Search

In this research, the focus is on addressing the capacitated vehicle routing problem (CVRP), a prominent logistical challenge within transportation management. The discrete salp swarm algorithm (DSSA) has been introduced as an innovative adaptation of the traditional salp swarm algorithm (SSA), specifically redesigned to cater to discrete characteristics inherent in problems like the CVRP. This algorithm integrates the core principles of SSA with mutation and crossover techniques, enhancing its applicability for discrete problems. The paper contributes in two main areas: firstly, the development of DSSA, tailored to address the unique requirements of VRP scenarios and ensuring a balanced approach between exploration and exploitation in discrete optimization. Secondly, the effectiveness of DSSA is demonstrated through practical applications, including an 8-customer routing task and a real-world case study involving cement delivery in Vietnam. In these scenarios, DSSA consistently demonstrates superior performance over other meta-heuristic strategies, marking a noteworthy advancement in the domain of optimization and providing a potential tool for complex routing challenges in logistics and distribution systems. [ABSTRACT FROM AUTHOR]

Automatic road feature extraction from the remote sensing (RS) imagery has a significant role in various applications such as urban planning, transportation management, and environmental monitoring. In this paper, propose a method based on the U-Net model to extract the road features from the LANDSAT-8 operational land imager (OLI) images. This method aims to extract road features in OLI images that appear as curvilinear features and roads with widths greater than 25 meters, which are mostly covered within a single pixel of the OLI resolution of multi-spectral images. The U-Net architecture is well-known for its effectiveness in image segmentation tasks. However, to optimize the complexity in the U-Net model, simplified the architecture while retaining its key components and principles. The proposed model by decreasing the convolution layers and the parameters which are involved to optimize the model called as simplified U-Net model. To train this model, the label images were generated for LANDSAT-8 OLI images, by using the saturation based adaptive thresholding and morphology (SATM) method. This reduces the efforts to draw the labels in the vector format labels and convert to raster images. The model is able to effectively generate weights, which are able to extract the road features. This model weights applied on the OLI images which covers the urban and rural areas of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper. [ABSTRACT FROM AUTHOR]

Summarizing historical pandemic control experience can help the government better cope with the impact of uncertain public health events on taxi industry. This paper presents a summary of the relationship between various pandemic control measures and taxi system from the perspective of travel resilience. Additionally, we investigate the effectiveness of passenger subsidy schemes in improving the taxi travel resilience. To achieve these objectives, our research follows these steps: Firstly, we analyze the impact of different pandemic control measures on the performance variation curves of the taxi travel system. Secondly, using the travel resilience calculation formula, we evaluate the taxi travel resilience levels under different pandemic control measures and analyze other factors (such as virus characteristics) on taxi travel resilience through regression models. Finally, we construct a taxi travel resilience improvement model. Aiming at maximizing the social benefits which consider taxi travel resilience, we search for the optimal passenger subsidy result. And then make a comparison for two distinct subsidy types: fixed-amount and fare subsidy rate. Taking Ningbo city, China as a case study, the research findings demonstrate: (1) The ranking order of each pandemic control measure based on taxi travel resilience performance from low to high are as follows: city-wide lockdown control measure, district-wide lockdown control measure, targeted epidemic control measure, and fully lifted control measure. These findings suggest that a reduction in the scope and duration of pandemic control measures can maintain a high level of taxi travel resilience. (2) In addition to pandemic control measures, the virus's incubation period and infectivity have a significant impact on travel resilience. (3) Model results show that increasing passenger subsidies can quickly improve taxi travel resilience, but it cannot guarantee a consistent increase in social benefits. When the weight of travel resilience in the objective function is small, social benefits will initially increase and then decrease with higher passenger subsidy amounts. In such cases, the extreme value method can help determine the optimal passenger subsidy amount. (4) When decision-makers focus on improving taxi travel resilience, they can adopt the fixed amount subsidy scheme. When the focus is on maximizing social benefits, the fare subsidy rate scheme should be chosen. [ABSTRACT FROM AUTHOR]

This study focuses on the construction of a synthesis health index for metro vehicle wheelsets through theoretical foundation analysis, procedural steps, and practical examples. By analyzing the theoretical foundation, this study explains the advantages of the synthesis health index compared to traditional health indicators. The procedural steps describe the detailed process from data collection, preprocessing, and feature extraction to health index construction. Additionally, practical examples are used to validate the effectiveness and accuracy of this method in real-world applications. The results indicate that this synthesis health index construction method can accurately assess the real-time health status of in-service wheelsets and also reflect and predict the trends of their health status changes. [ABSTRACT FROM AUTHOR]

This study examines the incorporation of blockchain technology into Saudi Arabia's Intelligent Transportation Systems (ITS), concentrating on enhancing the bus permission procedure for religious mass gatherings in Makkah. The research illustrates how blockchain might improve the security and operational effectiveness of transportation management for major events. The research used a weighted significance methodology and Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis to identify the critical success factors (CSFs) influencing transportation at religious events. The research illustrates the strong interrelation of the CSFs, emphasizing the notable enhancement in transparency and efficiency in the approval and management processes using blockchain-based solutions. The research examines the impact of reformulated CSFs on the proposed blockchain-based transportation framework (BTF), emphasizing key domains such as people ( P 1 ), technology ( P 2 ), the environment ( P 3 ), and organization ( P 4 ). The findings indicate that blockchain-related CSFs exhibit the greatest influence, which is 21.62, while financial CSFs demonstrate the least influence of 0.25. This research significantly addresses current system limitations and stimulates wider blockchain usage inside Intelligent Transportation Systems by developing a thorough mathematical model. This paper presents a strategy framework for the successful management of large-scale transportation difficulties with blockchain technology, assuring optimum operations in organizational and environmental contexts. [ABSTRACT FROM AUTHOR]

