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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]

A motor resistance parameter identification scheme was proposed for the speed prediction of a direct-torque-controlled (DTC) induction motor (IM) drive. The DTC IM drive was established on the basis of the stator's current and flux, with the stator current acquired from an IM using the Hall effect current sensor. Rotor speed prediction was achieved using the electromagnetic torque and rotor flux. The stator resistance parameter identification scheme was developed using the model reference adaptive system based on the motor's active power, and the adaptation mechanism was designed using the modified particle swarm optimization algorithm. The MATLAB\Simulink® toolbox was utilized to simulate this system, and all the control algorithms were realized using a TI DSP 6713 and F2812 micro-control card to validate this approach. Simulation and experimental results confirmed the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Intelligent Vehicle–Infrastructure Collaboration Systems (i-VICS) put forward higher requirements for the real-time security of dynamic traffic information interaction. It is difficult to ensure the safety of dynamic traffic information interaction by means of traditional static information security. In this study, a method was proposed through machine learning-based lane-changing (LC) behavior recognition and information credibility discrimination, based on the utilization and exploitation of traffic business characteristics. The method consisted of three stages: LC behavior recognition based on Support Vector Machine (SVM), LC speed prediction based on Recurrent Neural Network (RNN), and credibility discrimination of speed information under LC states. Firstly, the labeling rules of vehicle LC behavior and the input/output of each stage model were determined, and the raw NGSIM data were processed to obtain data sets for LC behavior identification and LC speed prediction. Both the SVM classification and RNN prediction models were trained and tested, respectively. Afterwards, a model of credibility discrimination speed information under an LC state was constructed, and the real vehicle speed data were processed for model verification. The results showed that the overall accuracy of vehicle status recognition by the SVM model was 99.18%, and the precision of the RNN model was on the order of magnitude of cm/s. Considering transverse and longitudinal abnormal velocity, the accuracy credibility discrimination of LC velocity was more than 97% in most experimental groups. The model can effectively identify the abnormal speed data of LC vehicles and provide support for the real-time identification of LC vehicle speed information under i-VICS. [ABSTRACT FROM AUTHOR]

At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks. [ABSTRACT FROM AUTHOR]

In order to improve the punctuality and safety of urban rail transit trains during operation and achieve accurate parking, it is necessary to track and predict the speed curve during the train operation. This paper firstly calculates the instantaneous power of the train based on the measured data, and then uses the random forest model to classify the interval according to the power curve, and then establishes a real-time prediction method for the speed curve of urban rail transit trains based on neural network for different intervals. The train speed prediction model is tested. The results of model testing on the simulation data and actual line data show that the proposed algorithm can effectively predict the speed curve of the train in real time, improve the accuracy of speed tracking control. The error is reduced by 57.7% compared with the traditional neural network model, and the error is reduced by 73.9% compared with the random forest regression model.

Smart cars rely on sensors like LIDAR and high-precision map-based perception for driving environment sensing. However, they can't detect low-speed vehicles beyond visual range, affecting safety and comfort. Manual vehicles face similar challenges. Low-speed driving contributes to expressway accidents due to limited visibility, road design, and equipment performance. To enhance safety, an over-the-horizon potential safety threat vehicle identification method using ETC big data is proposed. It consists of three layers. The first layer is the vehicle section travel speed sensing layer based on the wlp-XGBoost algorithm. The second layer is the in-transit vehicle position estimation layer based on the DR-HMM algorithm. The third layer is the Multi-information fusion of potential safety threat vehicle identification layer. Dynamic real-time detection and identification of potential safety threats in expressway sections were achieved, and simulations were conducted using real-time ETC data from Quanxia section on an ETC platform. Results show accurate prediction of vehicle speed and position in different road sections and traffic situations, with over 95% accuracy and recall in identifying potential safety threat vehicles. It perceives changes in the traffic conditions of road sections in real-time based on the changing trend of potential safety threat vehicle numbers, providing a vital reference for speed planning and risk avoidance.

