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Road transportation is among the global grand challenges affecting human lives, health, society, and economy, caused due to road accidents, traffic congestion, and other transportation deficiencies. Autonomous vehicles (AVs) are set to address major transportation challenges including safety, efficiency, reliability, sustainability, and personalization. The foremost challenge for AVs is to perceive their environments in real-time with the highest possible certainty. Relatedly, connected vehicles (CVs) have been another major driver of innovation in transportation. In this paper, we bring autonomous and connected vehicles together and propose TAAWUN, a novel approach based on the fusion of data from multiple vehicles. The aim herein is to share the information between multiple vehicles about their environments, enhance the information available to the vehicles, and make better decisions regarding the perception of their environments. TAWUN shares, among the vehicles, visual data acquired from cameras installed on individual vehicles, as well as the perceived information about the driving environments. The environment is perceived using deep learning, random forest (RF), and C5.0 classifiers. A key aspect of the TAAWUN approach is that it uses problem specific feature sets to enhance the prediction accuracy in challenging environments such as problematic shadows, extreme sunlight, and mirage. TAAWUN has been evaluated using multiple metrics, accuracy, sensitivity, specificity, and area-under-the-curve (AUC). It performs consistently better than the base schemes. Directions for future work to extend the tool are provided. This is the first work where visual information and decision fusion are used in CAVs to enhance environment perception for autonomous driving. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence (AI) and the Internet of Things (IoT), public transportation systems in our daily lives are smarter than ever. A large number of wireless sensors are distributed in roadside units (RSUs), collecting traffic flow and public vehicle information to monitor and learn about road congestion, subway arrival, and traffic accidents in real-time. In this paper, we propose probabilistic model checking based path planning for a multimodal transportation system. First, a traffic network with different means of travel is formalized as a directional graph, and the traffic congestion probability is generated from IoT sensor data. Moreover, a discrete-time Markov chain (DTMC) is introduced as the formal model to support quantitative verification. Second, users may have different travel requirements except the shortest path, so user-oriented critical paths are proposed. Then, the minimum cost and the minimum congestion requirements are defined in the form of probabilistic computation tree logic (PCTL) to describe the verification property for evaluating the selected path. We focus on the temporal relations of key points that a user needs to visit to build the travel path. Third, the optimal path is identified and confirmed based on the quantitative results returned by the probabilistic model checker, PRISM, which is a supporting tool that verifies the property against the formal model. Finally, case studies are conducted to demonstrate the feasibility and availability of our proposed method for the smart transportation system. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the biggest challenges around the world, resulting in multiple harmful consequences such as air pollution, road fatalities, and traffic jams. Thus, it is a vital need to develop preventive mechanisms that allow better traffic management and alleviate the burden put on the transport network. Vehicle-to-anything (V2X) communication is gaining massive research interest as a promising solution to mobility challenges. This technology will enable vehicles and the infrastructure to form a distributed network constantly exchanging traffic information in real-time. The availability of timely information can help road users make optimal choices and enable effective autonomous traffic control. Therefore, to avoid traffic congestion, mobility models need to be established to study vehicular dynamics and to forecast future traffic conditions. The main goal of this study is to develop a traffic model based on Markov chain to tackle the congestion issue in a highway environment. Based on traffic data collected from vehicles through V2X technology, the model studies the evolution of traffic flow along a multiple lane divided highway and locally calculates estimates of the expected number of vehicles traveling on a highway segment. Performance measures are then inferred to detect possible congestion and then prevent it from happening. The numerical results presented in this study validate the model accuracy and show its ability to reproduce the fundamental mobility aspects in a highway environment. [ABSTRACT FROM AUTHOR]

In 5G networks, a massive number of connections of high data rate services, e.g., video streaming services, certainly make the networks deteriorated because of extreme traffic congestion at the backhaul links of macro base stations (MBSs). Although ultra-dense networks (UDNs) have been considered as a promising architecture to stimulate the 5G networks, the congestion problem hampers the UDNs to provide mobile users (MUs), i.e., represented by device-to-device (D2D) pairs and shared downlink resource users (SUs), with high video delivery capacity. In this paper, we propose a social-aware caching and resource sharing (SCS) strategy for video streaming services in 5G UDNs. Particularly, we formulate the SCS problem by taking into account the social relationship of each D2D pair, the available storage of femtocell base stations (FBSs) and transmitters (TXs) of D2D pairs, the target signal to interference plus noise ratio (SINR) of SUs, and the popularity of videos. The SCS problem is then solved for 1) optimal number of caching copies of each video and optimal caching placements in the FBSs and 2) optimal resource sharing allocation between the SUs and the D2D pairs for D2D communications. This way, the workload at the backhaul links of the MBSs can be reduced. Simultaneously, an arbitrary MU can retrieve the videos alternately from the MBSs, FBSs, and TXs at high cache-hit ratio and maximum delivery capacity. Simulation results are analyzed to show the benefits of the proposed SCS strategy compared to other conventional schemes. [ABSTRACT FROM AUTHOR]

