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The efficiency and responsiveness of supply chains are vitally dependent on inventory replenishment and transportation decisions. In this paper, we study a supply chain consisting of a single retailer ordering seasonal products within the newsvendor framework. The primary objective of the paper is to investigate the retailer's decision-making process, aimed at determining the optimal replenishment quantities and selecting the appropriate mix and size of the truck fleet. Initially, we formulate a mathematical model where the retailer exclusively manages a limited fleet of its own trucks for inbound transportation of a single seasonal product. In this context, we determine a lower breakeven point for the fixed transportation cost than what has been previously proposed in the literature. Subsequently, we examine a commonly encountered transportation scenario where the retailer has the opportunity to expand its fleet size by leasing trucks from the external market. The outcomes of the numerical example indicates that the flexibility resulting from the utilization of different types of trucks can lead to reduced overall costs. We also address the practical transportation problem of efficiently shipping various seasonal products solely with the retailer's own trucks. For this complex problem, we propose an optimal solution procedure based on Lagrangian method. We show that the joint replenishment of multiple products results in cost savings and enhances utilization of the trucks' capacities. [ABSTRACT FROM AUTHOR]

Although technical improvements to engines and aftertreatment systems have the greatest impact on pollutant emissions, there is also potential for reducing emissions through driver behavior. This potential can be realized in the very short term, while better emission-control technologies only take effect once they have penetrated the market. In addition to a change in driving style, the vehicle owner's choice of vehicle technology and size class will also have an impact on the future emissions of the vehicle fleet. The effects of different driving styles, the tire choice, the vehicle size class, and propulsion technologies on energy consumption and tailpipe and non-exhaust emissions are analyzed in this paper for different traffic situations and start temperatures for cars with petrol and diesel combustion engines and for battery electric vehicles. The analysis is completed with the corresponding upstream emissions from fuel and electricity production. The analysis is based on a vehicle simulation using the Passenger car and Heavy-duty Emission Model (PHEM), which is based on a large database of vehicles created using measurements of real driving conditions. For the assessment of the driving style, a novel method was developed in an H2020 project, which reproduces a measured trip with a virtual eco-driver. Carbon dioxide equivalent emissions (CO2eq) increase with increasing vehicle size, but can be reduced by around 20% for conventional vehicles and 17% for battery electric vehicles (BEVs) through an environmentally conscious driving style. On average, BEVs have around 50% lower CO2eq emissions than conventional vehicles, if the emissions from vehicle production are also taken into account. On an average journey of 35 km, the cold start of modern diesel vehicles accounts for around half of the total NOx emissions, while the proportion of cold starts for petrol vehicles is around 25%. Tire and brake wear together generate a similar amount of PN23 emissions as the exhaust gases from new cars. [ABSTRACT FROM AUTHOR]

This paper proposes a methodological way to compensate for the imbalance between energy generation and consumption using a battery block from electric vehicles as an energy reservoir through the well-known vehicle-to-grid system (V2G). This method is based on a simulation process developed by the authors that takes into consideration the daily fluctuations in energy consumption as well as the power level generated by an energy source, either conventional, renewable, or hybrid. This study shows that for very large electric vehicle fleets, the system is rendered non-viable, since the remaining energy in the battery block that allows the electric vehicle to be usable during the daytime avoids having to compensate for the energy grid imbalance, only allowing it to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. This method allows for predicting the optimum size of an electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance, and size of the electric vehicle fleet. [ABSTRACT FROM AUTHOR]

Accurate identification of vehicles and estimating density in traffic surveillance systems is a challenging task, particularly in scenarios with closely spaced lanes. Single Shot MultiBox Detector (SSD) is introduced in vehicle detection and classification due to its speed and accuracy. It utilizes a transfer learning technique that enables them to utilize features from pretrained Convolutional Neural Networks (CNNs). Although the utilization of multi-scale feature maps in SSD has achieved efficient results in vehicle detection, it struggles to identify key features of vehicles due to its one-stage detection approach. Furthermore, the use of VGG16 as the backbone network in these approaches leads to the loss of fine-grained details, posing challenges in accurately localizing and classifying small vehicles. Also, there is no interaction between high- and low-level features, which restricts the networks ability to effectively integrate and utilize both features for accurate vehicle detection. To overcome these limitations, an improved SSD approach is introduced in this paper, leveraging interactive multi-scale attention characteristics to accurately detect and classify vehicles. This approach utilizes ResNet50 as the network backbone to overcome the limitations of traditional SSDs in detecting small vehicles. Also, an attention block is incorporated into it to focus on key details and assign higher attention to relevant pixels within the feature map. Also, the network employs a parallel detection framework and shares multi-scale layers (both high and low), enabling efficient detection of vehicles with various sizes. Then, traffic density is estimated based on the weights assigned to the categorized vehicles obtained from the improved SSD and the recorded area. Finally, traffic is classified as high, low, or moderate by comparing the estimated density to a threshold value. By adding attention characteristics of different scales to the original detection branch and replacing the VGG16 with ResNet50 of the SSD technique using our method, the feature representation capability and detection accuracy are both significantly improved. [ABSTRACT FROM AUTHOR]

