Transportation Management Research Articles

Building A Simple Weather Forecast Application using Python Django

Weather forecasting plays a crucial role in various sectors such as agriculture, aviation, transportation, and disaster management. Accurate and timely weather predictions are essential for making informed decisions and mitigating potential risks. This project presents a comparative study of different Application Programming Interfaces (APIs) for weather data retrieval and their effectiveness in weather forecasting. The objective is to evaluate the performance, reliability, and accuracy of various APIs in providing weather information for accurate forecasting. This project presents the development of a sophisticated weather forecasting application using the Python Django framework. In response to the growing demand for accurate and accessible weather information, this application provides users with timely updates on current weather conditions, temperature variations, and detailed forecasts for the upcoming six hours. The application dynamically adapts its interface to reflect hourly changes in weather patterns, ensuring that users receive up-to-date and reliable information at all times. One of the key features of this application is its user-friendly interface, which allows users to conveniently search for weather data anytime and anywhere. Whether planning a trip, scheduling outdoor activities, or simply staying informed about local weather conditions, users can rely on this application to deliver precise and relevant information tailored to their needs. Furthermore, the application enhances user experience by providing comprehensive coverage of global weather data. Users can easily access weather information for any location worldwide, enabling them to plan travel itineraries, explore new destinations, and make informed decisions about their future holiday plans.

Resilience-based post-disaster repair strategy for integrated public transit networks

The resilience of public transit systems has become a critical issue due to their essential role in urban mobility. However, the challenges of repair selection and sequencing in integrated public transportation networks (IPTN) have not been fully explored. Therefore, we propose a bi-level resilience-based repair strategy optimisation (RRSO) model to investigate the optimisation of resilience in IPTN. To consider trip selection and account for cascade failure processes, a lower-level traffic assignment model was integrated into a single-layer RRSO model. In the traffic assignment model, perceived travel time reflects the traffic impedance of public transportation. Additionally, to assess the performance of the RRSO model under emergency repair conditions, random variables were introduced to represent uncertain factors in the traffic environment. Finally, the results demonstrate that the RRSO model outperforms standard empirical repair strategies. This study provides theoretical insights into the management of urban public transportation.

Implications of the Progression to Sustainable Public Transportation: An Insight into Modern Jeepney Shift from the Philippines

Modern jeepneys represent a notable step forward in the Philippines’ modernization efforts. However, there is a need for further study since this has caused a lot of criticism in the country, and no studies have established its service quality and the behavioral intentions among passengers. This study assessed the factors influencing passengers’ intention to use modern jeepneys, employing a higher-order reflective construct using partial least square structural equation modeling. An online questionnaire gathered data from 502 commuters using modern jeepneys, employing convenience sampling for a diverse sample. Significant relationships (p-value < 0.05) were found between service quality (β: 0.559), attitude (β: 0.169), perceived behavioral control (β: 0.183), and behavioral intentions (β: 0.160), with assurance emerging as the most influential factor for higher-ordered constructs (β: 0.869). Meanwhile, satisfaction’s significant influence on behavioral intention was highest for lower-ordered constructs (β: 0.673). The study underscored the importance of building trust with passengers, prioritizing safety and security, and integrating modern jeepneys with traditional designs to enhance commuter experiences and promote modernized and sustainable transportation alternatives. It highlights the critical role of providing high-quality service in fostering positive intentions toward using modern jeepneys and its utilization. Despite the backlash of the majority on the redevelopment of traditional jeepneys, results presented that passengers are more inclined to use public transportation based on travel experiences rather than societal approval. The study offers theoretical and practical implications for future works and government organizations, transportation management, and modern jeepney manufacturers, emphasizing the importance of improving service quality.