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy. [ABSTRACT FROM AUTHOR]

The implementation of an energy management strategy plays a key role in improving the fuel economy of plug-in hybrid electric vehicles (PHEVs). In this article, a bi-level energy management strategy with a novel speed prediction method leveraged by reinforcement learning is proposed to construct the optimization scheme for the inner energy allocation of PHEVs. First, the powertrain transmission model of the PHEV in a power-split type is analyzed in detail to obtain the energy routing and its crucial characteristics. Second, a Q-learning (QL) algorithm is applied to establish the speed predictor. Third, the double QL algorithm is introduced to train an effective controller offline that realizes the optimal power distribution. Finally, given a reference battery's state of charge (SOC), a model predictive control framework solved by the reinforcement learning agent with a novel speed predictor is proposed to build the bi-level energy management strategy. The simulation results show that the proposed method performs with a satisfying fuel economy in different driving scenarios while tracking the corresponding SOC references. Moreover, the calculation performance also implies the potential online capability of the proposed method.

The "white hole effect" alters the driving environment during a tunnel's exit phase, making it more difficult and uncertain for drivers to access information and control their behavior, thereby endangering traffic safety. Consequently, the driving risk at the exit of a long spiral tunnel served as the subject of this study, and the Jinjiazhuang spiral tunnel served as the object of the natural vehicle driving experiment. Following the theory of a non-linear autoregressive dynamic neural network, a vehicle speed prediction model based on driver characteristics was developed for the exit phase of the tunnel, taking driver expectations and behavioral changes into account. It also classifies the driver's behavior during the tunnel's exit phase to assess the risk posed by the driver's behavior during the tunnel's exit phase and determine a dynamic and safe comfort speed. The study's results indicate that the driver's behavioral load changed significantly as the vehicle approached the tunnel exit. At the exit of the spiral tunnel, the vehicle's actual speed was 71 km/h, which is below the speed limit of 80 km/h. This demonstrates that the expected change in the driver's behavior in the tunnel exit phase was substantial. Therefore, setting the emotional safety and comfort speed so that the driver maintains a smooth comfort level in the tunnel exit phase can reduce the tunnel exit driving risk. The results of this study provide a benchmark for tunnel traffic safety and lay the groundwork for further development of vehicle risk warning settings for the tunnel's exit phase. [ABSTRACT FROM AUTHOR]

Inclement weather and environmental factors impact traffic operations resulting in travel delays and a reduction in travel time reliability. Precipitation is an example of an environmental factor that affects travel conditions, including traffic speed. While Intelligent Transportation Systems services aim to proactively mitigate congestion on roadways, these services are often not sensitive to weather conditions. This paper investigates the application of high-resolution weather data in improving the performance of proactive transportation management models and proposes short-term speed prediction models that fuse real-time high-resolution weather surveillance radar data with traffic stream data to conduct spatial and temporal prediction of the speed of roadway segments. Extreme gradient boosting weather-aware speed prediction models were developed for a 7-km segment of Interstate 270 in St. Louis, MO, USA. The performance of the weather-aware models was compared with the performance of weather-insensitive speed prediction models that did not take precipitation into account. The results indicated that in the majority of instances, the weather-aware models outperformed the weather-insensitive models. The extreme gradient boosting models were compared with the K-nearest neighbors algorithm and feed-forward neural network models. The extreme gradient boosting model consistently outperformed the other two methods. In addition to speed prediction models, van Aerde speed-flow traffic stream models were developed for rain and no-rain conditions to study the impact of precipitation on the traffic stream across the corridor. Results indicated that the impact of precipitation is not identical across the corridor, which was mirrored in the results obtained from weather-aware speed prediction models. [ABSTRACT FROM AUTHOR]

Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based architecture for network-wide traffic speed prediction. We propose a deep-learning-based model consisting of a fully convolutional neural network, bidirectional long short-term memory, and attention mechanism. Our design aims to consider both backward and forward dependencies of traffic data to predict multistep network-wide traffic speed. Thus, we propose a model named AttBDLTSM-FCN for multistep traffic speed prediction. We augmented the attention-based bidirectional long short-term memory recurrent neural network with the fully convolutional network to predict the network-wide traffic speed. In traffic speed prediction, this is the first time that augmentation of AttBDLSTM and FCN have been exploited to measure the backward dependency of traffic data, as a building block for a deep architecture model. We conducted comprehensive experiments, and the experimental evaluations illustrated that the proposed architecture achieved better performance compared to state-of-the-art models when considering the short and long traffic speed prediction, e.g., 15, 30, and 60 min, in multistep traffic speed prediction. [ABSTRACT FROM AUTHOR]

At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks.

The future motion prediction of vehicles in the front is widely valued for its great potential to improve a vehicle's safety, fuel consumption, and efficiency. However, due to the uncertainty of a driver's driving intentions and vehicle dynamics, future motion prediction faces great challenges. In order to break the bottleneck in the prediction of leading vehicle motion, this paper proposes a prediction idea of decoupling the prediction of leading vehicle motion into vertical vehicle speed prediction based on the Gaussian process regression algorithm and horizontal heading angle prediction based on the long short-term memory method, which combines the predicted vehicle speed and heading angle to derive the future trajectory of the leading vehicle. Moreover, we propose a prediction algorithm of the leading vehicle motion based on the combination of driving intention recognition and multimodel prediction results by the Fuzzy C-means algorithm, which tries to solve the problem of the unclear driving intention of the predicted object and the nonlinearity between the future motion of the vehicle and the environment. Finally, the algorithm is validated using real vehicle data, proving that it has high prediction accuracy. [ABSTRACT FROM AUTHOR]

The aim of this work is to forecast wind energy by fitting the wind speed logged data, that have been measured over a year period (Nov. 2019–Mar. 2021), on a unique probability density function selected among a number of similar probability functions, as it is not always possible to select one distribution function that fits all wind speed regimes. The wind speed and direction data were measured at Fujairah site, which are affected by long-term fluctuation of ± 10% of wind speed, and short-term fluctuation of more than ± 20%. Based on the foregoing measurements, five different probability density functions can be fitted, namely Weibull, Rayleigh, Gamma, Lognormal and Exponential, with their associated parameters. A procedural algorithm is proposed for wind speed forecasting with best selected fitting distribution function, using a procedural forecast-check method, in which forecasting is performed with time on the most suitable distribution function that fits the foregoing data, depending on minimum errors accumulated from preceded measurements. Different error estimation methods are applied. The algorithm of selecting different distribution functions with time, makes energy prediction more accurate depending on the fluctuation of wind speed. A detailed probabilistic analysis is carried out to predict probable wind speed, and hence wind energy, based on variations of the parameters of the selected fitting distribution function. [ABSTRACT FROM AUTHOR]

In this study, an adaptive speed prediction scheme based on reactive power was established for direct torque-controlled (DTC) permanent magnet synchronous motor (PMSM) drives. The current and flux of a stator were used to establish a DTC PMSM drive. Hall effect current sensors with a non-contact sensing technique were used to detect the stator current of the PMSM. The voltage space vector pulse width modulation (VSVPWM) DTC scheme was used in place of a traditional switching table (ST) DTC scheme to reduce current and torque ripples. Model reference adaptive control (MRAC) was utilized to develop a speed prediction scheme, and its adaptation mechanism was designed using the elephant herding optimization (EHO) algorithm. The torque, flux, and speed controllers were designed using a proportional–integral (P–I)-type controller. The MATLAB/Simulink© toolbox was used to establish the simulation scheme, and all control algorithms were realized using a microprocessor control card. The simulation and experimental results confirmed the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy.

