Search

Driver Fingerprinting (DF), reflecting unique driving characteristics, has received extensive attention for its prominent applications in various domains such as driver identification in automobile sharing paradigms, driver-based insurance, and vehicle anti-theft. Previous research has primarily concentrated on utilizing data from sober states to establish DF models while rarely considering driving fatigue, a common and risky driving state. This study proposes a scheme to analyze the effects of fatigue on DF identification and emphasizes the necessity of incorporating fatigue in DF models. Firstly, we conducted simulated driving experiments to collect the drivers’ behavior data, facial videos, and the Karolinska Sleepiness Scale (KSS). Secondly, we calculated DF indicators using a double-layer sliding time window and used the Kruskal-Wallis test to extract the DF features. Finally, we divided the full sample set into three subsets: a sober sample subset, a fatigue sample subset, and a double state sample subset containing both sober and fatigue data. We used each sample subset to train DF models based on principal component analysis and long short-term memory. We then utilized all three sample subsets to evaluate the DF models trained by each subset. Combined with the correlation between the indicators and KSS, we analyzed the influence of fatigue on DF identification. The results indicated that the identification accuracies of DF models built solely on sober data significantly decreased when applied to fatigue data. The average accuracy of the 7-driver groups showed the most substantial reduction of 54.59%. Conversely, DF models considering fatigue demonstrated a notable increase in the identification accuracy of drivers, and the maximum increase of 25.47% was from the 7-driver groups. Additionally, we discussed the effects of time windows and the number of drivers on DF identification accuracy and an application framework for driver identification systems based on DF integrating driving fatigue detection.

Vehicle Group Intelligence (VGI) represents a significant research domain that contributes to the advancement of intelligent transportation systems. This study employs bibliometric approaches to uncover knowledge bases and prevailing research perspectives within the VGI field. We collected 2821 publications from the SCIE and SSCI databases for the literature survey. A visual analysis is conducted from five distinct perspectives to reveal the overall distribution of these publications. Two analytical techniques (document co-citation and keyword co-occurrence) are utilized to highlight highly co-cited publications and popular research topics. According to the analysis of the document co-citation, six representative knowledge bases are identified: string stability of vehicle platoons, wireless communication of vehicle platoons, cooperative driving in merging areas, the influence of Cooperative Adaptive Cruise Control (CACC) platoon on traffic flow, platoon management of heavy-duty vehicles, and cooperative control of multi-agent systems. These knowledge bases have provided significant contributions to the exploration and development of the VGI field. Based on the knowledge mapping related to the keyword co-occurrence, the core research trends include five categories: vehicle platooning in different traffic scenarios, cooperative merging of Connected and Automated Vehicles (CAVs), active safety control of vehicle groups, stability analysis of vehicle platoons, and wireless communication of cooperative driving. Furthermore, we illustrate potential research opportunities at four levels, including control architectures, issues in the application of vehicle platoons, cooperative strategies of vehicle groups, and communication protocols and security. The research findings provide valuable guidance for researchers by clarifying the direction of academic exploration in the VGI field.

To optimize a vehicle platoon system in terms of car-following behavior, a decentralized model predictive control (MPC) strategy for longitudinal velocity control was established (namely, CF-MPC). Firstly, considering the influence of car-following behavior on vehicle states, a longitudinal velocity control model for platoons of connected and automated vehicles (CAV) was designed. Based on that model, an upper-level MPC controller was built to obtain the desired acceleration of the vehicles. Secondly, a lower-level controller received the desired acceleration signal and converted it into the expected throttle opening/braking pressure, to control acceleration/deceleration. Then, the Lyapunov stability method was used to detect the stability conditions that the model should satisfy. Finally, three simulation procedures—constant speed, acceleration, and deceleration were tested, and the validity of the CF-MPC method was verified from the perspectives of a model strategy and a control strategy. The simulation results show that with the proposed CF-MPC method, CAV platoons quickly completed velocity tracking and maintained a safe distance, thereby improving traffic efficiency, fuel economy, driving safety, and transportation capacity.

