A properly designed public transport system is expected to improve traffic efficiency. A high-frequency bus service would decrease the waiting time for passengers, but the interaction between buses and cars might result in more serious congestion. On the other hand, a low-frequency bus service would increase the waiting time for passengers and would not reduce the use of private cars. It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users. It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices. However, as far as the authors know, most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function, and do not properly consider the congestion dynamics and their impacts on mode choices. To fill this gap, this paper proposes a bi-level optimization model. A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics. A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm. A surrogate model-based algorithm is used to solve the bi-level programming problem.
This paper provides a review of predictive analytics for roads, identifying gaps and limitations in current methodologies. It explores the implications of these limitations on accuracy and application, while also discussing how advanced predictive analytics can address these challenges. The article acknowledges the transformative shift brought about by technological advancements and increased computational capabilities. The degradation of pavement surfaces due to increased road users has resulted in safety and comfort issues. Researchers have conducted studies to assess pavement condition and predict future changes in pavement structure. Pavement Management Systems are crucial in developing prediction performance models that estimate pavement condition and degradation severity over time. Machine learning algorithms, artificial neural networks, and regression models have been used, with strengths and weaknesses. Researchers generally agree on their accuracy in estimating pavement condition considering factors like traffic, pavement age, and weather conditions. However, it is important to carefully select an appropriate prediction model to achieve a high-quality prediction performance system. Understanding the strengths and weaknesses of each model enables informed decisions for implementing prediction models that suit specific needs. The advancement of prediction models, coupled with innovative technologies, will contribute to improved pavement management and the overall safety and comfort of road users.
This article is a compilation of teen driver crash contributing factors typically extractable from the crash data collection system in the United States. Tremendous research effort has been undertaken over the decades to comprehend teen driver crash risks, as teen drivers continue to be over-involved in crashes even when accounting for the driving exposure. This article presents the contexts of crash factors related to operating conditions, roadway, vehicle, and driver and their unique influences on teen driver crashes in terms of estimated risk, prevalence, and estimated likelihood mainly from descriptive and analytical studies. The key variables are selected based on the number of studies that considered each risk factor for analysis. The understanding of crash factors could be translated into graduated driver licensing and other teen driver safety programs. While the discussions were grounded in crash studies carried out in the United States, the insights gleaned from these studies hold the potential to offer valuable guidance to other countries. For example, the insights and discussions can serve as a catalyst for the development and improvement of driver education programs tailored to address the specific requirements and difficulties confronted by their teenage drivers.
An important issue in analyzing accident blackspots is the estimation of severity levels of different types of accidents. This study aims to estimate the severity level of accidents in Bahrain using crash costs. These crash costs were calculated by the Human Capital Approach (HCA) and total reported costs from the victims. The data was collected from the General Directorate of Traffic, insurance companies, Ministry of Works (MoW) and Ministry of Health. It was found, from the survey responses, that there was no significant effect of victim characteristics on the total cost of the accidents. The severity levels were found to be higher than those found in previous literature or adopted by local authorities which could be attributed to the economic conditions of Bahrain. Moreover, the weights found by both approaches were different from each other. Therefore, it is recommended to use the HCA approach due to its comprehensive calculations involving future costs.
To effectively mitigate the short-term fatigue effects of driving in extra-long tunnels, this study conducted natural driving experiments in five extra-long tunnels of varying lengths and tunnel group sections. Utilizing data obtained from natural driving fatigue experiments, this study identified perclos P80, variable coefficient of pupil diameter, and acceleration as fatigue sensitivity indicators, determined through significance tests of difference and correlation analysis. This study employed an ordered multi-class Logistic model to investigate the factors that influence driving fatigue in extra-long tunnels. The most significant variable in the model was perclos P80, which served as an indicator for classifying and identifying fatigue levels in extra-long tunnels. Following this, a dimensionless quantitative metric, the Fatigue Driving Degree, was formulated, and the Threshold of Driving Fatigue was established. Using the quantitative framework for driving fatigue, this paper standardized the definition of the fatigue arousal zone in extra-long tunnels. The study analyzed the operational principles and validated the key parameters of the fatigue arousal zone in extra-long tunnels. These parameters encompass the placement location, length, form, and traffic induction design of the fatigue arousal zone. The research findings can serve as a theoretical reference for the development of fatigue arousal technology in extra-long highway tunnels in China.
The highway capacity manual (HCM) provides a formula to calculate the heavy vehicle adjustment factor (f HV) as a function of passenger car equivalent factors for the heavy vehicle (E T). However, a significant drawback is that the methodology was established solely based on human-driven passenger cars (HDPC) and human-driven heavy vehicles (HDHV). Due to automated passenger cars (APCs), a new adjustment factor (f AV) might be expected. This study simulated traffic flows at different percentages of HDHVs and APCs to investigate the impacts of HDHVs and APCs on freeway capacity by analyzing their influence on f HV and f AV values. The simulation determined observed adjustment factors at different percentages of HDHVs and APCs (f observed). The HCM formula was used to calculate (f HCM). Modifications to the HCM formula are proposed, and vehicle adjustment factors due to HDHVs and APCs were calculated (f proposed). Results showed that, in the presence of APCs, while f observed and f HCM were statistically significantly different, f observed and f proposed were statistically equal. Hence, this study recommends using the proposed formula when determining vehicle adjustment factors (f proposed) due to HDHVs and APCs in the traffic stream.
The time cost of ridesharing rental represents a crucial factor influencing users' decisions to rent a car. Researchers have explored this aspect through text analysis and questionnaires. However, the current research faces limitations in terms of data quantity and analysis methods, preventing the extraction of key information. Therefore, there is a need to further optimize the level of public opinion analysis. This study aimed to investigate user perspectives concerning travel time in ridesharing, both pre and post-pandemic, within the Twitter application. Our analysis focused on a dataset from users residing in the USA and India, with considerations for demographic variables such as age and gender. To accomplish our research objectives, we employed Latent Dirichlet Allocation for topic modeling and BERT for sentiment analysis. Our findings revealed significant influences of the pandemic and the user's country of origin on sentiment. Notably, there was a discernible increase in positive sentiment among users from both countries following the pandemic, particularly among older individuals. These findings bear relevance to the ridesharing industry, offering insights that can aid in establishing benchmarks for improving travel time. Such improvements are instrumental in enabling ridesharing companies to effectively compete with other public transportation alternatives.
