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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]

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection. • We show the utility of using adjacent traffic sensor measurements to reduce TTDA. • XGBoost classifier produces the best results as quickly as 1.0 min. • XGBoost classifier produces the best results (AUC-ROC 83% and AUC-PR 49%). • We show that features of both upstream and downstream sensors are the most important. • We report results on two different datasets (i.e., I-75 and I-24) in Chattanooga, TN. [ABSTRACT FROM AUTHOR]

Maritime Traffic Situation (MTS) is crucial for enhancing the efficiency and safety of maritime navigation, with the complexity of ship traffic serving as a key aspect of identifying and managing maritime risks. This paper proposes a ship traffic complexity measurement model for MTS. The proposed model quantifies the complexity between ships by evaluating factors such as density, proximity factor, and mitigation index, and then integrates these evaluations into multiple ships interaction scenarios to enable a comprehensive assessment of ship traffic complexity. The results of case study have fully demonstrated the model's capability in identifying potential collision risks during the interaction processes of multiple ships situation within a navigational sea. Research on the ship traffic complexity measurement model can provide strong theoretical and practical support for maritime traffic management and navigational safety. • Building on an in-depth exploration of the intrinsic attributes of ships, this paper introduces ship domain (SD) into the calculation of proximity factor within the ship traffic complexity measurement model. By constructing different SDs for different ships, the model can more accurately assess collision risks. • Incorporating the mitigation index, based on ships' speed and course parameters, into the ship traffic complexity measurement model enables the quantification of ships' movement trends within specified temporal and spatial domains. This integration allows the model to accurately assess the mitigation complexity of ships in various MTS, taking into account their dynamics. • Expanding the ship traffic complexity measurement model from focusing on pairs of ships to encompassing the quantification of traffic complexity in multi-ship interactive scenarios has significantly enhanced the model's practical application value. [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]