Transportation Management System Research Articles

ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting

ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting

Predicting Bus Travel Time in Cheonan City Through Deep Learning Utilizing Digital Tachograph Data

Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

Real-time prediction of transit origin–destination flows during underground incidents

Efficient transportation planning and management are critical for ensuring the smooth operation of rail transit systems, particularly in urban areas with high passenger demand. Real-time prediction of transit flow during underground incidents is vital for ensuring passenger safety, minimizing disruptions, and optimizing resource allocation. This study presents a comprehensive framework to address the considerable challenges associated with short-term origin–destination (OD) flow prediction, particularly during incidents. We propose integrating convolutional long short-term memory cells with self-attention cells to learn the long-range spatiotemporal information of historical OD flows. To overcome the absence of a real-time OD matrix, we introduce an improved graph convolution operation, considering the local connection of ridership, to capture vital spatiotemporal interaction patterns. Quantitative and qualitative analyses were conducted to evaluate the performance of the framework, providing insights into the role of each module. Experimental results suggest that incorporating incident matrix theory enhances the accuracy of capturing declining OD flows during incidents by up to approximately 25%. Furthermore, integrating external features like weather and time comprehensively assesses the impact of erratic change factors, especially during off-peak periods. Additionally, introducing more than one cycle of historical OD flow as an input may introduce noise, and increased attention is needed for OD flows with average daily flows exceeding 30 passengers. Designed with a high degree of modularity and adaptability, the proposed framework serves as a valid tool for transportation professionals and researchers to address evolving challenges in the management and operation of urban transportation systems.

Secured intelligent transportation with privacy retention through blockchain framework

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture.

Multivariable forecasting approach of high‐speed railway passenger demand based on residual term of Baidu search index and error correction

AbstractAccurate prior information of passenger flow demand on high‐speed railway is of great significance for the operation and the management of transportation systems. Various factors in modern social life have caused uncertainty at demand. Recently, individuals are increasingly depending on the online search results when choosing among different transportation modes, services, and destinations, which provide important basic information for forecasting the travel demand. This study employs Baidu search index to assist in capturing volatility of high‐speed railway passenger demands, offering insights into the travel inclinations and travelers' actions. Furthermore, we have given more in‐depth attention and analysis to their residual term accounting for the random nature caused by various factors. To this end, a sophisticated deep analysis mechanism based on data decomposition has been devised to extract and analyze the valuable information concealed within the residuals, so as to enhance the comprehension of the variability inherent in the high‐speed railway passenger flow. Meanwhile, an error correction strategy is implemented for all residual terms to improve further their forecasting accuracy. Experimental results from two real‐world datasets demonstrate the effectiveness and robustness of the developed hybrid approach across several popular evaluation indicators. Therefore, this study can function as a reliable instrument, provide sensible data‐driven guidance for resource allocation and make scientific decisions in the railway industry.

Exploring the Application of Blockchain Technology in Logistics and Transportation System

This paper explores the application of blockchain technology in improving the management and efficiency of logistics and transportation systems. By leveraging blockchain technology, real-time recording, traceability, data sharing, security, and trustworthiness of logistics information can be achieved, thereby enhancing the efficiency and safety of logistics operations. The paper discusses the theoretical foundations of blockchain technology and logistics and transportation systems, highlighting key concepts such as decentralization, distributed ledger, consensus mechanisms, smart contracts, operations research, supply chain management, and transportation economics. Additionally, the literature review presents an overview of existing studies on logistics and transport systems and blockchain technology, illustrating various applications and benefits, such as sustainable transport frameworks, transportation and logistics management models, smart logistics systems, and supply chain traceability platforms. In conclusion, the exploration of blockchain technology's application in logistics and transportation systems offers comprehensive support for improving logistics efficiency, reliability, and safety, promising more efficient and reliable transportation services for the logistics industry in the future.

OSTM-NET: Joint scale variation and occlusion handling deep network for real-time vehicle counting and volume estimation

Vehicle counting and traffic volume estimation using videos are difficult tasks crucial for efficient traffic control in smart cities. Several existing techniques rely in tracking and detecting mechanisms of the vehicles. These methods are ineffective for detecting occluded and small vehicles. Also, contextual information loss occurs in deep counting networks. An innovative Internet-of-Things (IoT)- driven Intelligent Transportation Management (ITM) system is proposed to address these issues. Initially, traffic videos are converted to temporal-spatial images instead of using complex detection and tracking methods. The density map for the temporal spatial image is estimated using an occlusion-aware spatio-temporal multi-scale network (OSTM-Net). It consists of two sub-networks for capturing occluded and small vehicles simultaneously. The scale-aware column network (SCNet) accurately captures small vehicles and preserves contextual information through enhanced scale representation. At the same time, the occlusion management network (OM-Net) uses position-sensitive regions of interest (PSRoI) deformable pooling to address the occlusion issues. Finally, volume estimation and counting are calculated in accordance with the density map obtained from OSTM-Net. Every path in the videos is processed separately using OSTM-Net to calculate the vehicle count in every path for effective traffic control in this proposed approach. Furthermore, the effectiveness of the sub-networks (SCNet and OM-Net) is validated using ablation experiments. The proposed IoT-based ITM achieves high performance in counting vehicles and estimating traffic volume compared to other existing approaches.