The fuel efficiency of the transportation sector has become a key factor to reduce greenhouse gas emissions and fuel consumption in response to the negative impacts of global warming. As an approach to energy saving and environmental sustainability, eco-driving has attracted considerable research interest in the past decades. This review aims to provide a comprehensive review of the research on eco-driving using methodologies of literature bibliometrics and content analysis through VOSviewer software. The following keywords “ecological-driving”, “ecological-routing”, “ecological-bus”, “ecological-car”, “ecological-vehicle”, “eco-driving”, “eco-routing”, “eco-driver”, “eco-bus”, “eco-car” and “eco-vehicle” are used for paper retrieval. The query was conducted on January 20, 2021. The results take account of all journal articles, proceedings papers, and reviews without time limitation. Finally, a total of 767 documents were retrieved as total publications, which were viewed over the period 2001–2020 based on the Web of Science (WoS) Core Collection database. The publication year, leading countries, leading sources, leading institutions, leading authors, document citation, and document co-citation were analyzed to explore the primary trends. The In-depth analysis reveals five clusters of keywords, and the review of relevant studies on eco-driving from five different perspectives is carried out to identify potential trends and future research hot spots of eco-driving.

Energy management is a fundamental task and challenge of plug-in split hybrid electric vehicle (PSHEV) research field because of the complicated powertrain and variable driving conditions. Motivated by the foresight of intelligent vehicle and the breakthroughs of deep reinforcement learning framework, an energy management strategy of intelligent plug-in split hybrid electric vehicle (IPSHEV) based on optimized Dijkstra’s path planning algorithm (ODA) and reinforcement learning Deep-Q-Network (DQN) is proposed to cope with the challenge. Firstly, a gray model is used to predict the traffic congestion of each road and the length of each road calculated in the traditional Dijkstra’s algorithm (DA) is modified for path planning. Secondly, on the basis of the predicted velocity of each road, the planned velocity is constrained by the vehicle dynamics to ensure the driving security. Finally, the planning information is inputted to DQN to control the working mode of IPSHEV, so as to achieve energy saving of the vehicle. The simulation results show the optimized path planning algorithm and proposed energy management strategy is feasible and effective.

Drivers’ take-over reaction time in partially automated vehicles is a fundamental component of automated vehicle design requirements, and take-over reaction time is affected by many factors such as distraction and drivers’ secondary tasks. This study built cognitive architecture models to simulate drivers’ take-over reaction time in different secondary task conditions. Models were built using the queueing network-adaptive control of thought rational (QN-ACTR) cognitive architecture. Drivers’ task-specific skills and knowledge were programmed as production rules. A driving simulator program was connected to the models to produce prediction of reaction time. Model results were compared with human results in both single-task and multi-task conditions. The models were built without adjusting any parameter to fit the human data. The models could produce simulation results of take-over reaction time similar to the human results in take-over conditions with visual or auditory concurrent tasks, as well as emergency response time in a manual driving condition. Overall, R square was 0.96, root mean square error (RMSE) was 0.5 s, and mean absolute percentage error (MAPE) was 9%. The models could produce simulation results of reaction time similar to the human results from different task conditions. The production rules are plausible representations of drivers’ strategies and skills. The models provide a useful tool for the evaluation of take-over alert design and the prediction of driver performance.

It is of significant importance to select an appropriate speed for a vehicle to drive through an upcoming curve. Previous studies have mainly taken into account the vehicle–road interaction, which lacks quantitative analysis of drivers’ driving behavior related to curve speed selections. In this study, a curve speed model derived from the vehicle–road coupling effect analysis is combined with drivers’ driving styles which are classified into aggressive and moderate styles. Moreover, a driver behavior questionnaire based analysis is carried out for quantitative identification of the above two groups of drivers, compared with the traditional vehicle-motion-indexed classification of driving styles. Unlike previous curve speed models, the proposed model not only takes the vehicle–road coupling effect into consideration, but also introduces a driving style factor which is quantified with both driver behavior questionnaire analysis and vehicle-motion-indexed classification. The proposed curve speed model was validated with the road test data. It is found that the proposed curve speed model considering both the vehicle–road interaction and drivers’ driving styles could effectively guarantee traffic safety and riding comfort in sharp curves.

Aggressive driving has attracted significant attention recently with the increase in related road traffic collisions occurring in China. This study aims to investigate the effect of driving skills on aggressive driving behaviors and traffic accidents to find implications for traffic safety improvement in China. A total of 735 Chinese drivers were recruited to complete a self-reported survey including demographic information, the translated Driver Skill Inventory (DSI), and Driver Aggression Indicator Scale (DAIS). Exploratory factor analysis was first conducted to investigate the factor structures of DSI and DAIS among Chinese drivers. Unlike the two-factor solution (i.e., perceptual-motor and safety skills) found in other studies, the current study result revealed a three-factor solution (i.e., perceptual-motor, safety, and emotional control skills) of DSI. Then, the interaction between DSI factors on DAIS factors, demographic variables, and the number of self-reported traffic accidents and offenses was tested by using moderated regression methods. The results revealed the interaction between perceptual-motor skills and safety skills on aggressive warnings committed by drivers themselves. The interactive effect between safety skills and emotional control skills on perceived aggressive warnings was also found. The results suggested that higher ratings of safety skills are essential for buffering the effect of high-level perceptual-motor skills and emotional control skills on aggressive driving in China. In conclusion, policy makers should be interested in understanding the effect of Chinese drivers’ skills on the aggression drivers committed and conceived in traffic. Successful intervention strategies should include all skill factors in the driver training contents.

