy and offering subsidies strategy in carpooling applications. Subsequently, an empirical analysis is conducted to examine and compare the sensitivity of the parameters across various scenarios. The findings suggest that: firstly, providing subsidies to passengers and drivers, along with deductions for drivers through carpooling platforms, is an effective way to promote wider adoption of the PUDO strategy. Then, the decision-making process is divided into three stages: initial stage, middle stage, and mature stage. PUDO strategy progresses from initial rejection to widespread acceptance among drivers in the middle stage and, in the mature stage, both passengers and drivers tend to adopt it under carpooling platform subsidies; the factors influencing the costs of waiting and walking times, as well as the subsidies granted to passengers, are essential determinants that require careful consideration by passengers, drivers, and carpooling platforms when choosing the PUDO strategy. Our work provides valuable insight into the PUDO strategy's applicability and the declared results provide implications for traffic managers and carpooling platforms to offer a suitable incentive. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), commonly known as drones, have drawn significant consideration thanks to their agility, mobility, and flexibility features. They play a crucial role in modern reconnaissance, inspection, intelligence, and surveillance missions. Coverage path planning (CPP) which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest (ROI). However, the flight time of the UAV is limited due to a battery limitation and may not cover the whole region, especially in large region. Therefore, energy consumption is one of the most challenging issues that need to be optimized. In this paper, we propose an energy-efficient coverage path planning algorithm to solve the CPP problem. The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region. To do so, the flight path is optimized and the number of turns is reduced to minimize the energy consumption. The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint. Then, the coverage path planning problem is formulated, where the exact solution is determined using the CPLEX solver. For small-scale problems, the CPLEX shows a better solution in a reasonable time. However, the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems. Thus, to solve the model for large-scale problems, simulated annealing forCPP is developed. The results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX solver. Finally, we compare the simulated annealing against the greedy algorithm. The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality. [ABSTRACT FROM AUTHOR]

Group awareness is playing a major role in the efficiency of mission planning and decision-making processes, particularly those involving spatially distributed collaborative entities. The performance of this concept has remarkably increased with the advent of the Internet of Things (IoT). Indeed, a myriad of innovative devices are being extensively deployed to collaboratively recognize and track events, objects, and activities of interest. A wide range of IoT-based approaches have focused on representing and managing shared information through formal operators for group awareness. However, despite their proven results, these approaches are still refrained by the inaccuracy of information being shared between the collaborating distributed entities. In order to address this issue, we propose in this paper a new belief-management-based model for a collaborative Internet of Drones (IoD). The proposed model allows drones to decide the most appropriate operators to apply in order to manage the uncertainty of perceived or received information in different situations. This model uses Hierarchical Analysis Process (AHP) with Subjective Logic (SL) to represent and combine opinions of different sources. We focus on purely collaborative drone networks where the group awareness will also be provided as service to collaborating entities. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) have been a rapidly emerging phenomenon in recent years, with some automated features already available in vehicles. AVs are expected to potentially revolutionize the existing inefficient state of urban transportation and be a step closer to environmental sustainability. This study focuses on simulation modeling in assessing the potential effects of autonomous vehicles (AVs) and on mobility and safety by developing a framework model based on traffic microsimulation for a real network located in Al-Madinah, Saudi Arabia. The market penetration rates (MPRs) will not reach 100% in the near future; instead, penetration will progressively increase. As a result, in our study, we investigated the potential effect of AV technology in five different AV market penetration rates: 0% (baseline), 25%, 50%, 75%, and 100%. The results suggest that Avs significantly improve the network's safety and operational performance at high penetration rates. Specifically, estimated vehicle delays decreased by 26%, 34.4%, 63.7%, and 74.2% for 25%, 50%, 75%, and 100% AV penetration rates, respectively. Finally, we think this study will help decisionmakers over in the long-term in their attempts to achieve sustainable development through the optimal integration of innovative and novel technologies. [ABSTRACT FROM AUTHOR]

Nowadays, resilience has become an indispensable term in several aspects and areas of research and life. Reaching consensus on what actually constitutes "resilience," "community," and "community resilience" is still a task that guarantees a vivid exchange of opinions, sometimes escalating into debates, both in the scientific community and among practitioners. Figuring out how to practically apply resilience principles goes even a step further. This study attempts to circumvent the need for a universal agreement on the definition of "community resilience," which may still be immature, if not impossible, at this time. We accomplish this by proposing a practical methodological approach with concrete methods on how to agree and implement commonly accepted community resilience principles in the context of technology development and pilot testing for disaster management. The proposed approach was developed, tested, and validated in the context of the Horizon 2020 EU-funded project Search and Rescue. Major aspects of the approach, along with considerations for further improvement and adaptation in different contexts, are addressed in the article. [ABSTRACT FROM AUTHOR]

