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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]

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]

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 "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]

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]

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]