A simplified system dynamics model to investigate the effects of urban transportation management policies on reducing CO2 and greenhouse gas emissions

Kuwait suffers from severe air pollution levels making it on the top ten list worldwide pollution wise based on the records of Air Quality Index (AQI). The transport and energy sectors represent the major players in contributing to the current unprecedent pollution situation Kuwait is suffering from. The unique harsh arid weather associated with high levels of humidity, high temperatures and frequent sandstorms in the region intensifies the extent of the pollution crisis. There is almost no single study in the literature that focused on testing different scenarios on transport and energy sectors effects into the reduction of prevailing pollution levels in this region of the world. This work comes to fill this gap in the literature. A simplified system dynamics model (covering the years from 2004 to 2040) was developed for Kuwait to simulate the effects of different urban transportation management policies on reducing CO2 and greenhouse gas emissions levels. Two policies were tested: a gasoline tax with low, medium, and high rates, as well as a free bus service. Combined policies resulting from merging the gasoline tax at different levels with the free bus service were also tested, which outperformed the two separate plans being implemented alone. The best combined strategy achieved was to encourage citizens to use public transportation by utilizing the free bus service rather than using private vehicles in addition to impose a high fuel tax. This action was anticipated to reduce CO2 emissions by 14 million metric tons and the number of private vehicles by 3 million vehicles by the end of 2040. Such approach proved to be helpful in reducing the pollution levels and in turn decrease the severity of global warming phenomenon in the Arabian desertic peninsula.

Achieving solar-thermal driven dual functional device utilizing flexible PDA coated sponge integrated thermoelectric module design for water purification and power generation

The design of an efficient solar energy collector for solar-thermal-driven water purification and power generation is a promising strategy to concurrently mitigate the shortage of fresh water and energy crises. However, harvesting low-grade thermal energy in an eco-friendly and cost-effective manner through solar-thermal materials for water evaporation and electric cogeneration remains challenging. Herein, we demonstrate a dual-functional device integrating solar-thermally driven water purification and thermo-induced power generation. This typical design, fabricated via polydopamine (PDA) tightly deposited onto melamine sponges (PDA@Sponge) via H-bonds, along with a thermoelectric generator (PDA@Sponge/TEG), showcases superior solar-thermal-driven dual-functional application. The critically structurally adjustable PDA@Sponge possesses excellent homogeneity and stability, weakening interfacial hydrogen bonding between water molecules, favoring the evaporation and overflow of water molecules. The optimal serrated design achieves a water evaporation rate of up to ∼1.50 kg −2h−1, with an efficiency of ∼ 94.04 % under one sun, surpassing the most of previous reports, owing to its special tip thermal localization effect and rational water transportation management. Simultaneously, the designed PDA@Sponge/TEG yields an open-circuit voltage and short circuit current of 55 mV and 22 mA, respectively, along with a maximal output power of 162 μW (40.5 μW/cm2) at a load resistance of 7.5 Ω, under one sun and room temperature. Notably, such a device, after multiple series connections, can charge a capacitor to 1.5 V within 1 min, capable of powering low-energy electronic devices such as smartwatches and LED lights. By efficiently integrating of multiple solar-thermal-driven processes, our research promotes the design of multifunctional devices to promise an approach for desalinating seawater, purifying wastewater, and powering low-energy electronics, especially in remote areas lacking power stations and/or water treatment facilities.

IoT based Efficient and Secure Building Architecture with Constrained Application Protocol (CoAP)

The Internet of Things (IoT), one of the newest technologies, has great potential for many industry businesses. Smart building technology is one of the biggest IoT industries. IoT is used on a regular basis in many different industries. It is used in a variety of contexts, including physical defense, e-health, smart buildings, grids, cities, homes, asset management, and transportation management. IoT-level, networked smart buildings offer affordable assistance. Comfort, accessibility, security, and energy management are necessary for commercial space. Naturally, IoT-based solutions can satisfy these requirements. With the depletion of the energy supply and the increase in demand for energy, there has been a growing focus on energy usage and building maintenance. This paper uses emerging IoT technology to demonstrate a smart building design that is both safe and energy-efficient. Constrained Application Protocol (CoAP) is a crucial internet transmission protocol that assigns a unique address to every device. Data is transferred using this application layer protocol outside of secure channels. While there are several recommended techniques to protect sensitive data transfer, one likely way to improve the security aspect of CoAP for authentication and end-to-end security is through the use of Datagram Transport Layer Protection (DTLS).This paper propose a smart building design in which the Internet of Things is used to control the operation of all technological components. The simulation results show that compared to the situation of employing Message Queuing Telemetry Transport (MQTT), adopting CoAP in a smart building saves energy use by around 34.86%. In order to increase security in a smart building, this article also explains how to include the DTLS protocol inside of CoAPutilising enhancements from the Certificate Authority (CA).Finally, using the ContikiCooja (CC) simulator to simulate system performance and data collecting in terms of latency, throughput, and resource consumption, CoAP is utilized to modify orderer and peer nodes for performance research in smart building.