The shared transportation systems of many countries in the Middle East and Arab world are commonly challenged by sociocultural constraints that need to be satisfied to attract people to ridesharing. As an example, in this article, we address the chartered vanpooling problem, which relates to a ridesharing form widely used in many of these countries, and propose the concept of socially structured vanpooling as an accommodation of the sociocultural preferences of riders. The proposed framework aims at improving the social satisfaction of riders by grouping them into socially compatible pools of co-riders who are comfortable using chartered van services. We present the proposed framework through a case study of female students in Salalah, Oman, and implement it as a three-step clustering algorithm consisting of 1) crisp spatiotemporal pooling, 2) fuzzy agglomerative clustering based on riders’ social connections, and 3) fuzzy clustering according to riders’ preferences. Based on data collected from 500 students, our experimental results show that the proposed framework leads to a better tradeoff between riders’ satisfaction and van operators’ benefits. [ABSTRACT FROM AUTHOR]

Fog computing (FC) is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network (close to the Internet of Things (IoT) devices). Fog nodes provide services in lieu of the cloud. Thus, improving the performance of the network and making it attractive to social media-based systems. Security issues are one of the most challenges encountered in FC. In this paper, we propose an anomalybased Intrusion Detection and Prevention System(IDPS) againstMan-in-the-Middle (MITM) attack in the fog layer. The system uses special nodes known as IntrusionDetection System (IDS) nodes to detect intrusion in the network. They periodically monitor the behavior of the fog nodes in the network. Any deviation from normal network activity is categorized as malicious, and the suspected node is isolated. ExponentiallyWeightedMovingAverage (EWMA) is added to the system to smooth out the noise that is typically found in social media communications. Our results (with 95% confidence) show that the accuracy of the proposed systemincreases from80% to 95% afterEWMAis added. Also, with EWMA, the proposed system can detect the intrusion from 0.25-0.5 s seconds faster than that without EWMA. However, it affects the latency of services provided by the fog nodes by at least 0.75-1.3 s. Finally, EWMA has not increased the energy overhead of the system, due to its lightweight. [ABSTRACT FROM AUTHOR]

Enforcement of traffic rules and regulations involves a wide range of complex tasks, many of which demand the use of modern technologies. variable speed limits (VSL) control is to change the current speed limit according to the current traffic situation based on the observed traffic conditions. The aim of this study is to provide a simulation-based methodological framework to evaluate (VSL) as an effective Intelligent Transportation System (ITS) enforcement system. The focus of the study is on measuring the effectiveness of the dynamic traffic control strategy on traffic performance and safety consideringvariousperformanceindicatorssuchastotaltraveltime, averagedelay, and average number of stops. United Arab Emirates (UAE) was selected as a case study to evaluate the effectiveness of this strategy. A micro simulation software package VISSIM with add-on module VisVAP is used to evaluate the impacts of VSL. It has been observed that VSL control strategy reduced the average delay time per vehicle to around 7%, travel time by 3.2%, and number of stops by 48.5%. Dynamic traffic control strategies also alleviated congestion by increasing the capacity of the bottleneck section and improving safety. Results of this study would act as a guidance for engineers and decision makers to new traffic control system implementation. [ABSTRACT FROM AUTHOR]

Ambient data is generated from a collective of smart devices, connected environments and pervasive platforms, so well-integrated and finely tuned that we no longer think about connecting, syncing, or controlling. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-020-01371-w 7 Ahmed A, Outay F, Zaidi SOR, Adnan M, Ngoduy D (2020) Examining queue-jumping phenomenon in heterogeneous traffic stream at signalized intersection using UAV-based data. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-019-01333-x 3 Beg A, Qureshi AR, Sheltami T, Yasar A (2020) UAV-enabled intelligent traffic policing and emergency response handling system for the smart city. [Extracted from the article]