The Dynamic Interval (DI) graph models the updating uncertainty of the arc cost in the graph, which shows great application prospects in unstable-road transportation planning and management. This paper studies the Real-time Shortest Path (RTSP) problems in the DI graph. First, the RTSP problem is defined in mathematical equations. Second, three models for RTSP are proposed, which are the Dynamic Robust Shortest Path (DRSP) model, the Dynamic Greedy Robust Shortest Path (DGRSP) model and the Dynamic Mean Shortest Path (DMSP) model. Then, three solution methods are designed. Finally, a numerical study is conducted to compare the efficiency of the models and corresponding solution methods. It shows that the DGRSP model and DMSP model generally present better results than the others. In the real road network test, they have the minimum average-regret-ratio of DGSP 7.8% and DMSP 7.1%; while in the generated network test, they both have a minimum average-regret-ratio of 0.5%. [ABSTRACT FROM AUTHOR]

Understanding intra-urban travel patterns through quantitative analysis is crucial for effective urban planning and transportation management. In previous studies, a range of distribution functions were modeled to lay the groundwork for human mobility research. However, few studies have explored the nonlinear relationships between travel distance patterns and environmental factors. Using travel distance data from ride-hailing services, this research divides a study area into 1 × 1 km grid cells, modeling the best travel distance distribution and calculating the coefficients of each grid. A machine learning framework (Extreme Gradient Boosting combined with Shapley Additive Explanations) is introduced to interpret the factors influencing these distributions. Our results emphasize that the travel distance of human movement tends to follow a log-normal distribution and exhibits spatial heterogeneity. Key factors affecting travel distance distributions include the distance to the city center, bus station density, land use entropy, and the density of companies. Most environmental variables exhibit nonlinear and threshold effects on the log-normal distribution coefficients. These findings significantly advance our understanding of ride-hailing travel patterns and offer valuable insights into the spatial dynamics of human mobility. [ABSTRACT FROM AUTHOR]

Accepted by: Aris Syntetos Machine learning (ML) has evolved into a crucial tool in supply chain management, effectively addressing the complexities associated with decision-making by leveraging available data. The utilization of ML has markedly surged in recent years, extending its influence across various supply chain operations, ranging from procurement to product distribution. In this paper, based on a systematic search, we provide a comprehensive literature review of the research dealing with the use of ML in supply chain management. We present the major contributions to the literature by classifying them into five classes using the five processes of the supply chain operations reference framework. We demonstrate that the applications of ML in supply chain management have significantly increased in both trend and diversity over recent years, with substantial expansion since 2019. The review also reveals that demand forecasting has attracted most of the applications followed by inventory management and transportation. The paper enables to identify the research gaps in the literature and provides some avenues for further research. [ABSTRACT FROM AUTHOR]

In order to analyze the passenger flow characteristics of single line bus and improve the operation of public transportation vehicles through combination optimization, this paper establishes a short-term bus passenger flow prediction model based on existing research, data characteristics, and solving objectives, and selects indicators for comparison and analysis of results. The research is based on a long short-term memory (LSTM) network, bidirectional long short-term memory (BiLSTM) network, and gated recurrent unit (GRU) network for modeling, and public health event management is included as an important influencing factor in the model establishment process. Through comparative analysis of the model prediction results, a short-term bus passenger flow prediction method based on BiLSTM network is finally proposed. Compared with existing methods, this method not only ensures prediction accuracy, but also ensures solution speed and universality performance. The research results further improve the existing theoretical and methodological system for optimizing the operation of conventional public transportation and have certain practical value for formulating more efficient public transportation scheduling plans, achieving refined management of public transportation, and improving the decision-making level of urban public transportation management. [ABSTRACT FROM AUTHOR]

The accurate prediction of pavement performance is essential for transportation administration or management to appropriately allocate resources road maintenance and upkeep. The international roughness index (IRI) is one of the most commonly used pavement performance indicators to reflect the surface roughness. However, the existing research on IRI prediction mainly focuses on using linear regression or traditional machine learning, which cannot take into account the historical effects of IRI caused by climate, traffic, pavement construction and intermittent maintenance. In this work, a long short-term memory (LSTM)-based model, LSTM+MA, is proposed to predict the IRI of pavements using the time-series data extracted from the long-term pavement performance (LTPP) dataset. Effective preprocessing methods and hyperparameter fine-tuning are selected to improve the accuracy of the model. The performance of the LSTM+MA is compared with other state-of-the-art models, including logistic regressor (LR), support vector regressor (SVR), random forest (RF), K-nearest-neighbor regressor (KNR), fully connected neural network (FNN), XGBoost (XGB), recurrent neural network (RNN) and LSTM. The results show that selected preprocessing methods can help the model learn quickly from the data and reach high accuracy in small epochs. Also, it shows that the proposed LSTM+MA model significantly outperforms other models, with an R2 of 0.965 and a mean square error (MSE) of 0.030 in the test datasets. Moreover, an overfitting score is proposed in this work to represent the severity degree of the overfitting problem, and it shows that the proposed model does not suffer severely from overfitting. [ABSTRACT FROM AUTHOR]