TRANSFORMATION OF PASSENGER TRANSPORT MANAGEMENT STRUCTURE IN RAILWAY TRANSPORT: WORLD EXPERIENCE AND ITS ADAPTATION TO MODERN CONDITIONS IN UKRAINE

Objective. To enhance the competitiveness of railway passenger transportation in Ukraine, it is necessary to explore the experience of structural transformations in passenger enterprises of foreign railways, analyze the current state of the domestic railway passenger transportation management system, and propose an effective organizational structure for the passenger complex of Ukrainian Railways PJSC in modern conditions. Methodology. To achieve the stated objective, methods of analogy, modeling, comparative, and retrospective analysis were applied. Results. The world experience of interaction between organizational units of railway passenger complexes has been considered. An analysis has been conducted, and problems in the interaction of departments in the existing railway passenger transportation management system have been identified. A management structure for the passenger complex of Ukrainian Railways PJSC has been proposed, which will help optimize its operation and contribute to the increased efficiency of railway passenger transportation in Ukraine. Scientific novelty. The stages of transformation of the management structure of the passenger complex after the formation of Ukrainian Railways PJSC have been generalized. The passenger complex is considered as a single system: based on the visualization of the distribution of work with trains of different categories, problems of delineation of responsibility zones between the departments of the passenger complex have been identified. A model of an improved organizational structure for managing the passenger complex of railway transport has been developed in accordance with modern conditions. Practical significance. The proposed measures can be applied by Ukrainian Railways PJSC to improve the organizational structure of the passenger complex in the process of transitioning to a vertically integrated management model.

Conceptual awareness levels of digital logistics among Turkish university students

The logistics industry has witnessed the emergence of digital technology as a central topic of discourse, and terminology associated with digital logistics has gained extensive usage among professionals in the transportation, logistics, and international trade sectors. Digital logistics is highly dependent on the accumulation of data from the logistical activities conducted by organizations. The implementation of a digitalized process presents organizations with novel prospects, such as decreased operational expenses, increased efficiency, and reduced uncertainties associated with order fulfillment. Additionally, digital logistics facilitates the enhancement of distribution methods for businesses. The post-graduation knowledge levels of students become significant factors for potential employers. University education plays a crucial role in imparting academic and technical expertize in the domain of logistics; therefore, it is critical that logistics programs integrate a greater number of digital courses. Academic institutions play a pivotal role in enabling students to gain consciousness and comprehension of emerging patterns and concepts. In light of this, the purpose of this research was to determine “the extent of digital logistics and digitalization conceptual awareness” among university students. The results of the study will be used as a guide for educational institutions like colleges to follow when it comes to incorporating digital courses into their curriculum. It was decided to use questionnaires for the purpose of data collecting. A statistical analysis was conducted on the information gathered from the university students. While most students had a good grasp of the concepts of AI, VR, digital supply chain, and transportation management systems, female students showed less familiarity with these phrases than their male counterparts. It is possible for universities to provide students with the opportunity to acquire knowledge of digital logistics concepts through the implementation of training courses, workshops, programs, and term projects. It is advisable to promote students’ acquisition of knowledge regarding digital technologies through the implementation of additional courses. The educational experience and students’ overall level of awareness can be greatly enhanced through partnerships between IT firms and institutions.

Enhancing sustainable urban air transportation: Low-noise UAS flight planning using noise assessment simulator

Low-altitude flights of unmanned aircraft systems (UASs), such as drones, can cause substantial noise pollution in urban areas. This study aims to tackle the crucial issue of noise in UAS flight planning and analyze the feasibility of implementing low-noise urban flights at a micro level. We employ a virtual flight simulator to accurately model the UAS noise impact in realistic flights and optimize the flight path via a heuristic algorithm accordingly. The flight noise assessment incorporates a sound source modeling technique for practical UAS models and an efficient Gaussian beam tracing method for outdoor noise propagation in realistic environments. Case studies for flight simulations are conducted in a representative resident community and metropolitan area to evaluate the noise impact resulting from different flight paths. By comparing our approach with benchmark shortest-distance paths, we validate its effectiveness in significantly reducing en-route noise exposure, encompassing both instantaneous and continuous noise levels. This method holds great potential for contributing to the planning and management of urban air transport systems. It effectively mitigates noise impact, promoting quieter and more environmentally friendly UAS operations and sustainable urban transportation.

