Transportation Engineering最新文献

This study investigates the combination of audio and image data to classify road conditions, particularly focusing on loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturing gravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep Convolutional Networks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forest classifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decision-level processing with logical disjunction and conjunction gates (AND and OR) in combination with individual classifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121), demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusion method enhances adaptability by compensating for limitations in one modality with information from the other. Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. This method can help to identify loose gravel on gravel roads, substantially improving road safety and implementing a precise maintenance strategy through a data-driven approach.

The emerging demand for electric bicycles in recent years has prompted several Bike-Sharing Systems around the world to adapt their service to a new wave of commuters. Many of these systems have incorporated electric bikes into their network while still maintaining the use of regular mechanical bicycles. However, the presence of two types of bikes in a Bike-Sharing network may impact how rebalancing operations should be conducted in the system. Regular and electric bikes may exhibit distinct demand patterns throughout the day, which can hinder efficient planning of such operations. In this paper, we propose a new model that provides rebalancing recommendations based on the demand prediction for each type of bike. Additionally, we simulate the performance of our model under different scenarios, considering commuters’ varying inclination to substitute their preferred bike with one of a different type. Our empirical experiments indicate the potential of our model to improve user satisfaction, reducing the total lost demand by approximately 10%, while reducing the lost demand for electric bikes by around 30%, on average, when compared to the existing rebalancing strategy used by the real-world Bike-Sharing System under study. Remarkably, this was accomplished while maintaining an almost identical average hourly count of rebalancing operations.

The current study examines the impact of Advance Amber Warning Signal (AAWS) on driver behaviour and decision-making processes at two junctions on a roadway. Through on-road observations and decision tree analysis, data was collected to assess the effects of AAWS on driver responses to a yellow dilemma. The results indicated that AAWS led to a significant increase in the percentage of drivers who accelerated before stopping at the junction, while reducing uncertain changes between braking and accelerating. Moreover, AAWS resulted in improved stopping propensity, and a substantial reduction in red light violations. These findings demonstrate the positive influence of AAWS on driver behaviour, improved decision-making, and enhanced compliance with traffic regulations. The presence of AAWS equips drivers with the necessary tools to navigate challenging scenarios on the road and make safe decisions during critical moments. As a result, the implementation of AAWS has the potential to significantly enhance junction safety, optimise driver behaviour, and contribute to overall road safety measures.

Having the capability of estimating both the number of crashes and their severity levels, crash prediction models are a precious tool in highway safety. However, there hasn't been any research on predicting traffic crashes on Indian mountainous rural highways. The primary objective of this research is to develop safety performance functions (SPFs) for traffic crashes occurring on rural roads located in the mountainous region of Uttarakhand, India.

For analysis, the study utilized five years of crash data collected from different types of rural roads. The road network was divided into constant segments of 500m each, and separate models were developed for single (TSV_C) and multi-vehicle (TMV_C) crashes using the negative binomial regression approach. These SPFs highlight important significant variables in terms of positive and negative association and a potential change in subject crash frequencies. The results concluded that different types of risk factors impact both types of crashes, with horizontal (H_C) and vertical curves (V_C) in common. For instance, spot speed increases TSV_C crashes by 3.87 %, whereas H_C and V_C tend to increase subject crashes by 8.32 % and 29.95 %, respectively.

Similarly, TMV_C is influenced by carriageway (C_W) and shoulder width (S_W). The result proposed that an increase in C_W and S_W can decrease frequencies by 0.668 times and 0.819, respectively. Additionally, the model highlighted the importance of rut-depth and the presence of pavement markings in the road safety analysis. At last, further research scope is suggested based on the limitations of this study.

Current rail traffic management and control systems cannot be easily upgraded to the new needs and challenges of modern railway systems because they do not offer interoperable data structures and standardized communication interfaces. To meet this need, the Horizon 2020 Shift2Rail OPTIMA project has developed a communication platform for testing and validating the new generation of traffic management systems (TMS), whose main innovative features are the interoperability of the data structures used, standardization of communications, continuous access to real-time and persistent data from heterogeneous data sources, modularity of components and scalability of the platform. This paper presents the main components, their functions and characteristics, then describes the testing and validation of the platform, even when federated with other innovative TMS modules developed in separate projects. The successful validation of the system has confirmed the achievement of the objectives set and allowed a new set of objectives to be defined for the reference platform for the railway TMS/Traffic Control systems.