Currently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distributed resources such as shore-based facilities and cloud computing centers to execute a variety of ship applications. Due to the increasing number of MASS and asymmetrical distribution of traffic flows, the transportation management must design an efficient cloud–shore–ship collaboration framework and smart resource allocation strategy to improve the performance of the traffic network and provide high-quality applications to the ships. Therefore, we design a cloud–shore–ship collaboration framework, which integrates ship networking and cloud/edge computing and design the respective task collaboration process. It can effectively support the collaborative interaction of distributed resources in the cloud, onshore, and onboard. Based on the global information of the framework, we propose an intelligent resource allocation method based on Q-learning by combining the relevance, QoS characteristics, and priority of ship tasks. Simulation experiments show that our proposed approach can effectively reduce task latency and system energy consumption while supporting the concurrency of scale tasks. Compared with other analogy methods, the proposed algorithm can reduce the task processing delay by at least 15.7% and the task processing energy consumption by 15.4%. [ABSTRACT FROM AUTHOR]

Highway toll stations are equipped with electronic toll collection (ETC) lanes and manual toll collection (MTC) lanes. It is anticipated that connected autonomous vehicles (CAVs), MTC human-driven vehicles (MTC-HVs), and ETC human-driven vehicles (ETC-HVs) will coexist for a long time, sharing toll station infrastructure. To fully leverage the congestion reduction potential of ETC, this paper addresses the problem of ETC lane allocation at toll stations under heterogeneous traffic flows, modeling it as a mixed-integer nonlinear bilevel programming problem (MINLBP). The objective is to minimize total toll station travel time by optimizing the number of ETC lanes at station entrances and exits while considering ETC-HVs' lane selection behavior based on the user equilibrium principle. As both upper-level and lower-level problems are convex, the bilevel problem is transformed into an equivalent single-level optimization using the Karush–Kuhn–Tucker (KKT) conditions of the lower-level problem, and numerical solutions are obtained using the commercial solver Gurobi. Based on surveillance video data from the Liulin toll station (Lianhuo Expressway) in Zhengzhou, China, numerical experiments were conducted. The results illustrate that the proposed method reduces total vehicle travel time by 90.44% compared to the current lane allocation scheme or the proportional lane allocation method. Increasing the proportion of CAVs or ETC-HVs helps accommodate high traffic demand. Dynamically adjusting lane allocation in response to variations in traffic arrival rates is proven to be a more effective supply strategy than static allocation. Moreover, regarding the interesting conclusion that all ETC-HVs choose the ETC lanes, we derived the relaxed analytical solution of MINLBP using a parameter iteration method. The analytical solution confirmed the validity of the numerical experiment results. The findings of this study can effectively and conveniently guide lane allocation at highway toll stations to improve traffic efficiency. [ABSTRACT FROM AUTHOR]

To ensure the safe application of hydrogen energy in utility tunnels, it is crucial to study the propagation process of hydrogen explosion. In this paper, the explosion propagation process of non-premixed hydrogen-air mixture in gas compartments with different bending angles and with various hydrogen status was investigated. By analyzing overpressure, flame evolution state and flame velocity, the characteristics of the hydrogen explosion in utility tunnels were revealed. The results show that the explosion overpressure, flame velocity and flame brightness all first increase and then decrease with the increase of hydrogen concentration, due to the suppression effects in both fuel-lean and fuel-rich environments. Moreover, it was found that, in 90°, 120° and 150° bending compartments, the maximum overpressure values of hydrogen were respectively 28.52%, 3.52% and 8.53% higher than that in the straight compartment, while the average flame velocity values were respectively 34.19%, 11.96% and 34.13% lower than that in the straight compartment. This was closely related to the intense reflection of shock waves in the bending section, where the accumulation of reflected shock waves not only increased the maximum overpressure but also slowed down flame velocity. This research provides valuable guidance for the design and safety management of hydrogen transportation in utility tunnels and similar confined spaces. • Non-premixed hydrogen explosion shows enhanced overpressure and flame velocity. • Higher explosive power occurs in concentration slightly above stoichiometric ratio. • The effect of bending utility tunnel structure on hydrogen explosion is discovered. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an important part of transportation management and planning. For example, accurate demand prediction of taxis and online car-hailing can reduce the waste of resources caused by empty cars. The prediction of public bicycle flow can be more reasonable to plan the release and deployment of public bicycles. There are three difficulties in traffic flow prediction to achieve higher accuracy. Firstly, more accurately to capture the spatio-temporal correlation existing in historical flow data. Secondly, the weight of each channel in the traffic flow data at the same time interval affects the prediction results. Thirdly, the proportion of closeness, period and trend of traffic flow data affects the prediction results. In this paper, we design a deep learning algorithm for short-term traffic flow prediction, called SE-MAConvLSTM. First, we designed Spatio-Temporal Feature Extraction Module (STFEM), which is composed of Convolutional Neural Network (CNN), Squeeze-and-Excitation Network (SENet), Residual Network (ResNet) and Convolutional LSTM Network (ConvLSTM) to solve the above two problems mentioned. In addition, we design multi-attention modules (MAM) to model the closeness, period and trend of traffic flow data to solve the third problem mentioned above. Finally, the aggregation module was used to integrate the output of the last time interval in STFEM and the output of the multi-attention module. Experiments are carried out on two real data sets, and the results show that the proposed model reduces RMSE by 4.5% and 3.7% respectively compared with the best baseline model. [ABSTRACT FROM AUTHOR]

