Young Novice Drivers’ Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control Behavior

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver’s distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers’ cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers’ vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction

Optimizing guidance signage system to improve drivers’ lane-changing behavior at the expressway toll plaza

Although ETC (Electronic Toll Collection) has been widely implemented in vehicles in China in recent years, the channel guidance signage system on expressways has not been updated accordingly. Late lane-changing occurs when drivers are confused about the correct channel to enter, leading to increased crash risks and traffic congestion in front of the toll. The paper aims to optimize the current guidance signage systems, given the high proportion of ETC vehicles on Chinese expressways, and evaluate its effects on the drivers’ lane-changing behavior when passing through the expressway toll channels. A driving simulator experiment was conducted to test four scenarios: Original sign plan, Partial Manual Toll Collection (MTC) sign plan, Complete MTC sign plan and Complete MTC sign with Voice warning plan. Forty participants with a valid driver's license completed the four scenarios, and their behavior performances (e.g., decision-making of lane-changing, response time, average speed and deceleration) in the main lane in front of the toll booth were analyzed. The results showed that compared to the Original sign plan and Partial MTC sign plans, the Complete MTC sign plans (with and without voice warning) played a significant role in improving the MTC vehicle drivers’ lane-changing behaviors. The improvement included earlier initiated lane-changing, shortened response time, lower deceleration rate and extended lane-changing duration distance. The findings of this study have important implications for expressway designers and relevant management departments to optimize the current guidance signage and enhance traffic safety and efficiency at the toll plaza.</p

Influencing Mechanism of Potential Factors on Passengers’ Long-Distance Travel Mode Choices Based on Structural Equation Modeling

Understanding the public transportation users’ preferences to long-distance travel modes would contribute to reasonable developing policies and resource allocation. This paper aims to explore the influencing mechanism of potential factors on the long-distance travel mode choice. A survey was conducted to collect the data. The analysis of variance (ANOVA) approach was applied to analyze the correlation relationship between potential factors and travel mode choice behavior. The results showed that, except gender, service demand for safety and departure time, all of the other factors significantly influenced the travel mode choice behavior. Specifically, passengers with higher education level and income level were more likely to choose high-speed railway (HSR) and plane; passengers caring about travel expense were more likely to choose ordinary train, whereas plane and HSR may be chosen more by passengers caring more about comfort, punctuality and efficiency; the more passengers were satisfied with travel modes’ service performance, the more they would be likely to choose them; the most competitive distance ranges for coach, ordinary train, HSR and plane were below 500 km, 500–1000 km, 500–1500 km and over 1500 km, respectively. Besides, the structural equation modeling (SEM) technique was applied to investigate the influencing mechanism of factors on the long-distance travel mode choice. The results revealed that travel distance was the most significant variable directly influencing passengers’ mode choices, followed by the service demand, performance evaluation, and personal attributes. Furthermore, personal attributes were verified to have an indirect effect on travel mode choice behavior by significantly affecting the service demand and performance evaluation

Uncertainty Analysis of Rear-End Collision Risk Based on Car-Following Driving Simulation Experiments

Rear-end collisions are one of the most common types of accidents, and the importance of examining rear-end collisions has been demonstrated by numerous accidents analysis researches. Over the past decades, lots of models have been built to describe driving behaviour during car following to better understand the cause of collisions. However, it is necessary to consider individual difference in car-following modelling while it seems to be ignored in most of previous models. In this study, a rear-end collision avoidance behaviour model considering drivers’ individual differences was developed based on a common deceleration pattern extracted from driving behaviour data, which were collected in a car-following driving simulation experiment. Parameters of variables in the model were calibrated by liner regression and Monte Carlo method was adopted in model simulation for uncertainty analysis. Simulation results confirmed the effectiveness of this model by comparing them to the experiment data and the influence of driving speed and headway distance on the rear-end collision risk was indicated as well. The thresholds for driving speed and headway distance were 18 m/s and 15 m, respectively. An obvious increase of collision risk was observed according to the simulation results

Individuals’ Acceptance to Free-Floating Electric Carsharing Mode: A Web-Based Survey in China