Investigating travel time distribution and associated variability is important for a variety of transport planning, traffic management, and control projects. Studies that investigated travel time distribution tend to be limited to explore changes in characteristics of distribution with respect to space and time of day. Given the availability of big dataset that contains seven different types of vehicle trajectories in the city of Athens for around 56,000 trips which are traversing on more than 1.8 million road links, this study presents the detailed investigation of travel time distribution in different spatiotemporal settings. The study considered four different types of urban roads and six time intervals along with consideration of weekdays and weekends. The empirical investigation employed Kruskal-Wallis, Chi-square, and Kolmogorov-Smirnov tests to fit travel time data into seven unimodal statistical distributions that are found in the literature to describe travel time distribution. It is found that lognormal distribution outperformed other distribution, and all of the considered categories of travel time data are well-fitted to this distribution. Additionally, parameters of lognormal distribution for different categories of travel time data are not significantly different from each other, which led to the conclusion that travel time distribution is roughly independent of space and time, which is in agreement with a few earlier studies that are limited in their scope especially in relation with availability of data. With this important finding, this study estimate values of travel time variability for different classes of individuals employing a standard approach that requires time of day independent standardized distribution of travel time. It is estimated that for Athens population value of travel time variability is approximately half of the value of travel time. This is useful to carry out cost-benefit analyses for mobility-related projects in Athens, Greece. [ABSTRACT FROM AUTHOR]

In addition to their environmental impact, road traffic congestions have been recognized to seriously affect commuters' safety as well as the performance of transportation systems. To address these issues, various Traffic Incident Management (TIM) systems have been implemented. Recent systems are particularly focusing on the integration of promising emergent technologies such as the Internet of Things. However, thorough studies are still necessary to make sure that these technologies are compatible with existing systems and effective within their context of use. The main goal of this research is to develop a smart TIM system which is based on Car2X communications and which aims at improving both traffic safety, commuters' mobility, and gas emissions. To assess the effectiveness of our solution, we use the following measures: stops delay, stops all, vehicle delay, travel time, gas emissions, and fuel consumption. This paper also outlines how we use our simulation platform (which was developed based on VISSIM and Python) to quantify the benefits of using Car2X communications. We run our simulations on Muscat Expressway in the Sultanate of Oman. Results are promising and include (1) the travel time decreased by 6%; (2) the average stop delay and vehicle stops were reduced by at least 9% and 27% respectively; and (3) there is a total decrease in fuel consumption and carbon monoxide emissions by approximately 16%. [ABSTRACT FROM AUTHOR]

Visitor mobility in protected areas is often a challenge as manual map reading and finding a route consume the time. This paper presents a digital tool that provides valuable information for both the visitors and the managers of protected areas. The app provides assistance to users to generate, access, and record hiking routes within the protected area. A route planner was created to generate user-specific alternative routes taking into account the user's preferences in terms of distance, preferred landscape, level of accessibility, etc. The app also provides the users with basic information on the park, including captivating points, typical flora, and fauna. Moreover, visitors and park management can communicate with each other through exchanging messages and suggestions. The pilot validation test performed with 20 participants and real-time test from 68 park visitors revealed that all the functionality of the app is in order and the app enhances the mobility experience of the user within Hoge Kempen National Park. Furthermore, regular use of the app from users will generate rich data that is valuable for visitor management purposes, for example, the number of trips per day, the average duration of visits and popular segments. [ABSTRACT FROM AUTHOR]

Despite of their increasing popularity, Internet of Things (IoT) platforms are still suffering from major security problems, particularly during communications between the IoT devices. Indeed, these devices are commonly prone to malicious attacks and require prior mutual authentication to guarantee the confidentiality and security of data being shared as well as a proper network operation. In order to deal with this issue, several researchers have proposed the use of the emergent blockchain paradigm, which has emerged as a key technology that will transform the way in which information will be shared. As this paradigm is facing scalability and flexibility problems, the use of multi-agents systems has been adopted in recent works. However, current solutions are using agents for the rudimentary role of data collection only. In this paper, we propose to secure the IoT platform with a multilevel blockchain system (MBS) where the speed and flexibility of blockchain transactions are enforced by mobile agents which are migrating throughout the IoT network. The simulations of our solution through the Hyperledger Fabric are showing relevant results in terms of response time and energy consumption. [ABSTRACT FROM AUTHOR]

