Vehicle Drivers Research Articles

Impact of stochastic vehicle generation on traffic microsimulation model output

One of the most important characteristics of microsimulation is the ability to model the temporal and spatial nature of traffic demand. Every traffic microsimulation has a vehicle generation model that determines how and when the driver–vehicle units are introduced in the simulation. While microsimulation use has become increasingly popular, it is unclear how the vehicle generation options affect the final result. The purpose of this paper is to examine the stochastic component of the vehicle generation model and how it may affect the simulation output. Three scenarios, including the passenger car equivalent (PCE) model of the Highway Capacity Manual (HCM-7), were used for assessing stochastic volumes and their impacts on performance measures. Results indicate a statistically significant impact of the variability of stochastic volumes on the estimation of performance measures at the breakdown flow phase and synchronized flow phase for interrupted and uninterrupted flow conditions, respectively. This finding highlights the importance of reporting vehicle generation as a calibration parameter, enabling others to replicate the experiments. When utilizing microsimulations for the assessment of roadway/system performance, it is crucial for users to have a thorough understanding of demand generation.

Reconfiguring Vehicles for Drivers with Disability: A Knowledge-Based Decision Support System

Driving a car is pivotal to supporting Persons with Disabilities (PwDs) independence and quality of life. The problem of reconfiguring a vehicle to meet both the PwD’s needs and the (local or supranational) regulations is far from trivial since it requires the identification of the appropriate modifications and adaptations to be installed on the driver’s car. However, PwDs may not be acquainted with the mechanical modification, aids, and devices installed on their cars to allow them to drive, nor may they be aware of the possible configurations available. In the Italian context, this knowledge is strictly regulated by local and European regulations, which—according to the type(s) of impairments a driver has—indicate the possible configurations for the vehicles and the aids and mechanical modifications that need to be implemented. Therefore, to support PwDs in understanding the possible modification(s) their cars could undergo, a novel knowledge-based Decision Support System (DSS) was developed with the support of the Italian National Institute for Insurance against Accidents at Work (INAIL). The DSS exploits ontological engineering to formalize the relevant information on cars’ modifications, PwDs’ impairments, and a rule engine to match candidate drivers with the (sets of) car configurations that can be installed on their vehicles. Thus, the proposed DSS can enable the drivers to acquire more insights on the types and functionalities of the driving aids they will use. It also supports INAIL in administering the “special driving license”.

Bridge Monitoring Using Smartphones Installed on Driving Vehicles: Oriented to Crowdsourcing Model

The acquisition of bridge frequency by using the ubiquitous ordinary passenger vehicles and smartphones in cities has attracted an increasing amount of attention. This study proposes a bridge monitoring method and develops a vehicle–bridge crowdsourcing monitoring (VBCM) platform for public participation. It takes smartphones carried by vehicles as monitoring terminals for a long-term bridge monitoring task. To validate the feasibility of available smartphone brands as a signal collector, a small shake table test is carried out and the processing scheme for the smartphone sampling data is investigated. A smartphone sensor frequency gain technique is proposed to satisfy the fusion of multisensor data in different smartphones. Furthermore, a magnetic target identification technique is proposed to pick up and extract the bridge response signal segment corresponding to the vehicle’s entering and leaving, as well as to eliminate redundant data. To this end, the network of multiple smartphones is synchronized. For the sake of applicability and feasibility, a series of scaled experiments are conducted in the laboratory considering an adapted toy car driving over a simply supported steel bridge. The results demonstrated the practicality of the proposed methodology. The combination of easily accessible smartphones oriented to a crowdsourcing model and a driving vehicle lowers the threshold for the bridge monitoring process, which also pinpoints a potential for future structural health monitoring development.

Integrated Control of Intelligent Vehicle Driving Stability Using Three-Dimensional Phase Space

