Cervical spine injuries are a major concern for motorcyclists in traffic accidents and racing competitions. Neck braces aim to prevent cervical spine injuries during accidents by reducing the neck range of motion, and keeping it under physiological limits. This work aims to evaluate the ability of neck braces to reduce neck mobility for two driving postures associated with PTW configurations. The neck mobility of twelve volunteer subjects testing four neck braces on two powered two-wheelers (scooter and racing motorbike) is measured using an optoelectronic motion capture system. With the tested neck braces worn, neck mobility is significantly reduced as compared to the physiological range of motion in all degrees of freedom. However, only flexion/extension is reduced by all neck braces tested. This suggests that these brace designs do not provide protection against all the cervical spine loading directions that may occur in a trauma. Furthermore, specific type of each powered two-wheeler considered significantly affects the neck mobility in axial rotation, as well as the postero-anterior and caudo-cranial translations, thus underscoring the need to consider the driving posture when evaluating neck brace devices.

The aim of this research was to examine the impact of aircraft noise on communities near the Belgrade Airport by conducting short-term noise measurements. Apart from the noise abatement procedure published in the Aeronautical Information Publication for Belgrade Airport, there are still neither publicly available reports of the actual efforts made towards the aircraft noise reduction nor the description of the current noise situation. In order to estimate the current noise situation, eighteen aircraft overflight noise measurements were taken in two settlements in specific sound-sensitive community areas around the Belgrade Airport. The results showed that level differences between background noise and aircraft overflights were higher than 10 dB for each measurement and could be considered significant. Furthermore, preliminary compatibility analysis with acoustic zoning was performed. Average daily noise levels were estimated from these short-term measurements and were compared to legal noise limits for different acoustic zones. The results indicate that in some cases noise levels exceed the legal threshold, which should encourage land use planners to include the issue of Belgrade acoustic zoning on the agenda, but also prompt Belgrade Airport to implement continuous noise and flight tracks monitoring.

Existing parking guidance systems only provide road guidance outside the parking lot but do not provide accurate guidance to specific parking spaces inside the parking lot. By using a Kalman filter, the Grubbs test, and a neural network algorithm to improve the RSSI-based location fingerprint identification technology, an accurate location method based on indoor Wi-Fi is obtained, which implements precise route guidance and a reverse search function for parking spaces. We utilize Beidou positioning to develop a Gaode map for outdoor navigation and use an integrated system of ultrasonic detector/indicators and ground locks to manage parking spaces. Through the secondary development of an Android system and the application of a MySql database, an app for precise parking guidance was developed. The system makes full use of the Internet and parking information, eliminates information asymmetry, improves the utilization ratio of the urban static traffic resources, allocates parking spaces in real-time, breaks information islands, provides parking search and recommendation functions for users, achieves parking information-sharing, and effectively improves parking efficiency and the parking utilization ratio.

In terms of the travel demand prediction from the household car ownership model, if the imbalanced data were used to support the transportation policy via a machine learning model, it would negatively affect the algorithm training process. The data on household car ownership obtained from the study project for the expressway preparation in the Khon Kaen Province (2015) was an unbalanced dataset. In other words, the number of members of the minority class is lower than the rest of the answer classes. The result is a bias in data classification. Consequently, this research suggested balancing the datasets with cost-sensitive learning methods, including decision trees, k-nearest neighbors (kNN), and naive Bayes algorithms. Before creating the 3-class model, a k-folds cross-validation method was applied to classify the datasets to define true positive rate (TPR) for the model’s performance validation. The outcome indicated that the kNN algorithm demonstrated the best performance for the minority class data prediction compared to other algorithms. It provides TPR for rural and suburban area types, which are region types with very different imbalance ratios, before balancing the data of 46.9% and 46.4%. After balancing the data (MCN1), TPR values were 84.4% and 81.4%, respectively.

Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network.

Double queue concept has gained its popularity in dynamic user equilibrium (DUE) modeling because it can properly model real traffic dynamics. While directly solving such double-queue-based DUE problems is extremely challenging, an approximation scheme called first-order approximation was proposed to simplify the link travel time estimation of DUE problems in a recent study without evaluating its properties and performance. This paper focuses on directly investigating the First-In-First-Out property and the performance of the first-order approximation in link travel time estimation by designing and modeling dynamic network loading (DNL) on single-line stretch networks. After model formulation, we analyze the First-In-First-Out (FIFO) property of the first-order approximation. Then a series of numerical experiments is conducted to demonstrate the FIFO property of the first-order approximation, and to compare its performance with those using the second-order approximation, a point queue model, and the cumulative inflow and exit flow curves. The numerical results show that the first-order approximation does not guarantee FIFO and also suggest that the second-order approximation is recommended especially when the link exit flow is increasing. The study provides guidance for further study on proposing new methods to better estimate link travel times.