Automatic Shrimp Fry Counting Method Using Multi-Scale Attention Fusion.

Shrimp fry counting is an important task for biomass estimation in aquaculture. Accurate counting of the number of shrimp fry in tanks can not only assess the production of mature shrimp but also assess the density of shrimp fry in the tanks, which is very helpful for the subsequent growth status, transportation management, and yield assessment. However, traditional manual counting methods are often inefficient and prone to counting errors; a more efficient and accurate method for shrimp fry counting is urgently needed. In this paper, we first collected and labeled the images of shrimp fry in breeding tanks according to the constructed experimental environment and generated corresponding density maps using the Gaussian kernel function. Then, we proposed a multi-scale attention fusion-based shrimp fry counting network called the SFCNet. Experiments showed that our proposed SFCNet model reached the optimal performance in terms of shrimp fry counting compared to CNN-based baseline counting models, with MAEs and RMSEs of 3.96 and 4.682, respectively. This approach was able to effectively calculate the number of shrimp fry and provided a better solution for accurately calculating the number of shrimp fry.

Pemetaaan Partisipatif Potensi Wisata Bantaran Sungai Sebagai Upaya Perencanaan Pembangunan Desa

The current management paradigm of the river area is no longer seen as a protected area without any exploitation activity in it. The involvement of the community around the river in the management is an effective alternative inining the sustainable functioning of the riverside area. One of the early approaches to achieving sustainable management of river transportation is by conducting participatory mapping for administrative boundaries and development of tourism activities. The objectives of this activity: 1) provide education on the functioning of the river transport area, potential threats and strategies for sustainable river management; 2) accompany the preparation of participatory maps of the administrative ducks and the development plan of tourist activities in the village of Peniti Besar. Dedication to the community is carried out by the method of community development. The stages are surveys, potential identification, base map making, participatory mapping, map finalisation, and evaluation. Through participatory mapping, the sustainable management of river basin areas based on tourist activities, has provided extensive space for the apparatus of the village, as well as the community in developing its territory independently and intelligently, as listed in the indicator of achievement of the Sustainable Development Goals (SDGs).

Unlocking urban charm for liveable street: transformative design strategies to boost pedestrian appeal in Asian cities

Walking as a mode of transportation faces challenges in Asian cities due to the proliferation of private motorized vehicles and lacking public transportation management. Despite contributing to people’s physical and mental well-being, walking is often considered the last option for transportation. Previous research has highlighted the link between walking, urban design qualities, individual reactions, and a sense of place. Other studies have suggested that a sense of place fosters a sense of belonging and attachment to the location. This paper aims to propose transformative design strategies to enhance pedestrians’ willingness to walk through the design of street junctions. Street junctions, especially those featuring corner buildings, offer opportunities for walking, observing, and resting, all of which are crucial elements for vibrant public spaces. The findings reveal that street junctions in cities like Surabaya (Indonesia) are predominantly perceived as “spaces to move” rather than “places to be.” To advance walkability in street junctions, transformative design strategies need to be addressed. Encouraging pedestrians’ willingness to walk can focus on their experiences and perceptual qualities. Developing policies aimed at enhancing street junctions in Asian cities emerges as a pivotal strategy for strengthening a culture of walking.

Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data

This study proposes a data-driven framework for understanding the space-time patterns of exposure and contribution of different activities to traffic congestion in urban road networks by using GPS trajectory and Point-of-Interest (POI) big datasets. Taking taxi trips related to traffic congestion in Wuhan, China as a case study, we first infer the types of individual activities from GPS trajectories and POIs and identify traffic congestion on each road. Then we develop two indicators to measure the congestion exposure of different activity types. Further, we reveal the space-time patterns of activity-related congestion through spatiotemporal analysis of the indicators of traffic congestion associated with different activities. The findings of this study shed light on how different types of activities contribute to the space-time heterogeneity of traffic congestion, and highlight the significance of considering the space-time patterns of congestion related with different activity types in urban transportation management.