<div>To enhance vehicle dynamic stability during driving, we developed a three-dimensional phase space model that incorporates the sideslip angle of center of mass, yaw rate, and lateral load transfer rate. This model enabled real-time evaluation and active control of vehicle stability. First, longitudinal and lateral controllers were implemented to ensure precise vehicle trajectory. Second, a hierarchical control strategy was designed to actively manage the desired sideslip angle, yaw rate, and roll angle based on the vehicle’s destabilizing conditions, thereby maintaining the vehicle within a stable state space. We simulated and tested the stability analysis methods and integrated control strategies for both cars and trucks under DLC (double lane change) and CDC (circular driving condition) scenarios using joint simulations with CarSim/TruckSim and Simulink. The proposed integrated stability control strategy, which combined MPC-based trajectory tracking with direct yaw moment control and active suspension control, enhanced the vehicle’s directional and roll stability. This approach effectively mitigated vehicle instability under extreme conditions. Compared to the MPC lateral tracking control system, the performance of the integrated control system was significantly improved. In the DLC scenario, the maximum values of the sedan’s lateral deviation, sideslip angle, yaw rate, and vehicle roll angle decreased by 22.6%, 33.9%, 5.5%, and 1.2%, respectively. In the CDC scenario, the truck’s lateral acceleration, sideslip angle, yaw rate, and vehicle roll angle decreased by 7.5%, 46.8%, 8.2%, and 80%, respectively. Additionally, open-loop simulation tests were conducted under fishhook steering conditions for both passenger cars and trucks. The results further validated the effectiveness of the integrated control strategy, demonstrating its ability to significantly improve yaw rate and roll response, thereby enhancing overall vehicle stability under challenging driving conditions.</div>

Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on Türkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09 % across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77 %. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72 %.

GAN-Based Generation of Synthetic Data for Vehicle Driving Events

GAN-Based Generation of Synthetic Data for Vehicle Driving Events

Assessment of occupational musculoskeletal disorders (MSDs) among heavy vehicle drivers.

Professional driving requires long hours of work, uncomfortable seats, negotiating rough terrain and highways, and possibly minor repairs and other auxiliary transportation duties. Heavy vehicle drivers driving vehicles such as trucks, bulldozers, etc. due to such working structures are more prone to various musculoskeletal disorders (MSDs) and pain, which is of great concern. In the present study, it is planned to investigate possible ergonomic risk factors such as age, weight, driving exposure, seat suspension systems, lifting heavy weights causing MSDs in drivers of various heavy vehicles. The results of the study are expected to help drivers reduce the risk of MSDs. For the present study, the Nordic questionnaire on musculoskeletal disorders was modified and standardized and was administered to the 48 heavy vehicle drivers randomly selected to collect the data. The analysis divulged that over the past 12 months, lower back pain (LBP) emerged as the most dominant pain experienced by 56% of drivers, followed by knee pain (KP) (43%) and neck pain (NP) (39%) respectively. The prevalence of shoulder pain (SP) was observed to be much lower than in previous literature. The logistic regression model further revealed that increasing age, poor suspension system and poor body posture were significantly associated with lower back pain. Additionally, a poor suspension system and lifting heavy weights had significant effect on the drivers' knee pain. The results demonstrated the evident necessity for ergonomic consideration in vehicle designing and ergonomic training for heavy vehicle drivers.

Annoyance caused by simultaneous exposure to noise and seat vibrations inside of a mini excavator: multimodal interaction and the effects of changes in low-frequency content of the sound signal

Drivers of commercial vehicles are exposed to noise and vibrations during their occupation. The signal characteristics of these exposures can lead to different degrees of annoyance perception, even below the legal daily exposure limits. In addition, simultaneous exposure to sound and vibration may lead to potential interaction effects between the two stimuli, influencing the total annoyance perception. Therefore, a perception experiment was designed to ask the participants about the perceived total annoyance caused by simultaneous noise and seat vibrations in commercial vehicles, presenting modified sound and seat vibrations, recorded inside of a mini-excavator. Multimodal interaction effects of noise and vibrations were investigated by changing the intensity of both stimuli. Furthermore, the influence of low-frequency signal components of the sound signal with and without simultaneous seat vibrations was investigated by specific changes in the sound spectrum.

Multimodal Dataset Construction and Validation for Driving-Related Anger: A Wearable Physiological Conduction and Vehicle Driving Data Approach