Energy management strategies (EMSs) based on speed prediction are widely used in fuel cell hybrid electric vehicles (FCHEVs). However, two fundamental issues must be addressed: short-term speed prediction and power allocation across multi-scales. To address these issues, a hierarchical EMS for FCHEV based on multi-scale information fusion is developed in this study. In this strategy, a novel speed predictor that incorporates visual information and the motion states of a vehicle is used to predict the short-term speed. At the global level, the average power demand of each road segment is predicted based on real-time traffic information, which is used to plan the global reference state of charge (SOC) trajectory before departure. At the local level, fuzzy rules are utilized to determine the short-term reference SOC. The short-term optimal co-state of the Pontryagin minimal principle-based strategy is then updated. The results demonstrate that the proposed visual information speed predictor (VISP) enhances the prediction accuracy by 12%–32.5% and leads to a 3.36% reduction in the total cost compared with the EMS utilizing the conventional predictor. In addition, the proposed EMS makes the terminal SOC close to the target value and reduces the total cost by 20.33% compared to the benchmark strategy. • A hierarchical energy management strategy based on multi-scale information fusion is proposed. • A novel speed predictor that incorporates visual information and vehicle motion states is designed. • Global reference SOC trajectory method based on the real-time temporal and spatial traffic information of road segments is proposed. • Adaptive fuzzy rules are used to determine the shot-term reference SOC for each horizon. • Real urban driving cycle is used to verify the performance of the energy management strategies. [ABSTRACT FROM AUTHOR]

With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter referred to as “road speed”) is a key parameter for forecasting road congestion. This paper proposes a hybrid radial basis function (RBF) neural network algorithm for forecasting road speed. First, it proposes a fuzzy RBF neural network structure by combining the fuzzy logic system with the RBF neural network. Then, it incorporates factors such as weather, holidays and road grades into the input layer. Considering the uncertainty and sensitivity of the initial centre of the traditional membership function layer, it uses fuzzy C-means clustering to determine the centre and other parameters of the membership function layer. Then using the gradient descent method, it trains the weights between the fuzzy inference layer and the output layer. Finally, this paper trains the proposed hybrid RBF neural network with the traffic road network data and weather data of a city, and uses the trained hybrid neural network to predict the road speed and the congestion status. The prediction results show that, compared with simplex prediction methods, such as BP neural network, time series method, and RBF neural network, the hybrid RBF neural network has a higher forecasting accuracy, with the mean absolute percentage error (MAPE) being reduced to 6.4%. Experimental results verify the accurate forecasting, enhanced learning feature and mapping capability of this method in short-term road speed forecasting, indicating that it can provide reliable predicted values to help solve urban congestion problems.

Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based architecture for network-wide traffic speed prediction. We propose a deep-learning-based model consisting of a fully convolutional neural network, bidirectional long short-term memory, and attention mechanism. Our design aims to consider both backward and forward dependencies of traffic data to predict multistep network-wide traffic speed. Thus, we propose a model named AttBDLTSM-FCN for multistep traffic speed prediction. We augmented the attention-based bidirectional long short-term memory recurrent neural network with the fully convolutional network to predict the network-wide traffic speed. In traffic speed prediction, this is the first time that augmentation of AttBDLSTM and FCN have been exploited to measure the backward dependency of traffic data, as a building block for a deep architecture model. We conducted comprehensive experiments, and the experimental evaluations illustrated that the proposed architecture achieved better performance compared to state-of-the-art models when considering the short and long traffic speed prediction, e.g., 15, 30, and 60 min, in multistep traffic speed prediction.

