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Short vehicle speed prediction is important in predictive energy management strategies, and the accuracy of the prediction is beneficial for energy-saving performance. However, the nonlinear feature of the speed series hinders the improvement of prediction accuracy. In this study, a novel hybrid model that combines an autoregressive integrated moving average (ARIMA) and a long short-term memory (LSTM) model is proposed to handle the nonlinear part efficiently. Generally, the ARIMA component filters out linear trends from the speed series data, and the parameters of the ARIMA are determined with the analysis. Then the LSTM handles the residual normalized nonlinear items, which is the residual of ARIMA. Finally, the two parts of the prediction results are superimposed to obtain the final speed prediction results. To assess the performance of the hybrid model (ARIMA-LSTM), two tested driving cycles and two typical driving scenarios are subjected to rigorous analysis. The results demonstrate that the combined prediction model outperforms individual methods ARIMA and LSTM in dealing with complex, nonlinear variations, and exhibits significantly improved performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE). The proposed hybrid model provides a further improvement for the accuracy prediction of vehicle traveling processes. [ABSTRACT FROM AUTHOR]

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This research aims to improve the performance and economics of fuel cell hybrid electric vehicles (FCHEVs), validated and established by introducing an innovative energy management strategy (EMS) based on a speed-predictive fusion model. Firstly, a mixed prediction model was built based on BiLSTM, TCN, and Self-attention (SA) mechanism to accurately search, capture and fuse multi-granularity features in time series. Then, Harris-Hawk Optimization (HHO) was used to optimize the dropout rate and model learning rate of the combined BiLSTM-TCN-SA time series model to improve the prediction accuracy and generalization ability of the model. Finally, stochastic model predictive control was combined with BiLSTM-TCN-SA to form SMPC-NSGA III algorithm, which was used for multi-objective optimization of fuel economy, fuel cell durability and battery durability. In this study, the effectiveness of the proposed strategy was verified under the condition of CLTC-P driving cycle. The experimental results showed that RMSE and R2 of HHO-BiLSTM-TCN-SA velocity prediction model are 1.169 and 0.998, respectively. In addition, the output of the model is within the confidence interval of 97.5 % of the real speed, and there is no significant difference, which is statistically significant. Under the SMPC-NSGA III strategy, the average efficiency of the fuel cell was increased by 12 % and 1 % respectively. • A new energy management strategy was created to improve fuel cell durability. • HHO-based optimisation of BiLSTM-TCN-Self-Attention hyperparameter fusion model improves speed prediction performance. • NSGA-III multi-objective optimization algorithm was combined with SMPC for collaborative optimization results. • The average efficiency of the fuel cell in the SMPC strategy is higher than the DP and FLC. [ABSTRACT FROM AUTHOR]

To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km. • A scenario-oriented driving cycle construction method is proposed. • A novel hybrid neural network model with higher prediction accuracy is proposed. • The scenario-oriented adaptability of the proposed strategy is enhanced. • Both battery SOC maintaining capability and fuel economy are improved. [ABSTRACT FROM AUTHOR]

Accurate steady state speed prediction is one of the key indexes to improve vehicle safety. This paper presents a steady state speed prediction method for expressway vehicles under snow and ice conditions. Firstly, the torque balance equation of vehicle rollover time is established, and the critical state of vehicle stability is judged by the lateral acceleration of vehicle. Secondly, the steady-state speed prediction parameters, including friction coefficient, braking distance, driver reaction time and vehicle yaw Angle, were calculated according to the judgment results of stable critical state, and the steady-state speed prediction model was built according to the predicted parameters. Experimental results show that this method can accurately detect the critical state of vehicle stability, and the steady-state speed prediction results are basically consistent with the actual speed. [ABSTRACT FROM AUTHOR]

Transport practitioners need a universally applicable speed prediction model to estimate average speeds on any road. Average annual speed is a key input to the economic assessment of transport infrastructure where reliable estimates of future average speeds are necessary to calculate economic costs and benefits. The relationship between Annual Average Daily Traffic (AADT) and average annual speed was investigated on higher-order roads across South Africa, revealing a high level of variability in this correlation at different locations. This variation is influenced by road characteristics, such as alignment and cross-section, complicating the formulation of a universal speed prediction model. Two novel speed prediction models are proposed in this article that use AADT to forecast future average annual speed. The speeds of heavy vehicles and light vehicles can be estimated separately, as well as the average speed of all vehicles simultaneously. Both models are self-calibrating, accounting for the variation in the AADT–speed relationship. This calibration step is unique to speed prediction models and increases the reliability of these models to estimate future average speeds considerably. Furthermore, self-calibrating average annual speed prediction models are universally applicable and will simplify economic assessment of transport infrastructure. [ABSTRACT FROM AUTHOR]