Carsharing is growing rapidly in popularity worldwide. When the vehicles involved are Battery Electric Vehicles (BEV), carsharing has been proven to remarkably contribute to easing energy and environment crises. In this study, individuals’ acceptance to carsharing in China was measured from three aspects: carsharing mode choice behavior, highest acceptable price to use carsharing, and willingness to forgo car purchases. The data were collected by a web-based survey. The hierarchical tree-based regression (HTBR) method was applied to explore the effects of potential influencing factors on individuals’ acceptance, and some interesting findings were obtained: participants who know about carsharing were more likely to use carsharing, pay higher prices and forgo car purchases; the most competitive trip purpose and trip distance for choosing carsharing were, respectively, business activities and 11–20 km; most participants (47.1%) were willing to pay 1–2 Yuan per minute to use carsharing, and males or participants with higher income-level could accept higher price; and when car purchase restrain policy (CPRP) was carried out in a city or the urban public transport service level (UPTSL) was high, participants were more willing to forgo car purchases. Based on the above findings, corresponding policies were proposed to provide guidance for successful establishment of carsharing in China

Using a V2V- and V2I-based collision warning system to improve vehicle interaction at unsignalized intersections

Introduction: Unsignalized intersections are critical components of the road network where traffic collisions occur frequently. Method: This study aims to design a Vehicle-to-Vehicle (V2V)- and Vehicle-to-Infrastructure (V2I)-based unsignalized intersection collision warning system (UICWS) to improve driver performance and enhance driver safety at unsignalized intersections. A multi-user driving simulator experiment was conducted with 48 participants divided into 24 pairs. The dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions was examined under different warning conditions (i.e., with vs without UICWS) and intersection field of view (IFOV) conditions (i.e., standard vs improved IFOV). Results and conclusions: The experimental results showed that the UICWS could effectively help drivers make appropriate operation decisions and reduce the number of right-angle collisions and near-collisions at unsignalized intersections. In the condition without UICWS, improved IFOV could prompt drivers to make crossing decisions in advance and adjust speed smoothly. Moreover, drivers’ crossing maneuvers changed with the relative distance between the subject and conflict vehicles and the intersection. The risks of collisions and near-collisions increased significantly when the two vehicles were at a similar distance to the intersection before they initiated any actions. Practical Applications: The findings show that the proposed UICWS can effectively reduce collisions or near-collisions at unsignalized intersections in a connected vehicle environment. On this basis, some active intervention strategies, such as specific speed guidance depending on the dynamics of the conflict vehicle, can be developed to ensure vehicles passing through unsignalised intersections safely.</p

Optimizing the guiding sign system to improve drivers’ lane-changing behavior at freeway exit ramp

A high incidence of traffic accidents is often observed in freeway exit ramp areas. Slowing down, wandering, and changing lanes suddenly and continually in a short interval near the exit ramp are important reasons for accidents. Helping drivers start changing lanes sooner and more efficiently in freeway exit ramp areas is a feasible solution to vehicle interweaving. This paper aims to optimize the current guiding sign system and improve drivers’ lane-changing behavior before the exit ramp. Three guiding sign optimization measures (sign symbols, ground signs and voice prompts) had been considered before five guiding sign plans were made for driving simulation experiments: original sign (OS) plan, new type sign (NTS) plan, ground guiding sign (GOS) plan, voice prompt (VOS) plan, and voice-ground sign (VGOS) plan. The decisions, reactions, and operation processes of 43 Chinese drivers were compared to confirm the optimal guiding sign plan. The results showed that updating sign symbols, adding ground signs and voice prompts all contributed to the drivers’ shorter response time, earlier arrival at the lane-changing location, higher average speed and greater longitudinal distance of lane-changing. These findings can help freeway designers optimize the guiding sign system for freeway exit ramps.</p

Osteoarthritis versus psoriasis arthritis: Physiopathology, cellular signaling, and therapeutic strategies

Osteoarthritis and psoriasis arthritis are two degenerative forms of arthritis that share similar yet also different manifestations at the histological, cellular, and clinical levels. Rheumatologists have marked them as two entirely distinct arthropathies. Given recent discoveries in disease initiation and progression, potential mechanisms, cellular signaling pathways, and ongoing clinical therapeutics, there are now more opportunities for discovering osteoarthritis drugs. This review summarized the osteoarthritis and psoriasis arthritis signaling pathways, crosstalk between BMP, WNT, TGF-β, VEGF, TLR, and FGF signaling pathways, biomarkers, and anatomical pathologies. Through bench research, we demonstrated that regenerative medicine is a promising alternative for treating osteoarthritis by highlighting significant scientific discoveries on entheses, multiple signaling blockers, and novel molecules such as immunoglobulin new antigen receptors targeted for potential drug evaluation. Furthermore, we offered valuable therapeutic approaches with a multidisciplinary strategy to treat patients with osteoarthritis or psoriasis arthritis in the coming future in the clinic