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]

The future motion prediction of vehicles in the front is widely valued for its great potential to improve a vehicle’s safety, fuel consumption, and efficiency. However, due to the uncertainty of a driver’s driving intentions and vehicle dynamics, future motion prediction faces great challenges. In order to break the bottleneck in the prediction of leading vehicle motion, this paper proposes a prediction idea of decoupling the prediction of leading vehicle motion into vertical vehicle speed prediction based on the Gaussian process regression algorithm and horizontal heading angle prediction based on the long short-term memory method, which combines the predicted vehicle speed and heading angle to derive the future trajectory of the leading vehicle. Moreover, we propose a prediction algorithm of the leading vehicle motion based on the combination of driving intention recognition and multimodel prediction results by the Fuzzy C-means algorithm, which tries to solve the problem of the unclear driving intention of the predicted object and the nonlinearity between the future motion of the vehicle and the environment. Finally, the algorithm is validated using real vehicle data, proving that it has high prediction accuracy.

Bypass pipeline inspection gauges have the advantages of low cost and bringing no consumption in transportation efficiency and have been widely used in pipe cleaning, inspecting, and maintaining operations. The moving speed of bypass pipeline inspection gauges will seriously affect the results of the operations, so there are strict requirements on the moving speed of bypass pipeline inspection gauges. Because the moving speed of pipeline inspection gauge is difficult to measure or control in real time, it is important to predict it. This paper studies the influencing factors and their impact methods of pipeline inspection gauges' motion. Through the combination of computational fluid dynamics simulation and friction mathematical model, the relationship between the value of the bypass hole diameter and the pipeline inspection gauges' moving speed was studied. Under the selected research conditions, when the diameter of the bypass hole is increased from 0.1 to 0.5 m, the moving speed of pipeline inspection gauge in water and crude oil is, respectively, decreased from 2.779 to 0.589 m/s and from 2.777 to 0.373 m/s, and the relationship between them can be approximately described by a function. Based on this principle, the moving speed of pipeline inspection gauge can be predicted mathematically. The experiments also indicate that the density and dynamic viscosity of the transport medium and the deformation amount of the bypass pipeline inspection gauge sealing disk will affect the movement state of pipeline inspection gauge in the pipeline. This research has guiding significance for the design of the pipeline inspection gauges' structure size, which is beneficial to the pipeline robot to better meet the needs of cleaning, inspecting, and maintaining operations, and has reference value for related researches. [ABSTRACT FROM AUTHOR]

Featured Application: This work provides a tool for determining the adequate spacing between vertical traffic calming measures as a function of the target speed in the segment. Traffic calming measures (TCM) are placed in urban areas to improve road safety, and among them, vertical TCMs are widely employed. Many researches are focused on the influence of the geometry of each measure on speed reduction, but it is demonstrated that drivers forget its effect and speed up after it. Therefore, placing consecutive TCMs can help to maintain a safe area. However, scarce literature can be found about the adequate spacing between them. Hence, the aim of this paper is to analyze the adequate distance between TCMs. Various streets with variable distances and different vertical TCMs were evaluated in Poland and Spain, including raised crosswalks, raised intersections, speed humps and speed cushions. The intermediate point between two TCMs was selected as the place where the maximum speed is achieved. Results showed that there was a good correlation between the speeds at intermediate points and the distance between TCMs, with a determination coefficient around 0.80. For an 85th percentile of the speed under 50 km/h, a maximum distance of 200 m between TCMs is recommended, and for a value of 40 km/h, 75 m. [ABSTRACT FROM AUTHOR]

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy.

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R2 are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy.

Due to the increase of congestion on highways, providing real-time information about the traffic state has become a crucial issue. Hence, it is the aim of this research to build an accurate traffic speed prediction model using symbolic regression to generate significant information for travellers. It is built based on genetic programming using Pareto front technique. With real world data from microwave sensor, the performance of the proposed model is compared with two other widely used models. The results indicate that the symbolic regression is the most accurate among these models. Especially, after an incident occurs, the performance of the proposed model is still the best which means it is robust and suitable to predict traffic state of highway under different conditions.

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.