Heavy rainfall events are becoming more common in many areas with escalating climate change, and these events can considerably affect travel speed and road safety. It is critical to understand when and how rainfall events affect congestion in the transportation network to help improve decision making for infrastructure planning and real-time operations. This study incorporates high-resolution rainfall and wind data into a travel speed prediction model, along with other data related to weather conditions, incidents, and real-time speeds, to assess if localized rainfall data can inform travel speed prediction during light and heavy rainfall events, and how this compares with the classical method of using a single city-wide rain gauge data point. The travel speed prediction model holistically selects the most related features from a high-dimensional feature space by modeling by wind direction, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) to overcome overfitting issues and is applied to two urban arterials for case studies located in Pittsburgh, Pennsylvania. The results indicate that high-resolution rainfall features in many instances are better predictors of future rainfall on the target segments, leading to overall better prediction results (in 30-min lag time), when compared with models that use a single city-wide rain gauge. This has implications for other cities that are interested in improving travel speed prediction modeling and traffic modeling under increasing impacts from climate change and extreme weather. [ABSTRACT FROM AUTHOR]

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]

Resource scarcity, global climate change and environmental pollution are increasingly constraining the development of the automotive industry. China proposes to reach the carbon peak by 2030; to reach the carbon neutral double carbon target by 2060 and gradually promote a green and low-carbon transition in energy development. The development of new energy vehicles is an important approach for China to realize its energy structure transformation in the automobile industry. HEV, as a transitional product of automobile energy transformation, has the advantages of both internal combustion engine vehicles and electric vehicles, which can improve the fuel efficiency and the emission problem of internal combustion engine vehicles and the range is longer compared to electric vehicles. One of the important aspects of HEV research is the design of whole vehicle energy management strategy based on the model predictions. Particularly, model-based predictive control is one of the mainstream energy management strategies nowadays, and its optimization effect is mainly subject to the model prediction accuracy. In this study, we constructed the ITS environment of a local roadway through simulation, compared the speed prediction effects of different speed prediction methods in different prediction time domains, and fused the historical information of vehicles (speed of the vehicle in front, distance, signal status, distance, and remaining time). It is found that N-BEATS is more effective in predicting vehicle speed in different prediction time domains, and the prediction accuracy of the speed prediction model is effectively improved after its fusion of multivariate information. • Speed prediction based on real driving data. • Comparing the accuracy of different speed prediction methods in different prediction time domains. • Fusion of multivariate information (vehicle history, front vehicle speed, distance, signal light status, remaining time, and other factors) for speed prediction. [ABSTRACT FROM AUTHOR]

• The greatest speed change occurred during the process of switching from the passenger lane to the truck lane. • The inner lane demonstrated higher mean speeds compared to the outer lane. • The 85th percentile speed of the adjacent main lane was lower than that of the acceleration lane. • The time-series speed prediction model using transformer networks could achieve an accuracy of 98.35% in predicting lane-level driving speed for freeway interchange areas. This study investigates the lane-level speed distribution in the freeway interchange area and develops a time-series speed prediction model using Transformer networks. The full-sample real-time speed data from interchange areas under heavy traffic was extracted by the YOLO v3 detection algorithm and Deep-Sort tracking algorithm based on Unmanned Aerial Vehicle (UAV) videos. A short-term prediction model of lane-level driving speed was constructed using the Time-Series Transformer (TST) framework. Results showed that the greatest magnitude of speed change occurred during the process of switching from the passenger lane to the truck lane. The inner lane consistently demonstrated higher mean speeds compared to the outer lane. The TST model proposed in this study could achieve an accuracy of 98.35% in predicting lane-level driving speed. These findings suggest the need to consider the speed transition between passenger and truck lanes in freeway engineering design, marking setting, and optimization of safety facilities. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate ship speed prediction is not only important for economic benefits and emission reduction, but also plays a vital role in making satisfactory routing plans. To investigate a speed prediction method of the largest bulk carriers ever constructed, we firstly compare several widely-used data-driven methods and then pick out the most appropriate model for the prediction task. Being as the input attributes of the data-driven models, the metocean data are collected from forecasting products and carefully cleaned up by data pre-processing steps. However, we have observed from literatures that the data for training are mostly collected from observational systems, such as the noon-reports, sensor measurements, on-board monitoring systems and reanalysis products, which may have certain differences with the data from forecasting systems. To figure out whether the data inconsistency problem will affect the model effectiveness on prediction, exhaustive experiments of a sensitivity study are carried out by utilizing observational data for training models while testing the prediction performance with forecasting data. • Various data-driven models are used for predicting the speed of a Valemax vessel. • The sensitivity of using inconsistent met-ocean data for training is investigated. • Keeping data consistency is significant to obtain satisfactory and reliable results. [ABSTRACT FROM AUTHOR]