The presence of water on wet roads due to rainfall has emerged as a critical factor influencing driving safety. During severe weather, the non-uniform water distribution on the road can significantly diminish pavement friction, elevating the risk of traffic accident. Furthermore, the tire-pavement friction coefficient varies with vehicle velocity, rendering wet braking analysis complex. Conventional braking risk analysis often assumes a fixed friction coefficient, neglecting the impact of uneven pavement-induced water depth irregularities. This paper introduces a novel braking performance assessment method based on water-depth estimation and vehicle-pavement simulation. The uneven water-depth distribution is first estimated using LiDAR-measured pavement geometry. A co-simulation framework is then proposed to analyze the tire-pavement friction and study the dynamic braking performance. The 85th percentile stopping distance is adopted as the evaluation index for quantifying braking risk. Results from case studies highlight the substantial influence of rainfall intensity and vehicle velocity on braking risk. Additionally, pavement rutting accumulates deeper water depth, thereby elevating braking safety risks. The proposed model offers a novel perspective on vehicle braking risk analysis and can serve as a valuable safety indicator for highway transportation management and decision-making in driving strategies during wet weather conditions. [ABSTRACT FROM AUTHOR]

Transport mode is one of the important travel characteristics for citizens, which is crucial to the planning and management of urban transportation. With the natural advantages of large sample sizes and a wide coverage of people, more and more researchers adopt mobile phone signaling data (MSD) to detect transport modes. However, due to their low positioning accuracy and temporally irregular nature, identifying transport modes with similar spatiotemporal features, such as the bus and car modes, is particularly challenging. We propose a transport detection framework using MSD combined with bus GPS data to distinguish between the car and bus modes. First, a trajectory matching algorithm is proposed to obtain the most probable bus that mobile phone users may take. Then, more features are mined to improve the accuracy of transport mode detection with different classification models. Furthermore, for signaling trajectories identified as the bus mode, more bus travel information is recognized, including the boarding and alighting station and timestamp. Finally, we built a ground truth dataset and compared the recognition accuracies under different features and classification models. The result shows that the transport mode detection accuracies of the proposed framework with the GBDT, XGBoost, and LightGBM algorithms are all higher than 94%. [ABSTRACT FROM AUTHOR]

The aim of this work is to clarify whether electric-powered aviation can comply with requirements from the Danish government for a zero-emission carbon dioxide footprint by 2030 on domestic flight routes. The battery types are presented, in an attempt to determine whether electric air transportation can become commercially viable. The gap in evaluating the feasibility of moving by electric aircraft by 2030 is met through this research, by analysing the feasibility, through current developments, of delivering enough energy in terms of Wh/kg as compared to conventional fuel for a combustion-driven aircraft. The calculations, based on lithium (Li)-ion batteries with an energy density of 260 Wh/kg, showed that the goals will be met only with the development of new battery technology. The results indicate that there will be a reduction in carbon dioxide emissions by approximately 17%. According to aircraft manufacturers, it will be possible to accommodate up to 186 passengers in a fully electric-driven aircraft, which can meet the demand for domestic flights in Denmark. Other types of batteries, such as lithium–sulfur (Li–S) and lithium–oxygen (Li–O2) are relevant to investigate because of their higher energy density and usage of natural minerals other than just lithium. [ABSTRACT FROM AUTHOR]

An -polar fuzzy ( F) model offers a practical framework for decision-making by providing higher flexibility in handling uncertainties and preferences. The ability of F sets to tackle multiple reference points permits for a more nuanced analysis, leading to more accurate results in complex decision scenarios. This study was mainly devoted to introducing three novel aggregation operators (AGOs) for multi-criteria decision-making (MCDM) based on generalized geometric Heronian mean (GGHM) operations comprise the concept of F sets. The presented operators consisted of the weighted F power GGHM (W FPGGHM), ordered weighted F power GGHM averaging (OW FPGGHM), and hybrid F power GGHM (H FPGGHM) operators. Some essential fundamental properties of the proposed AGOs were investigated: idempotency, monotonicity, boundedness, and Abelian property. Furthermore, an algorithm based on the initiated W FPGGHM operators was developed to address diverse daily-life MCDM scenarios. Next, to validate the efficiency of the established algorithm, it was implemented in a daily-life MCDM problem involving urban transportation management. At last, a sensitivity analysis of the initiated AGOs was provided with existing F set-based operators involving Dombi, Yager, and Aczel-Alsina's operations-based AGOs. [ABSTRACT FROM AUTHOR]

Urban mobility systems generate a massive volume of real-time data, providing an exceptional opportunity to understand and optimize transportation networks. To harness this potential, we developed UrbanFlow Milano, an interactive map-based dashboard designed to explore the intricate patterns of shared mobility use within the city of Milan. By placing users at the center of the analysis, UrbanFlow empowers them to visualize, filter, and interact with data to uncover valuable insights. Through a comprehensive user study, we observed how individuals interact with the dashboard, gaining critical feedback to refine its design and enhance its effectiveness. Our research contributes to the advancement of user-centric visual analytics tools that facilitate data-driven decision-making in urban planning and transportation management. [ABSTRACT FROM AUTHOR]

Progressive and sudden deteriorations are the main reasons affecting the functionality of a transportation network. This paper presents a general probabilistic approach in which the ensembles of regression trees (ERT) are innovatively adopted to predict the life-cycle system reliability of asphalt pavements using Monte Carlo simulations, and pavement segments are considered with bridges in the analysis, prediction, and management of the functionality of transportation networks under both progressive and sudden deterioration due to multiple hazards. Four performance indicators of asphalt pavement subjected to multiple hazards were modeled using ERT trained with the Long-Term Pavement Performance database. The specific hazard types corresponding to each pavement performance indicator for the associated ERT model training were identified. The structural performance associated with bridge superstructures and substructures was analyzed by considering corrosion, traffic loading, and seismic hazards. The proposed approach is illustrated on an existing transportation network in Pennsylvania. The essential retrofitting timing, importance measure, and retrofitting priority associated with the individual component were investigated utilizing the calculated time-variant connectivity-based functionality and resilience associated with the network. The results demonstrate that asphalt pavements have a significant impact on the network functionality and should be considered in the postevent decision-making process of retrofitting strategies. [ABSTRACT FROM AUTHOR]

