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Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs' coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large.

Coronavirus 2019 (COVID-19) and its variants are still spreading rapidly with deadly consequences and profound impacts on the global health and world economy. Without a suitable vaccine, mobility restriction has been the most effective method so far to prevent its spreading and avoid overwhelming the heath system of the affected country. The compartmental model SIR (or Susceptible, Infected, and Recovered) is the most popular mathematical model used to predict the course of the COVID-19 pandemic in order to plan the control actions and mobility restrictions against its spreading. A major limitation of this model in relation to modeling the spreading of COVID-19, and the mobility limitation strategy, is that the SIR model does not include mobility or take into account changes in mobility within its structure. This paper develops and tests a new hybrid SIR model; SIR-M which is integrated with an urban activity travel model to explore how it might improve the prediction of pandemic course and the testing of mobility limitation strategies in managing virus spread. The paper describes the enhanced methodology and tests a range of mobility limitation strategies on virus spread outcomes. Implications for policy and research futures are suggested.

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.

Automotive infotainment systems are expected to be first deployed on highways to service drivers travelling long distances, who are more likely to utilize the infotainment applications. In order to meet the stringent requirements of the infotainment systems, road side units (RSUs) are installed along the highway to facilitate a continuous vehicle-to-infrastructure (V2I) connectivity. Due to the long travelling distance and small coverage of the individual RSU, a more cost-effective solution would be to combine V2I with the vehicle-to-vehicle (V2V) communications to maintain the continuous connectivity. In this paper, we propose a new dynamic cooperation scheme that employs a dynamic forwarder selection strategy to generate an adaptive multi-hop V2V path for connectivity maintenance and throughput enhancement at a vehicle located outside of the RSU's coverage range. For the commonly assumed scenario that all vehicles travel in the same direction and at the same speed, we develop an analytical model and derive closed-formed expressions for the average out-of-range connection time, number of service resumptions and achieved throughput. The developed analytical model provides insights into the impacts of inter-RSU distance, vehicles' assistance willingness and the target vehicle's buffer size to the network performance. Simulation results with practical parameter settings show that our proposed scheme is effective in improving connectivity while offering a high throughput for the target vehicle. In particular, a high vehicle density, more assistance willingness by the forwarders and a large buffer size at the target vehicle are shown to be helpful in sparse RSU deployments.

We consider a fifth-generation (5G)-empowered future Industrial IoT (IIoT) networking problem where IIoT machines are capable of communicating and sharing their data networking knowledge gained (and experiences) with other neighboring devices/tools. For such an IIoT setting, deep-learning (DL)-based communication protocols are known to be highly efficient but having a computationally complex training procedure in terms of both time/space and volume of data sets. One solution for such training is to be completed offline for each equipment and machines of IIoT before deployment. A better approach would be to replicate the model from the expert existing machine and implant it into new machines. Such training for the transfer of knowledge can be done by manufacturers using high computational power, even for large-scale DL models. After sufficient training and the desired level of accuracy, the trained machines can be deployed in the smart factory equipment to perform life-long collaborative learning. We design a novel distributed transfer learning (TL) framework to maximize multipath communication networking performance for Industry 4.0 environment. To conduct seamless sharing of knowledge gain by the multipath TCP (MPTCP) agents and tackle retraining issues of DL-based approaches, we investigate TL for MPTCP from the IIoT networking perspective. With relevant insights from transfer and collaborative learning, we develop a distributed TL-MPTCP framework to accelerate the learning efficiency and enhance the performance of newly deployed machines. Our approach is validated with numerical and emulated NS-3 experiments in comparison with the state-of-the-art schemes.

A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching between streets of different safety, convenience, and comfort levels. As a consequence, the quality of bicycle networks should be evaluated not based on one but multiple factors and by considering the different user preferences regarding these factors. More comprehensive methodologies to assess urban bicycle networks are essential to the operation and planning of modern city transportation. This work proposes a multi-objective methodology to assess—what we refer to as—bikeability between origin–destination locations and over the entire network, useful for evaluation and planning of bicycle networks. We do so by introducing the concept of bikeability curves which allows us to assess the quality of cycling in a city network with respect to the heterogeneity of user preferences. The application of the proposed methodology is demonstrated on two cities with different bike cultures: Amsterdam and Melbourne. Our results suggest the effectiveness of bikeability curves in describing the characteristic features and differences in the two networks.

For station-based bike-sharing systems, the balance between user demand and bike allocation is critical for the operation. As a basic operational index, the short-term prediction of bike numbers (f...