Despite the increased interest in battery electric vehicles (BEV), limited range abilities unsettle customers, which is often related to range anxiety. A better understanding of energy consumption and the possibility to accurately predict the remaining battery energy along an upcoming route may help to reduce this stress perception by means of advanced in-vehicle information systems. Addressing the trend towards vehicles with on-board cloud communication and information systems, the present research focuses on electric powertrain consumption and speed profile forecasts. A meaningful prediction of a speed profile for a given route is a basic prerequisite for an accurate consumption forecast. This study proposes a methodology to derive such a speed profile from real-time traffic data obtained from HERE Technologies while considering individual driving style characteristics. Given the predicted speed profile, a detailed BEV consumption model which accounts for BEV specific energy management strategies and environmental factors is used to obtain a consumption forecast. Prediction uncertainties are analyzed and parameter sensitivities with respect to energy consumption are derived as a function of the route dependent mean vehicle speed. Within a field study with Mercedes Benz EQC experimental vehicles, covering thirty-two test cycles, it is shown that the proposed methodology can accurately predict energy consumption for long look-ahead horizons and significantly reduces the variance in prediction compared to a typical baseline strategy. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective hierarchical prediction energy management strategy is proposed to achieve optimal fuel cell life economy and energy consumption economy for a range extended fuel cell vehicle. First, a global state of charge rapid planning method is proposed based only on the expected driving distance. Then, the vehicle speed information in the prediction horizon is estimated by a vehicle speed prediction module based on the back propagation neural network. According to the predicted speed and state of charge reference, a novel fusion algorithm that combines the direct configuration method and sequential quadratic programming is proposed to achieve optimal fuel cell life economy and energy consumption economy in the prediction horizon. Simulation results validate that the proposed strategy can effectively reduce the operating costs compared with that of the charge depletion-charge sustaining strategy and the equivalent consumption minimization strategy, thereby proving the feasibility of the proposed strategy. • A multi-objective hierarchical energy management strategy is developed for FCV. • A global SOC planning method is proposed based on expected driving distance. • A fusion strategy is employed to optimize hydrogen consumption and fuel cell life. [ABSTRACT FROM AUTHOR]

Traffic calming measures (TCM) are placed in urban areas to improve road safety, and among them, vertical TCMs are widely employed. Many researches are focused on the influence of the geometry of each measure on speed reduction, but it is demonstrated that drivers forget its effect and speed up after it. Therefore, placing consecutive TCMs can help to maintain a safe area. However, scarce literature can be found about the adequate spacing between them. Hence, the aim of this paper is to analyze the adequate distance between TCMs. Various streets with variable distances and different vertical TCMs were evaluated in Poland and Spain, including raised crosswalks, raised intersections, speed humps and speed cushions. The intermediate point between two TCMs was selected as the place where the maximum speed is achieved. Results showed that there was a good correlation between the speeds at intermediate points and the distance between TCMs, with a determination coefficient around 0.80. For an 85th percentile of the speed under 50 km/h, a maximum distance of 200 m between TCMs is recommended, and for a value of 40 km/h, 75 m.

The main goal of the current study is to take advantage of advanced numerical and intelligent tools to predict the speed of a vehicle using time series. It is clear that the uncertainty caused by temporal behavior of the driver as well as various external disturbances on the road will affect the vehicle speed, and thus, the vehicle power demands. The prediction of upcoming power demands can be employed by the vehicle powertrain control systems to improve significantly the fuel economy and emission performance. Therefore, it is important to systems design engineers and automotive industrialists to develop efficient numerical tools to overcome the risk of unpredictability associated with the vehicle speed profile on roads. In this study, the authors propose an intelligent tool called evolutionary least learning machine (E-LLM) to forecast the vehicle speed sequence. To have a practical evaluation regarding the efficacy of E-LLM, the authors use the driving data collected on the San Francisco urban roads by a private Honda Insight vehicle. The concept of sliding window time series (SWTS) analysis is used to prepare the database for the speed forecasting process. To evaluate the performance of the proposed technique, a number of well-known approaches, such as auto regressive (AR) method, back-propagation neural network (BPNN), evolutionary extreme learning machine (E-ELM), extreme learning machine (ELM), and radial basis function neural network (RBFNN), are considered. The performances of the rival methods are then compared in terms of the mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute percentage error (MDAPE), and absolute fraction of variances (R2) metrics. Through an exhaustive comparative study, the authors observed that E-LLM is a powerful tool for predicting the vehicle speed profiles. The outcomes of the current study can be of use for the engineers of automotive industry who have been seeking fast, accurate, and inexpensive tools capable of predicting vehicle speeds up to a given point ahead of time, known as prediction horizon (HP), which can be used for designing efficient predictive powertrain controllers.

