Structure Optimization of Mixed-Speed Train Traffic for Cyclic Timetable: Model and Algorithm Development

Trains can be optimally spread over the period of the cyclic timetable. By integrating sequencing issue with headway time together, this paper studies the structure optimization of mixed-speed train traffic for a cyclic timetable. Firstly, by taking it as a job-shop problem with sequence-dependent setup times on one machine, in the type of infinite capacity resource with headway (ICR + H), the problem is transformed to alternative graph (AG) and then recast to the mixed-speed train traffic planning (MSTTP) model. For the multiobjective in MSTTP, three indicators are optimized, i.e., heterogeneity, cycle time, and buffer time, which correspond to diversity of train service toward passenger, capacity consumption of rail network, and stability of train operation, respectively. Secondly, the random-key genetic algorithm (RKGA) is proposed to tackle the sequence and headway simultaneously. Finally, RKGA is coded with visual studio C# and the proposed method is validated with a case study. The rail system considered is a line section encompassing a territory of 180 km with 15 mixed-speed trains in each cycle of the timetable. Results indicate the comprehensively balanced train plan for all stakeholders from random variations of train sequence and headway time. Both the quantitative proportion of heterogeneity/homogeneity (e.g., 2.5) about the optimized distribution of the mixed train traffic and the link between train headway time and the sequence for each traffic scenario are found. All the findings can be used to arrange the mixed-speed train traffic more scientifically.

Quantifying the Impact of Traffic on Electric Vehicle Efficiency

While the influence of several factors on battery electric vehicle (BEV) efficiency has been investigated in the past, their impact on traffic is not yet fully understood, especially when driving in a natural environment. This paper investigates the influence of driving in intense traffic conditions while considering the ambient temperature and driving behavior on BEV energy efficiency in a field study. A total of 30 BEV inexperienced drivers test drove a 2017 Volkswagen eGolf on a route with various road types in two different traffic intensity scenarios: During morning commute hours with higher traffic congestion and lower congestion hours throughout the middle of the day. Results support the hypothesis that traffic conditions significantly impact the vehicle’s efficiency, with additional consumption of approximately 4–5% in the high traffic scenario. By creating and comparing driving in traffic to an underlying base case scenario, the additional range potential by avoiding traffic for this particular vehicle can be quantified as up to seven miles. New patterns of BEV efficiencies emerged, which can help stakeholders understand how eco-driving can be strategically improved by selecting trip times and routes that avoid high traffic intensity.

Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%.

Traffic Scenarios for Automated Vehicle Testing: A Review of Description Languages and Systems

Testing and validation of the functionalities and safety of automated vehicles shifted from a distance-based to a scenario-based method in the past decade. A number of domain-specific languages and systems were developed to support scenario-based testing. The aim of this paper is to review and compare the features and characteristics of the major scenario description languages and systems (SDLS). Each of them is designed for different purposes and with different goals; therefore, they have their strengths and weaknesses. Their characteristics are highlighted with an example nontrivial traffic scenario that we designed. We also discuss some directions for further development and research of these SDLS.

Sidelink optimizations for layer-3 based IoT Relaying in 5G NR

<div>The effective support of 5G-Internet of Things (IoT) requires cellular service in deep coverage areas while providing long battery life for IoT devices which perform infrequent small data transmission towards the base station. Relaying is a promising solution to extend the coverage while at the same time meeting the battery life requirements of the IoT devices. Considering this, we analyze the suitability of layer-3 relaying over the 3GPP Release 16 NR-PC5 interface to support massive IoT applications. More precisely, we study the unicast connection establishment mechanism over the NR PC5 interface in a partial coverage scenario. Further, a set of optimizations on the Release 16 NR-PC5 procedure to effectively support massive IoT applications are proposed and analyzed. The obtained performance evaluation results which are presented in terms of data success probability, device power consumption, and signaling overhead, quantify how effectively the Release 16 NR-PC5 interface can support the requirement of IoT in the 5G and beyond era. The proposed sidelink small data transmission and frame-level access provides the largest gain overall and can reduce the device power consumption by an average of 68%, and signaling overhead by 15% while maintaining a data success probability of more than 90% in an IMT-2020 defined IoT traffic scenario.</div>