Research on Comprehensive Control and Traceability Management of Food Cold Chain Transportation

With the rapid development of economic globalization and cross-border e-commerce, the food import and export trade has encountered growth opportunities. Meanwhile, food safety has gained increasing attention from consumers worldwide. Most food products require cold chain transportation. However, in recent years, various safety hazards have emerged in the process of food cold chain logistics, with disruptions and food spoilage causing significant waste. Ensuring the safety and traceability of the entire food cold chain has become a critical factor limiting its development. This paper proposes strategies for the development of food cold chains, including the establishment of a comprehensive standard system, promoting orderly development of traceability systems, and enhancing traceability supervision platforms to achieve transparency in food cold chain safety.

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.

Climate Mitigation And Waste Management In The Tourism Industry For A Sustainable Ecosystem

Climate is one of the primary factors that influence international tourism. Most international travelers are interested in visiting nations with a distinct and more agreeable environment. This study aims to define the responses of tourism industry to face the climate change impacts through mitigation initiatives. Climate is a tourism resource that defines the viability of areas to sustain tourist activities, influences tourism demand, and affects operating expenses such as heating and cooling, artificial snow prices, and water and irrigation availability. Some mitigation efforts and waste management for the tourism industry to facing the climate change impacts are concerning on energy efficiency, resources and product supply, transportation, waste and wastewater management, and human resources development and awareness.

Stochastic Game Network-inspired intelligent framework for quality assessment in logistic industry

The widespread utilization of IoT-Fog-Cloud computing has revolutionized customer-oriented service delivery aspects in the global logistics industry. With industrial globalization, the logistics industry is often challenged to address quality issues. Moreover, with improper product management and transportation, the logistics industry has to face considerable financial loss. Conspicuously, a novel Stochastic Game Network (SGN) is proposed in the current study to ensure enhanced service product quality in the logistics industry. Specifically, the smart logistic industry-based decision-making framework is presented by incorporating several parameters of product quality. In the proposed method, each IoT device acts as a game player with a preset action set and strategy. Moreover, each IoT sensor at 4 levels of the logistic industry including Production Management Sensor (IMS), Inventory Management Sensor (IMS), Transportation Management Sensor (TMS), and Delivery Management Sensor (DMS) is incorporated as a game player node. Quality parameters are analyzed using the proposed Bayesian Belief Model (BBM) technique for effective classification. Moreover, Game-theoretic decision-making is used to automate enhanced product quality management and control units in IoT-based logistic industries for effective analysis and customer-oriented service delivery. For validation purposes, experimental results are compared with state-of-the-art decision-making techniques. Based on the results, the proposed model has registered enhanced statistical measures in terms of Precision (92.98%), Specificity (93.78%), Sensitivity (96.78%), Reliability (96.54%), and Stability (78%). To assess the theoretical efficacy of the proposed strategy, mathematical analysis is performed to quantify customer satisfaction. Conclusively, the proposed model presents an effective mechanism for quality assurance by incorporating stochastic behavior in the logistics industry.

An Analysis of Verification Systems and Cybersecurity Dangers

Global infrastructure provides all nations essential services such as Internet, electricity, banking, finance, public services, transportation, and water management. Each country has a distinct plan for critical infrastructure based on its sector. As IoT-based solutions become more prevalent, critical infrastructures require network and Internet access. As a result, critical systems within information networks are also vulnerable to cyber-attacks. Identifying possible cyber-attacks, developing defence tactics, and implementing preventative measures are critical. Protecting critical infrastructures from cyber-attacks is crucial nowadays. This paper examines current attacks on critical infrastructures. Security steps are addressed to prevent Internet protocol-based innovations. The IoT has enabled online connectivity for almost anything. The Internet of Things (IoT) refers to networked computing devices that may send data without human input. In contrast, the Internet is valuable; it can pose a significant cybersecurity risk to critical infrastructure.