TrafficGPT: Viewing, processing and interacting with traffic foundation models

With the promotion of ChatGPT to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, large language models (LLMs) struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT—a fusion of multiple LLMs and traffic foundation models. This integration yields the following key enhancements: 1) empowering LLMs with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git.

Software and Architecture Orchestration for Process Control in Industry 4.0 Enabled by Cyber-Physical Systems Technologies

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

Transportation-Enabled Services: Concept, Framework, and Research Opportunities

As a result of rapid advancements in urban infrastructure and technology, transportation is transforming a variety of innovative and emerging services. This paper introduces the concept of transportation-enabled services (TRENS), which is a service model that uses transportation systems to enable and enhance the delivery, accessibility, and effectiveness of nontransportation services. We establish a general framework in which the transportation-enabled services involve four key stakeholders: customers, suppliers, TRENS providers, and transportation carriers. Within this general framework, we present five specific service models: a one-sided market, three variants of two-sided markets, and a three-sided market, each characterized by the interactions among suppliers, TRENS providers, and transportation carriers. Taking different models of transportation-enabled services into consideration, we highlight crucial research opportunities to enhance understanding of the planning, operations, evaluation, and regulation of transportation-enabled services. These research opportunities include demand and supply management, transportation system management and operations, coordination among stakeholders, and the evaluation and regulation of transportation-enabled services. We believe that the emergence, widespread adoption, and popularity of transportation-enabled services will significantly enhance and reshape many services and thus contribute to the overall improvement of future mobility in the broader context of smart cities. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: The work of Hai Wang was supported by the Lee Kong Chian Fellowship awarded by Singapore Management University. The work of Hao Sun was supported by the National Natural Science Foundation of China [Grants 72301179].

Optimizing municipal solid waste collection vehicle routing with a priority on infectious waste in a mountainous city landscape context

The growth of the economy, urban sprawl, and population expansion precipitate a substantial increase in solid waste generation, particularly in vital regions like the capital area and economically significant cities, including renowned tourist destinations. Furthermore, the protracted spread of the COVID-19 virus has led to a notable increase in the quantity of infectious waste. In this regard, the safe and effective management of municipal solid waste (MSW) collection and transportation emerges as a major concern in MSW management. This paper aims to determine routes for waste collection, both infectious and noninfectious, from waste collection points to diverse waste disposal sites in the mountainous Khao Kho district of Phetchabun province, located in northern Thailand, with the objective of minimizing total fuel consumption and service time. Fuel consumption is contingent upon both the distance traveled and the prevailing condition of the road. The road condition exerts an influence on the vehicle's speed, thereby directly impacting fuel efficiency. In the context of service time, priority is assigned to collection points with substantial quantities of infectious waste, given the imperative to promptly dispose of such waste to mitigate the risk of transmission. In this paper, a mixed-integer programming model and three modified differential evolution (MDE) algorithms were developed to solve the problem. The computational results reveal that the MDE outperformed the traditional DE and is effective for the MSW transportation management system.

FAST-CA: Fusion-based Adaptive Spatial–Temporal Learning with Coupled Attention for airport network delay propagation prediction

The issue of delay propagation prediction in airport networks has garnered increasing global attention, particularly due to its profound impact on operational efficiency and passenger satisfaction in modern air transportation systems. Despite research advancements in this domain, existing methodologies often fall short of comprehensively addressing the challenges associated with predicting delay propagation in airport networks, especially in terms of handling complex spatial–temporal dependencies and sequence couplings. In response to the complex challenge of predicting delay propagation in airport networks, we introduce the Fusion-based Adaptive Spatial–Temporal Learning with Coupled Attention (FAST-CA) framework. FAST-CA is an innovative model that integrates dynamic and adaptive graph learning, coupled attention mechanisms, periodicity feature extraction, and multifaceted information fusion modules. This holistic approach enables a thorough analysis of the interplay between flight departure and arrival delays and the spatial–temporal correlations within airport networks. Rigorously evaluated on two extensive real-world datasets, our model consistently outperforms current state-of-the-art baseline models, showcasing superior predictive performance and the effective learning capabilities of its intricately designed modules. Our research highlights the criticality of analyzing spatial–temporal relationships and the dynamics of flight coupling, offering significant theoretical and practical contributions to the advancement and management of air transportation systems.