Predicting urban traffic volume is of great significance to traffic management and urban construction. An accurate prediction model can help drivers optimize driving routes, allocate resources reasonably and reduce urban traffic congestion. Most of the existing studies do not consider the complex nonlinear spatio-temporal relationship. In the spatial dimension, they do not consider the impact of regional semantics and regional interactions. In the temporal dimension, they ignore the impact of long-term historical information and key time points. Aiming at the complexity of traffic data, in this paper, we design a ResNet-TCN model to predict the urban traffic volume. Firstly, we construct and extract features from the vehicle GPS tracking and external information, such as velocity, time, location and weather. Then, we obtain regional semantic information by the ResNet model and combine the weights of the regional division with the average vehicle velocity into a two-channel matrix. We extract the key features of the matrix sequence and predict the velocity by the TCN model. Finally, we estimate the traffic volume through a traffic volume inference model in the traffic field. We conduct a large number of experiments on the actual dataset of Chengdu and compare our model with the existing models. The experimental results show that our method has better performance on prediction accuracy. [ABSTRACT FROM AUTHOR]

Bike sharing system as mode of public transport is very popular in the world. This smart solution can be described as answer to an increasingly frequent traffic congestion and parking problems in many cities all around the world. This issue is beginning to relate to some cities in Slovakia as well. Bicycles address traffic congestion as they form a valid substitution for cars on short trips, contribute to the use of public transport by providing effective last-mile connectivity and simply take up less space on the road. As the system of shared bicycles works from 2016, it is relatively new in Slovakia. This is a reason why this system still has some problems and deficiencies that need to be optimized. Presented paper focused on the city of Nitra, which is currently struggling with the issue of traffic congestion. The main aim of paper is to point out the opportunities and constraints arising from the concept of shared bicycles in the conditions of city of Nitra. Our proposals and recommendations are based on the opinions of the citizens of Nitra obtained from conducted marketing survey(625 respondents – citizens of Nitra). The results of the survey have brought important insights into improving the strategy of shared bicycles, focusing on attractiveness for citizens, and ultimately, urban transport solutions. [ABSTRACT FROM AUTHOR]

The slow traffic in cities with pedestrians and bicycles is becoming more and more important due to their environmental protection and low carbon. However, the driving at the road intersections is seriously mixed and easy to jam. Traffic delays mainly occur here. Improving the traffic efficiency of the intersections plays an important role for the alleviation of traffic congestion. In this paper, a multi-objective signal timing optimization model is established about the vehicle average delay, slow traffic delay, vehicle stopping times and traffic capacity. Then an improved particle swarm optimization algorithm (PSO) is used to solve it. The experimental results show that the model is optimized to reduce the vehicle delay, slow traffic delay and stopping times of an intersection, and then increase the traffic capacity. So the multiple indicators of an intersection are improved, which have significant effects on the increase of operational efficiency of an intersection. [ABSTRACT FROM AUTHOR]

The use of Information and Communication Technology (ICT) as a copilot for the drivers has a potential to improve traffic safety and efficiency. A key challenge in integrating ICT in vehicular networks is to provide the mechanisms for the delivery of safety messages called beacons. In particular, finding the trade-off between providing sufficient coverage and controlling channel congestion remains the focus in the stipulated amendments for safety message transmissions. In this paper, we handle this trade-off by proposing a Multi-metric Power Control (MPC) approach, which uses application requirements and channel states to determine a transmit power for safety messages. The MPC gives a best-effort approach to satisfy the coverage range requirement of a message as specified by the application. Moreover, the concept distinguishes among message types to provide coverage differentiation. We show that the best-effort approach of providing coverage for different messages can control congestion and as a result improve awareness by minimizing beacon collisions. The performance analysis of MPC using discrete event simulation confirms its practicality. [ABSTRACT FROM AUTHOR]

In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activities. Consequently, valuable insights can be gained by applying data mining techniques to the data generated through social networks. In this work, a practical approach to combine data mining techniques with statistical analysis is proposed to implement crowd sensing in a smart city. A case study to analyze the relationship between weather conditions and traffic congestion in Beijing based on tweets posted on Sina Weibo platform is presented to demonstrate the proposed approach. Following the steps of data pre-processing and topic determination, we applied Granger Causality Test to study the causal relationships between weather conditions, traffic congestion and human outdoor activity. The mediation analysis is also implemented to verify human outdoor activity as a mediator variable significantly carrying the influence of good weather to traffic congestion. The result demonstrates that outdoor activity serves as a mediator transmitting the effect of good weather on traffic congestion. In addition, the causes of negative emotion are also studied. [ABSTRACT FROM AUTHOR]