Next-generation intelligent transportation systems are based on the acquisition of ambient data that influence traffic flow and safety. Among these, is the ambient visibility range whose estimation, in the presence of fog, is extremely useful for next-generation intelligent transportation systems. However, existing camera-based approaches are based on "engineered features" extraction methods that use computer algorithms and procedures from the image processing field. In this contribution, a novel approach to estimate visibility range under foggy weather conditions is proposed which is based on "learned features" instead. More precisely, we use AlexNet deep convolutional neural network (DCNN), trained with raw image data, for feature extraction and a support vector machine (SVM) for visibility range estimation. Our quantitative analysis showed that the proposed approach is very promising in estimating the visibility range with very good accuracy. The proposed solution can pave the way towards intelligent driveway assistance systems to enhance awareness of driving weather conditions and hence mitigate the safety risks emanating from fog-induced low visibility conditions. [ABSTRACT FROM AUTHOR]

As modern cities expand and develop, the resultant increase in population density gives rise to the need for smart solutions to cope with the demands applied to the infrastructure of the city. In this paper, we investigate the shortcomings of traffic policing and emergency response handling systems; propose an intelligent, autonomous UAV-enabled solution; and describe the system in a simulated environment. Several scenarios of traffic monitoring and policing system are considered in the simulation: traffic light violations and accident detection, mobile speeding traps and automated notification, congestion detection and traffic rerouting, flagged stolen vehicles/pending arrest warrants and vehicle tracking using UAVs, and autonomous emergency response handling systems. Furthermore, smart city infrastructure enable intelligent handling of emergencies by providing traffic light prioritization for ground emergency response units to reduce delay for patient care, automated physical bollard on routes with congested points due to accidents or hazards, first responder support UAV units—medical supplies UAV, fire fighting UAV to combat or control small fires, and numerous other benefits. Lastly, we present the results of the simulated system and discuss our findings. [ABSTRACT FROM AUTHOR]

Non-recurrent congestion, which is mainly due to traffic incidents, may seriously impact the performance and operation of a traffic system. Reacting quickly and in a uniform and structured way is vital. In particular, choosing the appropriate response strategy with only a short delay may mitigate the impact of incidents, improve traffic efficiency, and increase safety in the transportation system. This paper proposes STIMF: a smart traffic incident management framework to reduce the burden on traffic incident operators by assisting them in selecting the most appropriate response strategy when an incident occurs. STIMF includes two software systems: (a) a simulation environment used to evaluate traffic incident management strategies and (b) a fuzzy-logic inference system that allows the traffic operator to get prompt recommendations on the best response strategies based on the current context and conditions. Moreover, the STIMF framework also describes the process of preparing and building the simulation environment. To evaluate the proposed framework, we tested it on a section of the Muscat expressway in Oman. [ABSTRACT FROM AUTHOR]

Recently, simulation models have been widely used around the world to evaluate the performance of different traffic facilities and management strategies for efficient and sustainable transportation systems. One of the keys factors for ensuring the reliability of the models in reflecting local conditions is the calibration and validation of microsimulation models. The majority of the existing calibration efforts focus is on the experimental designs of driver behaviour and lane-changing parameters. Towards this end, this paper describes the necessary procedure for the calibration and validation of a microscopic model using the VISSIM software, during peak hours. The procedure is applied on Muscat Expressway in the Sultanate of Oman. The calibration parameters and the measure-of-effectiveness are identified by using multi-parameter sensitivity analysis. The optimum values for these parameters are obtained by minimising errors between simulated data and field data. In our proposed model, we used traffic volume and travel speed for model calibration, as well as average travel time for validation of the calibrated model. The achieved results showed that driving characteristics significantly impacted the merging/diverging traffic flow ratio in the merging area, the link length and the distance between on-ramps and off-ramps, as well as the percentage of heavy vehicles. The results also showed that having both the advanced merging and cooperative lane-change settings active, along with safety distance reduction factor, necessary lane change, minimum headway (front/rear), and emergency stop, had a significant influence on simulation precision, especially at on-ramps and off-ramps. Finally, our proposed model can be utilized as a base for future traffic strategy analysis and intelligent transportation systems evaluation to help decision makers with long-term and sustainable development decisions. [ABSTRACT FROM AUTHOR]