Given an input graph Gin=(V,Ein) , we consider the problem of designing a sparse subgraph G=(V,E) with E⊆Ein that supports a large matching after some nodes in V are randomly deleted. We study four families of sparse graph designs (namely, clusters, rings, chains, and Erdős–Rényi graphs) and show both theoretically and numerically that their performance is close to the optimal one achieved by a complete graph. Our interest in the stochastic sparse graph design problem is primarily motivated by a collaboration with a leading e-commerce retailer in the context of its middle-mile delivery operations. We test our theoretical results using real data from our industry partner and conclude that adding a little flexibility to the routing network can significantly reduce transportation costs. This paper was accepted by David Simchi-Levi, optimization. Funding: This work was supported by the University of Chicago Booth School of Business, an Alibaba Cainiao Research Grant, and the Singapore Ministry of Education [NUS Startup Grant WBS A-0003856-00-00]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.01588. [ABSTRACT FROM AUTHOR]

In the realm of traffic signal operations, the Signal Timing Manual second edition (STM2) serves as a foundational guide for professionals engaged in multimodal signal retiming projects. However, it is acknowledged that the STM2 has its limitations, and real-world conditions often necessitate adaptations in the established procedures. Considering this context, this research endeavors to bridge this gap by conducting a comprehensive survey aimed at traffic signal professionals. This study presents the findings of a comprehensive survey conducted among traffic signal professionals to explore the methodologies, challenges, and practices involved in multimodal signal retiming projects. The survey aimed to obtain detailed insights into the current state of signal retiming, the types of data and tools utilized, and the adaptations necessary to address the complexities of multimodal urban transportation networks. The survey highlights and summarizes responses from 36 professionals across North America, providing insight into both the common strategies and unique challenges faced by those responsible for optimizing signal timings in diverse and dynamic urban environments. The survey results reveal a reliance on diverse tools and data types for signal optimization, highlighting the complexities of accommodating different transportation needs. The findings underscore the importance of tailored approaches and advanced technologies in enhancing signal retiming processes. The insights gained from this study will inform future strategies and enhance the effectiveness of signal retiming procedures in urban areas, thereby contributing to improved traffic management and multimodal transportation efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a longitudinal controller for the mixed vehicle platoon in the presence of driver response time‐delay and communication time‐delay based on the pinning consensus of heterogeneous multi‐agent system. Firstly, an adaptive pinning consensus control protocol and derive sufficient conditions for the heterogeneous non‐linear multi‐agent system are designed to achieve pinning consistency. Then, the heterogeneous characteristics of the vehicle are described based on the car‐following model, then the mixed vehicle platoon system model is constructed and a mixed vehicle platoon controller based on heterogeneous multi‐agent pinning consensus is proposed. Besides, the driver response time‐delay and communication time‐delay are introduced into the model, and a controller based on time‐delay heterogeneous multi‐agent pinning consensus is designed. Finally, the effectiveness of the proposed controller is verified by numerical simulations, and the effect of number of connected autonomous vehicle, different types of vehicle order, driver response time‐delay, communication time‐delay and the parameter of the controller on stability of mixed vehicle platoon is also quantitatively demonstrated. [ABSTRACT FROM AUTHOR]

With tidal traffic becoming an important feature of urban transportation, the impact of tidal lanes on traffic flow is a research topic for many scholars. This paper explores the setting scheme of tidal lanes at toll stations based on the conditions and rules for setting up tidal lanes on urban roads. A highway tidal toll station model is established based on VISSIM. The traffic flow characteristics are simulated when the main traffic flow directions in tidal traffic are located at the exit and entrance of the toll station, respectively. We use a tidal coefficient of 0.7 as an example to analyze the impact of the switching ratio of tidal lanes at toll stations on the traffic volume in the exit and entrance directions under different traffic flow states. We determine the optimal number of tidal lanes to open through analysis of traffic growth rate and total traffic volume changes at toll stations, and propose an opening model for tidal lanes at toll stations under different tidal traffic flow states. In addition, we take the Jingxiong Highway in China as an example to design the traffic organization of the tidal lane, which provides reference for the control of tidal toll stations. [ABSTRACT FROM AUTHOR]

The grain export in Brazil is operated by several players and regulated by a few governmental agencies. Although players have particular interests, sustainability is an increasing concern that poses a common challenge to rail operations: reducing CO2 emissions. This work proposes a simulation-optimisation approach for assigning trains to routes in a complex queuing system, minimising CO2 emissions of the country-wide rail operations without compromising the delivery of planned volume exports. The model considers a multi-modal logistic system, i.e., terminals, railways, and ports integrated into a closed circuit. The computational experiments using real-world data of 2021 reveal that the CO2 emissions could be reduced by 5.32 kilo tons and still increase the grain export by 2.20% per year. [ABSTRACT FROM AUTHOR]

This research was carried out quantitatively to analyze the management of road transport of cargo and goods in Isfahan province with an emphasis on the political and security dimensions. Through semi-structured interviews with 8 subject matter experts, the management components of the road transport industry were identified. In the next step, the data extracted by the survey method was used as the basis for compiling a researcher-made questionnaire in the dimensions of environmental, economic, technical, process, operational, legal and human resource damages. The statistical sample in this phase was estimated to be 269 people through Cochran's sampling formula and was selected by the available random method. The validity of the data was confirmed through formal, content, and construct validity, as well as its reliability through Cronbach's alpha greater than 0.7. The results show that the average of all identified dimensions is higher than average (3); The results also show that the highest influence coefficient in process damages is (0.147) and the highest predictive power belongs to technical damages (0.245). The model fit criterion shows that the influence and predictor coefficients of the model are reliable. [ABSTRACT FROM AUTHOR]