Currently, almost every family has at least one car; thus, vehicle density is increasing annually. However, road capacity is finite; consequently, traffic accident frequency may increase due to increasing vehicle density. Typically, car accidents result in traffic congestion because vehicles behind the accident are not aware of the event and continue to follow the front queue. To address this problem, some emergency services, such as emergency message broadcasting, have been proposed. However, not all drivers want to receive such messages because they intend to exit the route prior to the accident scene, which means that communication resources may be wasted. In this paper, we propose a prediction model to forecast vehicles behavior based on a Markov chain and identify which vehicles require the emergency message. In addition, the proposed model includes an efficient policy based on the shortest path for police cars and ambulances such that they can attend the accident scene quickly and relieve traffic congestion. Simulation results show that the proposed method reduces unnecessary message transmission and increases road utilization efficiently. [ABSTRACT FROM AUTHOR]

Traffic congestion is a growing problem in many countries around the world. It has been recognized that instead of constructing more roads and freeways to counter this problem it is prudent to improve the utilization of existing road network through a judicious combination of advances in control engineering, communication and information technology. The traffic control architecture proposed in this paper is a combination of communicating Urban Traffic Control Architecture (UTCA) and Freeway Traffic Control Architecture (FTCA). The UTCA combines context-awareness, Cyber-Physical Systems (CPS) principles, and Autonomic Computing System (ACS) principles to optimize traffic congestion and enforce safety in urban traffic network. The UTCA includes a network of adaptive intersection traffic controllers and their immediate supervisory systems, who are also networked. The central piece of each traffic controller is an arbiter, which is a mini CPS. It is aware of the traffic dynamics at the intersection managed by it, by virtue of continuous input from monitoring sensors. Due to this context-awareness ability and its communication ability to exchange traffic information with its neighbors, it can execute policy-based reactions in order to enable safe and efficient traffic throughput at its intersection. Each urban traffic supervisory system is designed with ACS principles in order to minimize downtime caused by environmental emergencies and maximize security of the subsystem under it. A supervisory subsystem will also collect global traffic flow information and contextual constraints from its neighbors. Based on this input it will modify policies and communicate them to its traffic controller for timely adaptation. The urban traffic flowing into freeway traffic will be mediated by Intelligent Ramp Meters (IRM). An IRM interacts with the urban traffic control system and its nearest Intelligent Roadside Unit (IRSU) to regulate the flow of traffic from urban to freeway network. The FTCA consists of a network of mutually interacting IRSUs which monitor traffic flow, communicate with IRMs for providing traffic guidance for freeway drivers. An IRSU will communicate with the vehicles in the zone managed by it in order to provide information on rerouting when road and weather conditions warrant it. It also facilitates exchange of information between vehicles, guide them in lane changes and maintaining safe distance in order to avoid collision. [ABSTRACT FROM AUTHOR]

Abstract  Recently IEEE 802.16 WiMAX has attracted a lot of attention in wireless networking research and applications. To enable a flexible and cost-effective deployment, mesh networking mode is defined in WiMAX standard. In this paper, we introduce a system model of WiMAX mesh networking with the focus on entry process, frame structure, centralized and distributed scheduling. The state-of-the-art WiMAX mesh networking research is reviewed. In addition, we propose an effective QoS differentiation scheme for the IEEE 802.16 WiMAX mesh networks. Both collocated scenario and general topology are theoretically analyzed and compared. Illustrative numerical examples are presented to demonstrate the effectiveness of the proposed strategy. The impact of key parameters on the performance is discussed for differentiating multiple classes of services. Several open issues are summarized as a guideline for future topics in WiMAX mesh networking research. [ABSTRACT FROM AUTHOR]

Abstract  There has been a growing interest in the use of wireless mesh networks. Today’s wireless technology enables very high data rate up to hundreds of Megabits per second, which creates the high demand of supporting real-time multimedia applications over wireless mesh networks. Hence it is imperative to support quality of service (QoS) in wireless mesh networks. In this paper, we design a framework to provide parameterized QoS in 802.11e based wireless mesh networks. Our framework consists of admission control algorithms and scheduling algorithms, which aim at supporting constant bit-rate (CBR) traffic flows, as well as variable bit-rate (VBR) traffic flows. We first present deterministic end-to-end delay bounds for CBR traffic. We then prove that the delay of VBR traffic can be bounded if the traffic flow conforms to a leaky-bucket regulator. We further study different admission control algorithms for VBR traffic. Our simulation results show that, by taking advantage of statistical multiplexing, much more traffic flows can be admitted. [ABSTRACT FROM AUTHOR]