Vehicle fleet electrification is the main target of the following decades, as a measure to decarbonize the transport sector. Electric urban busses consist of a solution for upgrading the bus fleet aiming to an improvement of urban areas and city centres air quality. To electrify the bus fleet at the city of Athens, Greece, the responsible organization, Athens Urban Transport Organization S.A. (OASA), organized a pilot program for using battery electric buses (BEB) in existing bus lines. The goal of the pilot program was to evaluate the energy consumption of the selected busses under real operation scenarios. The main target of this study is to describe the combined experimental and simulation methodology followed for the evaluation of the BEBs’ . The experimental part of the study is based on the monitoring of the BEBs under real operation for a specific bus line. During the operation of the vehicles, energy consumption along with environmental conditions and A/C usage were monitored. The simulation models were used to predict the energy consumption under different driving conditions and quantify the impact of operating parameters on energy consumption. Experimental results showed that the average daily energy consumption ranged between 96 kWh/km and 220 kWh/km, values strongly related to ambient temperature. Simulations highlighted that A/C usage can lead to two times higher energy consumption for the same route and load. Finally, the expected electric range of buses considered in the study calculated between 130 km and 170 km for the selected line, load equivalent of 25 passengers and 7 kW of A/C consumption.

Motion Sickness (MS) remains a paramount concern in the evolving landscape of autonomous vehicles. As automation continues to reshape the passenger-vehicle interaction paradigm, the proliferation of diverse in-vehicle systems empowers passengers to disengage from the driving task. However, this new paradigm comes with the potential cost of exacerbating MS inside the car.

In this work, we intended to: (1) present and review available countermeasures to prevent or mitigate MS, found in the literature, that could also be implemented inside a car; (2) identify current trends and gaps in countermeasures to MS; and (3) suggest future avenues of research for potential use-cases aiming to mitigate MS in autonomous driving.

Through a comprehensive review of 65 publications, spanning from 2009 to 2023, we have organized existing literature into three distinctive categories and ten subcategories: (1) Vehicle-Centric Adaptation (Adaptation to Surface, Best Route ad Driving Style Adaptation), (2) In-Car Design and Environment (Design Guidelines for Seats, for Displays, for Windows and Climate Control), and (3) Sensory Cues (Visual, Haptic Audio and Olfactory Cues).

Our findings suggest that Visual Cues and Motion Planning are the two strongest trends in MS countermeasures. In contrast, Olfactory Cues are the least studied approach. Our results also substantiate the viability of multimodal approaches as a promising solution for passengers in autonomous vehicles. The simultaneous application of various countermeasures might hold potential in mitigating MS effectively.

As autonomous vehicles advance, these findings offer a strong basis for future research to decrease passenger's motion sickness and improve their well-being, safety, and comfort inside the car.

Climate change is causing an increase in the frequency and intensity of wildfires, demonstrating that our capacity to respond to them is insufficient. Therefore, it is necessary to reconsider wildfire management policies, practices, and decision-support tools, extending beyond emergency measures. This study presents the extension of a GIS-based methodology for fire analysis, providing decision-making support for the implementation of new fire-related policies for road transportation infrastructure. It represents a novel contribution that facilitates the transition towards proactive wildfire policies. The framework is demonstrated to support informed decision-making, addressing both reactive actions, i.e., emergency response, and the evaluation of proactive adaptation measures at a system level. The results suggest that landscape management policies can play an important role in improving the resilience of road networks to wildfires.

Online and offline shopping trips have a profound impact on various facets of urban life, including e-commerce, transportation systems, and sustainability. To assess the factors shaping consumers' decisions, we introduce a novel hybrid machine learning model that integrates the Gray Wolf Optimization (GWO) algorithm with a deep Convolutional Neural Network (CNN). This model is applied to predict shopping behavior based on a survey of 1000 active e-commerce users residing in areas 2 and 5 of Tehran. These individuals have made successful purchases through both online and offline services during the final 20 days of 2021. The GWO algorithm plays a pivotal role in selecting optimal features and hyperparameters for the deep Convolutional Neural Network, which is a powerful deep learning model for image recognition and classification. Notably, our model achieves an impressive accuracy of 97.81% while maintaining a MSE of 0.325, having identified seven out of ten key features as the most influential. To gage the effectiveness of our approach, we conduct a comparative analysis with alternative methods. The results unequivocally showcase the superiority of our proposed algorithm, which attains an accuracy of 97.81%. In contrast, other models such as CNN, LSTM, MLP, DT, and KNN yield accuracies of 95.63%, 94.04%, 90.12%, 86.49%, and 80.16%, respectively. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. Its primary objective is to assist them in formulating effective strategies aimed at reducing transportation costs, curbing pollutant emissions, mitigating urban traffic congestion, and enhancing user satisfaction all while fostering sustainable development.