In past years, the task of automatic vehicle trajectory analysis in video surveillance systems has gained increasing attention in the research community. Vehicle trajectory analysis can identify normal and abnormal vehicle motion patterns and is useful for traffic management. Although some analysis methods of vehicle trajectory have been developed, the application of these methods is still limited in practice. In this study, a novel adaptive vehicle trajectory classification method via sparse reconstruction and mutual information analysis based on video surveillance systems was proposed. The l0-norm minimization of sparse reconstruction in the method was relaxed to the lp-norm minimization (0 < p< 1). In addition, to consider the nonlinear correlation between the test trajectory and the dictionary, mutual information between the test trajectory and the reconstructed one was taken into account. A hybrid orthogonal matching pursuit–Newton method (HON) was developed to effectively find the sparse solutions for trajectory classification. Two real-world data sets (including the stop sign data set and straight data set) were used in the experiments to validate the performance and effectiveness of the proposed method. Experimental results show that the trajectory classification accuracy is significantly improved by the proposed method compared with most well-known classifiers, namely, NB, k–nearest neighbor, support vector machine, and typical extant sparse reconstruction methods.

With the recent rapid industrialization, occupational safety and health (OSH) has become an important issue in all industrial and human activities. However, incidents of injuries and fatality rates in the Ghanaian industry sector continue to increase. Despite this increase, there is no evidence regarding the element of OSH management in transport activities in Ghanaian industries. Thus, this study aims to examine the perceptions regarding the importance of safety and health in work-related transport activities in Ghanaian industries.

The development of autonomous vehicles provides effective solutions and opportunities for reducing the probability of traffic accidents. However, because of technical limitations and economic and social challenges, achieving fully autonomous driving is a long-term endeavor. One principal research question is how to choose the suitable driving mode of an intelligent vehicle during stressful traffic events. For this purpose, an on-road experiment with 22 drivers was conducted in Wuhan, China; multisensor data were collected from the driver, the vehicle, the road, and the environment. Driving modes were classified into three categories on the basis of the driver’s self-reported records, and two physiological indexes that use the k-means cluster method were adopted to calibrate the self-reported driving modes. A feature-ranking algorithm based on the information gained was adopted to identify significant factors, and a driving mode decision-making model was established with the multiclass support vector machine algorithm. The results indicated that the SD of the front wheel angle, driver experience, vehicle speed, headway time, and acceleration had significant effects on the driving mode decision making. The driving mode decision-making model demonstrated a high predictive power with a prediction accuracy of 0.888 and area under the curve values of 0.918, 0.91, and 0.929 for the receiver operating characteristic curves. The conclusions provide theoretical support for decision making by the controller of a semiautomated vehicle.

The parameter value chosen to measure driving performance affects the accuracy of the estimated fatigue level. Methods to analyze the sensitivity of these parameter values were proposed. Standard deviation of lane position (SDLP) and steering reversal rate (SRR) were considered to assess fatigue, and the sensitivity of these parameters was analyzed from the time domain and value domain. Thirty-six male drivers participated in a field test. Lane position, steering wheel angle data, and self-reported fatigue level (scored on the Karolinska sleepiness scale) were recorded. SDLP results indicate that the maximum average coefficient with fatigue level reached .11, with a unified statistical interval of 202 s when the consecutive analysis method was used; the maximum average coefficient was .12 with a unified interval of 120 s when the maximum analysis method was used. SRR results indicate that a steering angle difference of 6° was the most sensitive threshold for driver fatigue level and has an average correlation coefficient of .42, which demonstrated that SRR was more reliable than SDLP for monitoring fatigue level. With the use of the optimal parameter value, the variation results of SDLP and SRR at each fatigue level were examined, and results indicate that driving ability was impaired as fatigue level increased. The methods and results can be applied to analyses of fatigued or drowsy driving.