Revenue management for passenger rail transportation has a vital role in the profitability of public transportation service providers. This study proposes an intelligent decision support system by integrating dynamic pricing, fleet management, and capacity allocation for passenger rail service providers. Travel demand and price-sale relations are quantified based on the company's historical sales data. A mixed-integer non-linear programming model is presented to maximize the company's profit considering various cost types in a multi-train multi-class multi-fare passenger rail transportation network. Due to market conditions and operational constraints, the model allocates each wagon to the network routes, trainsets, and service classes on any day of the planning horizon. Since the mathematical optimization model cannot be solved time-efficiently, a fix-and-relax heuristic algorithm is applied for large-scale problems. Various real numerical cases expose that the proposed mathematical model has a high potential to improve the total profit compared to the current sales policies of the company. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study on the departure time choice behavior during the morning rush hours under different road capacity conditions. Experimental data are analyzed from the aspects of the equilibrium state of a traffic system and the choice behavior of subjects. The experimental results showed that the user equilibrium is easy to achieve in the medium-capacity scenario; however, it is difficult in the low- and high-capacity scenario. This implies that the user equilibrium cannot predict the aggregate behavior well when the bottleneck capacity is too low or too high. A reinforcement learning model is constructed to reproduce experimental results and uncover subjects' learning mechanism. Simulation results are in good agreement with the experimental results. The results presented in this study could provide the theoretical support for developing measures for transportation management and control during the morning rush hours. [ABSTRACT FROM AUTHOR]

The editorial discusses recent advances in deep learning for traffic probabilistic prediction, emphasizing the importance of probabilistic models in handling uncertainties in traffic systems. Deep learning methods such as Bayesian Deep Learning, Deep Gaussian Processes, Quantile Regression, and diffusion models are explored for their effectiveness in providing a range of possible traffic states and their corresponding probabilities. The practical implications of probabilistic traffic prediction include proactive congestion control, refined passenger information systems, improved risk assessment for autonomous driving, and enhanced public transport planning. Future research directions focus on developing efficient and scalable deep Bayesian inference techniques, integrating various data sources for more precise predictions, and incorporating uncertainty estimates into decision-making frameworks for robust traffic management solutions. [Extracted from the article]

In an interconnected world dominated by global trade and intricate supply chain management, the transportation and management of dangerous cargo such as flammable liquids, toxic chemicals and radioactive materials, present multifaceted challenges. These hazardous substances pose significant environmental and health risks, necessitating rigorous safety measures and regulatory oversight. This comprehensive overview examines the various types of dangerous cargo, their environmental implications and notable case studies, highlighting the critical importance of international cooperation and stringent regulations. It delves into the regulatory frameworks governing the transport of hazardous materials by rail, sea, air and land, emphasizing the pivotal role of institutions like the International Maritime Dangerous Goods Code and the Environmental Protection Agency. Analysis indicates a need for improved response times in monitoring programs, necessitating adaptability to diverse environments and specific circumstances. Monitoring and impact assessment programs within emergency response frameworks differ from those aimed at detecting long-term trends in physical, biological and chemical variables. [ABSTRACT FROM AUTHOR]

This paper discusses logistics collaboration applied to a vehicle-manufacturing setting. We reach out to a larger segment of the supply chain by employing a novel unit of analysis, a logistics triad. The triad encompasses an Original Equipment Manufacturer, a Third-Party Logistics Service Provider, and first-tier suppliers. We use the framework known as Collaborative Transportation Management (CTM) to study collaborative transportation activities, focusing on the important relationship between logistics collaboration and logistics performance. Using a qualitative research orientation and a multi-case research strategy, we interviewed logistics practitioners in three countries to identify the enablers of CTM, the salient collaborative practices, and the performance outcomes. The study uncovers the extent to which CTM contributes to the operational and relational performance of the logistics triad. We provide empirical evidence of an ad hoc implementation of this notion. We propose avenues of intervention to preserve logistics collaboration and to enhance logistics performance. [ABSTRACT FROM AUTHOR]

The transport sector has seen significant development in recent years. Algerian cities, like other cities in the world, have grown and expanded their geographic area, and the population has increased as well, leading to increased demand for transportation. However, this increase and growth have not been accompanied by development in the policies adopted in organizing transportation and its means. The city of Mila, like other Algerian cities, suffers from several problems, including marginalization, which dominates several neighborhoods. This is characterized by isolation and difficulty of access to an area or region within the city, and results from several internal and external factors. What has worsened the situation in these neighborhoods is that they are victims of urban development tools and planning policies, along with poor management and organization of transportation and traffic movement. This has deepened the crisis, depriving them of mobility and vitality, and has led to their continued marginalization and isolation from other neighborhoods in the city. The objective of this research is to implement innovative solutions and proposals for the marginalized neighborhood, aimed at fostering balance and harmony while ensuring its integration into the dynamics of development through the enhancement of public urban bus transportation. [ABSTRACT FROM AUTHOR]