• A probability distribution model of platoon size of connected vehicles is proposed. • A fundamental diagram model of the mixed traffic flow is developed. • An analytical model of the capacity of mixed traffic flow is proposed. • A numerical experiment is designed to discuss the impact of penetration rate and maximum platoon size on road capacity. This paper proposes a capacity analysis model for a mixed traffic flow environment that considers the heterogeneity and maximum platoon size of connected vehicles. Firstly, we explore how the organization mode of platoon affects car-following characteristics in mixed traffic flow with connected and automated vehicles, connected human-driven vehicles, automated vehicles, and human-driven vehicles. Different car-following models are used to describe the car-following characteristics of different types of vehicles. Secondly, the probability distribution model is developed for the size of platoons considering the penetration rate and the maximum platoon size of connected and automated vehicles, connected human-driven vehicles. Thirdly, the fundamental diagram of mixed traffic flow is further derived based on the probability distribution of platoon size and car-following models. Then, the capacity model of mixed traffic flow is proposed. Finally, a numerical experiment is designed to evaluate the impact of different penetration rates and maximum platoon sizes of connected and automated vehicles, connected human-driven vehicles on the capacity of the mixed traffic flow. The results show that (1) with a low penetration rate of connected and automated vehicles, connected human-driven vehicles, the change in the maximum platoon size has no noticeable effect on the distribution of platoon size of connected and automated vehicles, connected human-driven vehicles. (2) Road capacity increases with the maximum platoon size and the penetration rate of connected vehicles. (3) Compared with connected human-driven vehicles, the penetration rate of connected and automated vehicles has a more significant impact on road capacity. [ABSTRACT FROM AUTHOR]

Vehicle detection is an important component of intelligent transportation systems and autonomous driving. However, in real-world vehicle detection scenarios, the presence of many complex and high uncertainty factors, such as illumination differences, motion blur, occlusion, weather, etc., makes accurate and real-time vehicle detection still challenging. In order to reduce the influence of these uncertainties in real scenarios and improve the accuracy and real-time performance of vehicle detection, this paper proposes a type-1 fuzzy attention (T1FA), in which fuzzy entropy is introduced to re-weight the feature map in order to reduce the uncertainty of the feature map and facilitates the detector's focus on the target center as a way to effectively improve the accuracy of vehicle detection. Furthermore, to detect vehicles with different sizes more effectively, mixed depth convolution in MetaFormer (MDFormer) is employed as a token mixer to capture multi-scale perceptual fields. And a novel YOLO detector based on fuzzy attention (YOLO-FA) is proposed. Experimental results show that T1FA can boost 3.2% AP50 on challenging vehicle detection dataset UA-DETRAC, which is better than other commonly used attention mechanisms, especially in scenarios of rain and nighttime with higher uncertainty by 4.2% and 8.1% AP50, respectively. Finally, without pretraining on extra data, YOLO-FA achieves 70.0% AP50 and 50.3% AP on UA-DETRAC, which achieves better balance between accuracy and speed compared with state-of-the-art detectors. The remarkable improvement of T1FA in different detectors and datasets also shows the considerable generalization of T1FA. • A type-1 fuzzy attention (T1FA) mechanism-based YOLO (YOLO-FA) detector is proposed. • Mixed depthwise convolution is employed as a token mixer in MetaFormer. • T1FA can boost 3.2% AP50 in UA-DETRAC by reducing the uncertainty. • YOLO-FA trained from scratch achieves 70.0% AP50 and 50.3% AP on UADETRACA. [ABSTRACT FROM AUTHOR]

• Contact patch size and excitation are considered in the theoretical VBI model. • Contact point response is confirmed the same from vehicle and bridge response. • Contact patch size is discussed for both pneumatic and solid wheel scenarios. • Arbitrary time step is determined by contact patch length and vehicle speed. • Theoretical identification of damping ratios shows an average error of 0.52%. This paper presents an improved theoretical damped single-axle vehicle-bridge dynamic interaction model to consider the effect of the contact patch size and motor-induced vehicle excitation. The contact patch issue is critical as it determines the minimum time step for simulation and maximum identifiable frequency, while the inclusion of the motor-induced vehicle excitation benefits the design of autonomous self-driven rather than towed vehicles. Estimations of the contact patch size for both the pneumatic tire and solid wheel scenarios are discussed. The contact patch responses, which degenerate into contact point responses when the contact patch size is assumed to be infinitely small, were derived for the first time both from the vehicle and bridge responses to confirm their equivalence. The minimum time step, which determines maximum identifiable frequency but is arbitrarily chosen in literature, is proposed to be determined by the vehicle speed and contact patch length. The procedures to extract multiple bridge triad information including natural frequencies, mode shapes, and damping ratios from the vehicle responses are also presented. Based on extensive parametric analyses, the sinusoidal vehicle excitation becomes more prominent as its amplitude and/or frequency increase and may overshadow the analysis of bridge frequencies of interest. The vehicle acceleration leads to a more accurate extraction of bridge mode shapes and damping ratios than the vehicle displacement since the displacement is dominated by the fundamental mode of bridge vibration. The damping ratio extraction shows an average error of 0.28% from the instantaneous amplitude of the vehicle acceleration signal. [ABSTRACT FROM AUTHOR]