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.

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.

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.

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.

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.

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.

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.

Implementing autonomous bus services in several cities has garnered substantial research attention worldwide. However, the benefits and challenges of this emerging mode remain insufficiently understood. Consequently, VOSviewer was employed for a bibliometric analysis involving 300 publications, investigating the associations among authors, journals, and keywords. Subsequently, we comprehensively reviewed the current state of research on two topics and proposed future recommendations. Results indicate that the first document related to autonomous bus services was published in 2009. Most user attitude -related research data are obtained via questionnaires and analyzed using statistical techniques. Autonomous bus services are expected to benefit passengers regarding travel time, cost, safety, etc., while passenger preferences are inconsistent. However, integrating the service into existing bus systems requires careful consideration of the schedule sequences. Notably, modular autonomous bus services present a new opportunity for the further optimization of bus services. In future studies, standardized data acquisition procedures should be developed to achieve comparable results. Regarding traveler choice behavior, the effect of specific autonomous bus service policies over time and the heterogeneity due to cultural or social contexts across regions should be assessed. To further promote autonomous bus services, based on fluctuating travel demands, the effects of vehicle capacity, speed, and cost of fleet composition should be evaluated comprehensively to optimize the bus network and schedule sequence. Owing to the protracted nature of the transition from conventional to fully autonomous buses, one should prioritize semi-autonomous bus services. Another essential future research direction is to integrate modular autonomous bus assembly or disassembly strategies with different fine-grained operation optimization techniques in various scenarios.

The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems. Monitoring and accurately forecasting urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion. Compared with traditional short-time traffic prediction, this study proposes a machine learning algorithm-based traffic forecasting model for daily-level peak hour traffic operation status prediction by using abundant historical data of urban traffic performance index (TPI). The study also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation, including day of week, time period, public holiday, car usage restriction policy, special events, etc. Based on long-term historical TPI data, this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm (XGBoost). The model validation results show that the model prediction accuracy can reach higher than 90%. Compared with other prediction models, including Bayesian Ridge, Linear Regression, ElatsicNet, SVR, the XGBoost model has a better performance, and proves its superiority in large high-dimensional data sets. The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.

Connected vehicle (CV) is regarded as a typical feature of the future road transportation system. One core benefit of promoting CV is to improve traffic safety, and to achieve that, accurate driving risk assessment under Vehicle-to-Vehicle (V2V) communications is critical. There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones: (1) the CV environment provides high-resolution and multi-dimensional data, e.g., vehicle trajectory data, (2) Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment, e.g., the multiple interacting objects and the time-series variability. Hence, this study proposes a driving risk assessment framework under the CV environment. Specifically, first, a set of time-series top views was proposed to describe the CV environment data, expressing the detailed information on the vehicles surrounding the subject vehicle. Then, a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment. It is proved that this model can reach an AUC of 0.997, outperforming the existing machine learning algorithms. This study contributes to the improvement of driving risk assessment under the CV environment.

Countdown signals for motorized vehicles, which are intended to ensure safety on the road and regulate motor vehicle speed limits at road intersections, are still considered a relatively novel concept. These signals have been adopted by only a few countries, and the number of cities that use them is limited. This review aims to summarize the effects of countdown signals on traffic safety and efficiency and to determine the consistency and differences of existing research propositions on the matter. Based on the review, considerable research presents evidently different conclusions in the areas of driver red-light running and traffic safety. Particularly, some studies propose that countdown signals reinforce traffic safety, whereas others consider that such signals adversely affect traffic safety. Meanwhile, related literature provides varying conclusions on the aspect of traffic efficiency for vehicle headway. At present, the number of studies conducted regarding the driving behaviors of motorists toward countdown-signalized intersections is insufficient. Accordingly, such inadequate diversity in research causes difficulty in completely assessing the benefits and disadvantages of countdown signals. In this paper, an important future research direction on microcosmic driving psychological and physiological data combined with macro-driving behavior is proposed.

After some tragic fire events, Directive 2004/54/EC was issued to ensure a minimum safety level for tunnels belonging to the Trans-European Road Network longer than 500 m. Nowadays, most of the Italian road tunnels are still not in compliance with the minimum safety requirements, thus refurbishment works are often planned. By developing a traffic macro-simulation model, this paper aims at assessing the resilience of an existing twin-tube motorway tunnel when one of its tubes is partially or completely closed due to planned activities. Several scenarios were investigated, also considering the availability or not of an alternative itinerary in the surrounding transportation network. The average vehicles’ speed was used as a functionality parameter, while the resilience metrics were the resilience loss, the recovery speed, and the resilience index. The findings showed higher resilience losses for complete closure rather than partial closure of the tube under planned refurbishment works. The implementation of digital technologies, such as variable message signs, might reduce the resilience loss of the tunnel system. This research might represent a reference for tunnel management agencies in the choice of the most appropriate traffic control strategy to improve tunnel resilience in the event of planned activities.

Taxi demand prediction is a crucial component of intelligent transportation system research. Compared to region-based demand prediction, origin-destination (OD) demand prediction has a wide range of potential applications, including real-time matching, idle vehicle allocation, ride-sharing services, and dynamic pricing, among others. However, because OD demand involves complex spatiotemporal dependence, research in this area has been limited thus far. In this paper, we first review existing research from four perspectives: topology construction, temporal and spatial feature processing, and other relevant factors. We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory. Next, we discuss ongoing challenges in OD prediction, such as dynamics, spatiotemporal dependence, semantic differentiation, time window selection, and data sparsity problems, and summarize and compare potential solutions to each challenge. These findings offer valuable insights for model selection in OD demand prediction. Finally, we provide public datasets and open-source code, along with suggestions for future research directions.

Traffic flow prediction is an important component of intelligent transportation systems. Recently, unprecedented data availability and rapid development of machine learning techniques have led to tremendous progress in this field. This article first introduces the research on traffic flow prediction and the challenges it currently faces. It then proposes a classification method for literature, discussing and analyzing existing research on using machine learning methods to address traffic flow prediction from the perspectives of the prediction preparation process and the construction of prediction models. The article also summarizes innovative modules in these models. Finally, we provide improvement strategies for current baseline models and discuss the challenges and research directions in the field of traffic flow prediction in the future.