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For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms. [ABSTRACT FROM AUTHOR]

Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector's recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model's ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1-8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios. [ABSTRACT FROM AUTHOR]

The accuracy of stress estimates in Orthotropic Bridge Decks (OBD) may be negatively impacted by the complex load transfer and asphalt properties. Yet, accurate stress estimates are crucial for optimal fatigue verifications. A field measurement and modelling study has been conducted to evaluate the accuracy of finite element (FE) models. The level of detail of the FE model resembled engineering practice. Measurements comprised of strains caused by normal flowing traffic and by single vehicles with known load. The study gave insight into the distribution of the transverse vehicle location, dynamic vehicle–bridge deck interaction, and the asphalt influence. Comparing with international guidelines, the prediction error of FE models of OBDs is on the edge of acceptable. • Vehicle-induced strains are measured in an Orthotropic Bridge Deck (OBD). • Single vehicle tests and flowing traffic measurements are performed. • Data give insight in distribution of transverse location and asphalt influence. • Dynamic amplification appears a random factor with limited effect on fatigue. • Prediction errors of finite element models of OBDs are on the edge of acceptable. [ABSTRACT FROM AUTHOR]

Weigh-in-motion (WIM) systems provide the opportunity to measure the weight of vehicles, including individual axles, as they pass over the pavement. This study focuses on the evaluation of different WIM systems, the applications of WIM sensors, and the comparison of existing WIM sensors, particularly smart concrete sensors with self-sensing capabilities. To achieve this objective, various types of concrete sensors were investigated. These sensors are capable of measuring weight while in motion, monitoring the structural health of pavements, and collecting traffic data. Smart concrete sensors with self-sensing capabilities are composed of concrete composites that contain conductive or semi-conductive fillers, enabling the measurement of stress and strain through piezoresistive properties. The findings indicate that self-sensing concrete sensors can be effectively utilized for traffic properties measurement; however, ensuring their reliability in real-field conditions and practical applications necessitates further research. [ABSTRACT FROM AUTHOR]

• A systematic review of recently published articles on traffic conflict measurements based on PRISMA. • Validation of SSMs is needed in heterogeneous traffic environment, especially in the context of developing countries. • Importance of SSMs in new technologies like driver warning systems in driverless cars. • SSMs are not adequately capturing the likelihood and severity of accidents. • Study on the driver behavior at the unsignalized intersections. In recent times, the assessment of unsignalized intersection safety has received significant research attention because of the complex and diverse traffic movements and driving behaviour at such locations. However, priority traffic regulations are not well followed in comparison to the unsignalized junctions, which leads to more conflicts. Additionally, the severity of conflicts increases with continuous traffic manoeuvres, including right-turns and through traffic, combined with different driving behaviours. Several studies have compared crash-based analysis to proactive traffic safety measures. Current research outcomes imply that surrogate safety measures (SSMs) have the potential to elucidate the sequence of events that result in collisions, their underlying causes, and their outcomes. Therefore, to further understand the appropriateness of SSMs, further study is required based on heterogeneity in traffic along with driver behaviour that incorporates turning vehicle factors. This study presents an all-inclusive evaluation of the recent advancements in SSMs and their practical implementation, with a particular emphasis on unsignalized intersections in developing nations. The findings of this investigation would be helpful in identifying the appropriate safety indicators for evaluating traffic safety at unsignalized intersections. [ABSTRACT FROM AUTHOR]

Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart cities. Travelers as well as urban managers rely on reliable traffic information to make their decisions for route choice and traffic management. However, noisy or missing traffic data poses a problem for accurate and robust traffic forecasting. While data-driven models such as deep neural networks can achieve high prediction accuracy with complete datasets, sensor malfunctions, and environmental effects degrade the performance of such models, as these models rely heavily on precise traffic measurements for model training and estimation. Consequently, incomplete traffic data poses a challenge for robust model design that can make accurate traffic forecasts with noisy/missing data. This research proposes the Robust Spatiotemporal Graph Convolutional Network (RT-GCN), a traffic prediction model that handles noise perturbations and missing data using a Gaussian distributed node representation and a variance based attention mechanism. Through experiments conducted on four real-world traffic datasets using diverse noisy and missing scenarios, the proposed RT-GCN model has demonstrated its ability to handle noise perturbations and missing values and provide high accuracy prediction. • Gaussian distribution representation enhance inherent robustness of neural network. • Variance-based attention mechanism reduce the propagation of perturbation. • Batch Random Noise help improve robustness during training phase. • Models are tested on both noisy and missing datasets. • Diverse perturbation scenarios are considered in experiments. [ABSTRACT FROM AUTHOR]