Factors Influencing Efficiency of Cross-Border Transport Management System Using Rfid Sensor Technology in Cross-Border Transport Management System, Nong Khai Border Checkpoint

Purpose: This study aimed to examine the factors influencing the efficiency of the cross-border transport management system using RFID sensor technology in the cross-border transport management system, Nong Khai border Checkpoint and develop a Structural Equation Model (SEM) for cross-border transport management system Nong Khai border Checkpoint. Method: This study was a quantitative study. The sample group consisted of 450 cross-border logistics service providers in Thailand and used a questionnaire to collect data to analyze the data by percentage, mean, standard deviation statistics, Confirmatory Factor Analysis (CFA) and Structural Equation Model analysis (SEM). Results and Conclusion: The research findings revealed that 1) Service and Performance variables influenced cross-border transport management system at the statistical significance level (p<.05) which was consistent with the theory. The observed variables had component weights between 0.241 to 0.79 and were significantly different from zero at the 0.01 level for 11 variables, one variable different from zero at the 0.05 level, and the reliability values. of latent variables were between 0.058 - 1.153 and 3) The harmonization of models based on assumptions and empirical data and found that all thresholds were within the acceptable (pass) criteria, Structural Equation Model analysis (Chi-Square = 44.113, df = 35, p = 0.139, RMSEA = 0.026, GFI =0.026, AGFI = 0.960). Research Implications: In order to have satisfied or effective service level, cross-border transport management system using RFID sensor technology in Cross-border Transport Management System must have a one-stop service to fulfil all the requirement to cross the border of all vehicles every day, the service must be done in a speedy manner. For the service itself, it has to be reliable and service with equitability and adequate. Originality/Value: This research provides the knowledge and practice for Cross-border Transport Management System, Nong Khai Border Checkpoint locating at Thailand and Laos borders to improve its facility, infrastructure as well as its service component to meet the requirement of logistics companies that use the service.

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

Collective transportation system management in Algiers area: a game theoretic Approach

In Algiers area several collective transportation means such as: public buses, private buses, tramway, metro, train, etc are available; nevertheless the lack of coordination among them arises some relevant questions concerning the profitability of these means, as well as the satisfaction of travellers. In order to answer these questions, a statistical survey is conducted by the urban dynamics team of our research centre. Furthermore, the present work aims to figure out optimal strategies of both public authority and the private transport union, taking into account the travellers’ reactions. In this direction, a three steps game is considered. To compute a solution to the game under consideration, an ABC algorithm combined to TOPSIS method are used. The whole methodology is applied to Algiers area and bring solutions that motivate the private operators to cooperate with the authorities’ plans. In addition, the developed algorithm has the advantage to be flexible, since it allows multiple optimal solutions.

Inclusive Smart Cities: An Exploratory Study on the London Smart City Strategy

Smart city strategies have been developed in several cities worldwide to improve the well-being of residents, social life and economic welfare through digital transformation and technology-based interventions. The need for cities to supply assistive services and inclusive strategies for all people has also increased with the increasing urban population and smart city applications. Although technology offers several benefits for more inclusive and liveable environments, there are also drawbacks due to difficulties in adapting to digitalization and due to limited accessibility. The present study aims to explore smart city strategies from an inclusiveness perspective by analysing the London smart city strategy as a case study using a qualitative research methodology. Qualitative data analysis was conducted based on secondary data from London smart city strategy documents and semi-structured interviews with domain experts. Although inclusiveness is embedded in the London smart city strategy, there is still room for improvement. The results of this study indicate that spatial inclusion is the major focus of the London smart city policy where inclusive housing, transport and health management systems are promoted with a variety of assistive technologies with some scope on social and economic inclusion. This study proposed ways of boosting inclusiveness by improving citizen engagement through collaborations, increased transparency, and measures for preventing data misuse and misinterpretation. Using London as a case study, potential barriers in implementing inclusive strategies for smart cities in practice are highlighted, which may provide valuable learned lessons for other cities.

Improving Transportation Management Systems (TMSs) Based on the Concept of Digital Twins of an Organization

Transportation process reliability is a competitive advantage in the market of cargo delivery services. Transportation customers are interested in shipping cargoes within agreed timeframes and ensuring their safety. These are the two main parameters of transportation process reliability, compliance with which depends on a number of factors. In particular, changes in the external environment where road transportation companies operate inevitably affect the transportation process stability. The purpose of this study is to develop an approach to road transportation reliability and risk mitigation based on the digital twin concept. The variability of business processes is usually the reason for deviations from the transportation terms agreed with the customer. These deviations can be interpreted as failures in delivering cargoes. A prerequisite for ensuring smooth cargo transportation by a road transportation company is the reduction of deviations in the performance of its business processes. The article proposes methods of computer modeling and creating a digital twin of a company reflecting the reference business processes for transportation. As a result, various deviations from the reference digital model that business processes exhibit when there are changes in the external environment can be promptly identified and corrected.