Cloud computing is attracting a lot of traffic on the Internet. Due to the distributed nature of the ad hoc networks, there is no clear path for the communication between nodes. Additionally, there is no centralized or master node to guide the rest of the nodes in the distributed networks. Since the networks are continually expanding, it is vital to mention that there is a substantial increase in the number of attacks. As such, we became interested to consider monitoring the wireless network using cloud. This way, we will be able to deal with varies attacks and to provide better guidance for the networks. In this paper, we propose a new approach for acknowledgement scheme, using a one hop cloud technology, called Cloud Acknowledgement Scheme. This approach strengthens the wireless network by introducing cloud as a monitoring tool as well as to act as a leader node. An efficient cloud provisioning needs a better scheduling scheme. Towards this end, this paper is proposing a new approach to support the short-lived nature of acknowledgment packets in an efficient manner. Our proposed approach constitutes of two developed algorithms that act as one. These scheduling algorithms help to accomplish the purpose of cloud acknowledgment scheme. It is also important and significant to consider the cloud's awareness of energy consumption to reduce the impact on the environment. Towards this end, monitoring energy consumption is achieved by assigning priorities on the given tasks and balancing the performance and the energy of the cloud. The proposed approach is demonstrated and proved as working models using simulations. The results suggest that our proposed approach has the potential to alternate other existing acknowledgement schemes. [ABSTRACT FROM AUTHOR]

Recommender systems are used to suggest items that are useful to users. The recommendations can be surprising and may be categorized as serendipitous recommendations. One of the limitations with serendipitous recommendations is that the user interface of such a recommender system rarely supports the user to switch from accuracy orientation to serendipity facilitations. Using serendipitous recommendations can be challenging. This is because the user might not fully benefit from and understand the serendipitous recommendations. One main advantage of this type of system is that a serendipity-oriented recommender system can be used for the supervision of research students. It can help them to find a novel topic in the area of their research interests. This paper reports on a novel user interface design for facilitating serendipitous recommendations generation in educational environments. The user interface of this recommender system provides students with user controls and visualization in order to explore research articles. This research comprises user experience experiments conducted in an academic environment and evaluated by means of a user centered design evaluation. It involves research articles recommender system named JabRef. The recommender systems were used by students at the undergraduate level. Users reported an enhanced user experience while using the user controls and visualization and serendipitous resource discovery. It was found that user interface design can facilitate a serendipity recommender system in the learning environment. University professors supervising students during the research can also benefit from the recommender system. [ABSTRACT FROM AUTHOR]

Road geometric design standards provide various possibilities for merging freeways with a decreasing number of lanes. In this study, an alternative design (i.e. taper design) is investigated and compared with the standard design under three different heavy vehicle compositions to understand driving performance in relation to the flow of traffic. Taper design is not always the first choice in the road geometric design guidelines and the designer has to provide arguments for selecting this design. Taper design and its comparison with other alternatives are also not well explored in literature. In this study, a driving simulator was used to examine and compare the performance of these two designs under different heavy vehicle compositions. Qualitative results showed that the perceived safety was better for the standard design compared to the taper design. Mean speed, acceleration, standard deviation of acceleration/deceleration, and cumulative lane changes were chosen as behavioural parameters to compare these two designs using MANOVA and repeated measures ANOVA. Results revealed that drivers' discomfort in performing merging manoeuvres was greatest in case of a taper design and when the percentage of heavy vehicles was moderate (15%). Overall, the standard design was found to be more favourable. Practitioner summary: Driving behaviour at merging freeways with a decreasing number of lanes is underexplored. We analysed safety in driving behaviour considering heavy vehicles for taper and standard designs provided in Dutch guidelines using a driving simulator. The standard design was found to be safer and the presence of moderate heavy vehicles caused more disturbances in driving behaviour. [ABSTRACT FROM AUTHOR]

Recently, we have witnessed unprecedented use of cloud computing and its services. It is influencing the way software is built, as well as company' resources such as servers, workstations or generally hardware are used. This paper aims to examine the benefits of cloud usage to support real-time service systems, using the Salesforce platform. First, we explore the meaning and the role of cloud computing for the real-time service systems efficient functioning. Then, we build a service management platform for the Polish Billiards and Snooker Association (PBSA), based on a real-time system located in the cloud. This way, PBSA managers are able to complete their tasks in this system on-demand. Moreover, it is set up as a private cloud to grant access only to the snooker organization employees. [ABSTRACT FROM AUTHOR]