Government subsidy is an important responsibility of fiscal expenditure in public-private partnership (PPP) projects. However, an improper subsidy strategy may cause over-compensation or under-compensation. In this research, an iteration game model combining game theory and real option is established to describe the periodic decision-making process. The strategy game model is applied to characterize the behavioral interactions between stakeholders, and the real option theory is used to predict the project performance under the influence of their decisions. Besides, two new indicators, the efficiency of fund (SE) and the total extra cost paid by the private sector (ME), are proposed to evaluate the extra project revenue caused by each unit of the subsidy and the incentive effects of the subsidy. Consequently, the preliminary results indicate that a periodic and iterative negotiations regarding the subsidy will effectively improve the efficiency of fund compared to the traditional way. The results also show that it is important for the public sector to give incentives, encouraging the private sector to make more efforts on the project, rather than merely providing fund support. Further study will focus on more detailed and complicated behaviors of stakeholders based on the model proposed in this paper.

In this paper, smart card data collected from the Nanjing Metro over 2-hour time periods are used to characterize within- and between-day human mobility patterns within the metro network. Results show that the OD (origin to destination) flows can be characterized well by shifted power law distributions with similar exponents around 2, which reflects the fact that a few OD pairs in the system play a dominant role and undertake disproportionately large OD flow distribution. The different exponents signify heterogeneous human movement in within- and between-day ranges. In addition, we analyze the metro community structures over different time periods based on the community detection method using random walks to visualize and understand passenger movement from a spatial perspective. Normalized mutual information is used to compare community partitions over different time-intervals. The results show that the properties of human mobility during different time periods have a similar rhythm, although some nuances exist, and the community structure is usually divided according to the line distribution. This empirical study provides spatiotemporal insights into understanding urban human mobility and some potential applications for transportation management.

The aim of this paper is to develop a model for estimating the urban logistics improvements potential based on success factors of intermodal urban transport. There were two aspects considered for building the urban logistics time efficiency model: achieving an improved transport capacity without purchasing new vehicles, and transferring responsibility of poor shipment planning to its owners by implementing the intermodal transport success factors. The model is to establish functional relationship among the shipment distribution requests (urbanization) and urban logistics inefficiencies management (market inconsistencies), and their impact on business operations. The applicability of the proposed model was tested on urban population growth data and time inefficiencies in urban distribution. The results provide both theoretical and practical confirmation of time efficiency importance of urban logistics and potential for introduction of new intermodal solutions in urban logistics. Different case scenarios for Sarajevo prove that reducing inefficiencies in urban logistics could reduce the number of delivery vehicles by less than a half. Since the delivery vehicles are sources of pollution, the subsequent conclusion is valid for externalities levels. The model, therefore, complements the existing knowledge and represents a practical tool for urban planners and logistics professionals for creating an efficient, innovative, and integrative approach to the development of urban logistics services.

With the rise of city logistics (CL) problems in the last three decades, various methods, approaches, solutions, and initiatives were analyzed and proposed for making logistics in urban areas more sustainable. The most analyzed and promising solutions are those that take into account cooperation among logistics providers and consolidation of the flow of goods. Furthermore, technological innovations enable the implementation of modern vehicles/equipment in order to make CL solutions sustainable. For several years, drone-based delivery has attracted lots of attention in scientific research, but there is a serious gap in the literature regarding the application of drones in CL concepts. The goal of this paper is to analyze four CL concepts that differ in consolidation type, transformation degree of flow of goods (direct and indirect, multi-echelon flows), and the role of drones. Two of the analyzed concepts are novel, which is the main contribution of the paper. The performances of the analyzed concepts are compared to the performances of the traditional delivery model – using only trucks without prior flow consolidation. The results indicate that CL concepts which combine different consolidation models and drones in the last phase of the delivery could stand out as a sustainable CL solution.

Following the sustainable transport policy, environmental criteria are becoming a competitive factor within the maritime shipping industry. The use of greener fuels in internal combustion engines, including electric drive, is a measure that can reduce external costs of transport. Alternative fuels in maritime transport, benefits, and potential attainable savings have been examined on the Kamenari–Lepatane ro-ro ferry route in the Bay of Kotor located in Montenegro. The results indicate higher total fuel cost savings by switching to LNG compared with electric power. However, the external costs of the latter are considerably lower, especially using renewable energy sources rather than fossil ones in the production process. The results obtained, relative to the magnitude and assumed complete internalization of external costs, justify the incentive to use the renewable sources as energy providers on the examined ro-ro ferry route. Environmental criteria should play a decisive role in assessing the overall benefit value, under the current trends and regulations of emissions reduction in maritime transport.

Traditional all-stop train operation mode cannot meet the demand of long travel distance and centralized travel of commuters very well. To meet this special travel demand, a zonal train operation mode based on “many-to-many” train stops is proposed. The coefficient of passenger exchange is used to locate suburban areas by depicting travel characteristics of commuters. Operational separating points within the suburban area are used as decision variables to analyze the combined cost components of this model, including passenger travel costs and railway operating costs. An integer programming model with the lowest overall cost is established, and the genetic algorithm is employed to solve it. The results proved good relative benefits in operation costs and travel time. And the sensitivity analysis of both coefficient of passenger exchange and passenger intensity has shown that the zonal operation mode is suitable for suburban railways with centralized travelers. However, the research also shows that when the passenger volume rose to a very high level, the number of zones would be limited by the maximized capacity of railway lines, which may cause the decline of the relative operational efficiency.