International Journal of Transportation Science and Technology最新文献

Signal timings at signalized intersections are frequently optimized by considering commonly used vehicle delay models. It is generally believed that reducing the average number of stops can also decrease the average vehicle delay. Therefore, the aim of this research is to address the question: “Can similar performance outcomes be achieved through the Minimization of Average Vehicle Delay (MAVD) and the Minimization of Average Number of Stops (MANS)?” The first phase of the study entails the creation of two distinct signal timing optimization models based on the Akcelik average vehicle delay and average number of stops models. Subsequently, scripts were developed in MATLAB to identify the optimal signal timings for both approaches utilizing the Differential Evolution Algorithm. In the third phase, 30 traffic scenarios were generated, each varying in overall traffic volumes at the intersection. Subsequently, the signal timings derived from the MAVD and MANS approaches were applied independently to these scenarios, and performance indicators (average vehicle delay and average number of stops) were compared. The results reveal that the utilization of MANS-based signal timings instead of MAVD may lead to an increase in average vehicle delays of up to 113.55%. Additionally, it is demonstrated that when MAVD-based signal timings are applied instead of MANS, the average number of stops can increase by up to 16.28%. Finally, it is concluded that as the overall traffic volume at the intersection increases, these growth rates tend to decrease.

Fatigue is an important cause of traffic crashes, and effective fatigue detection models can reduce these crashes. Research has found large differences in fatigued driving performance from driver to driver, as well as a significant cumulative effect of fatigue on a given driver over time. Both sources of variation can decrease the accuracy of detection systems, but previous studies have not done enough to evaluate these differences. The purpose of this study is therefore to develop a fatigue detection model that considers individual differences and the time cumulative effect of fatigue. Data on the lateral position of the car in its lane, steering wheel movement, speed, and eye movement were collected from 22 drivers using a driving simulator with an eye-tracking system. Drivers’ subjective fatigue scores were collected using the Karolinska Sleepiness Scale. State space models (SSMs) were built to detect fatigue in each driver, considering his or her individual features. As a time series model, the SSM can also address the time cumulative effect of fatigue, and it does not require a large dataset to achieve high levels of accuracy. The differences in SSM results confirm that diversity does exist among drivers’ fatigued driving performance, so the ability of the SSM to take into account driver-specific information from each individual driver suggests that it is more suitable for fatigue detection than models that use aggregated driver data. Results show that the fatigue detection accuracy of the SSM (77.73%) is higher than that of artificial neural network models (61.37%). The advantages of accuracy, high interpretability, and flexibility make the SSM a comprehensive and valuable individualized fatigue detection model for commercial use.

Access to healthcare services using public transportation (PT-based healthcare accessibility) is a crucial aspect in achieving healthcare equity as it affects individuals’ ability to receive healthcare. Previous research has focused on the spatial features of healthcare accessibility. However, less attention has been given to its temporal characteristics, which can be influenced by transit schedules, multimodal connectivity, congestion, and other factors. This study proposes a framework to better understand the impacts of temporally varying PT-based healthcare accessibility on healthcare equity. A case study of Shanghai, China is used to illustrate the temporal variation of healthcare accessibility, with a focus on hourly inter- and intra-regional disparities. These disparities are captured using the Gini coefficient and Theil index. Additionally, the study introduces bivariate local Moran’s I to identify healthcare shortage areas and evaluate the spatial autocorrelation between population density and healthcare accessibility. The findings of this study reveal that the accessibility to healthcare services experiences significant fluctuations throughout the day, leading to temporal variations in healthcare equity. Subway service quality contributes more to temporal variations than bus service quality. The lowest point of such equity is reached when PT operates at its full capacity. On a spatial level, individuals residing in newly developed regions, which surround the historical urban core or recently planned city subcenters, tend to experience decreased accessibility to healthcare via public transportation. Consequently, it results in a heightened reliance on motorized transportation in these areas. These findings provide insights that can inform the design of PT accessibility-based strategies, healthcare improvement plans and inclusive housing policies, to address healthcare equity issues in metropolitan areas. By considering both spatial and temporal factors, we can better understand the complex relationships between transportation and healthcare accessibility to promote equitable access to healthcare services and foster social equity.