First and last-mile access to and from public transport stations/stops is a major problem for encouraging public transport use. Bicycle sharing schemes have shown potential to fill this gap. Consequently, railway operators in the Netherland and Germany have started their own bike sharing schemes. Majority of the studies examined the preferences for using bike share schemes for larger cities. This study analyses the collected stated preference survey data for the use of bicycle sharing scheme for last mile travel, which is recently launched in small/medium sized cities of Belgium. Within this scheme a single docking station is available and users need to return bicycle at the same station. The survey also includes questions on respondents attitudes towards friendliness-to-cycling. The hybrid choice modelling framework is used to investigate preferences of users. Usual explanatory variables such as temperature, rain conditions, distance, rental cost, gender and age are found significant, which confirms the findings of earlier studies. Along with these; last-mile (to home) and an interaction term between rental cost and duration to keep bicycle are found significant, which indicate the negative effects of having a single docking station. Availability of escorting facility from parents/colleague/friends (a more common phenomenon in small/medium cities) also has a negative effect on the use of the examined bike share scheme. Paper also discusses promotional campaigns and marketing efforts according to the obtained results for making such schemes more successful. [ABSTRACT FROM AUTHOR]

High mobility in ITS, especially V2V communication networks, allows increasing coverage and quick assistance to users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better QoS during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future IoT-based edge computing networks. In this context, this article contributes in three distinct ways: to develop a QoS-aware, green, sustainable, reliable, and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT-driven edge computing networks; to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time datasets of vehicles to demonstrate how it outperforms conventional techniques, making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms. [ABSTRACT FROM AUTHOR]

In past years, 3D models of virtual worlds have been used in several applications such as urban planning, simulation and design. In the railway field, that we chose as a field of application to illustrate our proposals in this article, simulation makes sense because of the complexity involved and the risk for personnel during upstream phases of validation. To be able to simulation with enough precision, the model of the virtual universe is a key point. Especially For train component simulation, an accurate and detailed model of the train component is mandatory. For training the drivers within a serious game, a high visual quality is required. Obtaining a single model supporting these two constraints at the same time at the lowest cost is still an open issue and involved many actors. Our contribution is a methodology and a process for creating a virtual universe model, based on automatic model generation, in order to allow the creation of large-scale universes while guaranteeing a level of details appropriate to the need, a model of constant quality and including semantic data necessary for simulation, while reducing the modeling costs and the modeling duration. The proposed process is applied to train simulation. [ABSTRACT FROM AUTHOR]

This special issue is based on the best papers from 8th International Conference on Ambient Systems, Networks and Technologies (ANT-2017, http://cs-conferences.acadiau.ca/ANT-17/) and the 7th International Conference on Sustainable Energy Information Technology (SEIT 2017, http://cs-conferences.acadiau.ca/seit-17/) held in Madeira, Portugal, from May 16-19, 2017. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. Another paper is by Al-Ruithe et al. [[14]] entitled " I A systematic literature review of data governance and cloud data governance i ". 14 Al-Ruithe M, Benkhelifa E, Hameed K (2018) A systematic literature review of data governance and cloud data governance. [Extracted from the article]

Since decades, road traffic congestions have been recognized as an escalating problem in many metropolitan areas worldwide. In addition to causing substantial number of casualties and high pollution rates, these congestions are decelerating economic growth by reducing mobility of people and goods as well as increasing the loss of working hours and fuel consumption. In order to deal with this problem, extensive research works have successively focused on predicting road traffic jams and then predicting their propagations. In spite of their relevance, the proposed solutions to traffic jam propagation have been profoundly dependent on historical data. They have not also used their predictions to intelligently allocate traffic control resources accordingly. We, therefore, propose in this paper a new two-stage traffic resource dispatching solution which is ultimately aiming to implement a self-organizing traffic control system based on Internet of Things. Our solution uses in its first phase a Markov Random Field (MRF) to model and predict the spread of traffic congestions over a road network. According to the obtained predictions, the solution uses Markov Decision Process (MDP) to automatically allocate the road traffic resources. Our simulations are showing satisfactory results in terms of efficient intervention ratios compared to other solutions. [ABSTRACT FROM AUTHOR]

Informational interventions are important to bring positive changes in attitudes and perception among individuals. In relation to the individual's mobility behavior, habits, attitudes, and perceptions are difficult to change. Therefore, it is vital to identify relatively soft aspects of travel behavior with a potential to reduce the negative impacts of mobility on the environment and individual health. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. A variety of rules that are part of the algorithm are derived from the transport modeling literature, where constraints and factors were examined for activity-travel decisions. Three major aspects of activity-travel behavior, such as reduced car use, cold start of car engines, and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to collected small datasets from citizens of Hasselt (Belgium), Bologna (Italy), and Guildford (UK). A significant replaceable potential for car trips within 3 km to cycling and car trips to public transport has been found. The replaceable potential of excessive cold starts and participation in non-mandatory outdoor activities were also found, to some extent, to bring positive changes in the environment. In future research, these identified potentials are reported back to individuals with their consequence as part of a mobility-based informational intervention. [ABSTRACT FROM AUTHOR]