Abstract In this paper, a data driven approach, deep learning, for vehicle speed prediction is presented. Deep learning based on the deep neural network structure is applied to predict a future short-term speed with the collected dataset including the historical vehicle speed and its corresponding acceleration, steering information, location and driving date. The influence of the driving factors on the accuracy of vehicle speed prediction is analyzed. And four standard driving cycles are used to test the generalization ability of the proposed speed prediction method. The results show that when the training set is the information of the historical speed and the driving date, the prediction effect is the best, and RMSE is 1.5298. And the proposed prediction method has good generalization ability. [ABSTRACT FROM AUTHOR]

The traditional speed prediction generally utilizes the Global Position System (GPS) and video images, and thus, the prediction precision mainly depends on environmental factors (i.e., weather, ionosphere, troposphere, air, and electromagnetic waves).We study the Radio Frequency Identification (RFID) data-driven vehicle speed prediction and proposed an improved extended kalman filter (i.e., the adaptive extended kalman filter, AEKF) algorithm. Firstly, the on-board RFID reader equipped in the vehicle reads the information (i.e., current speed and time) from the tag deployed on the road. Secondly, the received information is transmitted to the on-board information processing unit, and it is demodulated and decoded into available information. Finally, based on the vehicle state space model, the AEKF algorithm is proposed to predict vehicle speed and improve the prediction results, so that the vehicle speed gradually approaches the actual vehicle speed. The simulation results show that compared with the conventional extended kalman filter (EKF) algorithm, our proposed AEKF algorithm improves the dynamic performance of the filtering and better suppresses the filtering divergence process. Moreover, the AEKF algorithm also improves the precision of the Mean Square Error (MSE) and Mean Absolute Error (MAE) by 57.4% and 32.4%, respectively. [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]

The efficiency performance of multi-motor-driven system highly depends on the power management. Three aspects of contribution have been made in this study. 1) A predictive power management for a DMPS is developed. To improve the performance of the predictive power management, an adaptive velocity predictor is proposed and the coefficients of proposed predictor can update its parameters according to identified driving patterns. Simulation results show that the new velocity predictor have best prediction performance compared with traditional predictors. 2) A neural network based power management is proposed. According to the optimization results of dynamic programming, radial-basis-function neural network is trained. The input dimensions and the number of hidden layer neurons of the neural network are optimized. 3) The performance of proposed control strategies are compared with three different drive cycles including MANHATTAN cycle, Japanese 1015 cycle, and UDDSHDV cycle. Simulation results indicate that compared with original control strategy, the predictive control strategy and neural network based control strategy show better efficiency performance. The neural network based strategy is verified by hardware-in-loop experiment and experiment results indicate that the control performance in real hardware shows similar property with simulation results. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of a vehicle speed prediction from tire yaw marks. In forensic science, the evidences from accident scene in most cases are the main information preparing the accident report. Analysis of tire yaw marks when vehicle speed is too great to maintain the proposed circular path is the main object of this paper. Errors caused by yaw mark trajectory measurement could have influences on choice of calculation methodology and on obtained results. Photographs of traffic accident scene eliminate the uncertainties of evidences measurement and enable expert to use photogrammetric methods for determination of necessary geometrical parameters. [ABSTRACT FROM AUTHOR]

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy. [ABSTRACT FROM AUTHOR]

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver's intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R2 are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy. [ABSTRACT FROM AUTHOR]