Travel-time reduction is a primary objective for managing connected emergency vehicles (CEVs) to save people’s lives or put out a fire. With the integrity of internet of things (IoT) and connected autonomous vehicles (CAVs), it has been a research challenge to find a safe, reliable, and optimal strategy that not only minimizes the CEV’s travel time but also lessens the undesirable side-effects on other road users. This article introduces multiple intelligent control strategies in one framework to boost the potential of CEVs traveling via multiple traffic intersections. The framework includes a path-planning mechanism adapting to sudden traffic delays, traffic signal preemption controller adapting to the urgency level associated with the emergency event, and a deep-learning model for CAVs to predict the time required for giving way to the CEV. All modules are implemented through a microscopic traffic simulation environment (PTV-VISSIM). This article holds significant implications for various scenarios involving CEVs and intelligent transportation systems (ITS). The path planning approach showcased notable improvements, reducing average path travel time by 9% when compared to existing benchmarks. The regression error for predicting the merging time of CAVs is minimized to be 0.4 second. Furthermore, the signal preemption controller demonstrated an important trade-off analysis between the level of intrusive preemption signal control and the undesired impacts on the traffic network. This finding enables traffic management authorities to make informed decisions regarding signal preemption strategies, considering both the travel time optimization for CEVs and the potential network-wide traffic impacts.

Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

The string stability of cooperative adaptive cruise control (CACC) platoons is largely affected by complex driving environment and abnormal driving behaviors. Fast and repetitive driving-state changes always occur during the period of changing driving states (such as leaving a platoon or lane-change), due to errors made in driver decisions or automatic driving system. This research proposes a framework which combines recognition of driving states with platoon operations and risk-prediction in order to reduce disturbance and unnecessary platoon operations resulting from driving-state jitters. First of all, long short-term memory (LSTM) neural networks were used in this research combined with a time-window in order to recognize driving states. Based on this research, the LSTM mode with an added time-window was found to be able to effectively reduce comparatively the jitters of recognition results. After that, an integrated mode which incorporates a recognition mode with danger probabilities was demonstrated to present better platoon operations. Monte Carlo simulation and importance sampling method will be given to predict platoons’ and vehicles’ trajectories and compute danger probabilities. In addition, an innovative strategy is implemented to identify an additional leader and execute a platoon splitting in order to improve driving smoothness, if a vehicle is recognized in an abnormal car-following state with a high danger-probability. In summary, this research has conducted extensive numerical tests to evaluate performances of the proposed system and the analysis results show that the proposed strategies will effectively increase smoothness and safety for a multi-platooning system.

Modern complexities associated with an arterial traffic makes existing safety prediction methods insufficient to meet desired standards required by recent developmental needs. This paper proposes an enhanced active safety prediction method based on big-data approach and Stacked AutoEncoder-Gated Recurrent Unit. Firstly, the big-data technology is used to construct a dynamic identification model to recognize real-time operation state and risk state. Secondly, the Stacked AutoEncoder-Gated Recurrent Unit is used to predict a level of safety based on associated recognition results. This paper uses data from working days of Sunset Boulevard, California, from January$1^{\mathrm{st}}$, 2020, to February$28^{\mathrm{th}}$, 2020. The results of analysis show that the accuracy of the proposed dynamic recognition model reaches 98.92%, which is better than existing models such as random forest, K-nearest neighbor, and naïve Bayes models. In addition, it is found that the Stacked AutoEncoder-Gated Recurrent Unit can achieve a prediction accuracy of 95.157% and has significant advantages in terms of efficiency. The proposed methods will provide feasible solutions for actively monitoring safety levels.

Automatic Dependent Surveillance-Broadcast (ADS-B) is one of the key technologies for future “e-Enabled” aircrafts. ADS-B uses avionics in the e-Enabled aircrafts to broadcast essential flight data such as call sign, altitude, heading, and other extra positioning information. On the one hand, ADS-B brings significant benefits to the aviation industry, but, on the other hand, it could pose security concerns as channels between ground controllers and aircrafts for the ADS-B communication are not secured, and ADS-B messages could be captured by random individuals who own ADS-B receivers. In certain situations, ADS-B messages contain sensitive information, particularly when communications occur among mission-critical civil airplanes. These messages need to be protected from any interruption and eavesdropping. The challenge here is to construct an encryption scheme that is fast enough for very frequent encryption and that is flexible enough for effective key management. In this paper, we propose a Staged Identity-Based Encryption (SIBE) scheme, which modifies Boneh and Franklin's original IBE scheme to address those challenges, that is, to construct an efficient and functional encryption scheme for ADS-B system. Based on the proposed SIBE scheme, we provide a confidentiality framework for future e-Enabled aircraft with ADS-B capability.

Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the future traffic state is a challenging research subject involving prediction reliability issues. Predictive performance measures, including the accuracy, efficiency, and stability, are generally considered as the most important priorities in the evaluation of prediction modules. Researchers have developed various K-nearest-neighbors-based searching algorithms that find the future state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging big data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the efficiency of the searching method used. This paper develops a novel sequential search strategy for traffic state predictions. The proposed sequential strategy is found to be outperforming the conventional single-level search approach in terms of prediction measures, which are prediction accuracy, efficiency, and stability. Compared with the conventional approach, the proposed sequential method yields significantly more accurate results via internal hierarchical improvements across sublevels while maintaining excellent efficiency and stability.

Abstract Automatic dependent surveillance‐broadcast is an emerging surveillance technology for the future “e‐enabled” aircrafts, which will make it possible for aircrafts to share their location data with neighboring aircrafts, ground controllers, and other interested parties. In order to provide the automatic dependent surveillance‐broadcast communications with a high level of accuracy and integrity, a reliable authentication mechanism is required. So far, however, very few cryptographic solutions have been offered to achieve this in the literature. Even existing solutions have faced the following challenges: (i) the authentication solutions based on regular digital signature require complex management of public‐key infrastructureell; and (ii) signing messages exchanged or broadcast frequently in aircraft‐to‐aircraft and aircraft‐to‐ground communication modes can cause a computational bottleneck easily. In order to address these challenges, we take a fresh approach to building up an authentication framework by introducing a new online/offline identity‐based signature scheme. Our scheme will resolve the public‐key infrastructure management issue by using the identities of aircrafts as public keys and will achieve a high efficiency through online/offline signature generation. Copyright © 2014 John Wiley & Sons, Ltd.

Establishing online services can bring significant advantages for users and for the businesses. However, it has many issues related to integrity and confidentiality. Adopting public key cryptography is important to provide high level of confidentiality and authentication services for online transactions, but it needs a trusted way of distributing public keys. Public Key Infrastructure (PKI) is a solution for assuring the authenticity of public keys via qualified digital certificates. The first part of this paper covers the basic concept of PKI and overview of its implementation, and the related issues on digital certificates and X.509 standard. The second part covers the case study of UAE's implementation of the PKI focusing on the Emirate ID's smart card experience. Finally, this paper discusses about the attacks that threaten the PKI.

Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models.