Current curve speed warning systems take into account mostly vehicle and road factors but not driver behavior. The systems aim at detecting sideslips of small cars on curves without consideration of rollovers for vehicles with an elevated center of gravity. In this study, a curve speed model that considers human, vehicle, and road factors is built to prevent not only sideslips but also rollover accidents for vehicles with an elevated center of gravity. In addition, a risk prediction model is presented to judge accident risk levels and determine levels of warning. Finally, the effectiveness of the presented system is validated with one skilled driver who carries out one test through a simulator under different curve scenarios. To verify the system, data from simulator tests were collected for offline checking of the system. The data were used to calculate safe speeds by using the curve speed model and to determine the levels of risk based on the risk prediction model. The results show that the system is highly compatible with the skilled driver in terms of warning accuracy and timing. Specifically, the correct alarm rate (i.e., the driver brakes and the system’s alarm goes off) of the system is 83.57% and the error alarm rate (i.e., the driver does not brake but the system’s alarm goes off) is 9.79%. Moreover, more than 80% of the time the difference between the system warning time and the operating time of the skilled driver is less than 2 s.

Some studies of driving behavior have been based on data mining to create a mechanism that relates data derived from vehicle monitoring, driver behavioral characteristics, and road safety to each other. To make the best of GPS data collected by transportation businesses and explore the potential rules of commercial vehicle driver behavioral characteristics, the parameters related to driving behavioral characteristics are extracted according to GPS data attributes based on factor analysis, and eight parameters of driving behavioral characteristics are transformed into a few aggregated variables containing clear information about driving behavior. With these variables as indicators, a cluster analysis of commercial vehicle driver behavioral characteristics in the selected case is carried out through hierarchical clustering. The results show that commercial vehicle driver behavioral characteristics can be effectively aggregated into four kinds: acceleration–deceleration, speeding-prone, acceleration, and deceleration. Of the four kinds, drivers with relatively serious acceleration–deceleration behavior are also characterized by three other relatively serious behaviors; such drivers have relatively high driving risks, so transportation businesses need to focus their supervision on those drivers. The research results have some relevance to the supervision and training of commercial vehicle drivers in China.

Circadian rhythms, inherent in all humans, consist of 24-h biological patterns that affect a person's fatigue level. The effect of circadian rhythms on driving performance was explored in an on-road driving study. Fifteen middle-aged professional daytime drivers were recruited to participate in the experiment. Participants were classified into three groups: (a) a morning group that started driving at 09:00, (b) a noon group that started driving at 12:00, and (c) an evening group that started driving at 21:00. Each group completed a 6-h driving task. The self-reported Karolinska sleepiness scale score was recorded every 5 min, and data on driving performance parameters, such as steering and lane positioning, were also acquired. The results indicated that both circadian rhythms and driving duration had significant effects on self-reported fatigue levels and that the fatigue level increased faster in the evening group than the morning and noon groups. The results of the circadian rhythm analysis showed that a driver was most likely to feel tired between 14:00 and 16:00 and between 02:00 and 04:00, when the ability to stay within designated lane lines (lane maintenance) was significantly impaired for drivers in all three groups. The evening group drivers were the most at risk. The steering performance did not show a significant relationship with the self-reported fatigue level. The self-reported fatigue level is the result of the interactive effect of circadian rhythms and driving duration. The standard deviation of lane position was more correlated with circadian rhythms than with the steering reversal rate.

Accurate prediction of vehicle motion status is critical for developing an advanced driver assistance system (ADAS), which can assess driving safety and detect dangerous scenarios in real time and in the near future. Although previous vehicle motion prediction models developed were mostly built on the basis of kinematic principles, driver behavior was largely ignored. Those models resulted in inaccurate trajectory predictions. To improve forecasting accuracy, the study reported here developed an improved vehicle motion model that includes consideration of both kinematic principles and real-time driver behavior. This improved vehicle motion model incorporates driver behavior into a constant acceleration (CA) model. Data on practical driver behavior, such as perception, identification, volition, and execution under traffic conditions and lane changes were collected. A quantitative approach based on a linear quadratic regulator optimal control method was used to acquire the driver's expected control input. In addition, a Kalman filter was applied to predict short-term vehicle motion, which was then used to analyze driving risks. Finally, CARSIM software was used to simulate driving scenarios. A Monte Carlo method was used to evaluate prediction accuracy and compare the results of the CA model and the improved vehicle motion model. The simulation results showed that the improved model can effectively simulate driver behavior in acceleration control by taking into full consideration the driver's volition and traffic environment. The proposed model yielded better predictions, provided an applicable way to improve the accuracy of vehicle motion prediction, and could be used to enhance the performance of ADAS.