Administrative management and service quality are fundamental aspects for the success and competitiveness of companies in the transportation industry. It is important to highlight that administrative management in transportation not only refers to the way in which the company is managed, but also to the implementation of actions to improve quality management. Sometimes companies focus solely on certification requirements and neglect the true nature of quality management in improving customer satisfaction. The objective of this study is to examine the relationship between administrative management and service quality in Carreño Trans S.A. Transport, a company located in Portoviejo, Manabí. For this purpose, a mixed, non-experimental cross-sectional investigation was carried out, with a correlational scope. A survey was applied from which an evaluation was developed, with a client focus. The Pearson correlation between the categories is 0.002, with a p-value of 0.985; showing that there is no direct correlation between how clients perceive administrative management and the quality of the service offered. Since no strong correlation was found, separate strategies are proposed to improve these areas such as independent evaluations and further investigations. Results indicate that accountability and safety are critical areas that require immediate attention. [ABSTRACT FROM AUTHOR]

Many transportation management agencies require premixed rapid-setting cementitious repair products that can be easily mixed and placed to return airport and roadway facilities to service and prevent downtimes. The US Air Force Civil Engineer Center oversees a rapid-setting cementitious material certification program to assist airfield managers and repair teams in selecting commercially available proprietary products to repair spalls in airfield concrete pavements. The program includes critical performance tests, such as compressive strength, flexural strength, and set time, as well as tests to evaluate each product's long-term resistance to more environment-based distress. One potential issue with the use of rapid-repair products is that they can become brittle and prone to cracking, causing potential foreign object debris (FOD) damage to aircraft. Proprietary products offer high early strengths, quick set times, and the ability to withstand traffic for many years. These products evolve, and new or modified products must be properly evaluated to ensure that they do not fail under traffic and that they can be placed without reaching the initial set prematurely. For this effort, the existing US Department of Defense (DoD) laboratory test protocol was evaluated by performing multiple tests on 25 repair products. Eleven products were also used to conduct permanent and emergency spall repairs that were load tested with simulated aircraft traffic to assess the protocol's effectiveness in identifying quality rapid-setting repair products. Larger emergency repairs (approximately 2.4×2.4 m) were also conducted with three products and load tested. Overall, several changes to the laboratory protocol were recommended, but full-scale tests indicated that the recommended protocol is still effective for selecting quality products. Larger emergency repair test results indicated that rapid-setting repair products can be effective for these size repairs, but the need for full-scale testing before certification was heavily emphasized. Practical Applications: The newly modified protocol for evaluating rapid-setting cementitious repair products in the laboratory is expected to have immediate use for airfield repair practitioners. There are many commercially available products, and the ability to identify high-performing products via a suite of laboratory tests should help extend the life of airfield repairs. In addition, the emergency spall repair and emergency large repair techniques discussed in this paper could be useful for civilian airport repair applications, where downtimes are very limited due to very full flight schedules. Conducting a very quick repair with high-quality materials can maintain flight operations until a more permanent repair can be conducted. [ABSTRACT FROM AUTHOR]

Traditional data storage models are inadequate in the face of the growing demand for big data in transportation and transportation management. Its poor horizontal scalability makes it difficult to deal with the growth of massive data; on the other hand, its complex management makes it challenging to achieve unified management and effective resource utilization due to the differences in equipment from different manufacturers. In order to effectively store and manage this huge amount of information, it is urgent to rely on advanced technical tools. In this context, while ensuring data security and simplifying data management, it is also necessary to meet the terminal's demand for high real-time performance, and these factors jointly promote the continuous attention and improvement of data storage terminal performance. This paper proposes a Hadoop solution based on distributed computing. As a distributed system infrastructure, Hadoop allows users to develop distributed programs without a deep understanding of distributed details, fully using the high-speed computing and storage capabilities of Hadoop clusters, which is especially suitable for big data processing tasks on the Internet of Things (IoT) platform. The experimental results show that for a 10 GB data file, the traditional terminal (Terminal 1) can store 7.8 GB, while the Hadoop-based terminal (Terminal 2) can store 9.9 GB. For 50 GB of data files, Terminal 1 and Terminal 2 can store 40.4 GB and 49.8 GB of data respectively. These results show that Hadoop terminals have significant advantages in processing large-scale data, especially in terms of data storage efficiency, and can use storage resources more effectively to meet the high-performance requirements of traffic and transportation management for data storage terminals. [ABSTRACT FROM AUTHOR]

Vehicle collision on bridges is an important issue for the transportation infrastructure management. This study explores the significance of bridge monitoring and the benefits of employing machine learning (ML) techniques to detect and classify vehicle-deck collisions on railway bridges. The ultimate goal is to transition from traditional bridge monitoring methods to a real-time monitoring system based on a ML approach, aiming to improve efficiency and accuracy in detecting bridge issues. Multiple supervised ML algorithms are evaluated to identify the most accurate model for collision detection and signal categorization. The selected ML model employs a distributed approach, enhancing its adaptability and integration into a comprehensive monitoring system for diverse bridge structures. The dataset comprises frequency, velocity, and displacement measurements collected over a one-year monitoring period from three distinct railway bridges. Additionally, a controlled experiment was conducted to identify signal patterns associated with collisions of different energy levels. The collected data underwent rigorous processing, including data cleaning, synchronization, pattern identification, and statistical analysis, to extract relevant features. The proposed model achieved an accuracy of 100% in detecting vehicle-deck collisions on railway bridges and demonstrated high accuracy in classifying other types of signals. The model provides bridge managers with a valuable digital decision support tool that aids in evaluating bridge conditions, minimizing maintenance costs, and ensuring train user safety. Furthermore, the developed approach aids in reducing disk storage and saving energy in embedded systems, enhancing its practicality and sustainability in real-world applications. [ABSTRACT FROM AUTHOR]

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature. [ABSTRACT FROM AUTHOR]