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

Rail transportation, a cornerstone of modern logistics and passenger transit systems, plays a pivotal role in facilitating the efficient movement of goods and people across vast distances. Operating on a network of interconnected tracks, rail systems offer a reliable and environmentally sustainable mode of transportation, particularly for long-distance travel and freight shipments. The paper presents a comprehensive investigation into the application of advanced computational techniques in the realm of rail transportation management. Specifically, Mamdani fuzzy logic and Backpropagation (BP) Neural Networks are employed to address critical challenges in scheduling and classification within rail networks. The utilization of Mamdani fuzzy logic facilitates nuanced decision-making in scheduling processes, considering uncertainties and complexities inherent in rail operations. Through linguistic rules and fuzzy sets, the scheduling system can effectively adapt to various operational constraints and disruptions, leading to more resilient and efficient scheduling solutions. Additionally, the integration of BP Neural Network enhances classification accuracy and prediction capabilities, enabling precise forecasting of train movements, passenger flows, and other key variables.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the “dual carbon” strategy, intelligent networked new energy vehicle technology plays a crucial role. This type of vehicle combines the advantages of new energy technology and intelligent network technology, effectively reduces carbon emissions in the transportation sector, improves energy utilization efficiency, and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles, making significant contributions. This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

DEVELOPMENT OF THE POTENTIAL OF INTELLIGENT TECHNOLOGIES IN MANAGING THE SECURITY OF A MODERN CITY

The article examines current challenges related to ensuring security and efficient functioning of urban infrastructure. The author analyzes the potential of smart technologies, such as video surveillance, data analytics, artificial intelligence and the Internet of things, in the context of creating innovative solutions for the management and development of the urban environment. The history of urban safety management, ethical and legal issues, as well as prospects for the development of smart technologies in this area, including smart transport management, the use of Big Data and analytics, the development of drones and autonomous systems, and the creation of ethical standards are briefly described.

Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic Information

Origin-destination (OD) flow contains population mobility information between every two regions in the city, which is of great value in urban planning and transportation management. Nevertheless, the collection of OD flow data is extremely difficult due to the hindrance of privacy issues and collection costs. Significant efforts have been made to generate OD flow based on urban regional features, e.g., demographics, land use, and so on, since spatial heterogeneity of urban function is the primary cause that drives people to move from one place to another. On the other hand, people travel through various routes between OD, which will have effects on urban traffic, e.g., road travel speed and time. These effects of OD flows reveal the fine-grained spatiotemporal patterns of population mobility. Few works have explored the effectiveness of incorporating urban traffic information into OD generation. To bridge this gap, we propose to generate real-world daily temporal OD flows enhanced by urban traffic information in this paper. Our model consists of two modules: Urban2OD and OD2Traffic . In the Urban2OD module, we devise a spatiotemporal graph neural network to model the complex dependencies between daily temporal OD flows and regional features. In the OD2Traffic module, we introduce an attention-based neural network to predict urban traffic based on OD flow from the Urban2OD module. Then, by utilizing gradient backpropagation, these two modules are able to enhance each other to generate high-quality OD flow data. Extensive experiments conducted on real-world datasets demonstrate the superiority of our proposed model over the state of the art.

Dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction

As a critical component of Intelligent Transportation Systems (ITS), traffic flow prediction is indispensable for vehicle routing and transportation management. However, traffic prediction is an intractable task due to the complex spatial–temporal dependencies. First, traffic data has multiple spatial dependencies that arise from different contexts and vary with time. Second, multi-scale temporal features naturally exist in the traffic data and have dynamic significance for predicting future traffic states. Insufficiently capturing such spatial and temporal dependencies will lead to the prediction performance degradation. To this end, we propose a dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction. Unlike existing methods, we propose a novel attention layer assigning weights to each feature of nodes to augment the dynamic spatial dependence modeling and a dynamic multi-branch temporal network learning temporal features from the time domain and frequency domain complementary to enhance the dynamic multi-scale temporal features learning capability. Extensive experiments on four well-known traffic datasets demonstrate that the proposed model outperforms the best baselines with a ratio of 5.27%, 2.44%, 3.27%, and 7.34% in terms of mean absolute error and also has superior long-term prediction performance than state-of-the-art baselines.