Planned special events attract thousands of attendees from nearby cities or suburbia by car and transit. In most cases, the majority of attendees use personal automobiles, and a high parking demand results in a short time, with a consequent parking shortage. Parking guidance information systems can solve the problem by displaying information on parking lot availability to dynamically divert vehicles. This study focused on optimizing dynamic parking guidance information for automobile drivers at special events. An original multimode traffic network was converted to a novel network by considering parking lots as dummy links; therefore the shortest path and traffic assignment could be implemented in this extended network. A bilevel programming model based on quasi-dynamic route choice and linear programming was proposed to optimize the dynamic parking guidance information. On the basis of travelers' reaction to the guidance, stochastic dynamic user optimal route choice was employed within the lower-level model. The upper-level model was a linear program aimed at minimizing network total travel time. The solutions of the bilevel programming model were based on discrete particle swarm optimization and the method of successive average algorithms. Results of a case study implemented with a hypothetical network indicated that the optimization model could reduce the system total travel time by 4%.

Purpose – The purpose of this paper is to propose a bi-level programming optimization model to reduce traffic congestion of transportation network while evacuating people to safe shelters during disasters or special events. Design/methodology/approach – The previous optimization model for contra flow configuration only considered the character of the manager. However, the traffic condition is not only controlled by managers, but also depended on the root choice of travelers. A bi-level programming optimization model, which considered managers and evacuees' character, is proposed to optimize the contra flow of transportation network in evacuation during special events. The upper level model aims to minimize the total evacuation time, while the lower level based on user equilibrium assignment. A solution method based on discrete particle swarm optimization and Frank-Wolfe algorithm is employed to solve the bi-level programming problem. Findings – It is found that the bi-level programming based contra flow optimization model can improve evacuation efficiency and decrease evacuation time 30 per cent or more. With the increase of traffic demand, the evacuation time will decrease significantly by contra flow configuration. Research limitations/implications – In the optimization model, the background traffic is ignored for simplification and the contra flow is configured absolutely as 0 or 1, which ensures vehicles do not go back into the evacuation area. Practical implications – An efficient optimization model for traffic managers to reduce congestion and evacuation time of evacuation network. Originality/value – The new bi-level programming model not only considers managers' character, but also considers evacuees' reaction. The paper is aimed to optimize contra flow for transportation network. [ABSTRACT FROM AUTHOR]

The prevention of severe injuries during crashes has become one of the leading issues in traffic management and transportation safety. Identifying the impact factors that affect traffic injury severity is critical for reducing the occurrence of severe injuries. In this study, the Fatality Analysis Reporting System data are selected as the dataset for the analysis. An algorithm named improved Markov Blanket was proposed to extract the significant and common factors that affect crash injury severity from 29 variables related to driver characteristics, vehicle characteristics, accidents types, road condition, and environment characteristics. The Pearson correlation coefficient test is applied to verify the significant correlation between the selected factors and traffic injury severity. Two widely used classification algorithms (Bayesian networks and C4.5 decision tree) were employed to evaluate the performance of the proposed feature selection algorithm. The calculation result of the correlation coefficient, accuracy of classification, and classification error rate indicated that the improved Markov Blanket not only could extract the significant impact factors but could also improve the accuracy of classification. Meanwhile, the relationship between five selected factors (atmospheric condition, time of crash, alcohol test result, crash type, and driver’s distraction) and traffic injury severity was also analyzed in this study. The results indicated that crashes occurred in bad weather condition (e.g. fog or worse), in night time, in drunk driving, in crash type of single driver, and in distracted driving, which are associated with more severe injuries.

Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays an important role in traffic management. The graph convolution network (GCN) is widely used in traffic prediction models to efficiently handle the graphical structural data of road networks. However, the influence weights among different road sections are usually distinct in real life and are difficult to analyze manually. The traditional GCN mechanism, which relies on a manually set adjacency matrix, is unable to dynamically learn such spatial patterns during training. To address this drawback, this study proposes a novel location graph convolutional network (location-GCN). The location-GCN solves this problem by adding a new learnable matrix to the GCN mechanism, using the absolute value of this matrix to represent the distinct influence levels among different nodes. Subsequently, long short-term memory (LSTM) is employed in the proposed traffic prediction model. Moreover, trigonometric function encoding was used in this study to enable the short-term input sequence to convey long-term periodic information. Finally, the proposed model was compared with the baseline models and evaluated on two real-world traffic flow datasets. The results show that our model is more accurate and robust than the other representative traffic prediction models. [ABSTRACT FROM AUTHOR]