Sidelink optimizations for layer-3 based IoT Relaying in 5G NR

<div>The effective support of 5G-Internet of Things (IoT) requires cellular service in deep coverage areas while providing long battery life for IoT devices which perform infrequent small data transmission towards the base station. Relaying is a promising solution to extend the coverage while at the same time meeting the battery life requirements of the IoT devices. Considering this, we analyze the suitability of layer-3 relaying over the 3GPP Release 16 NR-PC5 interface to support massive IoT applications. More precisely, we study the unicast connection establishment mechanism over the NR PC5 interface in a partial coverage scenario. Further, a set of optimizations on the Release 16 NR-PC5 procedure to effectively support massive IoT applications are proposed and analyzed. The obtained performance evaluation results which are presented in terms of data success probability, device power consumption, and signaling overhead, quantify how effectively the Release 16 NR-PC5 interface can support the requirement of IoT in the 5G and beyond era. The proposed sidelink small data transmission and frame-level access provides the largest gain overall and can reduce the device power consumption by an average of 68%, and signaling overhead by 15% while maintaining a data success probability of more than 90% in an IMT-2020 defined IoT traffic scenario.</div>

The role of eye movements in perceiving vehicle speed and time-to-arrival at the roadside

To avoid collisions, pedestrians depend on their ability to perceive and interpret the visual motion of other road users. Eye movements influence motion perception, yet pedestrians’ gaze behavior has been little investigated. In the present study, we ask whether observers sample visual information differently when making two types of judgements based on the same virtual road-crossing scenario and to which extent spontaneous gaze behavior affects those judgements. Participants performed in succession a speed and a time-to-arrival two-interval discrimination task on the same simple traffic scenario—a car approaching at a constant speed (varying from 10 to 90 km/h) on a single-lane road. On average, observers were able to discriminate vehicle speeds of around 18 km/h and times-to-arrival of 0.7 s. In both tasks, observers placed their gaze closely towards the center of the vehicle’s front plane while pursuing the vehicle. Other areas of the visual scene were sampled infrequently. No differences were found in the average gaze behavior between the two tasks and a pattern classifier (Support Vector Machine), trained on trial-level gaze patterns, failed to reliably classify the task from the spontaneous eye movements it elicited. Saccadic gaze behavior could predict time-to-arrival discrimination performance, demonstrating the relevance of gaze behavior for perceptual sensitivity in road-crossing.

CarLink: A Real-Time V2X Validation Platform for a Safe and Sustainable Mobility

<div>In this article, we present CarLink, a new simulation platform with hardware-in-the-loop (HiL), designed and implemented to reduce the time spent on field tests through the emulation of a complex vehicular scenario in a controlled laboratory environment. Specifically, CarLink can simulate a generic traffic scenario and let each vehicle in it communicate with a vehicle under test (VUT), which is actually physically available HiL and equipped with long- and short-range wireless communication capabilities. Communication between simulated vehicles and the VUT is provided by an external management unit (EMU) that integrates the virtual word with the physical one. The architecture is also designed to allow the integration of advanced driver assistance systems (ADAS) testing for the validation of future connected and automated vehicles.</div>

CarLink: A Real-Time V2X Validation Platform for a Safe and Sustainable Mobility

<div>In this article, we present CarLink, a new simulation platform with hardware-in-the-loop (HiL), designed and implemented to reduce the time spent on field tests through the emulation of a complex vehicular scenario in a controlled laboratory environment. Specifically, CarLink can simulate a generic traffic scenario and let each vehicle in it communicate with a vehicle under test (VUT), which is actually physically available HiL and equipped with long- and short-range wireless communication capabilities. Communication between simulated vehicles and the VUT is provided by an external management unit (EMU) that integrates the virtual word with the physical one. The architecture is also designed to allow the integration of advanced driver assistance systems (ADAS) testing for the validation of future connected and automated vehicles.</div>

EGMM video surveillance for monitoring urban traffic scenario

PurposeIn the traffic monitoring system, the detection of stirring vehicles is monitored by fitting static cameras in the traffic scenarios. Background subtraction a commonly used method detaches poignant objects in the foreground from the background. The method applies a Gaussian Mixture Model, which can effortlessly be contaminated through slow-moving or momentarily stopped vehicles.Design/methodology/approachThis paper proposes the Enhanced Gaussian Mixture Model to overcome the addressed issue, efficiently detecting vehicles in complex traffic scenarios.FindingsThe model was evaluated with experiments conducted using real-world on-road travel videos. The evidence intimates that the proposed model excels with other approaches showing the accuracy of 0.9759 when compared with the existing Gaussian mixture model (GMM) model and avoids contamination of slow-moving or momentarily stopped vehicles.Originality/valueThe proposed method effectively combines, tracks and classifies the traffic vehicles, resolving the contamination problem that occurred by slow-moving or momentarily stopped vehicles.