Introduction: This research aims to investigate the perceptions and reactions of drivers regarding freeway merging situation, utilizing a new approach with the basis of a multilevel simulation platform which incorporates virtual reality (VR) technology. Methods: A VR driving environment integrated with traffic micro-simulation was developed to evaluate driving behaviors and the impact of merging decisions in terms of traffic conflicts. The driving experiments were conducted under a variety of circumstances, including varying traffic flows and the presence of ramp metering. The Surrogate Safety Assessment Model (SSAM) was utilized to extract the number of conflicts from the micro-simulation results. Results: The final results indicated that the probability of conflict has a positive correlation with traffic flow, while conflict frequency at freeway merges is affected by the presence of ramp metering due to its potentiality to enhance driver decisions and reduce the drivers’ pressure when they make maneuvers. Practical Applications: The findings reveal that the proposed VR simulation platform is a useful tool to improve the safety of freeway merging. It has the potential to enhance driver skills and can also be used in the study of human–machine interaction.

Assuring the required quality of service, despite the growing volume and variety of Internet of Things (IoT) traffic, has remained an immense challenge in the popular WiFi networks. The IoT devices using short TCP flows often attain very different levels of service due to the complicated interactions between the transport layer protocol and the shared dynamic wireless medium. We develop in this paper a novel queue management policy by using a transient model to capture the interactions of IoT (short TCP) flows over traditional traffic in WiFi networks. Based on the Markov regenerative processes coupled with fluid model, we discover that the adaptive admission control (AAC) mechanism at the WiFi access point (AP) improves the response time and fairness of IoT traffic over the lossy WiFi links. To this end, under the proposed AAC, packets are admitted into the AP in such a way that the fractions of packets in the AP buffer belonging to the different IoT devices are balanced resulting in a comparable level of service. We furthermore prove the stability of the system dynamics under AAC which provides important practical insights in designing home WiFi IoT system.

Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method.

This paper develops a real-time agent-based simulation model to optimize the dynamic bus stop-skipping and holding schemes. The proposed agent-based simulation model is capable of modeling, schedul...

Perimeter control is an effective city-scale solution to tackle congestion problems in urban networks. To accommodate the unpredictable dynamics of congestion propagation, it is essential to incorporate real-time robustness against travel demand fluctuations into a pragmatic perimeter control strategy. This paper proposes robust perimeter control algorithms based on partial information feedback from the network. The network dynamics are modeled using the concept of the Macroscopic Fundamental Diagram (MFD), where a heterogeneously congested network is assumed to be partitioned into two homogeneously congested regions, and an outer region that acts as demand origin and destination. The desired operating condition of the network is obtained by solving an optimization program. Observer-based H∞ proportional (P) and proportional-integral (PI) controllers are designed based on Lyapunov theory, to robustly regulate the accumulation of each region and consequently to maximize the network outflow. The controller design algorithms further accommodate operational constraints by guarantying: (i) the boundedness of the perimeter control signals and (ii) a bounded offset between the perimeter control signals. Control parameters are designed off-line by solving a set of linear matrix inequalities (LMI), which can be solved efficiently. Comprehensive numerical studies conducted on the nonlinear model of the network highlight the effectiveness of the proposed robust control algorithms in improving the congestion in the presence of time-varying disturbance in travel demand.

Time-dependent origin–destination (OD) demand estimation using link traffic data in a large-scale network is a highly underdetermined problem. As a result, providing an accurate initial solution is crucial for obtaining a more reliable estimated demand. In this paper, we discuss the necessity of having a comprehensive demand profiling model that considers the spatial differences of OD pairs and we demonstrate its application in the calibration of large-scale traffic assignment models. First, we apply a departure choice model that adds a time dimension to the OD demand flows concerning their spatial differences. The time-profiled demand is then fed into the time-dependent OD demand estimation problem for further adjustment. Results show that in addition to reducing the error between simulation outputs and the observed link counts, the estimated demand profile more accurately reflects the spatial correlation of the OD pairs in the large-scale network being studied. Results provide practical insights into deployment and calibration of simulation-based dynamic traffic assignment models.

Due to its importance in Intelligent Transport Systems (ITS), traffic flow prediction has been the focus of many studies in the last few decades. Existing traffic flow prediction models mainly extract static spatial-temporal correlations, although these correlations are known to be dynamic in traffic networks. Attention-based models have emerged in recent years, mostly in the field of natural language processing, and have resulted in major progresses in terms of both accuracy and interpretability. This inspires us to introduce the application of attentions for traffic flow prediction. In this study, a deep learning based traffic flow predictor with spatial and temporal attentions (STANN) is proposed. The spatial and temporal attentions are used to exploit the spatial dependencies between road segments and temporal dependencies between time steps respectively. Experiment results with a real-world traffic dataset demonstrate the superior performance of the proposed model. The results also show that the utilization of multiple data resolutions could help improve prediction accuracy. Furthermore, the proposed model is demonstrated to have potential for improving the understanding of spatial-temporal correlations in a traffic network.