The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research. Bluetooth technology, for example, is already vastly used in vehicular transportation management solutions and services. Current studies discuss the potential of this emerging technology for pedestrian mobility research, but it has yet to be examined in a large urban setting. One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes. This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv, Israel, which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses. We propose a detection methodology that includes system calibration, effective travel time calculation, and classification by velocity that takes into consideration the probability of vehicular traffic jams. An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%. We showcase the results of pedestrian traffic analysis, together with a discussion on the data analysis challenges and limitations. To the best of our knowledge, this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traffic prediction is essential for transportation planning, resource allocation, congestion management and enhancing travel experiences. This study optimizes data preprocessing techniques to improve machine learning-based traffic prediction models. Data preprocessing is critical in preparing the data for machine learning models. This study proposes an approach that optimizes data preprocessing techniques, focusing on flow-based analysis and optimization, to enhance traffic prediction models. The proposed approach explores fixed and variable orders of data preprocessing using a genetic algorithm across five diverse datasets. Evaluation metrics such as root mean squared error (RMSE), mean absolute error (MAE) and R-squared error assess model performance. The results indicate that the genetic algorithm’s variable order achieves the best performance for the ArcGIS Hub and Frementon Bridge Cycle datasets, fixed order one preprocessing for the Traffic Prediction dataset and variable order using the genetic algorithm for the PeMS08 dataset. Fixed order 2 preprocessing yields the best performance for the XI AN Traffic dataset. These findings highlight the importance of selecting the appropriate data preprocessing flow order for each dataset, improving traffic prediction accuracy and reliability. The proposed approach advances traffic prediction methodologies, enabling more precise and reliable traffic forecasts for transportation planning and management applications. [ABSTRACT FROM AUTHOR]

Urban and suburban transport within Functional Urban Areas (FUAs) is now considered an integrated system. In these regions, many residents commute from the suburbs to the city daily for work, education, and social purposes. Transport planning must consider these dynamics to ensure consistent and convenient connections between the city and its suburbs. This article stresses the need for a standardized tool to collect data on transport management models in FUAs across 38 OECD-affiliated countries. The proposed tool, a survey questionnaire, aims to gather information on how transport management models are organized and operate in these regions. The article discusses research conducted in the Olsztyn FUA, revealing significant variations in transport management methods among municipalities. The questionnaire is categorized into four themes: public transport, transport infrastructure, FUA transport strategy and innovation, and risks and monitoring, offering a comprehensive view of the transport management model. The study also highlights varying development priorities among FUA municipalities; some focus on public transport, while others invest in road infrastructure. This study underscores the importance of a cohesive approach to transport management in FUAs, considering their diverse needs and requirements. [ABSTRACT FROM AUTHOR]

Traffic prediction is crucial for transportation management and user convenience. With the rapid development of deep learning techniques, numerous models have emerged for traffic prediction. Recurrent Neural Networks (RNNs) are extensively utilized as representative predictive models in this domain. This paper comprehensively reviews RNN applications in traffic prediction, focusing on their significance and challenges. The review begins by discussing the evolution of traffic prediction methods and summarizing state-of-the-art techniques. It then delves into the unique characteristics of traffic data, outlines common forms of input representations in traffic prediction, and generalizes an abstract description of traffic prediction problems. Then, the paper systematically categorizes models based on RNN structures designed for traffic prediction. Moreover, it provides a comprehensive overview of seven sub-categories of applications of deep learning models based on RNN in traffic prediction. Finally, the review compares RNNs with other state-of-the-art methods and highlights the challenges RNNs face in traffic prediction. This review is expected to offer significant reference value for comprehensively understanding the various applications of RNNs and common state-of-the-art models in traffic prediction. By discussing the strengths and weaknesses of these models and proposing strategies to address the challenges faced by RNNs, it aims to provide scholars with insights for designing better traffic prediction models. [ABSTRACT FROM AUTHOR]

With the escalation of global trade, the Chinese commodity futures market has ascended to a pivotal role within the international shipping landscape. The Shanghai Containerized Freight Index (SCFI), a leading indicator of the shipping industry's health, is particularly sensitive to the vicissitudes of the Chinese commodity futures sector. Nevertheless, a significant research gap exists regarding the application of Chinese commodity futures prices as predictive tools for the SCFI. To address this gap, the present study employs a comprehensive dataset spanning daily observations from March 24, 2017, to May 27, 2022, encompassing a total of 29,308 data points. We have crafted an innovative deep learning model that synergistically combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. The outcomes show that the CNN-LSTM model does a great job of finding the nonlinear dynamics in the SCFI dataset and accurately capturing its long-term temporal dependencies. The model can handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study's findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities. [ABSTRACT FROM AUTHOR]

Models of human mobility can be broadly applied to find solutions addressing diverse topics such as public health policy, transportation management, emergency management, and urban development. However, many mobility models require individual-level data that is limited in availability and accessibility. Synthetic populations are commonly used as the foundation for mobility models because they provide detailed individual-level data representing the different types and characteristics of people in a study area. Thorough evaluation of synthetic populations is required to detect data biases before the prejudices are transferred to subsequent applications. Although synthetic populations are commonly used for modeling mobility, they are conventionally validated by their sociodemographic characteristics, rather than mobility attributes. Mobility microdata provides an opportunity to independently/externally validate the mobility attributes of synthetic populations. This study demonstrates a spatially-oriented data validation framework and independent data validation to assess the mobility attributes of two synthetic populations at different spatial granularities. Validation using independent data (SafeGraph) and the validation framework replicated the spatial distribution of errors detected using source data (LODES) and total absolute error. Spatial clusters of error exposed the locations of underrepresented and overrepresented communities. This information can guide bias mitigation efforts to generate a more representative synthetic population. [ABSTRACT FROM AUTHOR]