Anger impairs a driver’s control and risk assessment abilities, heightening traffic accident risks. Constructing a multimodal dataset during driving tasks is crucial for accurate anger recognition. This study developed a multimodal physiological -vehicle driving dataset (DPV-MFD) based on drivers’ self-reported anger during simulated driving tasks. In Experiment 1, responses from 624 participants to anger-inducing videos and driving scenarios were collected via questionnaires to select appropriate materials. In Experiments 2 and 3, multimodal dynamic data and self-reported SAM emotion ratings were collected during simulated and real-vehicle tasks, capturing physiological and vehicle responses in neutral and anger states. Spearman’s correlation coefficient analysis validated the DPV-MFD’s effectiveness and explored the relationships between multimodal data and emotional dimensions. The CNN-LSTM deep learning network was used to assess the emotion recognition performance of the DPV-MFD across different time windows, and its applicability in real-world driving scenarios was validated. Compared to using EEG data alone, integrating multimodal data significantly improved anger recognition accuracy, with accuracy and F1 scores rising by 4.49% and 9.14%, respectively. Additionally, real-vehicle data closely matched simulated data, confirming the dataset’s effectiveness for real-world applications. This research is pivotal for advancing emotion-aware human–machine- interaction and intelligent transportation systems.

Highway Main Lane Vehicles Driving Behavior Prediction Based on Residual-Transformer

Highway Main Lane Vehicles Driving Behavior Prediction Based on Residual-Transformer

Vehicle Driving Behavior Recognition and Optimization Strategies Based on Cloud Computing and SSA-BP Algorithm

Vehicle Driving Behavior Recognition and Optimization Strategies Based on Cloud Computing and SSA-BP Algorithm

Geographic Factors Impacting the Demand for Public EV Charging: An Observational Study

The practicality and substitutability of electric vehicles depend on there being a fast, reliable way to recharge on round trips beyond the range of a single charge. Grouping such infrastructure into charging hubs benefits developers and operators through economies of scale and electric vehicle drivers in terms of travel logistics and passed-through cost savings. The need for charging capacity at en-route charging hubs is impacted by the following four identifiable geo-social parameters: (a) highway travel volumes, reflecting the rate at which electric vehicles are expending energy in the area; (b) local population, reflecting both the increased needs of electric vehicle owners without dedicated home chargers and the reduced needs of those commuting into a metropolitan center; (c) the quantity of competing charging stations; and (d) being on a critical interprovincial route. Twelve charging stations located in diverse locations around Nova Scotia, Canada, were evaluated in terms of these four parameters, and their recorded use was investigated from a dataset of 26,000 charging events between April 2022 and April 2024. The regression reveals that there are strong positive correlations between demand for fast charging and (a) traffic volumes (45%) and (c) being on an interprovincial route (42%), while there is only a very weak correlation with (b) local population (2%). Interestingly, there is only a weak negative correlation with (c) the number and capacity of nearby competing chargers (−6%), suggesting that either in short-term route choice or longer-term vehicle choice, the presence of chargers encourages electric vehicles.

Vienna road crashes: a multivariate approach to understanding driver-related risks

Road safety is worldwide researched field where crashes play a causal role and injuries are counted as outcomes. Approaches and techniques are diverse, but with same goal—contribute to safer road environment for everyone included. Drivers of personal vehicles are the most protected traffic group due to protection provided by vehicle and mandatory safety belt in comparison to pedestrians as the most venerable participants. Also, this group of drivers is characterized by the high presence of very young drivers whose psychological characteristics place them in a vulnerable risky subgroup. According to the original data of the traffic police on crashes of personal car drivers in the town of Vienna, since 2010 they have had the highest number of crashes in 2012 and after slow decrease is recorded. Still high in total number of crashes involving only drivers of personal vehicles from a traffic safety point of view this still represents an extremely worrying problem that has not received proper social attention. In response to knowledge gaps, this research is essential to address leading characterizations in crashes with aim to answer what is trend in crash occurrence during 2010–2020 inside Vienna municipality, and what is predicted trend. Is there significant and distinctive difference based on gender and age with conditions under which crashes are occurring influencing different injury degree. Multiple regression undoubtedly points fields for action in statistically based findings providing the most important answer to this research: why there are so many injuries and what is leading cause of them. Answering this question contributes toward higher safety environment for all participants.

Using Electric Vehicle Driver’s Driving Mode for Trip Planning and Routing

With the increasing adoption of electric vehicles worldwide, some limitations have emerged in their usage. The main limitations include low autonomy and a scarcity of charging points. In this work, we describe a software architecture for planning a stop at charging stations along a trip, by prediction of battery charge to be spent along the path. We describe the main components of this architecture and evaluate regression methods for the car consumption prediction module. We also use a real dataset built from an electric vehicle usage to validate the architecture concept and its viability analyzing multiple linear regression machine learning models. To further validate the architecture, we make comparisons between simulated and a real trips.

