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The digital twin, with its descriptive and predictive services provides promising prospects in the automotive field. Increased insights in customer behavior and vehicle states through descriptive models enable the predictive services of the digital twin to project these vehicles in the future. Currently, most of the predictive services of the digital twin focus on a singular dimension, the asset itself, without considering the influences of the environment and the driver, which hampers the reliability and accuracy of these models. The present work aims to provide a solution in the form of a holistic digital twin comprising of three descriptive models representing the motorcycle itself, its operating environment, and the motorcycle riding behavior. Based on data gathered during a large-scale measurement campaign, novel insights in the motorcycle riding behavior have enabled its representation into two mathematical formulations. Highlighting the capability of the digital twin to integrate data from heterogeneous sources, the environmental model is generated using geospatial data from a map provider, followed by a novel formulation of a safety driving line. Using a kinematic motorcycle model, the speed and banking angle over the defined route are predicted with high correlation to real-world motorcycle riding behavior. The insights generated by the developed digital twin can be used to enable data-driven development or as an input to provide individualized predictive services during the usage phase. • Generation of insights into real-world naturalistic motorcycle riding behavior. • Development of an environmental model using road-related topology data. • Three-dimensional digital twin of a real-world motorcycle fleet. • Validation of digital twin prediction with real-world measurements. [ABSTRACT FROM AUTHOR]

This paper aims to incorporate the throttle handle level prediction in high speed train(HST) operation prediction problem to enable the prediction of HST drivers' activities, in which the key instructions available to HST driver are difficult to determine. Specifically, we consider an end-edge-cloud orchestration system to capture the real-time responses for driver state changes. By adding edge computing nodes, the real-time performance of data collection, transmission, and processing is improved. Our ultimate goal is to guide and regulate train drivers' activities in the same way, regardless of uncertain factors affecting HST dynamic or kinematic performance. We formulate the problem as a physical-based and data-driven deep learning-aided prediction model and solve it using a novel long short-term memory (LSTM) deep neural network which combines: (i) an off-line approximate training model to learn the time series data in the cloud layer, and (ii) an online prediction process to determine driving strategies in the real-time windows, more in general expressed as driving skill level constraints. To evaluate the performance of our approach, some case studies using the real-world railway infrastructure and HST data have been conducted. The results show that the proposed models produce higher prediction accuracy for both speed and throttle handle level prediction tasks. Compared to the conventional HST operation prediction problem, which considers speed sequences only without throttle handle level consideration, this study finds that jointly modeling speed and throttle handle level actually improves the next operation prediction performance itself, potentially because throttle handle level observations capture the information on HST control dynamics, which may affect operators' driving choices. • Structure. Edge computing and prediction scheme are innovatively combined with HST data collection to meet the delay-sensitive and sequential-aware operation requirements, and a novel HST operation structure for end-edge cloud orchestration system is built. • Modeling. A combination of the physical-based and the data-driven deep learning model is formulated for HST operation prediction under uncertainty in throttle handle level and speed, which is a new model in the literature. As discussed, the existing research has only considered speed dynamics in real-time DAS limitedly. • Practice. From the field collected data in Beijingxi–Zhengzhoudong HSR (as shown in Figure 5), we test the effectiveness of our proposed model with seven cases. Since the real-time driving advice is calculated in a few milliseconds, our two-phase solution method can be practically relevant when designing DAS or ATO systems. More importantly, most time-consuming calculations are executed off-line in the cloud layer and the amount of on-line calculations in edge layer is limited to looking up a table. [ABSTRACT FROM AUTHOR]

Traffic parameters can show shifts due to factors such as weather, accidents, and driving characteristics. This study develops a model for predicting traffic speeds under these abrupt changes within regime switching framework. The proposed approach utilizes Hidden Markov, Expectation Maximization, Recursive Least Squares Filtering, and ARIMA methods for an adaptive forecasting method. The method is compared with naive and mean updating linear and nonlinear time series models. The model is fitted and tested extensively using 1993 I-880 loop data from California and January 2014 INRIX data from Virginia. Analysis for number of states, impact of number of states on forecasting, prediction scope, and transferability of the model to different locations are investigated. A 5-state model is found to be providing best results. Developed model is tested for 1-step to 45-step forecasts. The accuracy of predictions are improved until 15-step over nonadaptive and mean adaptive models. Except 1-step predictions, the model is found to be transferable to different locations. Even if the developed model is not retrained on different datasets, it is able to provide better or close results with nonadaptive and adaptive models that are retrained on the corresponding dataset. [ABSTRACT FROM AUTHOR]

Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets. [ABSTRACT FROM AUTHOR]