Sudden visibility reductions on highways due to foggy weather conditions often lead to a drastic increase in car crash risks. Indeed, fog formation distorts drivers' perception and judgment of inter-vehicular distances, vehicles' speeds, and braking distances. In order to support drivers in dealing with the impact of fog, various on-board warning systems are being deployed today. Despite their added value, these systems are still in need of efficient solutions supporting smooth vehicle's acceleration/deceleration profiles. This is to avoid sudden braking (hence, higher car crash risks) incurred by sensor technologies restricted to line of sight measurements. To meet this goal, we advocate in this paper a Wireless Fog Warning System (WFWS) where cooperative awareness messages are disseminated and used for calculating acceleration/deceleration activities. Without loss of generality, we build on IEEE 802.11p WLAN as a basis technology. Using simulations on the open-source vehicular network simulation framework Veins, we demonstrate both the potential of such a system for increasing safety and smoothing traffic flow—as well as of computer simulation as a means of its evaluation. [ABSTRACT FROM AUTHOR]

Considerable efforts are being spent worldwide to ensure comfortable living environments and decent, on-time assistance to elderly and people requiring healthcare services. Recent advances in data acquisition and communication are allowing revolutionary ambient assisted leaving (AAL) systems to be implemented, where healthcare data are collected and reported on-the-fly to dedicated medical servers for further analysis and actions. Due to the increasing rely on distributed, resource constrained sensing devices, AAL systems are being subject to large number of attacks. In addition to usual high energy consumption and communication overhead, current systems are not yet able to fully safeguard the private data of their users. To overcome these shortcomings, we propose in this paper to combine the use of cryptography, compressed sensing, and steganography into a new generic solution called Encryption Compression Aggregation Security Scheme (ECASS). While focusing on the specific case of Medical Healthcare Systems, ECASS aims to secure private data exchanges over wireless networks while achieving lower energy consumption and communication overhead. Our simulations with the NS-2.35 simulator are showing an improvement of 40 and 50% in terms of energy consumption and communication overhead respectively compared to the IBE-Lite security scheme. [ABSTRACT FROM AUTHOR]

In this paper, we address the recurring problem of node redundancy in Wireless Sensor Networks (WSNs). In addition to categorizing redundancy into different types, comprehensively formulating and representing redundancy as a dynamic matter as well as estimating it with a new analytical method, we propose an approach where sensors are competing for the identification and relocation of redundant sensors. In our approach, sensors with low redundancy weights force neighboring peers with high redundancy weights to relocate or go to sleep according to a bully strategy. To increase their chances of winning their individual competitions (also called micro competition), redundant sensors are allowed to use the support of close peers belonging to the same cluster, leading thereby to a macro competition. We extend our solutions to the scenario of charging electrical vehicles in a Smart City context. We, indeed, found that the charging stations, with their varying capabilities and schedules, are competing to provide redundant charging services to the moving electrical vehicles in a scenario very similar to sensor redundancy. Our aim is then to identify the extent of service redundancy to ultimately provide the right charging service, at the right location, at the right time, to the right electrical vehicle. We, therefore, mainly focus on proposing a mathematical formal model reflecting the competition of charging stations to support the requests of electrical vehicles. Our ongoing simulations with the ns3 tool are showing promising results. [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]

Growth of population in the capital city of Pakistan-Islamabad-is too high. This growth rate has caused a negative impact on the smooth flow of traffic system. The aim of this paper is to provide a solution to facilitate the car-based commuters to pick the route with minimal bottlenecks and to minimize the distance to reach the destination. The proposed solution is to manage and control the traffic system in the city of Islamabad. The current traffic data collection system in Islamabad and other urban areas does not provide timely and reliable data that can be useful to the Regional Transportation Authority for planning activities. To overcome this problem, our approach is based on collecting the missing data using a custom-built mobile app. Our system analyses and manipulates the collected information based on artificial neural network scheme that can indicate the bottlenecks for each route and predicts the shortest route based on the user's severity levels. To validate our proposed approach, we tested six different randomly selected routes in Islamabad with different bottlenecks. [ABSTRACT FROM AUTHOR]