The influence of vehicle size on perception and behavior toward drivers

ABSTRACT This study examines the effects of vehicle size on driver impressions and behavioral intentions. Study 1 tested whether vehicle size (large vs. small) affects perceived physical size (height, body shape) through socioeconomic status (SES). We found that large (vs. small) vehicle drivers were perceived as tall (vs. short), and this perception was mediated by the drivers’ estimated SES (but not by body shape). Study 2 focused on aggressive behavioral intentions (e.g. honking) toward other drivers, examining whether the relationship between vehicle size and intention was serially mediated by estimated physical size and traits (aggression, power). Here, large (vs. small) vehicle driver were perceived as tall (heavy) and possessing high power (high aggression), which is related to less (more) aggressive behavioral intention toward the driver. Our study suggests that individuals perceive other drivers’ physical sizes differently, and this perception is associated with differences in behavioral responses toward other drivers.

Economic Evaluation of a Novel Treatment of Attention-Deficit/Hyperactivity Disorder in US Motor Vehicle Drivers

Background: Attention-deficit/hyperactivity disorder (ADHD) affects approximately 4.4% of US adults. ADHD is associated with high-risk driving behavior and costly motor vehicle accidents. DYANAVEL XR (DXR) (Tris Pharma, Inc.) is a once-daily fast-acting amphetamine developed for ADHD treatment. A randomized controlled trial showed that DXR patients were 43% less likely to crash during a driving simulation than individuals taking placebo. Study outcomes suggest a DXR crash rate similar to that of a driver without ADHD, while patients treated with the current standard of care (SOC) have a 52% higher crash risk than non-ADHD drivers. Objective: The aim was to evaluate the economic benefits attributable to improved driving abilities and avoided crashes in DXR patients compared with patients treated with the SOC or those who are untreated. Methods: A cost-impact model estimated 1-year crash-related cost outcomes for DXR-treated patients compared with SOC-treated and untreated ADHD patients. SOC was assumed to consist of a combination of short-, intermediate-, and long-acting ADHD stimulant and non-stimulant medications. DXR crash risk was assumed equivalent to the non-ADHD population risk, as supported by trial data. Crash risk for untreated and SOC-treated ADHD patients were assumed to be 99% and 52% higher than the general US population, respectively. Model outcomes included the cost impact (medication- and crash-related costs) and the number of crashes, injuries, and fatalities avoided with DXR. Results: Treatment with DXR would avoid 0.82 crashes, 0.016 injuries, and 0.036 fatalities per year compared with untreated patients, and 0.036 crashes, 0.007 injuries, and 0.0001 fatalities per year compared with SOC-treated patients. Compared with a population of 25% SOC-treated patients and 75% untreated patients, DXR use would save an average of $4581 per person per year across all age groups when priced at $80 per month, assuming all SOC-treated and untreated patients utilized DXR. When the value of quality-of-life improvement is considered, savings increase over 7-fold. Discussion: Outcomes suggest that DXR may be an economically beneficial treatment compared with SOC for ADHD patients. Conclusions: The economic model showed that DXR is cost-saving compared with no treatment and SOC by reducing the number of motor vehicle crashes in the ADHD population.

Prevalence of Upper Cross Syndrome Among Heavy Vehicle Drivers (HVD): A Cross-Sectional Study

Background: Heavy vehicle drivers (HVDs) are at risk for musculoskeletal disorders, including Upper Cross Syndrome (UCS), due to prolonged sitting and repetitive tasks. UCS is characterized by muscle imbalances in the neck, shoulders, and upper back, leading to pain and functional impairment.Objective: This study aimed to assess the prevalence of UCS among HVDs and identify contributing factors such as working hours and pain intensity.Methods: A cross-sectional study was conducted from March 2024 to August 2024, involving 212 male HVDs aged 25-55 years, working for over a year, and experiencing neck and back pain for more than three months. Exclusion criteria included recent spinal trauma or deformities. Data were collected using the Numeric Pain Rating Scale (NPRS) and Neck Disability Index (NDI) and analyzed using SPSS version 25. Chi-square tests and linear regression were applied.Results: UCS was prevalent in 26.4% of drivers. A significant relationship was found between pain intensity and disability (χ² = 170.126, p = 0.000). Most drivers (66.9%) were aged 25-35 years, and 30.2% reported very mild pain.Conclusion: UCS is significantly prevalent among HVDs, with long working hours and poor ergonomics contributing to its onset.

