V2I-Based Platooning Design with Delay Awareness

This paper studies the vehicle platooning system based on vehicle-to-infrastructure (V2I) communication, where all the vehicles in the platoon upload their driving state information to the roadside unit (RSU), and RSU makes the platoon control decisions with the assistance of edge computing. By addressing the delay concern, a platoon control approach is proposed to achieve plant stability and string stability. The effects of the time headway, communication and edge computing delays on the stability are quantified. The velocity and size of the stable platoon are calculated, which show the impacts of the radio parameters such as massive MIMO antennas and frequency band on the platoon configuration. The handover performance between RSUs in the V2I-based platooning system is quantified by considering the effects of the RSU's coverage and platoon size, which demonstrates that the velocity of a stable platoon should be appropriately chosen, in order to meet the V2I's Quality-of-Service and handover constraints

Technical feasibility analysis and introduction strategy of the virtually coupled train set concept

Todays railway network capacity is limited by constraints imposed by traditional train protection systems. A way to overcome those limitations, maximize the railway network performance and also increase the operational flexibility is presented by the Virtually Coupled Train Set (VCTS) concept. This paper evaluates the technical feasibility of this approach, that was developed and is further evaluated in the framework of the Shift2Rail (S2R) project X2Rail-3. The main functionality of virtually coupled train sets is achieved by replacing the mechanical coupler between two railway vehicles by an electronic (virtual) coupling link. This operational change requires a permanent vehicle-to-vehicle communication and precise distance measurement, while enabling much faster coupling and decoupling procedures, increased interoperability and the operation of trains with a headway below absolute braking distance. To evaluate the technical feasibility of the VCTS concept, a series of technical and operational subsystem have been identified and analyzed. Interviews with experts from a variety of VCTS linked topics have been conducted, to evaluate the state of the art and new developments for those subsystems. Subsequently, the capabilities of the subsystems have been compared with the requirements of the VCTS system. In addition, different mitigations to overcome possible obstacles have been identified and evaluated. As the result, the most critical technical aspects for the implementation and success of VCTS have been identified as the requirement of controllable, fast and accurate responding braking systems, the availability of suitable communication technologies and frequency bands, the need for highly-accurate measurement of distance, speed and acceleration and the fast detection and monitoring of train integrity. Considering those results, a qualitative roadmap for the future VCTS development and introduction strategy is derived

Behavior of Vehicle Platoon with Limited Output Information Based on Constant Time Heading

This paper presents synchronization of vehicle platoon with limited-output information based on constant time heading spacing policy. Two control schemes, namely neighborhood controller neighborhood observer and neighborhood controller local observer designed based on constant time heading will be applied into a vehicle platoon. These control schemes are applicable for general topologies as long as spanning tree condition is fulfilled. Both control schemes have identical controller part but different approach in the observer parts. Neighborhood controller neighborhood observer utilizes completely neighborhood information in the observer part, while neighborhood controller local observer only uses the internal information in the observer part. The performance of both controllers will be analyzed numerically, and the results will be compared. Furthermore, the behavior of each follower in various vehicle-to- vehicle topologies in responding to disturbance will be presented and some remarks will be summarized

Traffic Time Headway Prediction and Analysis: A Deep Learning Approach

In the modern world of Intelligent Transportation System (ITS), time headway is a key traffic flow parameter affecting ITS operations and planning. Defined as “the time difference between any two successive vehicles when they cross a given point”, time headway is used in various traffic and transportation engineering research domains, such as capacity analysis, safety studies, car-following, and lane-changing behavior modeling, and level of service evaluation describing stochastic features of traffic flow. Advanced travel and headway information can also help road users avoid traffic congestion through dynamic route planning, for instance. Hence, it is crucial to accurately model headway distribution patterns for the purpose of analyzing traffic operations and making subsequent infrastructure-related decisions. Previous studies have applied a variety of probabilistic models, machine learning algorithms (for example, support vector machine, relevance vector machine, etc.), and neural networks for short-term headway prediction. Recently, deep learning has become increasingly popular following a surge of traffic big data with high resolution, thriving algorithms, and evolved computational capacity. However, only a few studies have exploited this emerging technology for headway prediction applications. This is largely due to the difficulty in capturing the random, seasonal, nonlinear, and spatiotemporal correlated nature of traffic data and asymmetric human driving behavior which has a significant impact on headway. This study employs a novel architecture of deep neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamics effectively to predict vehicle headway. LSTM NN can overcome the issue of back-propagated error decay (that is, vanishing gradient problem) existing in regular Recurrent Neural Network (RNN) through memory blocks which is its special feature, and thus exhibits superior capability for time series prediction with long temporal dependency. There is no existing appropriate model for long term prediction of traffic headway, as existing models lack using big dataset and solving the vanishing gradient problem because of not having a memory block. To overcome these critics and fill the gaps in previous works, multiple LSTM layers are stacked to incorporate temporal information. For model training and validation, this study used the USDOT’s Next Generation Simulation (NGSIM) dataset, which contains historical data of some important features to describe the headway distribution such as lane numbers, microscopic traffic flow parameters, vehicle and road shape, vehicle type, and velocity. LSTM NN can capture the historical relationships between these variables and save them using its unique memory block. At the headway prediction stage, the related spatiotemporal features from the dataset (HighwayI-80) were fed into a fully connected layer and again tested with testing data for validation (both highway I-80 & US 101). The predicted accuracy outperforms previous time headway predictions