Economic Evaluation of a Novel Treatment of Attention-Deficit/Hyperactivity Disorder in US Motor Vehicle Drivers.

Background: Attention-deficit/hyperactivity disorder (ADHD) affects approximately 4.4% of US adults. ADHD is associated with high-risk driving behavior and costly motor vehicle accidents. DYANAVEL XR (DXR) (Tris Pharma, Inc.) is a once-daily fast-acting amphetamine developed for ADHD treatment. A randomized controlled trial showed that DXR patients were 43% less likely to crash during a driving simulation than individuals taking placebo. Study outcomes suggest a DXR crash rate similar to that of a driver without ADHD, while patients treated with the current standard of care (SOC) have a 52% higher crash risk than non-ADHD drivers. Objective: The aim was to evaluate the economic benefits attributable to improved driving abilities and avoided crashes in DXR patients compared with patients treated with the SOC or those who are untreated. Methods: A cost-impact model estimated 1-year crash-related cost outcomes for DXR-treated patients compared with SOC-treated and untreated ADHD patients. SOC was assumed to consist of a combination of short-, intermediate-, and long-acting ADHD stimulant and non-stimulant medications. DXR crash risk was assumed equivalent to the non-ADHD population risk, as supported by trial data. Crash risk for untreated and SOC-treated ADHD patients were assumed to be 99% and 52% higher than the general US population, respectively. Model outcomes included the cost impact (medication- and crash-related costs) and the number of crashes, injuries, and fatalities avoided with DXR. Results: Treatment with DXR would avoid 0.82 crashes, 0.016 injuries, and 0.036 fatalities per year compared with untreated patients, and 0.036 crashes, 0.007 injuries, and 0.0001 fatalities per year compared with SOC-treated patients. Compared with a population of 25% SOC-treated patients and 75% untreated patients, DXR use would save an average of 80 per month, assuming all SOC-treated and untreated patients utilized DXR. When the value of quality-of-life improvement is considered, savings increase over 7-fold. Discussion: Outcomes suggest that DXR may be an economically beneficial treatment compared with SOC for ADHD patients. Conclusions: The economic model showed that DXR is cost-saving compared with no treatment and SOC by reducing the number of motor vehicle crashes in the ADHD population.

Cardiovascular Disease Among Commercial Motor Vehicle Drivers: A Focus on Truck Drivers.

Cardiovascular Disease Among Commercial Motor Vehicle Drivers: A Focus on Truck Drivers.

Vehicle-to-grid as a competitive alternative to energy storage in a renewable-dominant power system: An integrated approach considering both electric vehicle drivers' willingness and effectiveness

Vehicle-to-grid (V2G) technology, which enables bidirectional power flow between electric vehicles (EVs) and power grids, is a possible solution for integrating EVs and renewable energy (RE) into the power system. While EV drivers are indispensable components for the V2G applications, the extant power system studies have underexamined the willingness of EV drivers to participate in V2G. In addition, few studies investigate whether the small-scale and time-varying V2G supply can replace utility-level energy storage. Given this background, this study combined discrete choice experiments with energy storage capacity expansion planning (ES-CEP) to simulate time-varying V2G participation and effectiveness. The choice experiments identified drivers’ preferences for V2G, which was used to simulate V2G participation under different compensation schemes. Time-varying V2G participation was integrated into ES-CEP for renewable-dominant power system to assess the potential of V2G to replace ES at the utility scale. The results showed that most drivers were unlikely to engage in V2G, although this reluctance decreased during the night. Higher guaranteed state-of-charge, accessible V2G facilities, and monetary enrollment compensation encouraged the participation. The ES-CEP results showed that, even with a limited V2G (4.5–15.3 % of EV drivers), V2G significantly reduced the optimal ES capacity (36.5–45.6 % for power and 39.6–60.6 % for energy), shaved peak netload by 18.5 %, and saved the total cost by 3.35 %, even after accounting for V2G enrollment compensation and battery degradation. EVs that charged during the daytime and participated in V2G at night were the most utilized, resulting in 0.92 % of annual battery aging, but generated a profit of 636,749 KRW (553 USD). The findings of this study underscore the importance of a suitable compensation scheme for V2G and supplementary measures for incentivizing EV charging during off-peak hours, as these elements are essential for the successful implementation of V2G.